Dual-stiffness nanoparticles for compartment-specific drug delivery in stroke

Domain: biology
Published: 2026-03-12
Editorial outcome: major_revision

Abstract
Ischemic stroke presents a dual therapeutic challenge, involving dynamic and compartmentalized pathology in both the peripheral immune system and the central nervous system. Departing from conventional holistic treatment strategies, we developed a spatially tiered drug delivery approach using a combination of dual-stiffness nanoparticles (NPs) for compartment-specific therapy. Exploiting stiffness-dependent biodistribution, stiff NPs preferentially targeted peripheral immune cells, delivering anti-inflammatory agents to attenuate systemic inflammation and prevent immune cell infiltration into the brain. In parallel, soft NPs, shielded from immune uptake, efficiently penetrated the brain to deliver neuroprotectants and restore the damaged neural microenvironment. Mechanistic studies indicate that this selective distribution arises from the interplay between NP stiffness-dependent membrane energetics and competitive serum protein adsorption. This mechano-guided delivery strategy achieves synergistic “peripheral-central” intervention via a single administration, establishing a paradigm for precision nanomedicine, with broad potential in treating multi-compartment pathologies.

Introduction
Stroke remains a major global health challenge, with its burden steadily increasing. As of 2021, stroke ranked as the third leading cause of death, following ischemic heart disease and COVID-19, and the fourth leading cause of disability worldwide1. Among all stroke subtypes, ischemic stroke accounts for approximately 87% of cases2, imposing significant medical and economic burdens on both patients and society3.

Crucially, ischemic stroke is not a static or isolated injury but a complex spatially evolving disease, involving both the central nervous system and the peripheral immune compartment3,4. Acute ischemia induces neuronal necrosis, oxidative stress, and blood–brain barrier (BBB) disruption, leading to the leakage of damage-associated molecular patterns (DAMPs) into systemic circulation5–7. These signals activate the peripheral immunity, triggering rapid recruitment of neutrophils and monocytes to the injured brain8,9. The resulting central-peripheral crosstalk forms a self-amplifying inflammatory loop that exacerbates tissue damage and hinders endogenous repair mechanisms.

Despite growing recognition of the spatially compartmentalized pathology of ischemic stroke, most therapeutic strategies have continued to treat the brain and peripheral immune system as a unified target, aiming primarily for systemic immunomodulation or brain-wide neuroprotection10–13. For instance, one approach utilized PEGylated liposomes loaded with atorvastatin to enhance targeted drug delivery to ischemic brain tissue, resulting in improved neuroprotective outcomes10. Another strategy employed macrophage membrane-coated manganese dioxide (MnO₂) nanoparticles (NPs) loaded with fingolimod (FTY), which not only reduced oxidative stress but also promoted the phenotypic transition of microglia to the anti-inflammatory M2 type, thereby modulating the inflammatory microenvironment in ischemic stroke13. While these interventions offer partial benefits, their single-modality nature limits the ability to simultaneously suppress peripheral immune infiltration and facilitate neuronal repair. Moreover, the highly activated peripheral immune response during ischemic stroke accelerates the clearance of exogenous NPs, significantly compromising their targeting efficiency. These limitations may explain the modest clinical success of prior nanotherapeutic efforts to date.

To address these limitations, we developed dual-stiffness NP combination strategy, a spatially resolved therapeutic approach that independently targets i) the periphery and ii) the brain as distinct yet interacting compartments, each requiring tailored intervention (Fig. 1, 2a). NPs have transformed drug delivery by enabling programmable control over physicochemical properties such as size, charge, and surface ligands14–17. Our recent studies have identified NP stiffness, defined as the mechanical resistance to deformation under stress18, as a critical parameter influencing NP-cell interactions and cellular internalization19–21. Indeed, stiffness modulation has also been explored in cancer nanotherapy to improve treatment specificity and efficacy 22–24. Building on these insights, we engineered a mechano-guided NP system combining two types of NPs with distinct stiffness profiles. Rather than employing a uniform NP design, this dual-stiffness approach exploits differential uptake behavior to achieve compartment-specific delivery. When tested in a multilayer barrier model, stiff NPs were preferentially taken up by immune cells, while simultaneously shielding soft NPs from immune clearance and enhancing their uptake by neuronal cells. Leveraging this behavior, we loaded stiff NPs with the anti-inflammatory agent piceatannol (Pic) to suppress systemic immune overactivation and reduce the infiltration of neutrophils and monocytes into the brain. In parallel, the soft NPs carried the neuroprotective compound DL-3-n-butylphthalide (NBP), enabling efficient transport across the BBB and promoting neural repair and local microenvironmental restoration.

In a murine middle cerebral artery occlusion/reperfusion (MCAO/R) model, the drug-loaded dual-stiffness NPs strategy led to a substantial therapeutic benefit, increasing 28-day survival rates from 11.1% to 77.8%, while significantly improving neurological function. This approach not only addresses the spatially divergent pathophysiology of ischemic stroke but also presents a broadly applicable stiffness-graded delivery platform for treating other complex diseases, spatially partitioned diseases, such as Alzheimer’s disease or cerebral gliomas 25.

Results
Preparation and characterization of the dual-stiffness NPs
To construct the dual-stiffness NPs system, we selected two FDA-approved materials, liposomes and poly(lactic-co-glycolic acid) (PLGA). To ensure identical surface properties while achieving distinct mechanical stiffness, Liposomes were prepared as the soft NPs, while PLGA NPs coated with the same lipids (Lipo-PLGA) serve as the stiff counterpart (Fig. 2b). Liposomes were synthesized using a thin-film dispersion and ultrasonic hydration method. Lipo-PLGA NPs were prepared by microfluidics. By optimizing the synthesis parameters, we successfully integrated PLGA cores into liposomes without significantly altering particle size or zeta potential. The resulting Lipo-PLGA NPs exhibited an average size of 123.67 ± 2.52 nm, compared to 130.83 ± 1.01 nm for Liposome (Fig. 2c), both with a polydispersity index (PDI) of below 0.2, indicating uniform size distribution. The zeta potential of Lipo-PLGA is -30 ± 2.91 mV, comparable to that of Liposome (-34.5 ± 4.26 mV) (Fig. 2d). Transmission electron microscope (TEM) revealed the morphology of Lipo-PLGA (Fig. 2e) and Liposome (Fig. 2f), with the confocal imaging showing colocalization of both PLGA cores (DiI-labeled) and lipid shells (DiD-labeled, Pearson’s coefficient: 0.89 ± 0.05; yellow overlay in Supplementary Fig. 1). Atomic force microscopy (AFM) further demonstrated minimal deformation of Lipo-PLGA (Young's modulus of 14.08 ± 3.23 GPa) under mechanical probing, in contrast to significant deformation of Liposome (439.19 ± 82.06 kPa), confirming the enhanced stiffness in Lipo-PLGA (Fig. 2g). Endocytosis pathway assays demonstrated that incorporating a PLGA core preserves the internalization mechanism of both Liposome and Lipo-PLGA, confirming clathrin-mediated endocytosis as their main cellular entry route (Fig. 2h). This indicated that stiff Lipo-PLGA and soft Liposome share identical cellular entry mechanisms, given their similar size, surface charge, and composition. Subsequently, Lipo-PLGA and Liposome were designed as models of stiff and soft NPs, respectively, to systematically investigate the influence of NP stiffness on their in vitro and in vivo fate.

Cellular uptake efficiency of dual-stiffness NPs combination and cell selective preference
We next investigated the differences in the uptake efficiency of NPs with varying stiffness across different cell lines. Upon co-incubation with the immune cell line Raw264.7 cells, stiff NPs consistently exhibited significantly higher cellular uptake than the soft NPs (Supplementary Fig. 2, Fig. 3a). Notably, the intracellular accumulation of stiff NPs after 1 h incubation was equivalent to that of the soft NPs after 4 h, demonstrating faster and more efficient internalization of stiff NPs by Raw264.7 cells. Similarly, another immune cell line BV2 showed comparable stiffness-dependent uptake disparity (Fig. 3b), with significantly elevated uptake of stiff NPs over the soft ones. In contrast, the neuronal cell line PC12 displayed the opposite preference under identical conditions, revealed 1.5-fold higher endocytosis of the soft NPs compared to their stiff counterparts (Fig. 3c).

We then examined whether co-incubating cells with NPs of both stiffness would lead to differential cellular uptake efficiency. Notably, under co-administration conditions, stiff NPs exhibited 1.2-fold increase in accumulation within immune cells compared to single-NP administration (Fig. 3d), whereas soft NPs achieved ~50% evasion from immune clearance (Fig. 3e).

The distinct uptake behavior induced by differences in NP stiffness offer a strategy for spatially stratified drug delivery using a dual-stiffness NP combination. To test this hypothesis, we established an in vitro dual-compartment model with spatially separated neutrophils and PC12 cells (Fig. 3f). The dual-stiffness NP combination significantly enhanced the uptake of stiff NPs by the upper-layer neutrophils, resulting in fewer stiff NPs reaching the lower compartment for uptake by PC12 cells (Fig. 3g). Meanwhile, the shielding effect on soft NPs markedly reduced their uptake by neutrophils, thereby facilitating their penetration into the lower compartment and enhancing uptake by PC12 cells (Fig. 3h). These results indicated that the dual-stiffness NPs exploit the differential cellular uptake behaviors of stiff and soft NPs, achieving passive targeting to different cell types in separate compartments through a single administration. Specifically, immune cells preferentially engulf stiff NPs, while neural cells predominantly uptake soft NPs.

To explore the underlying mechanism, we performed coarse-grained molecular dynamics (CGMD) simulations of NP-membrane interactions (Fig. 3i-k, Supplementary Movie 1). The free energy landscape obtained from steered molecular dynamics (SMD) revealed that soft NPs benefit from stronger adhesion but require deeper penetration (43.6 nm) and nearly twice the total energy cost (538.8 𝑘 𝑇) compared to stiff NPs (274.6 𝑘 𝑇). Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment.

We further investigated how intrinsic cell mechanics influence this stiffness-dependent behavior. Neuronal cells such as PC12 are softer26 and possess a more diffuse actin network, reflecting their need for dynamic material exchange27,28, whereas immune cells are stiffer29 and exhibit actin–myosin enrichment at the membrane to support rapid phagocytosis30,31. Consistent with this, actin staining showed strong cortical enrichment in Raw264.7 cells but loose distribution in PC12 cells (Fig. 3l,m, Supplementary Fig. 3). Because actin polymerization mainly drives the internalization step of endocytosis rather than the initial adhesion, we next used cytochalasin D (CytD) to inhibit actin dynamics and assess its effect on NP uptake. CytD reduced stiff NP uptake in Raw264.7 cells, but had smaller effect on soft NPs or on PC12 uptake overall (Supplementary Fig. 4). This result suggests that stiff NP uptake in Raw264.7 cells is largely dependent on internalization. In contrast, PC12 cells showed negligible changes in NP uptake upon actin inhibition, consistent with their sparse actin networks and limited involvement of internalization.

Taken together, these results establish a mechanistic framework in which NP stiffness dictates membrane deformation energetics, while cellular cytoskeletal organization determines which uptake stage dominates. Their interplay drives cell-type-selective behavior—immune cells favor stiff NPs through efficient internalization, whereas neuronal cells prefer soft NPs due to binding advantages.

To verify this, we decoupled binding and internalization in a two-stage experiment, allowing sufficient binding before internalization to assess which step more critically influences cellular uptake efficiency (Fig. 3n). In Raw264.7 cells (Fig. 3o), stiff NPs maintained their uptake advantages after decoupling two stages, confirming that internalization efficiency governs their uptake preference. In contrast, in PC12 cells (Fig. 3p), uptake of stiff and soft NPs became comparable once binding and internalization were separated, abolishing the original soft NPs preference (Fig. 3c). This demonstrates that PC12 uptake is primarily governed by binding affinity, while immune-cell uptake depends on internalization kinetics. Collectively, these results are consistent with our initial hypothesis and align well with the simulated energetic profiles of NPs with different stiffness.

The fate of NPs with different stiffness in vivo and shielding effect mechanism
We further sought to validate this distinct spatial distribution in vivo. We established an MCAO/R model to simulate ischemic stroke, which causes cerebral hypoxia and metabolic failure, triggering massive neuronal apoptosis and releasing DAMPs into the periphery. Activation of circulating immune cells subsequently triggers rapid internalization of NPs. We then investigated whether combining NPs of different stiffness affects their uptake efficiency by peripheral immune cells. Due to the low abundance of leukocytes in the peripheral blood (0.1–0.2% of total blood cells), we used an integrated in vivo-in vitro approach (Fig. 4a) to avoid the difficulty of detecting sufficient positive immune cells by flow cytometry (Supplementary Fig. 5). This approach obtained disease-state leukocytes ex vivo while amplifying the visibility of stiffness-dependent phagocytic disparities. The results demonstrated that the dual-stiffness NPs enhanced the peripheral leukocytes uptake of the stiff NPs while markedly decreasing that of the soft NPs (Fig. 4b,c). We next traced the biodistribution of the soft NPs under the dual-stiffness regimen. The results showed that the “shielding” soft NPs evaded immune clearance under the protection of stiff NPs, thus significantly enhancing their accumulation in brain tissue (Fig. 4d) and significantly reducing hepatic retention (Fig. 4e). These results indicated that the dual-stiffness NP combination enabled delivery of stiff NPs to the periphery and soft NPs to the brain, creating a spatially tiered drug delivery system.

To understand this phenomenon, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment revealed that stiff NPs were more enriched with complement-related proteins, particularly those involved in the classical pathway (C1 complex, C6), compared with soft NPs (Supplementary Fig. 6, Fig. 4f~g). This indicates that NP stiffness is a key determinant of complement adsorption and immune recognition potential. We further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher immune cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 4h). Soft NPs showed a similar trend (Fig. 4i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that, compared with stiff NPs, soft NPs adsorb fewer complement-related components from serum and therefore experience a weaker complement-mediated increase in immune cell uptake. This trend was further supported by the quantification of C3b in NP protein coronas using ELISA, which revealed higher C3b levels on stiff NPs than on soft NPs (Supplementary Fig. 7).

We then compared the corona composition of each NP type when incubated alone or in dual stiffness NP combination. For stiff NPs (Supplementary Fig. 8, Fig. 4j), co-administration with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. In contrast, soft NPs in the dual-stiffness formulation exhibited substantially reduced adsorption of complement and immunoglobulin proteins (Supplementary Fig. 9, Fig. 4k). These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs. To further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Fig. 4l). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups diminished this effect. This supports the notion that complement activation contributes critically to the shielding behavior.

Beyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 3i-k, Supplementary Movie 1). In addition, as NP internalization by immune cells involves actin cytoskeleton remodeling, we performed CLSM to observe actin organization during NP-cell interaction (Supplementary Fig. 10). After co-incubation with stiff NPs, immune cells exhibited noticeable actin redistribution accompanied by progressive stiff NP internalization, whereas soft NPs induced minimal changes in actin organization. These observations suggest that stiff NPs not only benefit from energetic advantages in membrane wrapping but are also associated with increased engagement of the actin cytoskeleton during uptake. Such enhanced cytoskeletal involvement may reduce the availability of actin polymerization capacity, potentially limiting the phagocytic uptake of subsequently encountered NPs and thereby further reinforcing the complement-mediated shielding effect observed in the dual-stiffness NP system.

We further evaluated the immune cell uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Supplementary Fig. 11). Specifically, when stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Supplementary Fig. 11 a,b). In contrast, pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Supplementary Fig. 11 c,d). Regarding sequential dosing of the same stiffness NPs, we found that the addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Supplementary Fig. 11 e,f). Lastely, sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Supplementary Fig. 11 g,h).

Collectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms.

Biofunction of drug-loaded dual-stiffness NPs
After confirming that the dual-stiffness NP combination achieved dual-compartment distribution, we proceeded to load stiff and soft NPs with different agents, targeting the peripheral and central compartments, respectively, to mediate spatially segregated anti-inflammatory and neuroprotective effects.

CD18 (integrin β2), a critical subunit of functional integrins LFA-1 and Mac-132, mediates the binding of peripheral immune cells to vascular endothelial adhesion molecules (e.g., ICAM-1) under ischemic stroke conditions33, promoting massive infiltration of neutrophils, monocytes, and other leukocytes into the brain. The stiff NPs were designed to carry Pic34, a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate CD18 expression35, thereby suppressing leukocyte adhesion, migration, and peripheral inflammation in ischemic stroke model mice. The encapsulation efficiency of Pic@stiff reached at 68.52 ± 2.32 % (Supplementary Fig. 12a). In vitro release experiment showed that Pic@stiff exhibited an early rapid burst, releasing around 70% of Pic in the first 2 h followed by a slower sustained phase (Supplementary Fig. 13a). Such an early fast-release window aligns well with the acute inflammatory surge and the rapid neutrophil infiltration that occur within hours after ischemic stroke. We employed Pic@stiff to suppress immune cell migration, like neutrophils, into the brain. As outlined in Fig. 5a, the primary neutrophils isolated from mice could penetrate the in vitro BBB model, followed by N-Formyl-Met-Leu-Phe (fMLP) stimulation to induce inflammation. The Pic@stiff treatment significantly reduced CD18 expression on neutrophils, thereby markedly decreasing their transmigration into the lower chamber under inflammatory conditions (Fig. 5b, c). Meanwhile, NBP36,37, a neuroprotectant targeting oxidative stress and apoptosis, was encapsulated in the soft NPs (NBP@soft), achieving an encapsulation efficiency of 77.42 ± 4.89% (Supplementary Fig. 12b). NBP@soft demonstrated a gradual release pattern, with an initial release of 35% within the first 2 h and a continuous diffusion-dominated release that reached around 100% at 24 h (Supplementary Fig. 13b). This sustained release pattern matches the prolonged oxidative stress and delayed neuronal injury that persists during the ischemic stroke, thereby supporting the need for continuous neuroprotection. We further investigated whether NBP@soft could enhance the therapeutic effects of NBP on damaged neuronal cells. The results showed that NBP@soft significantly increased the viability of damaged PC12 cells in vitro compared to free NBP, confirming its enhanced neuroprotective role (Fig. 5d).

Therapeutic effects of drug-loaded dual-stiffness NPs altering their biodistribution
After validating the inhibitory effects of Pic@stiff on immune cell migration and the neuroprotective effects of NBP@soft, we further investigated whether their combination in the final formulation could provide both peripheral anti-inflammatory and central neuroprotective effects in vitro. During the acute phase of ischemic stroke, DAMPs released into the periphery activate immune cells that adhere to cerebral endothelial and infiltrate brain parenchyma—neutrophils infiltration peaks at 2-6 h, followed by monocytes influx peaking at 12-24 h, both exacerbating secondary neuronal death and infarct expansion38–40. Our drug-loaded dual-stiffness NPs were designed to break this vicious cycle by enabling compartment-specific therapeutic precision guided by spatial pathology.

We constructed an in vitro ischemic stroke BBB model to verify the compartment-specific therapeutic effects. Primary isolated neutrophils were utilized to mimic peripheral blood immune cells, with fMLP factor supplementation inducing inflammatory responses. An in vitro damaged BBB model was established by seeding endothelial cells in the upper chamber and PC12 neuronal cells in the lower chamber with oxygen-glucose deprivation/reperfusion (OGD/R) applied. This simulated post-ischemic stroke peripheral inflammation and central neural damage (Fig. 5e). Four groups were designed: Group A (dual-stiffness NPs: Pic@stiff + NBP@soft), Group B (single-soft NPs: Pic@soft + NBP@soft), Group C (single-stiff NPs: Pic@stiff + NBP@stiff), and Group D (no treatment). Although NPs with different stiffness can suppress neutrophils transmigration to the lower chamber and protect damaged neural cells, there were significant differences among these groups. In contrast to Group A, Group B (single-soft NPs) showed minimal ability to suppress neutrophils transmigration to the lower chamber. This deficiency exacerbated PC12 cell damage under high-inflammatory conditions, underscoring the necessity of stiff NPs to achieve robust immune cell uptake and anti-inflammatory in the upper chamber. Conversely, Group C (single-stiff NPs) exhibited substantial immune-mediated NPs clearance in the upper chamber, effectively limiting neutrophils infiltration. However, the poor penetration of stiff NPs resulted in inferior PC12 cell survival compared to Group A. This highlights the critical role of the soft NPs in bypassing upper-chamber clearance to deliver neurorestorative agents to PC12 cells (Fig. 5f, g).

Building on the findings in Fig. 3 regarding the spatially distinct uptake patterns of dual-stiffness NPs, we hypothesize that the therapeutic advantage of Group A arises from the following mechanism: (i) Pic@stiff is preferentially internalized by immune cells in the upper-chamber, releasing Pic to suppress inflammation-induced neutrophils activation and subsequent transmigration across the BBB; (ii) NBP@soft, protected by stiff NPs to evade immune surveillance, migrates to the lower chamber where they are internalized by PC12 neuronal cells, triggering localized NBP release to promote neuronal recovery. This dual-stiffness strategy enables simultaneous spatially targeted treatment of peripheral anti-inflammatory and central neuroprotective through a single administration.

We further evaluated the biodistribution of the drug-loaded dual-stiffness NPs in MCAO/R mice. The in vivo results demonstrated that Pic@stiff significantly increased their hepatic retention and decreased brain accumulation (Fig. 5h~k). Meanwhile, compared to the drug-free biodistribution (Fig. 4d, e), this drug-loaded dual-stiffness NPs strategy protected NBP@soft from peripheral immune clearance, reducing their hepatic accumulation while markedly improving brain delivery. These findings might result from Pic@stiff releasing Pic following internalization by peripheral immune cell, thereby suppressing immunity activation, while NBP@soft evaded immune clearance under protection, leading to significantly enhanced brain accumulation. This reaffirms that co-administration of drug-loaded stiff NPs and soft NPs enhances their distribution in peripheral and brain compartments, respectively, achieving synergistic dual-compartments therapy for ischemic stroke.

Peripheral immune suppression of drug-loaded dual-stiffness NPs
To validate the peripheral immune suppression of dual-stiffness NPs, we next examined the expression level of the immune cell migration-related protein CD18 and quantified immune cell retention in the brain during the acute phase of ischemic stroke (Fig. 6a). Our experimental results demonstrated that Groups A, B, and C all reduced CD18 expression (Fig. 6b, c). Notably, Group A (dual-stiffness NPs) and Group C (single-stiff NPs) exhibited significantly greater efficacy in suppressing CD18 expression in monocytes compared to Group B (single-soft NPs), beneficial for inhibiting immune cell migration into the ischemic hemisphere (Fig. 6d, e). While the inhibition effect of Group C was significantly weaker than that of Group A, analysis of the contralateral hemisphere using the same methods revealed minimal immune cell infiltration (1-3% of total cells), consistent with the acute phase of stroke (Supplementary Fig. 14). Group A still effectively reduced contralateral immune cell infiltration compared to untreated controls, suggesting a protective effect against secondary brain damage.

Massive immune cell infiltration during stroke promotes the release of tumor necrosis factor-alpha (TNF-α), which disrupts the BBB and induces adhesion molecule expression, further facilitating leukocyte migration. Concurrently, the extensive immune cell infiltration leads to the reactivation of inflammation in ischemic regions, subsequently activating microglia in the brain to release interleukin-1β (IL-1β). This cytokine promotes massive immune cell infiltration into brain tissue through the CXCL1/2 pathway, amplifying inflammatory responses and further compromising BBB integrity by increasing permeability. In early-stage ischemic stroke, Group A and Group C significantly reduced peripheral immune cell infiltration into the brain. When examining inflammatory cytokine levels in the lesioned brain hemisphere of MCAO/R mice on Day 1 of treatment, Group A showed a significant reduction in TNF-α levels and demonstrated a downward trend in IL-1β. Group C also exhibited significant reductions in both TNF-α and IL-1β levels during the early phase, indicating that the superior inhibition of Pic@stiff for immune cell infiltration into brain tissue facilitates early-phase inflammation control (Fig. 6f).

Group A maintained suppression of immune cell migration through Day 3, whereas other groups lacked sustained efficacy. By Day 3, Group A exhibited minimal neutrophil signals, confirming the efficacy of the dual-stiffness NPs combination in suppressing peripheral neutrophil recruitment via Pic@stiff (Supplementary Fig. 15a), consistent with Fig. 6d. Notably, either single stiff or soft NPs therapy reduced fluorescence signals compared to Group D but remained suboptimal, highlighting the necessity of combining NPs of differing stiffness to enhance peripheral immune cell uptake. Meanwhile, ICAM-1, a vascular endothelial adhesion molecule, exhibits upregulated expression post-stroke, peaking at 24-72 h before gradual decline. We evaluated ICAM-1 levels in the ipsilateral brain at Day 3, observing significant downregulation in Group A compared to the others (Fig. 6g). This indicates that the therapeutic approach of Group A effectively suppressed immune cell infiltration into brain tissue via the integrin-vascular endothelial adhesion molecule pathway, while single-stiffness NP Groups B and C failed to demonstrate such inhibition.

Central microenvironment regulation of drug-loaded dual-stiffness NPs
We next investigated the regulation of the central microenvironment following the administration of drug-loaded dual-stiffness NPs. To evaluate the modulatory effect of NBP@soft within this system on the ischemic microenvironment and its neuroprotective potential, key parameters in the ischemic hemisphere were assessed, including immune cell infiltration factors, brain inflammatory cytokines, BBB restoration, cerebral edema severity, infarct volume, and neural injury biomarkers. In Group A (dual-stiffness NPs), Pic@stiff shielded NBP@soft from peripheral clearance, thereby facilitating NBP delivery to neuronal cells and modulating the local microenvironment. As a result, the level of MPO, NE, and pro-inflammatory cytokines TNF-α and IL-1β were significantly reduced on Day 3 post-treatment. Group B (single-soft NPs) also exhibited reduced inflammatory burden, particularly in IL-1β suppression. In contrast, Group C (single-stiff NPs) showed cytokine levels that rebounded to near untreated levels, indicating that while stiff NPs initially inhibited peripheral immune infiltration, the absence of soft NPs compromised sustained neuroprotection and failed to remodel the pathological microenvironment, limiting their therapeutic durability in the MCAO/R model (Fig. 7a, b).

Persistent activation of immune cells and accumulation of inflammatory factors will exacerbate BBB disruption. In the untreated Group D, extensive dextran leakage and dense perilesional aggregates were observed, along with a pronounced decrease in Occludin expression—a tight junction protein essential for BBB integrity—indicating severe barrier disruption. In contrast, Group A exhibited minimal dextran deposition with only sparse isolated puncta, even within the infarct core (Supplementary Fig. 15b). Occludin level in Group A was restored to close to Sham values, effectively mitigating injury-induced BBB hyperpermeability (Fig. 7c). These results indicated that dual-stiffness NPs achieved timely modulation of the cerebral pathological microenvironment, significantly attenuating BBB permeability and suppressing degradation of tight junction proteins.

Moreover, Group A also restrained the expansion of edema and limited infarct progression. On Day 3 post-treatment, brain water content in Group A was significantly reduced (78.33 ± 0.36%) compared to controls (80.54 ± 0.67%, p < 0.0001), and infarct volume was markedly decreased (6.52 ± 4.21% vs. 48.73 ± 19.11% in controls, p < 0.0001). In contrast, Groups B and C, lacking spatial stage-selective delivery capacity during the acute phase, showed elevated edema and larger infarcts versus Group A (Fig. 7d, e).

Further assessment of neural cell recovery was performed on Day 3 post-treatment using TUNEL staining to label damaged neural cells. The TUNEL signal in brain tissue sections was significantly lower in Groups A and B compared to Groups C and D, indicating that the administration methods in experimental Groups A and B facilitated post-stroke neuronal recovery (Fig. 7f). This also underscores the critical importance of drug delivery via soft NPs in neuroprotection. These results demonstrated that combining drug-loaded NPs of varying stiffness—employing stiff NPs to rapidly suppress peripheral inflammatory storms while enabling soft NPs to deliver neuroprotective agents to the central environment under protection.

Therapeutic effect of drug-loaded dual-stiffness NPs in MCAO/R mice
We confirmed that drug-loaded dual-stiffness NPs achieved sustained peripheral anti-inflammatory and central microenvironment regulation through a single administration. Based on this finding, we applied this treatment strategy to MCAO/R mice over a 28-day period.

After inducing MCAO/R in mice, neurological severity was assessed using the modified Neurological Severity Score (mNSS), including sensory, motor, balance, reflex, and limb flexion parameters. Mice with mNSS >10 were randomized into groups and administered treatments according to the timeline in Fig. 8a. Survival rates, body weight changes, and mNSS scores were tracked from treatment initiation to Day 28.

The combined peripheral anti-inflammatory effect of stiff NPs and the central neuroprotective action of soft NPs synergistically enhanced survival rates and promoted neurological recovery in MCAO/R mice. The results showed that the drug- loaded dual-stiffness NPs (Group A) achieved the highest survival rate (77.8%), significantly surpassing the untreated group (11.1%, Fig. 8b). Group A maintained stable body weight starting from Day 2, while other groups exhibited continuous weight loss over the initial 3 days (Fig. 8c). It also significantly reduced mNSS scores in the early phase compared to untreated controls (Fig. 8d), demonstrating early neurofunctional improvement. Remarkably, Group A rescued severely injured mice and achieved uniformly low final mNSS scores during the long-term treatment period (Fig. 8e, f).

For Group B treated with single-soft NPs alone, the survival rate was only 22.2%. Due to the absence of stiff NPs to promptly suppress inflammation during the early phase, Group B mice exhibited substantial mortality in the first 4 days (Fig. 8b). Group B mice with high mNSS scores on Day 0 (indicating severe injury) did not survive post-treatment, while the two surviving mice showed significantly lower final mNSS scores and enhanced neurological recovery (Fig. 8e~g). Combined with Section before, this outcome may arise from the failure of soft NPs to rapidly suppress peripheral inflammation during the acute phase of ischemic stroke. In severe injury cases, intense ischemia-induced inflammation overwhelmed the delayed anti-inflammatory action of Group B's NPs, resulting in therapeutic failure and high mortality. Group C treated with single-stiff NPs showed higher survival rates than Group B. However, survivors retained elevated mNSS scores at the endpoint, indicating poor neurological recovery (e.g., persistent beam balance deficits and severe limb flexion). This might be attributed to stiff NPs predominantly exerting early-phase anti-inflammatory effects, while lacking sustained neuroprotective capabilities essential for long-term recovery.

To validate our interpretations and isolate the contribution of NPs delivery versus drug synergy, we conducted long-term control treatment experiments with five designed control groups (Supplementary Fig. 17, 18): Group E (only-soft NPs: NBP@soft), Group F (only-stiff NPs: Pic@stiff), Group G (dual-stiffness NPs without drug: stiff NPs and soft NPs without loading drug), Group H (drug-swapped dual-stiffness NPs: NBP@stiff + Pic@soft), Group I (free drug without NPs: NBP and Pic) and a Sham Group. Our results demonstrated all of the control treatment experiments showed lower survival rates and worse mNSS scores on Day 28 compared to the final formulation Group A. Group E showed high early-phase mortality, particularly among severely injured animals, indicating failure to provide acute protection. Groups F, G, and H, each containing stiff NPs, modestly delayed early mortality, likely through transient peripheral inflammation suppression via phagocytosis, but lacked durable neuroprotection. In assessing neurological functional recovery, drug-free Group G exhibited an average score of 10 on the mNSS at 28 days, indicating minimal neural repair. Group F which contained Pic@stiff retained high mNSS scores. In contrast, Group E utilized NBP@soft achieving significant attenuation of neurological deficits in surviving mice. As to Group H, swapped drug in dual-stiffness NPs system showed diminished survival rates and failed to rescue profoundly affected subjects, emphasizing the importance of compartment-specific drug allocation. And the free drug group (Group I) performed a survival rate of only 11.1%, likely due to rapid systemic clearance and minimal brain accumulation. These results demonstrated the therapeutic superiority of our stiffness-tiered drug delivery strategy - Pic-loaded stiff NPs combined with NBP-loaded soft NPs.

Neuronal recovery of drug-loaded dual-stiffness NPs in MCAO/R mice
To quantify neuronal recovery, brain tissue sections were stained and analyzed to assess neuronal status, population, and morphology (Fig. 8h). In Group D, Nissl bodies of untreated MCAO/R mice were significantly reduced with dissolution and fragmentation in the infarcted areas, indicating widespread neuronal damage. The mice in Group A substantially improved neuronal organization in the hippocampal region and increased Nissl body density with restored structural integrity, suggesting functional neuronal recovery. Surviving mice in Group B demonstrated comparable neurorestorative outcomes to Group A. In contrast, Group C exhibited minimal neuronal recovery, which aligns with their elevated mNSS observed in Fig. 8e. Neurofilament immunostaining revealed discontinuous and shrink fibers, and punctate aggregates with reduced density in Group D. In contrast, Groups A and B exhibited preserved filamentous structures and high positive signals. GFAP analysis demonstrated pronounced astrogliosis and scar formation in Groups C and D, whereas Groups A and B showed attenuated glial scarring and diminished apoptotic neuron signals in lesioned areas.

In summary, the dual-stiffness NPs delivery system synergistically addressed acute inflammation (via stiff NPs) and long-term neural repair (via soft NPs), significantly improving survival and functional recovery in ischemic stroke. This therapeutic regimen also demonstrated a favorable safety profile (Supplementary Fig. 19).

Methods
Ethical Statement
All research in this study comply with all relevant ethical regulations. All animal experiments were approved by th Laboratory Animal Welfare and Ethics Committee of Zhejiang University (AP CODE: ZJU20250300). The studies were conducted in strict accordance with the institutional guidelines provided by the Zhejiang University Laboratory Animal Center.

Materials
Genistein and poly (lactic-co-glycolic acid), PLGA (50:50) were purchased from Yuanye Bio-Technology Co., Ltd. (Shanghai, China). 5-(N-Ethyl-N-isopropyl) amiloride, EIPA and N Formylmethionyl leucyl phenylalanine, fMLP were purchased from Merck & Co., Inc. (America). Hoechst 33342 staining solution for live cells (100X), BeyoGel™ Plus PAGE Precast Gel (Tris-Gly, 4-15%, 15 well), polyvinylidene fluoride (PVDF) membrane (6.6 × 8.5 cm, 0.2 μm), protease and phosphatase inhibitor cocktail for mammalian cell and tissue extracts (50X), protease inhibitor cocktail for general use (100X), RIPA complete lysis buffer, SDS-PAGE electrophoresis buffer with Tris-Gly (Powder), western transfer buffer, blotting grade, Tween-20, QuickBlock™ protein-free secondary antibody dilution buffer for western blot (WB), 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate (DiI), 3,3′-dioctadecyloxacarbocyanine perchlorate (DiO), 1,1'-dioctadecyl-3,3,3',3'-tetramethylindodicarbocyanine,4-chlorobenzenesulfonate salt (DiD), one step TUNEL cell apoptosis detection k it and BeyoECL Moon were purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China). Red blood cell lysis buffer, DNase I, CollagenaseⅡ, Hanks balanced salt solutio n were purchased from Beijing Solarbio Science & Technology Co.,Ltd. (Beijing, China). Mouse TNF-α, IL-1β, MPO, NE, C3b ELISA kit were purchased from Shanghai EnzymCe linked Biotechnology Co., Ltd. (Shanghai, China). Pic was purchased from Meilun BiotecIhnology Co., Ltd. (Liaoning, China). NBP was obtained from CSPC-NBP Pharmaceutical Co., Ltd. (Hebei, China). Transwell-24 well, 8μm was purchased from Corning Inc. (America). MCAO suture was purchased from Beijing Cinontech Co. Ltd. (Beijing, China). Roswell Park Memorial Institute (RPMI)-1640 medium and Dulbecco's Modified Eagle Medium (DMEM) were purchased from Wuhan Pricella Biotechnology Co., Ltd. (Wuhan, China). FITC anti-mouse CD45, PE anti-mouse CD3, Brilliant Violet 421™ anti-mouse Ly-6C, APC anti-mouse/human CD11b, PE anti-mouse CD18, FITC anti-mouse Ly-6G were purchased from BioLegend Inc. (America). Anti-ICAM-1 antibody [EPR16608], anti-Neurofilament antibody [EPR22035-112], anti-GFAP antibody [EPR1034Y] and anti-Occludin antibody [EPR20992] were purchased from Abcam Trading Co., Ltd. (England).

Animals
Male C57BL/6 mice (5-6 weeks old, 16-18 g) were purchased from Shanghai SLAC Laboratory Animal Co., Ltd. (Shanghai, China). The mice were maintained under a 12h light/dark cycle at a temperature of 25℃ with 50% relative humidity and free access to food and water. All animal experiments were approved by th Laboratory Animal Welfare and Ethics Committee of Zhejiang University (AP CODE: ZJU20250300).

Cells
PC12 cells (Cat. TCR3) and Raw264.7 cells (Cat. TCM13) were purchased from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). Mouse microglia cells (BV2 cells, Cat. iCell-m011) were purchased from iCell Bioscience Inc. (Shanghai, China), originally derived from the German collection of microorganisms. bEnd.3 cells (Cat. CP-M108) were purchased from Wuhan Pricella Biotechnology Co., Ltd. (Wuhan, China). PC12 cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), 10% horse serum, and 1% penicillin-streptomycin. Raw264.7 cells, BV2 cells, and bEnd.3 cells were cultured in DMEM (high glucose for Raw264.7) supplemented with 10% FBS and 1% penicillin-streptomycin. All cells were maintained in a humidified incubator at 37°C with 5% CO2.

Synthesis and characterization of nanoparticles
To prepare PLGA NPs, 80 mg of PLGA was dissolved in 5 mL of acetone solution and ultrasonically dissolved as the oil phase. For the aqueous phase: ultrapure water was heated on a magnetic stirrer, and polyvinyl alcohol (P VA, 0.2%) was added while stirring. After complete dissolution, the oil phase was slowly added to the aqueous phase, stirred for about 30 min, transferred to an eggplant-shaped flask, and the organic solvent was evaporated under reduced pressure at 40 °C, 0.08 MPa for 1.5 h to obtain a PLGA NPs suspension. To prepare Lipo-PLGA NPs, the PLGA NPs suspension was filtered through a 0.8 μm membrane. Microfluidics was used to coat lipids onto the surface of PLGA. Based on preliminary experimental screening, the condition of microfluidic preparation method was: PLGA: lipid (mass ratio) = 4:1, aqueous phase: organic phase volume = 1:1, flow rate = 12 mL/min. The organic solvent was removed by overnight stirring. Confocal microscopy was used to examine the complete encapsulation of liposomes on the surface of PLGA, forming Lipo-PLGA. Anionic liposomes were prepared using the thin-film ultrasonic method. Certain amount of phosphatidylserine and soybean lecithin were mixed and dissolved in chloroform. Then chloroform was evaporated under reduced pressure using a rotary evaporator, followed by placement in a vacuum desiccator for 2 h to ensure complete removal of residual chloroform. After drying, 5 mL of dispersion medium was added to swell and hydrate the film. The mixture was then placed in an ultrasonic cleaner for bath sonication for 15 minutes, followed by transfer to an EP tube and probe sonication for 3 minutes to complete the preparation.

The particle size and zeta potential of the NPs were measured using a Malvern particle size and zeta potential analyzer (DLS). The morphology of the NPs was observed using transmission electron microscopy (TEM), and the Young's modulus and surface topography of the NPs were determined using atomic force microscopy (AFM). In this study, Lipo-PLGA and Liposome were designed as stiff NPs and soft NPs model respectively.

Cellular uptake pathways of NPs with different stiffness
Different endocytic inhibitors were utilized to investigate whether the pathways of NPs with similar size, zeta potential, and surface morphology but varying stiffness enter immune cells are consistent. Raw264.7 cells were selected as the cellular model for the experiment, seeded in 24-well plates at a density of 105 cells per well. Three endocytic inhibitors were chosen for the study: EIPA (inhibits macropinocytosis and phagocytosis, 10 μM), genistein (inhibits caveolae-mediated endocytosis, 10 μM), and chlorpromazine (inhibits clathrin-mediated endocytosis, 30 μM). After co-incubated with the cells for 30 min, 1 mg/mL Lipo-PLGA (DiD-labeled, 1:1000) and 1 mg/mL Liposome (DiD-labeled, 1:1000) were administered separately, followed by a 2 h co-incubation. Flow cytometry was then used to analyze the percentage of cellular uptake and the mean fluorescence intensity (MFI) within the cells.

Different cell lines uptake NPs with different stiffness
To determine the uptake kinetics of NPs with different stiffness levels by immune cells, this study utilized Raw264.7 cells as the cellular model. Raw264.7 cells (105 cells per well) were seeded in plates. 1 mg/mL of Lipo-PLGA (DiI-labeled, 1:1000) and Liposome (DiI-labeled, 1:1000) were administered at 100 μL per well, respectively. The cells were co-incubated with the NPs at 37°C for 1, 2, 3, and 4 h. After incubation, cells were detached using a cell scraper, collected and processed for flow cytometry analysis to compare the MFI within Raw264.7 cells at different time points, reflecting the differential uptake rates of stiff NPs and soft NPs. 2 h was selected as the timepoint to quantify the uptake of different stiffness NPs by different cell types. This experiment employed BV2 microglial cells and PC12 neuronal cells as in vitro models. Cells were seeded at a density of 105 cells per well in 24-well plates and cultured overnight. The next day, 100 μL of Lipo-PLGA and Liposome, at identical concentrations and dosages as above, were administered to the cells. After 2 h of incubation at 37°C, cells were gently collected into flow cytometry tubes. Post-processing flow cytometry was performed to compare the MFI of each NPs type across the two cell lines. The gating strategy was implemented as follows: Initial identification of cells was performed using a FSC-A versus SSC-A plotto exclude cell debris. To ensure data reflected individual cellular uptake rather than aggregates, singlets were gated using an FSC-A versus FSC-H plot. Only the linear population was retained for downstream analysis. The cellular uptake was quantified by measuring the MFI and the percentage of DiI-positive cells in the PE channel. For each sample, a minimum of 10,000 total events were collected. Data were processed using FlowJo software. Background fluorescence was determined using untreated cells as a negative control to set the threshold for DiI-positive populations.

Distribution of dual stiffness NP combination in vitro
Primary neutrophils were extracted from C57BL/6 mice. The mice were euthanized by cervical dislocation and sterilized by immersion in 75% alcohol for 5 min. The leg bones of the mice were removed, sterilized, and the primary cells from the bone marrow were flushed out using a syringe. The cell suspension was passed through a 40 μm cell strainer, and the liquid was centrifuged at 510 g for 5 min to collect the primary cells. Red blood cell (RBC) lysis buffer was used to remove RBCs from the primary cells. A density gradient centrifugation tube with gradients of 78%, 65%, and 55% was prepared using Percoll separation medium and NaCl solution. The primary cells were added to the tube and centrifuged at 1,780 g for 30 min. Cells collected at the interface between 78%-65% and the middle of 55% were considered primary neutrophils. These cells were resuspended in serum-containing culture medium for subsequent experiments.

In a Transwell plate (24-well, 8 μm), neutrophils were added to the upper chamber (2 × 105, 200 μL per well), while PC12 cells (105 cells, 1mL per well) were pre-seeded in the lower chamber. Fluorescently labeled NPs combination carriers of different stiffnesses were added to the upper chamber. After 2 h, the penetration into the lower chamber and uptake by PC12 cells, as well as the phagocytosis of NPs by neutrophils in the upper chamber, were examined. The gating strategy for flow cytometry analysis of neutrophils and PC12 cells was consistent with the procedures described in the “Different cell lines uptake NPs with different stiffness” section.

Mesoscopic model of nanoparticle endocytosis
The endocytosis of nanoparticles by cellular membranes was simulated based on a coarse-grained molecular dynamics (CGMD) method. The phospholipid membrane system used a three-bead solvent-free model that was developed by Cooke et al.44 In this model, the phospholipid molecules exhibit a three-particle form that consists of one hydrophilic head particle and two hydrophobic tail particles. The molecular structure is maintained by finite extension nonlinear elastic (FENE) bonds, given by,
𝑈 (𝑟) = − 1 𝑘 𝑅2 ln(1− 𝑟2 ), 0 < 𝑟 < 𝑅 (1)
𝐹𝐸𝑁𝐸 2 𝐹𝐸𝑁𝐸 0 𝑅2 0
where 𝑘 represents the spring stiffness and 𝑅 denotes the maximum range of the bond. In this work, 𝑘 is set to 3𝜖/𝜎2 and 𝑅 is set to 1.5𝜎 . This configuration enables the controlled extensibility of the chain.

An approximate unit system has been adopted for the CGMD simulations, where unit length and unit time are defined as 𝜎 and 𝜏, respectively, corresponding to physical values of 1 nm and 7.7 ns. Thermal energy has been maintained at 𝑘 𝑇 = 1.1𝜖, with the energy unit 𝜖 being equivalent to 3.89×10−21 J. All simulated particles have been assigned a diameter of 1.0𝜎 (1 nm).

The bilayer membrane contains 45,000 phospholipid molecules, constituting a 158×158𝜎2 planar structure with periodic boundary conditions. The planar membrane extends along the 𝑥 and 𝑦 directions, possessing a relative size sufficient to allow complete internalization under appropriate conditions. Two types of nanoparticles, with a diameter of 20𝜎, were designed to mimic the experimental setup: the soft particles were modeled as lipid bilayer vesicles with no core, while the rigid particles featured a PLGA core represented as a solid sphere, encapsulated by a lipid monolayer.

In addition to the intramolecular bond potential, pair potentials among molecules were established to maintain the structural properties of the membrane. The Weeks-Chandler-Andersen potential was employed to model the head-head and head-tail interactions44,45, mathematically expressed as follows,
𝑈 (𝑟) = 4𝛼 𝜖[( 𝑏 ) 12 −( 𝑏 ) 6 + 1 ], 0 < 𝑟 < 𝑟 = 2.5𝑏 (2)
𝑊𝐶𝐴 𝑊𝐶𝐴 𝑟 𝑟 4 𝑐
where the equilibrium bond length, denoted by 𝑏 , is set to 0.95𝜎 and the truncation radius, denoted by 𝑟 , is set to 21/6𝑏 . This configuration allows the beads to interact solely through repulsive interactions, resulting in a membrane that exhibits head-hydrophilic and tail-hydrophobic properties. The parameter 𝛼 in Eq. is a critical factor in regulating the magnitude of the interaction potential between the beads. Conversely, the COS potential was employed to model tail-tail interactions, as it is capable of expressing both repulsive and attractive interactions,
𝑈 (𝑟) = { 𝜖 +𝑈 (𝑟), 𝑟 < 𝑟
𝑊𝐶𝐴 𝑐
−𝛼 𝜖𝑐𝑜𝑠2( 𝜋(𝑟−𝑟 ) ), 𝑟 ≤ 𝑟 ≤ 𝑟 +𝑤 (3)
𝑐𝑜𝑠 𝐶𝑂𝑆 2𝑤 𝑐 𝑐 𝑐
𝑐
0, 𝑟 > 𝑟 +𝑤
𝑐 𝑐
where a larger equilibrium bond length, denoted by 𝑏, is equivalent to 𝜎, and the action potential decay range, denoted by 𝑤 , is set to 1.6𝑏. The membrane stiffness was modulated via the 𝛼 parameter, which governs the interaction strength.

The adhesion of nanoparticles to the membrane during endocytosis was facilitated by the implementation of the Lennard-Jones (LJ) 12-6 potential,
𝑈 (𝑟) = 4𝛼 𝜖[( 𝑏 ) 12 −( 𝑏 ) 6 ], 0 < 𝑟 ≤ 𝑟 = 2.5𝜎 (4)
𝐿𝐽 𝐿𝐽 𝑟 𝑟 𝑐
with an identical equilibrium bond length (𝑏 = 𝜎 ) between interacting particles. Two distinct spherical Liposomes, characterized by varying degrees of stiffness, were utilized in the study. In the CGMD simulations, the endocytosis process kept constant factors affecting the rate of endocytosis, including nanoparticles size, adhesion strength, planar membrane size and its initial tension. This modulation enables the assessment of the kinetic effects of the stiffnesses of the nanoparticles on the endocytic process.

Free energy calculations
To elucidate the superior uptake efficiency of stiff NP over soft NP from an energetic perspective, we computed the Gibbs free energy change during phagocytosis in the NPT ensemble using the steered molecular dynamics (SMD) module in LPAMMPS. In this setup, a harmonic spring linking a virtual particle to the NP pulled the latter towards the membrane interior at a constant velocity. The pulling speed was set low enough to approximate a quasi-static, reversible process. The free energy was evaluated using Jarzynski’s equality:
𝛥𝐹 = −𝛽−1 ln〈𝑒−𝛽𝑊〉 (5)
where 𝛥𝐹 = 𝐹𝐵 −𝐹𝐴 denote the free energy difference between the initial state A and the final state B, W is the work performed along the pulling trajectory, and 𝛽 = (𝑘 𝑇)−1. This identity rigorously connects non-equilibrium work statistics to equilibrium free energy differences in the limit of infinitely slow perturbation.

Observation of actin distribution in different cell types
Raw264.7 cells and PC12 cells were seeded into confocal dishes at a density of 105 cells per well. On the following day, cells were fixed with 4% paraformaldehyde for 10 min and washed three times with PBS. The actin cytoskeleton was stained with Actin-Tracker (1:200 dilution) for 30 min at room temperature, followed by two PBS washes. Nuclei were counterstained with DAPI for 10 min, washed twice with PBS, and covered with 500 µL PBS to keep cells hydrated and prevent detachment. Actin organization and distribution in both cell types were observed under a laser scanning confocal microscope under identical staining and imaging conditions.

Actin inhibited experiments
To inhibit actin dynamics and evaluate its role in NP uptake, cytochalasin D (CytD) was used. Raw264.7 cells and PC12 cells were seeded in 24-well plates at a density of 105 cells per well. On the following day, the culture medium was replaced with fresh medium containing CytD at final concentrations of 2 µM, 1 µM, 0.5 µM, or 0 µM (sterilized using a 0.22 µm filter). Raw264.7 cells were incubated with CytD for 12 h, while PC12 cells, being more fragile, were treated for only 4 h. After incubation, the medium was replaced with normal culture medium. Subsequently, 1 mg/mL of stiff NPs (DiD-labeled, 1:1000) and soft NPs (DiD-labeled, 1:1000) were added to each well (100 µL per well, respectively). After 2 h of incubation at 37°C, cells were gently harvested into flow cytometry tubes. Flow cytometry analysis was performed to compare the MFI of each NP type between the two cell lines.

Decoupling of cellular uptake stages
To separate the binding and internalization stages of NP uptake, temperature-controlled experiments were conducted. Raw264.7 cells and PC12 cells were seeded in 24-well plates at a density of 105 cells per well. On the following day, the medium was replaced with pre-cooled cultura medium, and the cells were incubated at 4°C for 1 h. Stiff NPs (DiD-labeled, 1:1000) and soft NPs (DiD-labeled, 1:1000) were then added at a concentration of 1 mg/mL (100 µL per well, respectively). The cells were maintained at 4°C for 1.5 h to allow sufficient NP binding, followed by incubation at 37°C for 3 h to enable internalization. Cells were subsequently collected into flow cytometry tubes, and the MFI was measured to quantify NP uptake.

Variation in the distribution of nanoparticles (NPs) with different stiffness in the bloodstream of MCAO/R mice following administration of dual-stiffness NPs
MCAO/R mouse model was constructed. Mice were anesthetized with 1% sodium pentobarbital and fixed in a supine position. Exposing the left external carotid artery (ECA) and internal carotid artery (ICA). A suture with a tip diameter of 0.23 ± 0.01 mm was inserted from the ECA into the ICA until significant resistance was encountered, blocking the blood flow of the middle cerebral artery (MCA). The wound was then sutured, and the mice were transferred to a 37 °C incubator to maintain body temperature. After 1 h, the suture was withdrawn to restore collateral circulation, initiating the reperfusion phase.

Within 6 h post-modeling, a combination of stiff NPs (DiO-labeled, 1:200) and soft NPs (DiO-labeled, 1:200) was administered intravenously to explore the distribution of NPs with different stiffnesses in the blood circulation of MCAO/R mice. At 5 min post-administration, 100 μL of peripheral blood was collected via the orbital sinus. The whole blood was centrifuged at 300 g for 5 min. The supernatant was discarded, and 1 mL of RBC lysis buffer was added to the pellet for 10 min of lysis. After lysis, adding equal volume of HBSS to terminate lysis, the sample was centrifuged at 300 g for 5 min, and the cell pellet was resuspended in 1 mL of HBSS (on ice). The sample was centrifuged again at 300 g for 5 min to wash away the lysis buffer. This step was repeated once. Antibody staining solution was prepared. Each million cells were treated with 0.2 μg of antibody. The cell pellet was resuspended in 80 μL of the mixed antibody staining solution, and an equivalent amount of Fc blocker was added. After incubating for 45 min, the free staining solution was washed away, and flow cytometry analysis was performed. Initial events were gated on FSC-A versus SSC-A and FSC-H versus FSC-A plots to identify the main cell population and ensure singlet discrimination, consistent with the procedures described in the section before. Within the singlet gate, CD45+ (PE/Cy7) events were identified as the total leukocyte population to exclude residual RBC debris and non-immune events. From the CD45+ parent gate, neutrophils were further identified as the CD11b+ (APC) and Ly6G+ (PerCP/Cy5.5) double-positive population. NP uptake was then quantified by measuring the MFI of DiO (FITC channel) within these specific subsets. Data were analyzed using FlowJo software.

Due to the low proportion of immune cells in whole blood, the number of immune cells with NPs signals observed in the mentioned experimental method was very limited. Therefore, further experiments combining in vivo and in vitro approaches were conducted to measure changes in the distribution of NPs with different stiffnesses in the blood circulation after combination administration. Specifically, blood was collected from the orbital sinus of mice 6 h post-modeling, and RBC lysis was performed following the steps described above. Leukocytes were isolated from the peripheral blood of the mice, resuspended in 2 mL of HBSS solution. The cells were then co-incubated with the stiff NPs and soft NPs in vitro for 1 h (at 37°C, 100 rpm on a shaker). Following the same steps as before, the cells were stained and labeled, and flow cytometry was used to investigate changes in the uptake of NPs with different stiffnesses under the soft and stiff combination drug delivery mode.

Variation in the distribution of soft NPs in the main organs of MCAO/R mice under the dual-stiffness NPs administration
MCAO/R mouse model was constructed. The experiment was divided into two groups: the soft NPs group (MCAO/R treated with soft NPs (DiD-labeled, 1:200)), the dual-stiffness NPs group (MCAO/R treated with soft NPs (DiD-labeled, 1:200) and stiff-NPs (no-label)), to compare the effect of dual-stiffness NPs administration on the biodistribution of soft NPs in model mice. After 24 h of drug circulation in vivo, the mice were subjected to cardiac perfusion, and tissues from the heart, liver, spleen, lungs, kidneys, and brain were collected. The fluorescence signals in the major organs of the mice were observed using an in vivo imaging system.

Formation and separation of the protein corona of NPs
Peripheral blood was collected from C57BL/6 mice via retro-orbital bleeding (1.5 mL per mouse) into Eppendorf tubes and allowed to clot for 1 h at room temperature. The samples were centrifuged at 2,810 g for 10 min, and the supernatant serum was collected. The obtained serum was diluted with saline at a 1:1 ratio to prepare 50% serum. This diluted serum was incubated with stiff or soft NPs for 2 h, followed by centrifugation at 20,000 g for 45 min. The supernatant was discarded, and the NP pellets were resuspended in 1 mL PBS to obtain NP suspensions coated with their respective protein coronas.

To evaluate how the combination of stiff and soft NPs influences the composition of each NP’s protein corona, a sequential serum preconditioning strategy was employed.

Taking stiff NPs as an example, peripheral blood was collected from C57BL/6 mice (1.5 mL via retro-orbital bleeding) and allowed to clot for 1 h. Soft NPs were added to the serum and incubated for 1 h to simulate their in vivo exposure, followed by centrifugation to remove the NPs. The resulting “soft NP–pretreated” serum was diluted with saline (1:1) to obtain 50% serum and subsequently incubated with stiff NPs for 2 h. After incubation, the mixture was centrifuged for 45 min at 20,000 g, the supernatant was discarded, and the pellet was resuspended in 1 mL PBS to obtain stiff NPs with adsorbed protein coronas. The same procedure was performed in reverse (preincubating serum with stiff NPs before adding soft NPs) to analyze protein corona composition changes on soft NPs.

Processing and analysis of protein corona samples
Protein Extraction and Digestion
Frozen NP–protein corona samples were thawed on ice and lysed in 8 M urea buffer containing 1% PMSF and 2 mM EDTA. Proteins were reduced with 10 mM DTT at 37°C for 1 h and alkylated with 22 mM iodoacetamide in the dark for 15 min at room temperature. After dilution with 50 mM ammonium bicarbonate, proteins were digested sequentially with trypsin and Lys-C at 37°C for 2 h. The reaction was stopped by acidifying the mixture to pH 2–3 with 20% TFA. Peptides were desalted using C18 cartridges (Millipore, Billerica, MA, USA) and quantified with the Pierce™ Quantitative Peptide Assay Kit (Thermo Fisher Scientific).

LC–MS/MS Analysis
Peptide samples were analyzed using a Vanquish Neo UHPLC system (Thermo Fisher Scientific). Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B was acetonitrile with 0.1% formic acid. Peptides were separated using a trap-and-elute dual-column setup, with a PepMap Neo Trap Cartridge (300 µm × 5 mm, 5 µm) and an Easy-Spray™ PepMap™ Neo UHPLC analytical column (150 µm × 15 cm, 2 µm). The column temperature was maintained at 55°C with a flow rate of 2.5 µL/min.

Mass Spectrometry Detection
Data-independent acquisition (DIA) was performed on an Orbitrap Astral high-resolution mass spectrometer (Thermo Fisher Scientific) in positive ion mode. Full MS scans were acquired from 380–980 m/z at a resolution of 240,000 (at 200 m/z), with an AGC target of 500% and a maximum injection time of 5 ms. MS2 scans were acquired with DIA mode using 299 variable windows (2 Th), a normalized collision energy of 25%, and a resolution of 120,000.

Database search and quantification
Raw DIA data were analyzed using DIA-NN (v1.8.1) in library-free mode against the UniProtKB Mus musculus reference proteome (UP000000589, 54,910 sequences). Spectral libraries were generated using deep-learning prediction and refined with the MBR (Match Between Runs) function. The false discovery rate (FDR) was controlled below 1% at both precursor and protein levels. Quantitative data from validated identifications were used for downstream analysis.

Influence of serum protein and complement activity on NP uptake
Peripheral blood serum was collected and prepared as described above. A portion of the serum was heat-inactivated by incubation at 56°C for 1 h to deactivate complement components. DiD-labeled stiff and soft NPs (1 mg/mL) were separately preincubated with normal serum, heat-inactivated serum, or PBS for 2 h.

Raw 264.7 cells were seeded in 24-well plates at a density of 105 cells per well. On the following day, the pretreated NPs were added to the cells and co-incubated for 2 h. Cells were then gently harvested into flow cytometry tubes, and the MFI was measured to quantify NP uptake under different serum conditions.

Quantification of C3b deposition on NP protein coronas by ELISA
NP protein coronas on stiff and soft NPs were prepared as described above. The total amount of corona proteins was quantified using BCA assay and adjusted to a final concentration of 200 μg/mL. C3b levels in the normalized protein solutions were then quantified using a commercial ELISA kit according to the instructions.

Observation of actin distribution during NP-immune cell interaction
Raw264.7 cells were seeded into confocal dishes at a density of 105 cells per well. On the following day, stiff NPs (DiD-labeled, 1:400) and soft NPs (DiD-labeled, 1:400) were then added at a concentration of 1 mg/mL (100 µL per well, respectively). After co-incubation at 10, 30, 60 min, removed free NPs and cells were fixed with 4% paraformaldehyde for 10 min and washed three times with PBS. The actin cytoskeleton was stained with Actin-Tracker (1:200 dilution) for 30 min at room temperature, followed by two PBS washes. Nuclei were stained with DAPI for 10 min, washed twice with PBS, and covered with 500 µL PBS to keep cells hydrated and prevent detachment. Actin organization and distribution in different time points were observed under a laser scanning confocal microscope under identical staining and imaging conditions.

Influence of different combination drug delivery modes on the uptake of NPs by immune cells
This experiment utilized a sequential administration protocol of stiff NPs followed by soft NPs. Respectively labeled stiff and soft NPs were added to track their cellular uptake dynamics.

Raw264.7 cells were seeded at 105 per well in plates and cultured overnight before treatment. Each groups processed flow cytometry 2 h after the second dose added. The experimental groups and order administration were designed as Supplementary Table 3-6.

Drug loading, releasing and in vitro immune cell migration inhibition ability of stiff NPs
Pic was encapsulated into stiff NPs by initially dissolving 2% Pic in the PLGA-acetone solution, followed by the preparation protocol to obtain Pic-loaded Lipo-PLGA (Pic@stiff). Unencapsulated Pic was removed via dialysis using a 3.5 kDa molecular weight cutoff membrane. Following 1-hour magnetic stirring, the unbound drug in the dialysate was collected and designated as W free. Subsequently, encapsulated Pic was extracted by demulsifying the retained dialysis bag contents with 9 volumes of methanol, yielding W Pic. All samples were analyzed using an Agilent 1260 HPLC system (Agilent Technologies, USA) equipped with a Diamonsil® C18 column (250 mm × 4.6 mm, 5 μm). Chromatographic separation was achieved under isocratic conditions with a mobile phase of water: acetonitrile (75: 25, v/v) at 1.0 mL/min flow rate, with UV detection at 325 nm. Encapsulation efficiency (EE) was calculated as:
EE (%) = ( 𝑊 𝑝𝑖𝑐 )×100% (6)
𝑊 +W
𝑝𝑖𝑐 𝑓𝑟𝑒𝑒
For the Pic release study, 1.0 mL of Pic@stiff was first dispersed in dialysis bags (3.5 kDa) after removal of free drug. The bags were then immersed in 20 mL of PBS buffer containing 0.5% Tween-80 and incubated at 37 °C with shaking at 100 rpm. At predetermined time points, 1 mL of the release medium was withdrawn and replaced with an equal volume of fresh buffer. After 24 h, the residual Pic within the dialysis bags was collected and emulsified with methanol to determine the unreleased drug. All collected samples were processed using the same procedure described above, and the Pic concentration was quantified by HPLC to calculate cumulative drug release.

An in vitro inflammatory BBB model was constructed by seeding 104 bEnd.3 cells onto the upper chamber of a Transwell plate (24-well, 8 μm). 1 mL of culture medium containing 10 μM of the fMLP stimulant was added to the lower chamber. According to the method described aboved, primary neutrophils were isolated from mice, adjusting the concentration to 106 cells/mL and divided into three groups: the untreated control group, the free Pic group (0.075 mg per 107 cells), and the Pic@stiff group (0.075 mg per 107 cells). Followed by a 4 h pretreatment period, pretreated neutrophils (200 μL, 5×106 cells/mL) were collected and added into the upper chamber after removing the original medium. The number of neutrophils that migrated to the lower chamber after 2 h was assessed using flow cytometry.

Drug loading, releasing and in vitro neuroprotective effect of soft NPs
NBP was encapsulated into soft NPs during the thin film dispersion (NBP@soft). Encapsulation efficiency of NBP@soft was assessed by HPLC. The specific method is the same as above, among which chromatographic separation was achieved under isocratic conditions with a mobile phase of 0.1% phosphoric acid water: acetonitrile (50: 50, v/ v) at 1.0 mL/min flow rate, with UV detection at 277 nm, EE% was calculated as:
EE (%) = ( 𝑊 𝑁𝐵𝑃 )×100% (7)
𝑊 +W
𝑁𝐵𝑃 𝑓𝑟𝑒𝑒
For the NBP release study, 1.0 mL of NBP@soft was first dispersed in dialysis bags (3.5 kDa) after removal of free drug. The bags were then immersed in 30 mL of PBS buffer containing 0.5% Tween-80 and incubated at 37 °C with shaking at 100 rpm. At predetermined time points, 1 mL of the release medium was withdrawn and replaced with an equal volume of fresh buffer. After 24 h, the residual NBP within the dialysis bags was collected and emulsified with methanol to determine the unreleased drug. All collected samples were processed using the same procedure described above, and the NBP concentration was quantified by HPLC to calculate cumulative drug release.

PC12 cells were seeded in a 12-well plate at a density of 105 cells per well. The following day, the PC12 cells were subjected to OGD/R treatment by replacing the culture medium with glucose-free RPMI 1640 medium and incubating under conditions of 1.0% O2 and 5% CO2 for 4 h. After 4 h, the medium was replaced with complete medium, and the cells were reoxygenated and re-glucose for 1 h to complete the pretreatment of the cells. Subsequently, drug administration was performed by adding 100 μL of free NBP (pre-dissolved in culture medium) and NBP@soft to each well. 2 h after administration, the viability of PC12 cells was measured to compare the survival of damaged PC12 cells across different experimental groups.

In vitro validation of the dual therapeutic effects exerted by the dual-stiffness NPs loading drugs
In a Transwell plate (24-well, 8 μm), 104 bEnd.3 cells were seeded in the upper chamber and 5×104 PC12 cells were seeded in the lower chamber. The following day, both the upper and lower chambers were subjected to OGD/R treatment by replacing the culture medium with glucose-free medium and incubating under conditions of 1.0% O2 and 5% CO2 for 4 h. After 4 h, the medium was replaced with complete medium, and the cells were reoxygenated and re-glucosated for 1 h to complete the pretreatment. The lower chamber medium was then replaced with 1 mL of medium containing 10 μM fMLP. Primary neutrophils were isolated from mice according to the method described aboved, and their concentration was adjusted to 5×106 cells/mL. After removing the original medium from the upper chamber, 200 μL of the neutrophil suspension was added. This setup simulated an inflammatory condition in vitro, modeling the damaged BBB in ischemic stroke. Drug administration was performed under this model, and 2 h later, the medium from the lower chamber was collected. The number of neutrophils migrated to the lower chamber was assessed using flow cytometry. Additionally, the viability of PC12 cells in the lower chamber was measured using the CCK-8 assay to compare the survival of damaged PC12 cells under inflammatory conditions across different experimental groups.

In vivo distribution of the dual-stiffness NPs loading drugs
MCAO/R mouse model was constructed. 6 h after modeling, drugs were administered via tail vein injection. The experiment was divided into four groups: the soft NPs group (NBP@soft, 10 mg/kg, DiD-labeled, 1:200), the dual-stiffness NPs group (NBP@soft (10 mg/kg, DiD-labeled, 1:200) and Pic@stiff (2 mg/kg, no-label)), to compare the effect of combined administration on the distribution of soft NPs in mice. The stiff NPs group (Pic@stiff, 2 mg/kg, DiD-labeled, 1:200), and the dual-stiffness NPs group (NBP@soft (10 mg/kg, no-label) and Pic@stiff (2 mg/kg, DiD-labeled, 1:200)), to compare the effect of combined administration on the distribution of stiff NPs in mice.

After 24 h of drug circulation in vivo, the mice were subjected to cardiac perfusion, and tissues from the heart, liver, spleen, lungs, kidneys, and brain were collected. The fluorescence signals in the major organs of the mice were observed using an in vivo imaging system.

Peripheral immune cells migration-related protein CD18 expression analyzed at 2 h post-injection
MCAO/R mouse model was constructed. 6 h after modeling, drugs were administered via tail vein injection. The severity of MCAO/R injury in mice was assessed using the mNSS. The higher the score, the more severe the nerve damage. The mNSS includes a comprehensive evaluation of motor (0-6), sensory (0-2), reflex (0-4), and balance (0-6) functions. Based on the scores, mice with a score exceeding 10 points were selected for further study and evenly divided into four groups: Group A (dual-stiffness NPs: Pic@stiff (2 mg/kg) + NBP@soft (10 mg/kg)), Group B (single-soft NPs: Pic@soft (2 mg/kg) + NBP@soft (10 mg/kg)), Group C (single-stiff NPs: Pic@stiff (2 mg/kg) + NBP@stiff (10 mg/kg)), and Group D (no treatment). 100 μL peripheral blood was collected via the orbital sinus at 2 h post-injection. The whole blood was centrifuged at 300 g for 5 min. The supernatant was discarded, and 1 mL of RBC lysis buffer was added to the pellet for 10 min of lysis. After lysis, adding equal volume of HBSS to terminate lysis, the sample was centrifuged at 300 g for 5 min, and the cell pellet was resuspended in 1 mL of HBSS (on ice). The sample was centrifuged again at 300 g for 5 min to wash away the lysis buffer. This step was repeated once. Antibody staining solution was prepared. Each million cells were treated with 0.2 μg of antibody (CD11b, Ly6G, Ly6C, CD18). The cell pellet was resuspended in 80 μL of the mixed antibody staining solution containing Ly6G (FITC), CD11b (APPC), Ly6C (BV421) and CD18(PE), and an equivalent amount of Fc blocker was added. After incubating for 45 min, the free staining solution was washed away. Utilizing flow cytometry analyzed the CD18 expression levels of peripheral immune cells. Initial gates were set on FSC-A/SSC-A and FSC-A/FSC-H to exclude cell debris and doublets. Within the singlet gate, a primary gate was set on CD11b+ (APC) events to identify the total myeloid cell population. Defined neutrophils and monocytes as the Ly6G+ (FITC) and Ly6C+ (BV421) population derived from the CD11b+ parent gate, respectively. For each specific subset (neutrophils and monocytes), a secondary gate was established to determine the percentage of CD18+ (PE) cells. The boundary for CD18 positivity was strictly defined using Fluorescence Minus One (FMO) controls to account for potential spectral overlap between FITC, PE, APC, and BV421.

Immune cell infiltration profiles in the brain of MCAO/R mice at 2 h post-injection
The mice were subjected to MACO/R modeling. 6 h after modeling, drugs were administered via tail vein injection. Following the method outlined aboved, the severity of MCAO/R injury in the mice was assessed using the mNSS. Based on the scores, mice with a score exceeding 10 points were selected for further study. 2 h post-injection (8 h post-modeling), mice were anesthetized with 1% sodium pentobarbital and subjected to cardiac perfusion with PBS to remove residual blood from the cerebral vasculature. The entire brain tissue was carefully extracted to prepare a single-cell suspension for flow cytometry analysis.

The preparation process involved removing the brainstem and cerebellum, dividing the brain into left and right hemispheres. The tissue was then minced and passed through a cell strainer. The homogenized sample was kept on ice. The sample was centrifuged (286 g, 5 min, 4 °C), and the supernatant was carefully discarded. The pellet was resuspended in 1.5 mL of digestion buffer (0.1% collagenase + 0.01% DNase I + 0.1% dispase, 0.5 mL each) and incubated at 37°C with gentle shaking for 1 h. The cell suspension was filtered through a 40 μm cell strainer, washed with 1.5 mL of DNase-containing wash buffer and 10 mL of HBSS, and centrifuged (286 g, 5 min, 4 °C). The supernatant was discarded, and the cell pellet was resuspended in 5 mL of 25% BSA, gently pipetted into a 15 mL centrifuge tube, and centrifuged (521 g, 20 min). The myelin layer and supernatant were carefully aspirated, leaving the cell pellet at the bottom of the tube. The pellet was resuspended in 10 mL of HBSS, transferred to a new 15 mL tube, and centrifuged (286 g, 5 min, 10 °C). The supernatant was discarded, and the pellet was treated with 1 mL of RBC lysis buffer at room temperature for 10 min, followed by centrifugation. Following the preparation of single-cell suspension from the MCAO/R mouse brain tissue, distinct marking strategies were employed to differentiate immune cells subsets. For neutrophils, Ly6G (PerCP/Cy5.5) and CD11b (FITC) were utilized. To identify monocyte within the brain parenchyma, F4/80 (BV510) and CD11b (FITC) were selected, since F4/80 is highly expressed on mature, tissue-resident, and recruited monocytes, while Ly6C is a marker for circulating monocytes. Initial gates were set on FSC-A/SSC-A and FSC-A/FSC-H to exclude cell debris and doublets. Within the singlet gate, a primary gate was set on CD11b+ (APC) events to identify the total myeloid cell population. Defined neutrophils and monocytes as the Ly6G+ and F4/80+ (BV421) population derived from the CD11b+ parent gate, respectively.

Measurement of immune cell migration products
The mice were subjected to MACO/R modeling. 6 h after modeling, drugs were administered via tail vein injection. The severity of MCAO/R injury in the mice was assessed using the mNSS, after which the mice were evenly divided into five groups: the Sham group, Group A (dual-stiffness NPs: Pic@stiff (2 mg/kg) + NBP@soft (10 mg/kg)), Group B (single-soft NPs: Pic@soft (2 mg/kg) + NBP@soft (10 mg/kg)), Group C (single-stiff NPs: Pic@stiff (2 mg/kg) + NBP@stiff (10 mg/kg)), and Group D (no treatment). 3 days after administration, the complete brain tissue was perfused and removed, and the left hemisphere (lesioned brain) was dissected. Using PBS as a buffer solution, prepare a 25% brain tissue homogenate. The homogenate was centrifuged to collect the supernatant (20,000 g, 4 °C, 10 min). According to the instructions of the ELISA kits for NE and MPO, the concentrations of NE and MPO in the lesioned brain tissue were measured. The protein concentration in the left hemisphere was quantified using a BCA kit, and the levels of NE and MPO in the brain tissue were calculated based on the protein content.

Measurement of immune cell migration-related factors
Following the method described aboved, the model was established, groups were assigned, and drug administration was performed. 3 days after administration, the complete brain tissue was perfused and removed. The left hemisphere (lesioned brain) was carefully dissected using a surgical scalpel and immediately immersed in pre-chilled PBS. After all hemispheres were collected, they were placed in grinding tubes, with 800 μL of RIPA (medium) lysis buffer and 8 μL of a mixed protease inhibitor (100X) added to each hemisphere. The tissue was then homogenized by grinding. After grinding, the samples were centrifuged (18,000 g, 20 min, 4 °C), and the supernatant was collected. Protein concentration was quantified using the BCA assay, and loading buffer (5X) was added to the samples, which were then boiled (95 °C, 10 min) to prepare them for analysis. WB was used to measure changes in the expression of proteins related to cell adhesion and migration in the brain tissue homogenate of MCAO/R mice. The full scan blots are provided in Source Data file.

Staining of brain tissue sections of neutrophil and tunel signals
3 days after treatment, coronal brain sections (4-6 µm thickness) were prepared. Immunofluorescence staining involved overnight incubation at 4°C with primary antibodies (anti-Ly6G), followed by species-matched secondary antibodies (Alexa Fluor 488-conjugated goat anti-rabbit IgG). One-step tunel cell apoptosis detection kit was utilized to stain the neuronal apoptosis signal. All sections were imaged using an Olympus VS200 digital slide scanner.

Detection of cerebral inflammation
Following the method described before, the model was established, groups were assigned, and drug administration was performed. On the 1st and 3rd days after administration, the complete brain tissue was perfused and removed, and the left hemisphere (lesioned brain) was taken. PBS was added as a buffer solution, and the tissue was homogenized using a pre-cooled automated rapid grinder to obtain a 25% brain tissue homogenate. According to the ELISA detection kits for mouse TNF-α and IL-1β, the concentrations of TNF-α and IL-1β in the lesioned brain side of mice from different experimental groups were measured. The protein concentration was determined using a BCA kit, and the inflammatory levels in the lesioned brain side of mice from different experimental groups were compared on the 1st and 3rd days after treatment.

Brain tissue sections staining
After long-term treatment, coronal brain sections (4-6 µm thickness) were prepared. For Nissl staining, sections were immersed in staining solution at 50°C for 30 minutes, rinsed twice with 70% ethanol, and mounted with neutral resin. Immunofluorescence staining involved overnight incubation at 4°C with primary antibodies (anti-neurofilament, anti-GFAP), followed by species-matched secondary antibodies (Alexa Fluor 488-conjugated goat anti-rabbit IgG). All sections were imaged using an Olympus VS200 digital slide scanner.

BBB permeability assessment
On the 3rd day after drug administration, the mice were anesthetized, and their brains were perfused with PBS and extracted. The brain tissues were processed according to the method described aboved. The expression of Occludin, a protein associated with tight junctions of the BBB, was measured using WB analysis. The full scan blots are provided in Source Data file.

3 days after drug administration, the permeability of the BBB was investigated using TRITC-labeled dextran (TRITC-Dextran). The dextran (2 μg, 200 μL/20 g) was intravenously injected and circulated in the mice for 2 h. Afterward, the brains were perfused with PBS and extracted intact. The extracted brain tissues were fixed with 4% paraformaldehyde, embedded in paraffin, and sectioned coronally at a thickness of 4-6 μm to facilitate observation of the cortical and hippocampal regions. Following dewaxing, the sections were stained with 10 μg/mL DAPI and mounted. The sections were then scanned using a virtual digital slide scanning system.

Cerebral edema content
The extent of brain tissue edema was assessed using the dry-wet weight method. On the 3rd day after drug administration, mice were anesthetized and subjected to cardiac perfusion with PBS. The brain tissue was then weighed and recorded as W wet. After freeze-drying (pre-frozen at -80 °C overnight and then lyophilized for 48 h), the tissue was weighed again and recorded as W dry. The percentage water content in the brain tissue of each group of mice was determined using formula:
Cerebral edema content(%) = (W wet −W dry) ×100% (8)
W wet

Cerebral infarction volume.
On the 3rd day following MCAO/R treatment, the mice were anesthetized and subjected to cardiac perfusion with PBS. The brain tissue was carefully extracted and stored at -20 °C for 1 h. Once the brain tissue was frozen solid, it was sliced coronally into approximately 2 mm-thick sections, yielding 5 uniform slices. The brain slices were immersed in a 1.5% solution of TTC and incubated at 37 °C, 30 min. After staining, each slice was divided along the midline into the infarcted hemisphere and the contralateral hemisphere, which were separately weighed and recorded as W infarcted and W contralateral. The brain tissue was then soaked in 3 mL of freshly prepared TTC extraction solution (anhydrous ethanol: DMSO = 1:1, v/v) and kept in the dark. After 24 h, the extraction solution was replaced with 3 mL of fresh solution, and the tissue was soaked for an additional 24 h in the dark. The two extraction solutions were combined, mixed thoroughly, and the absorbance at 490 nm was measured using a microplate reader, recorded as A infarcted and A contralateral. The percentage volume of the cerebral infarction was calculated using formula:
Cerebral infarction volume(%) = (1− A infarcted × W contralateral )×100% (9)
A contralateral × W infarcted

Long-term treatment experiment
Following the method outlined before, the mice were subjected to modeling, and drug administration. The survival status and behavior of the MCAO/R mice were meticulously recorded and assessed over a 28-day treatment period. By comparing survival rates, weight recovery, mNSS scores, the therapeutic efficacy of the dual-stiffness NPs administration on MCAO/R mice was investigated, along with the recovery of impaired neurological functions.

Safety evaluation
After long-term treatment, coronal brain sections (4-6 µm thickness) were prepared for H&E staining. Sections were imaged using an Olympus VS200 digital slide scanner.

Statistical analysis
All statistical analyses were performed using GraphPad Prism 10.4.1 software (USA). Data are expressed as mean ± SD. For comparisons between two groups, an unpaired two-tailed t-test was used. For comparisons among multiple groups with a single variable, one-way ANOVA followed by Tukey’s multiple comparison post-hoc test was performed. For comparisons among multiple groups involving two independent variables, two-way ANOVA followed by Sidak’s multiple comparison post-hoc test was performed. For measurements of recovery scores over the 28-day period, a two-way ANOVA (or mixed-effects model) was conducted using the Compare column means (main column effect) approach to assess overall treatment effects across time. To further investigate the time-specific differences, a two-way ANOVA (or mixed-effect model) with simple effects within rows was applied to compare the groups at each individual time point. Statistical significance was determined based on the calculated p-values.

Data Availability
The mass spectrometry proteomics data generated in this study have been deposited to the iProX database with the dataset identifier IPX0015471000. All data generated or analyzed during this study are included in this published article and its Supplementary Information files. The source data underlying all main figures (Figs. 2–8) and supplementary figures are provided with the paper as a Source Data file.

Code Availability
Data collection was performed using the open-source software LAMMPS (version 30 July 2016; https://lammps.sandia.gov). All simulation parameters, force fields, and boundary conditions required to reproduce the results are fully described in the Methods section. Data analysis and visualization were conducted using OVITO (version 3.4.4; https://www.ovito.org).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This article developed spatially tiered drug delivery approach using a combination of dual-stiffness nanoparticles (NPs) for compartment-specific therapy in ischemic stroke treatment. This topic is significant as NP stiffness is a critical parameter for the targeting delivery and cellular internalization, providing the possibility of simultaneously suppress peripheral immune infiltration and facilitate neuronal repair by single administration. The integration is a real strength for treating multi-compartment pathologies, making it a strong choice for publication. Generally, the idea is innovative and the manuscript is well organized and well wright. Several suggestions are as follows:\n1. Is there any difference in the protein crown formed of NP with different stiffness? Will protein crown affect the potential and stiffness of NP?\n2. For the dual-stiffness NPs drug delivery system, the stiff NP were designed to carry Pic and the soft NP were designed to carry NBP. What are the differences in drug release of NP with different stiffness?\n3. Why the Pic was loaded in PLGA NP instead of lipid layer outside?\n4. Is there a significant difference between the groups in Fig 6c and d? Why the four time points were selected for displaying the results of animal behavior experiments in Fig 6?\n5. This article uses Lipo-PLGA as stiff NP (Young's modulus of 14.08 ± 3.23 GPa) and Liposome as soft NP (Young's modulus of 439.19 ± 82.06 kPa). What other NPs can be considered as stiff NP or soft NP? What is the range of their Young's modulus?\n6. Why dose the substantial deformation of soft NPs result in delayed internalization? Is there any corresponding research that can prove this result?\n7. Why the author indicated the spatially tiered drug delivery approach by dual-stiffness NP can be applied to Alzheimer’s disease and glioblastoma? What are the similarities between these diseases and stroke?\n8. The schematic diagram in Fig 2 and 3 can be optimized.", "reviewer_2": "This manuscript proposes a spatially selective drug delivery strategy based on dual-stiffness nanoparticles, aiming to achieve coordinated intervention across both peripheral immune and central nervous system compartments in the pathological process of stroke. The concept is theoretically sound and attempts to leverage mechanical properties in the design of delivery systems, offering a potentially novel approach for precise treatment of complex diseases. However, at this stage the manuscript emphasizes that nanoparticle stiffness determines in vivo targeting behavior, the current experimental data do not convincingly support this central hypothesis. In particular, the study lacks comprehensive and systematic analysis of nanoparticle trafficking and cellular uptake mechanisms. Therefore, this manuscript needs major revision. The following is the questions and some comments in this manuscript:\nMajor concerns:\n1.The concept of dual hardness nanoparticle partition delivery proposed by the author (stiff NPs target peripheral immunity, soft NPs target brain) is heavily dependent on the hardness-cell uptake relationship established by previous generations (Sci Adv 2020, 6, eaaz4316; Nat Commun 2018, 9, 2607), but the breakthrough point in this study is not fully elucidated. The dual-stiffness combination is presented as innovative, but the manuscript fails to define a clear advance over prior work. For instance, how this design surpasses single-stiffness NPs or other dual-property systems.\n2. The mechanism of the shielding effect is ambiguous. It is claimed that stiff NPs can \"shield soft NPs from immune clearance\" (Lines 178-184), but the molecular mechanism of shielding (such as competitive receptor occupancy? secretion signal inhibition?) is not clarified; the shielding efficiency is not quantified (such as measuring the amount of serum opsonin adsorption, complement activation level).\n3.How can the difference in hardness precisely regulate cell selectivity? Only inhibition experiments have demonstrated that uptake is dependent on clathrin (Fig 1h), but this does not explain difference in uptake and in vivo targeting? Is it related to cytoskeleton remodeling (such as actin polymerization rate) or membrane tension sensing receptors (such as Piezo1)?\n4.In Fig 2l, soft nanoparticle brain targeting enhancement is attributed to hardness difference, but the MCAO model itself has blood-brain barrier damage, and the interference of passive penetration enhancement needs to be ruled out:\nMinor concerns:\n1.In Figure 1f, the liposome height is only 7.2 nm, which differs greatly from the DLS-reported hydrodynamic diameter. Please clarify whether AFM measurements were performed in liquid. If not, the morphology and the Young’s modulus data may not accurately reflect the native properties of the liposomes.\n2.In the cellular uptake experiment shown in Figure 1h, the authors used RAW264.7 cells. However, as stated on line 111, the stiff nanoparticles are intended to act on neutrophils and monocyte/macrophage populations, while the soft nanoparticles are designed to promote neural repair. Therefore, key cell types involved in the therapeutic pathway—such as neutrophils, brain endothelial cells, and neurons—should be included in the uptake or functional assays to better validate the proposed targeting mechanism.\n3.In Figures 3c and 3d, the free drug group shows significantly lower efficacy compared to the drug-loaded nanoparticle group. Please clarify this discrepancy. In principle, under in vitro conditions, free drugs should have greater direct accessibility to cells than encapsulated ones. In addition, the manuscript lacks drug release profiles and pharmacokinetic data.\n4.In Figure 5e, several brain sections appear incomplete or partially missing. Providing complete and representative brain slice images is essential to ensure accurate and reliable infarct volume evaluation.\n5.Figures 5f and 6 are missing a Sham group, which is necessary for accurately evaluating the therapeutic outcomes. Please include appropriate Sham controls.\n6.The manuscript states that the soft nanoparticles exhibit immune evasion capabilities. However, the liposomal formulation used is composed of conventional basic components that have been extensively studied. It remains unclear how stiffness alone accounts for the observed immune evasion. Would changing the lipid composition alter the immune interaction profile accordingly? Further clarification and mechanistic explanation are needed to support this key claim.\n7.The observation that dual-stiffness nanoparticles can selectively target different cell types is intriguing and worthy of further investigation. It is recommended that the authors expand the mechanistic discussion of this phenomenon to better support the rationale behind the design and therapeutic outcomes.", "reviewer_3": "Liu et al. developed a dual-stiffness nanoparticle system for compartment-specific drug delivery in ischemic stroke, combining stiff NPs and soft NPs. The approach demonstrated synergistic therapeutic effects in mice by suppressing systemic inflammation, reducing brain immune infiltration, and improving neuronal recovery. While the animal results are compelling, but the mechanistic concept is not innovative and the study lacks mechanistic depth (molecular drivers of stiffness-dependent uptake). This work is too preliminary to be published on Nature Communications.\n1.Key Issues: insufficient justification for nanoparticle design and mechanistic elucidation. This study proposes that stiff NPs (engulfed by immune cells) assist soft NPs (evading clearance) in crossing the BBB. However, numerous prior studies have already demonstrated that NPs can efficiently enter the brain via immune cell hitchhiking (e.g., monocyte- or neutrophil-mediated transport), such as: Nat. Nanotechnol. 2017, 12: 692-700; Nat. Comm. 2020, 11: 1126. Why is a dual stiff/soft NP system necessary? The current data fail to demonstrate a clear advantage over existing single-NP approaches.\n2.The NPs share identical lipid compositions, but does the PLGA core alter surface properties (e.g., protein corona formation) in vivo? Why can immune evasion be achieved merely by changing the hardness, without taking into account the surface properties?\n3.How does drug release correlate with NP stiffness? PLGA typically degrades slowly—does Pic release match the acute inflammation timeline (peaking at 6–24 h post-stroke)?\n4.Lack of Mechanistic Explanation for Soft NP BBB Penetration: How do soft NPs cross the damaged BBB? The study only provides passive accumulation data (Fig. 2l) but does not clarify: Whether they rely on transcytosis (e.g., LDL receptor-mediated pathways) or exploit inflammation-induced BBB disruption (e.g., MMP-9 upregulation)? Critical mechanistic experiments are missing, such as BBB model studies with endocytosis inhibitors or immunoelectron microscopy to visualize trans-endothelial transport.\n5.The \"shielding effect\" of stiff NPs on soft NPs is interesting but poorly dissected. The study relies on indirect flow cytometry data rather than real-time imaging (e.g., intravital microscopy) to demonstrate NP-NP or NP-cell interactions. Furthermore, the molecular basis for stiffness-dependent uptake (e.g., involvement of mechanosensors, cytoskeletal remodeling) is unexplored. A deeper mechanistic investigation is needed to justify the novelty beyond prior work on NP elasticity.\n6.Controls are incomplete in the in vivo experiments. Free drug combo (Pic + NBP) should be included to isolate the contribution of NP delivery vs. drug synergy. Single-drug NPs (e.g., Pic@stiff alone) should also be included to clarify whether dual-compartment targeting is essential or if peripheral immunomodulation suffices.\n7.What is the basis for choosing a treatment window of 6 hours? Survival benefits (77.8% vs. 11.1%) are impressive, but is the therapeutic timing optimized? The 6 h post-MCAO window may not reflect real-world stroke treatment delays.\n8.ANOVA post-hoc tests (e.g., Tukey vs. Sidak) are unspecified."}, "author_response": "We appreciate your insightful comments. The protein crown is one of the important factors that influence the bio-nano interface. The analysis of protein crown components is indeed crucial for targeted drug delivery systems. We confirm that the stiffness of nanoparticles (NPs) markedly affect the protein crown formation. Consistent with our previous work (J. Colloid Interface Sci , 2022, 606, 2: 1937-1744).\nWe performed a comprehensive proteomic analysis of the protein crown formed on the stiff and soft NPs used in the research, which revealed significant compositional differences. Gene Ontology (GO) enrichment analysis of these differentially expressed proteins highlighted processes related to immune response, complement activation, and extracellular matrix (Fig. S6). We specifically focused on terms related to opsonization and immune clearance (e.g., complement and immunoglobulin components), as these are critical for NP targeting and cellular uptake. This targeted analysis revealed that stiff NPs were significantly more enriched with complement-related proteins (C1 complex, C6), compared with soft NPs (Fig. 3f,g). These results further corroborate that the mechanical properties of NPs can modulate their bio-nano interface, which may influence their biological behaviors.\nRegarding the second part of your question: the protein corona forms a very thin adsorbed layer and is therefore expected to have negligible effect on NP stiffness.\nIn response to the comment, more information has been added to the manuscript as below:\nTo understand this phenomenon, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment revealed that stiff NPs were more enriched with complement-related proteins, particularly those involved in the classical pathway (C1 complex, C6), compared with soft NPs (Fig. S6, Fig. 3f–g). This indicates that NP stiffness is a key determinant of complement adsorption and immune recognition potential. We further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that, compared with stiff NPs, soft NPs adsorb fewer complement-related components from serum and therefore experience a weaker complement-mediated increase in immune cell uptake.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs. Select the top 20 GO Terms with P-value ranking in the enrichment analysis results (sorted from small to large), and draw a bubble chart of the enrichment items.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. The horizontal axis represents the enrichment factor (the ratio of the number of differentially expressed proteins enriched in this entry to the number of annotated proteins). The larger the enrichment factor, the higher the degree of enrichment of differentially expressed proteins. The vertical axis represents the name of the GO entry. The color of the dots changes from blue to red, representing the change in P-value from large to small. The smaller the P-value, the more statistically significant it is. The size of the dots represents the number of differentially expressed proteins annotated by the corresponding entries. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs. The MFI within Raw264.7 cells after co-incubation with (h) stiff NPs and (i) soft NPs under different serum pre-treated conditions.\n2. For the dual-stiffness NPs drug delivery system, the stiff NP were designed to carry Pic and the soft NP were designed to carry NBP. What are the differences in drug release of NP with different stiffness?\nThank you very much for your questions. In general, the drug release behavior of NPs is primarily determined by the NPs compositions, structure, and the interaction between the drug and NPs. Stiffness has not been demonstrated to directely alter the physicochemical drug release kinetics of NPs in current studies. Instead, stiffness mainly affects the biological fate of NPs, such as barrier penetration ability, recoginition and uptake by immune cells, and circulation time, which in the end exerts indirect influences on the overall pharmacodynamic performance.\nOur design was guided by the biodistribution tendencies of the stiff and soft NPs. In the dual-stiffness NP system, the stiff Lipo-PLGA NPs prefer to be more efficiently taken up by peripheral immune cells, while the soft Liposome NPs, being less recognized by the immune system, can passively accumulate in the brain.\nAccordingly, stiff NPs were designed to carry Piceatannol (Pic), a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate integrin β2 expression and suppress leukocyte adhesion, migration. Pic@stiff NPs achieved immune modulation in the peripheral compartment, where stiff NPs were predominantly distributed and internalized. Meanwhile, NBP, a clinically approved neuroprotective drug for ischemic stroke, was encapsulated in the soft NPs, enabling enhanced brain delivery and exertion of antioxidant and neuroprotective effects within the CNS.\nThis drug design aligns the pharmacological action of the drug with the spatial delivery behavior of NPs with different stiffness, realizing a rational dual-space therapeutic strategy.\nIn our system, the two types of NPs differ in their main material composition—PLGA for the stiff NPs and lipid bilayers for the soft ones—so the drug release profiles are primarily governed by the physicochemical properties of these materials rather than stiffness itself.\nIn response to this comment, the drug release data has been included and revisions have been made as follow:\nCD18 (integrin β2), a critical subunit of functional integrins LFA-1 and Mac-132, mediates the binding of peripheral immune cells to vascular endothelial adhesion molecules (e.g., ICAM-1) under ischemic stroke conditions33, promoting massive infiltration of neutrophils, monocytes, and other leukocytes into the brain. The stiff NPs were designed to carry Pic34, a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate CD18 expression35, thereby suppressing leukocyte adhesion, migration, and peripheral inflammation in ischemic stroke model mice. The encapsulation efficiency of Pic@stiff reached at 68.52 ± 2.32 % (Fig. S10a). In vitro release experiment showed that Pic@stiff exhibited an early rapid burst, releasing around 70% of Pic in the first 2 h followed by a slower sustained phase (Fig. S11a). Such an early fast-release window aligns well with the acute inflammatory surge and the rapid neutrophil infiltration that occur within hours after ischemic stroke. We employed Pic@stiff to suppress immune cell migration, like neutrophils, into the brain. As outlined in Fig. 4a, the primary neutrophils isolated from mice could penetrate the in vitro BBB model, followed by N-Formyl-Met-Leu-Phe (fMLP) stimulation to induce inflammation. The Pic@stiff treatment significantly reduced CD18 expression on neutrophils, thereby markedly decreasing their transmigration into the lower chamber under inflammatory conditions (Fig. 4b, c). Meanwhile, NBP36,37, a neuroprotectant targeting oxidative stress and apoptosis, was encapsulated in the soft NPs (NBP@soft), achieving an encapsulation efficiency of 77.42 ± 4.89% (Fig. S10b). NBP@soft demonstrated a gradual release pattern, with an initial release of 35% within the first 2 h and a continuous diffusion-dominated release that reached around 100% at 24 h (Fig. S11b). This sustained release pattern matches the prolonged oxidative stress and delayed neuronal injury that persists during the ischemic stroke, thereby supporting the need for continuous neuroprotection. We further investigated whether NBP@soft could enhance the therapeutic effects of NBP on damaged neuronal cells. The results showed that NBP@soft significantly increased the viability of damaged PC12 cells in vitro compared to free NBP, confirming its enhanced neuroprotective role (Fig. 4d).\nFig. S11 Cumulative release profiles of (a) Pic@stiff and (b) NBP@soft in PBS at 37 °C with shaking at 100 rpm, n=3.\n3. Why the Pic was loaded in PLGA NP instead of lipid layer outside?\nThank you very much for your question regarding the rationale of loading Piceatannol (Pic) into the PLGA core instead of the outer lipid layer, which indeed plays a crucial role in the therapeutic performance of our system.\nThere are two main considerations for this design:\n1. Physicochemical compatibility and stability.\nAccording to physicochemical data available from related databases (e.g. Drugbank), Pic exhibits a log P value of approximately 2.1 - 3.1, suggesting that it is a moderately hydrophobic small molecule. Previous studies have shown that small-molecule drugs with log P values in the range of 2 - 4 are preferentially encapsulated within PLGA NPs, where the hydrophobic polymer matrix can form stable intermolecular interaction with the drug molecules. This improved encapsulation efficiency and stability, while also reducing premature release of surface adsorption effects.\n2. Fabrication and encapsulation efficiency:\nIn our fabrication process, the PLGA NPs were prepared via nanoprecipitation (NPR), due to its simplicity and efficiency. Then we utilized microfluidics to prepare Lipid coated PLGA NPs (Lipo-PLGA). The nanoprecipitation step allows straightforward co-dissolution and co-precipitation of hydrophobic drugs like Pic with PLGA in the organic phase, yielding high drug-loading efficiency and uniform entrapment within the polymeric matrix.\nIf Pic was instead added during the microfluidics stage, it could interfere with the self-assembly of the lipid monolayer on the PLGA surface, leading to impaired core-shell formation and reduced loading efficiency.\nDrug release profiles of PLGA included four release mechanisms, containing diffusion through polymerc shells (governing the early stage of release) and degradation (as the particles degrade in the late stage). This phenomenon is well-documented in PLGA related literature (Sci. Rep., 2025,1:4218; Int. J. Pharm., 2006,314:198–206). This early fast-release window is compatible with the acute inflammation peak (6–24 h) following ischemic stroke.\nOur dual-stiffness system utilized a core-shell structure for the stiff NPs (Lipo-PLGA NPs), where we desired to leverage the release profiles of PLGA NPs. This feature is highly advantageous for treating acute ischemic stroke, as the inflammation cascade peaks rapidly (typically 6–24 hours post-stroke). Specifically, because Pic is encapsulated within the PLGA core during preparation, Pic@stiff displayed both an initial burst release and a subsequent sustained-release phase (Fig. S11a). This profile is crucial for maintaining stable peripheral immune suppression during the early inflammatory surge. If Pic were instead added during the microfluidic lipi-coating process, it would likely remain primarily associated with the lipid layer and undergo only a rapid release, without the sustained phase. Such a pattern would be insufficient to provide prolonged modulation of the peripheral immune system during the acute ischemic stroke.\nFig. S11 Cumulative release profiles of (a) Pic@stiff in PBS at 37 °C with shaking at 100 rpm, n=3.\nIn summary, our approach of encapsulating Pic within the PLGA core is a deliberate strategy to maximize drug loading and stability while ensuring a controlled release profile, which is essential for its therapeutic application.\n4. Is there a significant difference between the groups in Fig 6c and d? Why the four time points were selected for displaying the results of animal behavior experiments in Fig 6?\nThank you very much for your valuable comment. Based on your questions, we reanalyzed and presented the behavioral data in a more comprehensive way.\nTo address whether there were significant differences among the groups in Fig. 7c and e (original Fig. 6c and d), a two-way ANOVA (or mixed-effects model) was performed using the Compare column means (main column effect) approach to evaluate the overall treatment effects across the 28-day experimental period. The results demonstrated significant intergroup differences. Specifically, Group A (dual-stiffness NPs: Pic@stiff + NBP@soft) showed a markedly improved neurological recovery compared with Group D (no treatment, p < 0.0001) and Group C (single-stiff NPs: Pic@stiff + NBP@stiff, p < 0.0001). Moreover, group B (single-soft NPs: Pic@soft + NBP@soft) also exhibited significantly enhanced behavioral performance relative to Group D (p = 0.0034). These results are consistent with our experimental design. In contrast, body weight changes over the same 28-day period showed no significant differences among the groups.\nTo further investigate the time-specific differences, we applied a two-way ANOVA (or mixed-effect model) with simple effects within rows to compare the groups at each individual time point. The analysis revealed that significant differences were mainly concentrated within the first seven days after treatment. Specifically, Group A showed a clear therapeutic advantage during this early phase:\nIn the behavioral (mNSS) assessments, the following significant differences were identified:\n• Day 1: Group A vs Group D, p = 0.0467\n• Day 2: Group A vs Group D, p = 0.0228\n• Day 3: Group A vs Group B, p = 0.0108; Group A vs Group D, p = 0.0002\n• Day 4: Group A vs Group C, p = 0.0008; Group A vs Group D, p < 0.0001\n• Day 5: Group A vs Group D, p = 0.0073\nIn the body weight variation analysis, significant differences were also observed at specific time points:\n• Day 4: Group A vs Group D, p = 0.0028\n• Day 5: Group A vs Group D, p = 0.0128\nThese findings indicate that dual stiffness NPs formulation provided an early and sustained improvement in neurological function compared with other treatments.\nRegarding your question about why four time points were selected for displaying the results of animal behavior experiments in Fig. 7 (original Fig. 6): The four time points were chosen because they are evenly distributed across the 28-day observation period and represent the time points at which the most notable behavioral changes were observed. To avoid excessive length in the main text, we only presented data from these four representative time points. However, we have recorded and preserved videos and images for all experimental time points. Based on your valuable suggestion, we will include representative images for each time point in the Supplementary Information, as shown below.\nFigure S15. Beam balance test and walking test of MCAO/R mice during long-term treatment experiment.\n5. This article uses Lipo-PLGA as stiff NP (Young's modulus of 14.08 ± 3.23 GPa) and Liposome as soft NP (Young's modulus of 439.19 ± 82.06 kPa). What other NPs can be considered as stiff NP or soft NP? What is the range of their Young's modulus?\nThank you for this important question regarding the classification of NPs by their mechanical properties. In general, NPs can be categorized by their Young’s modulus: soft NPs such as liposomes, polymeric micelles, and hydrogel-based nanogels typically exhibit modulus ranging from several tens of kPa to several MPa, whereas stiff NPs such as PLGA NPs, silica NPs, and metallic or ceramic nanocarriers usually fall within hundreds of MPa to several GPa range.\nIt is crucial to note, as your question implies, that the precise boundary between “soft” and “stiff” is not universally defined. The reported values vary substantially across the literature due to differences in material composition, fabrication methods, and measurement approaches (e.g., Atomic Force Microscopy). For instance: Nature Communications (2018) demonstrated that NPs with Young’s moduli between 5 and 110 MPa displayed distinct cellular uptake and tumor penetration behaviors. Advanced Science (2020) reported stiffness-dependent interactions within the range of 60 MPa–1.41 GPa, while work from our group in Science Advances (2020) visualized the dynamic binding and internalization of NPs spanning 560 kPa–1.18 GPa.\nIn this study, we selected liposomes (439.19 ± 82.06 kPa) and Lipo-PLGA (14.08 ± 3.23 GPa) to represent the two mechanical extremes. This wide contrast (spanning nearly five orders of magnitude) provides a robust system to investigate the mechanobiological effects of NPs stiffness on drug delivery performance.\n6. Why dose the substantial deformation of soft NPs result in delayed internalization? Is there any corresponding research that can prove this result?\nThank you very much for your questions. In our previous study, we have directly observed the cellular internalization process of NPs with different stiffness using single-particle tracking and TEM (Science Advances, 2020,6:eaaz4316). It was found that stiff NPs maintained their spherical morphology throughout both the membrane binding and internalization processes, whereas the soft one displayed substantial deformation during these stages, representative TEM images shown below (Sci. Adv., 2020, Fig.3B, Fig. S6).\nBased on these established findings, we hypothesized that substantial deformation of soft NPs result in delayed internalization. To further support this hypothesis, we performed simulation using a coarse-grained molecular dynamics (CGMD) approach and tried to mimic more closely in this research. In this model, two types of NPs were designed to mimic the experimental setup: the soft NPs were modeled as lipid bilayer vesicles with no core, while the stiff NPs featured a PLGA core represented as a solid sphere, encapsulated by a lipid monolayer, which are almost the same as NPs used in this research (Fig. 2i). The free energy profile was obtained by integrating the spring force over the NP trajectory. Prior to membrane contact, the spring is compressed due to the NP’s free fall, performing positive work. From initial adhesion (~13 nm) to full immersion (~-12 nm), the spring extends against membrane deformation, resulting in a progressive increase in free energy cost. Notably, although soft NP exhibited a modest free energy benefit during early adhesion, full uptake required significantly greater penetration depth (43.6 nm), leading to a total energy cost of 538.8 𝑘 𝑇, being nearly double that of rigid NP (274.6 𝑘 𝑇). Simulations trajectories revealed that soft NP promote wrapping through persistent membrane neck elongation, suggesting that its compliance necessitates higher local membrane curvature and greater deformation energy (Fig. 2j,k). The setup enabled the assessment of the kinetic effects of the stiffnesses of the NPs on the uptake process. The simulation results confirmed that soft NPs indeed undergo noticeable deformation upon contact with immune cells, which could result in delayed uptake.\n[REDACTED]\nFig.3B TEM micrographs showing the morphological change of the stiffest (top) and softest (bottom) SNCs during receptor-mediated interactions with SKOV3 cells. (Adapted from Hui Y. et al., Science Advances, 2020, 6: eaaz4316.)\n[REDACTED]\nFig. S6. TEM images showing the morphological change of FA-PEG-modified stiffest and softest SNCs during FR-mediated interactions with SKOV3 cells. (a) Stiffest SNCs (100% TEOS): (i)-(v) on cell surface; (v) and (vi) in endosome. (b) Softest SNCs (0% TEOS): (i) and (ii) on cell surface; (iii) and (iv) during internalization; (v) in endosome; (vi)in lysosome. Scale bars = 200 nm. (Adapted from Hui Y. et al., Science Advances, 2020, 6: eaaz4316.)\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\n7. Why the author indicated the spatially tiered drug delivery approach by dual-stiffness NP can be applied to Alzheimer’s disease and glioblastoma? What are the similarities between these diseases and stroke?\nThank you for this insightful question. In our study, the spatially tiered delivery strategy based on dual-stiffness NPs aims to achieve coordinated regulation at two pathological interfaces, the peripheral immune system and central nervous system (CNS). Specifically, stiff NPs prefer to be phagocytosed by immune cells in the periphery, while soft NPs can evade peripheral clearance and penetrate the compromised BBB and accumulate in the brain, thus enabling dual-space therapeutic modulation.\nWe proposed that this concept can be extended to Alzheimer’s disease (AD) and glioblastoma (GBM) because these disorders share key pathological features with stroke:\n1.Structural and functional disruption of the BBB and neuroinflammatory in the brain\n2.Dysregulated peripheral immune responses\nIn AD, increasing evidence supports that it is not solely a neurodegenerative disease but a systemic disorder involving both central and peripheral immune dysregulation. During disease progression, feedback interactions among complement, activated microglia and astrocytes aggravate chronic neuroinflammation and promote Aβ and tau pathology. This subsequent release of IL (Interleukin) -1β and IL-18 further disrupts BBB integrity (Nat Rev Neurol, 2021,3:157-172). BBB dysfunction in AD has been verified by neuroimaging studies (Nat Rev Neurol, 2018,14:133-150). The compromised BBB facilities the infiltration of peripheral immune cells, further amplifying neuroinflammation (Translational Neurodegeneration, 2025,14:16). Meanwhile, AD will also induce a systemic immune activation, including immune cells such as neutrophils and natural killer (NK) cells has been observed in AD progress (Immunity & Ageing, 2024, 21:38).\nSimilarly, GBM exhibits both peripheral and central dysregulation. In GBM, the BBB is partially disrupted with increased permeability due to increased angiogenesis and the release of cytokines and chemical mediators (Int J Mol Sci, 2021,22:12654). Moreover, the blood–tumor barrier (BTB) is highly heterogeneous, with leaky vasculature in the tumor core but relatively intact barriers at infiltrative margins, posing a major challenge for uniform drug penetration (Nat Rev Cancer, 2020,20:26-41). In addition, clinical and preclinical studies indicate that GBM induces systemic immunosuppression characterized by reduced T-cell responsiveness and elevated myeloid-derived suppressor cells (MDSCs) in the circulation (Cell Mol Immunol, 2024,21:1354-1375; Front Oncol, 2021,11:770561).\nWhile ischemic stroke is characterized by an acute peripheral immune activation, AD involves a chronic, low-grade systemic inflammation, and GBM displays a profound systemic immunosuppression. Despite their different immune directions, all these conditions involve pathological crosstalk between the periphery and the brain. Therefore, our dual-stiffness NP strategy — in which stiff NPs target peripheral immune cells and soft NPs penetrate the compromised BBB to modulate the CNS microenvironment — could provide a generalizable approach to address such dual-space pathologies.\n8. The schematic diagram in Fig 2 and 3 can be optimized.\nThank you very much for this valuable suggestion. We have revised and refined the schematic diagrams in both Fig 2 and 4 (The original Fig.3 has been changed to Fig. 4). In addition, following your guidance, we have further improved the graphical abstract to ensure overall consistency. Under your advice, these adjustments have made Fig 2 and 4 more uniform in style and clear in structure.\nThe revised figures are shown below.\nFig. 2 The fate of NPs with different stiffness in vitro and analysis of cell selective preference mechanisms. The mean fluorescence intensity (MFI) within (a) Raw264.7 cells (b) BV2 cells (c) PC12 cells after co-incubation 2 h with stiff NPs and soft NPs, respectively, n=3. The effect of cellular uptake of (d) stiff NPs and (e) soft NPs when simultaneously added stiff and soft NPs into Raw264.7 cells, n=3. (f) Schematic diagram of different stiffness NPs distribution in different types of cells in vitro. The upper layer was supplemented with neutrophils extracted from mice, while the lower layer was seeded with PC12 cells to investigate the distribution patterns of dual-stiffness NPs combinations added in this model. After labeling (g) stiff NPs or (h) soft NPs, the changes in the percentage of cell uptake and MFI of upper-layer neutrophils and lower-layer PC12 cells, which indicate the impact of combined administration of stiff NPs and soft NPs on their uptake by different cells, n=3. (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ. CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm. (n) Schematic diagram of the cells binding and internalization of NPs during uptake process. The MFI within (o) Raw264.7 cells and (p) PC12 cells after binding and internalization, n=3. Stiff (Dye) and Soft (Dye) mean fluorescence labeled stiff NPs and soft NPs, respectively. Stiff (Dye) + Soft and Stiff + Soft (Dye) mean fluorescence only labeled stiff NPs or soft NPs respectively in the combined administration of stiff NPs and soft NPs.\nFig. 4 (a) Schematic diagram of neutrophils migration and infiltration through an in vitro BBB model under inflammatory conditions. (b) Flow cytometric assessment of CD18 level of neutrophils, n=3. (c) The number of neutrophils that migrated and infiltrated across the inflammatory BBB model after different treatments, n=3. (d) The survival percentage of PC12 cells following treatment with various drug administrations, n=3. (e) Schematic diagram depicting the construction of an in vitro damaged BBB model under ischemic stroke conditions, and the evaluation of how drug-loaded NPs with varying stiffness influence neutrophils migration and the recovery of underlying neuronal cell damage. (f) The number of neutrophils migrations in the in vitro disease model under different drug administration methods, n=3. (g) The survival rate of underlying damaged PC12 cells after different treatments, n=3. Post-administration of the drug-loaded combination, changes in Pic@stiff biodistribution in the (h) brain tissue and (i) other major organs of MCO/R mice, n=3. Changes in the biodistribution of NBP@soft in the (j) brain tissue and (k) other major organs of MCO/R mice after treatment, n=3.\nSchematic illustration of dual-stiffness nanoparticle (NP) system enabling peripheral immune modulation and central neurorepair after ischemic stroke.\nReviewer #2 (Remarks to the Author):\nThis manuscript proposes a spatially selective drug delivery strategy based on dual-stiffness nanoparticles, aiming to achieve coordinated intervention across both peripheral immune and central nervous system compartments in the pathological process of stroke. The concept is theoretically sound and attempts to leverage mechanical properties in the design of delivery systems, offering a potentially novel approach for precise treatment of complex diseases. However, at this stage the manuscript emphasizes that nanoparticle stiffness determines in vivo targeting behavior, the current experimental data do not convincingly support this central hypothesis. In particular, the study lacks comprehensive and systematic analysis of nanoparticle trafficking and cellular uptake mechanisms. Therefore, this manuscript needs major revision. The following is the questions and some comments in this manuscript:\nMajor concerns:\n1.The concept of dual hardness nanoparticle partition delivery proposed by the author (stiff NPs target peripheral immunity, soft NPs target brain) is heavily dependent on the hardness-cell uptake relationship established by previous generations (Sci Adv 2020, 6, eaaz4316; Nat Commun 2018, 9, 2607), but the breakthrough point in this study is not fully elucidated. The dual-stiffness combination is presented as innovative, but the manuscript fails to define a clear advance over prior work. For instance, how this design surpasses single-stiffness NPs or other dual-property systems.\nWe sincerely appreciate your insightful comment and fully agree that our work builds on the fundamental understanding of the stiffness–cell uptake relationship established by previous studies (Sci. Adv., 2020,6:eaaz4316; Nat. Commun., 2018,9:2607). These pioneering contributions, including one from our own team, provided essential mechanistic insights into how nanoparticle stiffness influences cellular internalization, tissue penetration, or in vivo distribution.\nSpecifically, the Nat Commun 2018 study was one of the earliest works to systematically demonstrate that stiffness of NPs (5-110 MPa) exhibits distinct cellular uptake, mucus penetration, and solid-tumor penetration profiles, revealing stiffness as an important parameter.\nThe Sci Adv 2020 work from our team further visualized the binding and internalization dynamics of NPs with varied stiffness ranging from 560 kPa to 1.18 GPa, highlighting how stiffness regulates cell-NPs interaction and engulfment.\nOur current study represents a significant conceptual and functional advance by transitioning from these foundational mechanistic insights to a novel, functional therapeutic strategy. The purpose of this design is to achieve spatially partitioned drug delivery and coordinate a multi-pronged therapeutic intervention across the two key spaces of stroke pathology: the peripheral immune system and the central nervous system (CNS).\nSuperiority over Single-Stiffness Nanoparticle Systems:\nA single-stiffness NP system, by definition, can only be optimized for one target compartment (periphery or CNS). Our dual-stiffness approach overcomes this fundamental limitation, achieving superior targeting and synergistic efficacy.\n1. Realized dual-space biodistribution: our results demonstrates that dual-stiffness NP combination enabled delivery of stiff NPs to the periphery and soft NPs to the brain, achieving spatially tiered drug delivery, a feat impossible for single-NP system.\n• In vitro, immune cells (e.g., Raw 264.7) preferentially engulfed stiff NPs, while neural cells (e.g., PC12) showed a higher uptake of soft NPs (Fig. 2a-c). Under co-culture (Transwell) conditions, immune cells mainly internalized stiff NPs, whereas PC12 cells accumulated more soft NPs through passive trans-compartmental diffusion (Fig. 2f-h).\n• In vivo, dual-stiffness approach significantly enhanced the accumulation of soft NPs in the brain (Fig. 3a-e). Stiff NPs “shield” soft NPs from peripheral immune clearance and reduce hepatic uptake, thereby improving circulation time and CNS availability. This critical partitioning cannot be achieved with soft NPs alone.\n2. Synergistic therapeutic efficacy: dual-space distribution translates directly to superior therapeutic outcomes. Ischemic stroke is a muti-compartment disease, requiring both peripheral immunosuppression and central neuroprotection.\n• Dual-stiffness NP system delivered two different drugs to two different locations: an anti-inflammatory agent (via stiff NPs) to peripheral immune cells and a neuroprotective compound (via soft NPs) to the brain (Fig. 5, 6).\n• This coordinated intervention resulted in synergistic therapeutic effects, including attenuated peripheral inflammation, improved neuronal survival, and enhanced behavioral recovery (Fig. 7).\n• This therapeutic efficacy was significantly greater than that achieved by administering either the stiff NP (anti-inflammatory) or the soft NP (neuroprotective) population alone, providing definitive proof that the dual-stiffness combination is superior to its single-stiffness counterparts (Fig. S16).\n3. Advantages over other dual-property systems:\nCompared to other dual-property systems that rely on complex multifunctional modifications (e.g., pH-, redox-, or ligand-responsive systems), our dual-stiffness strategy offers key advantages in:\n1. Simplicity and reproducibility: mechanical modulation can be precisely tuned via polymer composition and crosslink density without additional chemical synthesis or targeting ligands, making the system cost-effective and scalable.\n2. Drug utilization efficiency: each NP population carried a compartment-specific therapeutic, avoiding dilution of active drug across sites.\nIn summary, while our work is built on the established work about stiffness-uptake regularity, our innovation lies in utilizing this principle to design a functional, co-injected dual-NP system. This strategy exhibited superior biodistribution and synergistic therapeutic efficacy compared to single-stiffness systems, offering a new, highly translatable paradigm for treating complex, muti-compartment diseases.\n2. The mechanism of the shielding effect is ambiguous. It is claimed that stiff NPs can \"shield soft NPs from immune clearance\" (Lines 178-184), but the molecular mechanism of shielding (such as competitive receptor occupancy? secretion signal inhibition?) is not clarified; the shielding efficiency is not quantified (such as measuring the amount of serum opsonin adsorption, complement activation level).\nThank you for your raising this important question. As you said we have mentioned shielding effect in the abstract, we indeed need further investigation in this field. We are deeply inspired by some of the directions you mentioned.\nFirstly, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment analysis indicated that these differences predominantly involve immune responses, complement activation, and extracellular matrix-related processes (Fig. S6). Given the critical role of opsonization and immune clearance in NP fate, we focused on functional categories related to immune recognition, such as complement and immunoglobulin components. The analysis showed that stiff NPs were significantly enriched in complement-related proteins compared with soft NPs, particularly core components of the classical complement pathway (e.g., C1 complex and C6) (Fig. 3f,g). This suggests that NP stiffness is a key determinant of complement adsorption and immune recognition potential.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs.\nWe further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: normal serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. These results indicate that stiff NPs more readily adsorb complement components, thereby promoting complement-mediated immune cell uptake.\nFig. 3 The MFI within Raw264.7 cells after co-incubation with (h) stiff NPs and (i) soft NPs under different serum pre-treated conditions.\nAfter that, we compared the protein corona compositions of each NP type when incubated alone or in dual stiffness NP combination. For stiff NPs (Fig. S7, Fig. 3j), co-administration with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. In contrast, soft NPs under co-incubation (Fig. S8, Fig. 3k) exhibited markedly reduced adsorption of complement and immunoglobulin proteins, particularly C3, a key amplifier in the complement cascade. These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs.\nFig. S7 Bubble plot of GO enrichment analysis showing differences in protein corona formed on stiff NPs in dual-stiffness NPs combination vs. stiff NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (j) stiff NPs alone and stiff NPs in dual-stiffness NPs combination.\nFig. S8 Bubble plot of GO enrichment analysis showing differences in protein corona formed on soft NPs in dual-stiffness NPs combination vs. soft NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (k) soft NPs alone and soft NPs in dual-stiffness NPs combination.\nTo further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Fig. 3l). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups diminished this effect. This supports the notion that complement activation contributes critically to the shielding behavior.\nFig. 3 (l) The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled.\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k). This preferential membrane engagement of stiff NPs not only promotes their own uptake but also energetically hinders subsequent deformation required for soft NP internalization, thereby reinforcing the complement-mediated shielding in vivo.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nWe further evaluated the cellular uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Fig. S9).\n1) Stiff NPs-first, then-soft NPs: When stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Fig. S9 a,b).\n2) Soft NPs-first, then-stiff NPs: Pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Fig. S9 c,d).\n3) Stiff NPs-first, then-stiff NPs: Sequential addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Fig. S9 e,f).\n4) Soft NPs-first, then-soft NPs: Sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Fig. S9 g,h).\nFig. S9 The effect of the first stiff NPs and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\nCollectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms. However, we know that this mechanism warrants further validation in larger cohorts, across other NP stiffness combinations, and in different disease models, which will be pursued in future studies to establish a dual-stiffness delivery platform adaptable to disease-specific conditions.\nIn response to the comment, more information has been added to the manuscript as below:\nTo understand this phenomenon, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment revealed that stiff NPs were more enriched with complement-related proteins, particularly those involved in the classical pathway (C1 complex, C6), compared with soft NPs (Fig. S6, Fig. 3f–g). This indicates that NP stiffness is a key determinant of complement adsorption and immune recognition potential. We further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that, compared with stiff NPs, soft NPs adsorb fewer complement-related components from serum and therefore experience a weaker complement-mediated increase in immune cell uptake.\nWe then compared the corona composition of each NP type when incubated alone or in dual stiffness NP combination. For stiff NPs (Fig. S7, Fig. 3j), co-administration with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. In contrast, soft NPs in the dual-stiffness formulation exhibited substantially reduced adsorption of complement and immunoglobulin proteins (Fig. S8, Fig. 3k). These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs. To further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Fig. 3l). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups diminished this effect. This supports the notion that complement activation contributes critically to the shielding behavior.\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k, Movie 1). This preferential membrane engagement of stiff NPs not only promotes their own uptake but also energetically hinders subsequent deformation required for soft NP internalization, thereby reinforcing the complement-mediated shielding.\nWe further evaluated the immune cell uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Fig. S9).\n1) Stiff NPs-first, then-soft NPs: When stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Fig. S9 a,b).\n2) Soft NPs-first, then-stiff NPs: Pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Fig. S9 c,d).\n3) Stiff NPs-first, then-stiff NPs: Sequential addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Fig. S9 e,f).\n4) Soft NPs-first, then-soft NPs: Sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Fig. S9 g,h).\nCollectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. The horizontal axis represents the enrichment factor (the ratio of the number of differentially expressed proteins enriched in this entry to the number of annotated proteins). The larger the enrichment factor, the higher the degree of enrichment of differentially expressed proteins. The vertical axis represents the name of the GO entry. The color of the dots changes from blue to red, representing the change in P-value from large to small. The smaller the P-value, the more statistically significant it is. The size of the dots represents the number of differentially expressed proteins annotated by the corresponding entries. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs. The MFI within Raw264.7 cells after co-incubation with different way pre-treated (h) stiff NPs and (i) soft NPs. Differential relative abundance of complement and immunoglobulin components in the protein corona of (j) stiff NPs alone and stiff NPs in dual-stiffness NPs combination or (k) of soft NPs alone and soft NPs in dual-stiffness NPs combination. (l) The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs. Select the top 20 GO Terms with P-value ranking in the enrichment analysis results (sorted from small to large), and draw a bubble chart of the enrichment items.\nFig. S7 Bubble plot of GO enrichment analysis showing differences in protein corona formed on stiff NPs in dual-stiffness NPs combination vs. stiff NPs alone.\nFig. S8 Bubble plot of GO enrichment analysis showing differences in protein corona formed on soft NPs in dual-stiffness NPs combination vs. soft NPs alone.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nFig. S9 The effect of the first stiff NPs and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\n3.How can the difference in hardness precisely regulate cell selectivity? Only inhibition experiments have demonstrated that uptake is dependent on clathrin (Fig 1h), but this does not explain difference in uptake and in vivo targeting? Is it related to cytoskeleton remodeling (such as actin polymerization rate) or membrane tension sensing receptors (such as Piezo1)?\nThank you very much for your valuable comment on the mechanism underlying stiffness-dependent cell selectivity. In this study, we indeed observed opposite stiffness preferences across distinct cell types, immune cells preferentially uptake stiff NPs, whereas neuronal cells favored soft ones.\nWe hypothesize that this phenomenon arises from two complementary factors: (1) NP stiffness governs the energetic profile of membrane wrapping, and (2) cellular cytoskeletal mechanics determine the dominant uptake stage.\nTo examine the first factor, we developed a coarse-grained molecular dynamics (CGMD) model to simulate the interaction between membranes and NPs of varying stiffness (Fig. 2i-k). The Gibbs free energy change during phagocytosis was computed in the NPT ensemble using the steered molecular dynamics (SMD) module in LAMMPS (Fig. 2j). The results showed that soft NP exhibited a lower free energy benefit during early adhesion, but required deeper penetration (43.6 nm) and higher deformation energy (538.8 𝑘 𝑇) for full engulfment, being nearly double that of rigid NP (274.6 𝑘 𝑇). These results demonstrate that even in a purely membrane system, NP stiffness profoundly influences uptake efficiency (Fig. 2k). Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nFrom the cellular perspective, neuronal cells such as PC12 are inherently softer than macrophage, like Raw264.7 cells, as reported in previous studies (ACS Omega, 2020,5: 31115-31125; Proc Natl Acad Sci U.S.A., 2010,107:21016-21021). Reflecting their distinct physiological functions, neurons possess diffuse actin networks to enable dynamic extension (Mol biol Cell, 2016,27:3695-3704; Front Cell Neurosci, 2018,12:447), whereas immune cells exhibit dense actin accumulation at the plasma membrane to support rapid phagocytosis (Immunol Rev, 2014,256:1; Front Immunol, 2020,11:1097). Consistent with these features, our actin staining under identical imaging conditions revealed strong membrane-associated actin fluorescence in Raw264.7 cells, whereas PC12 cells displayed a looser and more dispersed actin organization (Fig. 2l,m; Fig. S3). Because actin polymerization mainly drives the internalization step of endocytosis rather than the initial adhesion, we next used cytochalasin D (CytD) to inhibit actin dynamics and assess its effect on NP uptake. CytD reduced stiff NP uptake in Raw264.7, but had smaller effect on soft NPs or on PC12 uptake overall (Fig. S4). This result suggests that stiff NP uptake in Raw264.7 cells is largely dependent on internalization. In contrast, PC12 cells showed negligible changes in NP uptake upon actin inhibition, consistent with their sparse actin networks and limited involvement of internalization.\nFig. 2 CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm.\nFig. S3 CLSM image of Raw264.7 cells and PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=100 μm.\nFig. S4 The MFI within (a) Raw264.7 cells and (b) PC12 cells co-incubated with stiff and soft NPs, respectively under different concentration of CytD, n=3.\nTo verify this, we decoupled binding and internalization in a two-stage experiment (Fig. 2n). We compared the differences in cell uptake of NPs between this decoupling group and normal experimental conditions, examining the effects of binding and internalization on the uptake efficiency of the two types of cells (Raw264.7 cells and PC12 cells). For Raw 264.7 cells (Fig. 2o), stiff NPs showed higher uptake of stiff NPs in the treated group, consistent with normal condition (Fig. 2a), confirming that stiffness preference is driven by internalization efficiency, while binding process between Raw264.7 cells and NPs has almost no effect on their uptake efficiency. In PC 12 cells (Fig. 2p), after we decoupled binding and internalization steps, the amount of stiff and soft NPs taken up by PC12 cells approaches consistency, despite a strong preference under normal conditions (Fig. 2c). This finding indicates that NP binding, rather than internalization efficiency, predominantly determines uptake in PC12 cells. Once the binding advantage of soft NPs at 37 °C is eliminated, the intrinsic thermodynamic favorability of stiff NPs emerges, resulting in comparable overall uptake and abolishing the apparent soft NP preference. Collectively, these results are consistent with our initial hypothesis and align well with the simulated energetic profiles of NPs with different stiffness.\nFig. 2 (n) Schematic diagram of the cells binding and internalization of NPs during uptake process.\nFig. 2 The MFI within Raw264.7 cells under (a) normal condition or (o) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nFig. 2 The MFI within PC12 cells under (c) normal condition or (p) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nTogether, these findings indicate that the NP stiffness-dependent membrane energetic profile and the two cell types differ in the dominant uptake phase governing NP uptake leading to the stiffness-dependent cell selectivity: Raw264.7 cells rely primarily on internalization, leading to a preference for stiff NPs, whereas PC12 cells are dominated by surface binding, showing higher association with soft NPs.\nHowever, we also know that there are other factors that contribute to the existence of this selectivity, like the involvement of mechanosensitive membrane receptors (e.g., Piezo1) in this process. We are trying to further explore and investigate the reasons behind it. We believe that such mechanism research will have great value and significance for the practical application of different stiffness NP combination administration in the future.\nIn response to the comment, more revisions have been made to the manuscript as below:\nTo explore the underlying mechanism, we performed coarse-grained molecular dynamics (CGMD) simulations of NP-membrane interactions (Fig. 2i-k, Movie 1). The free energy landscape obtained from steered molecular dynamics (SMD) revealed that soft NPs benefit from stronger adhesion but require deeper penetration (43.6 nm) and nearly twice the total energy cost (538.8 𝑘 𝑇) compared to stiff NPs (274.6 𝑘 𝑇). Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment.\nWe further investigated how intrinsic cell mechanics influence this stiffness-dependent behavior. Neuronal cells such as PC12 are softer25 and possess a more diffuse actin network, reflecting their need for dynamic material exchange26,27, whereas immune cells are stiffer28 and exhibit actin–myosin enrichment at the membrane to support rapid phagocytosis29,30. Consistent with this, actin staining showed strong cortical enrichment in Raw 264.7 cells but loose distribution in PC12 cells (Fig. 2l,m; Fig. S3). Because actin polymerization mainly drives the internalization step of endocytosis rather than the initial adhesion, we next used cytochalasin D (CytD) to inhibit actin dynamics and assess its effect on NP uptake. CytD reduced stiff NP uptake in Raw 264.7, but had smaller effect on soft NPs or on PC12 uptake overall (Fig. S4). This result suggests that stiff NP uptake in Raw 264.7 cells is largely dependent on internalization. In contrast, PC12 cells showed negligible changes in NP uptake upon actin inhibition, consistent with their sparse actin networks and limited involvement of internalization.\nTaken together, these results establish a mechanistic framework in which NP stiffness dictates membrane deformation energetics, while cellular cytoskeletal organization determines which uptake stage dominates. Their interplay drives cell-type-selective behavior—immune cells favor stiff NPs through efficient internalization, whereas neuronal cells prefer soft NPs due to binding advantages.\nTo verify this, we decoupled binding and internalization in a two-stage experiment, allowing sufficient binding before internalization to assess which step more critically influences cellular uptake efficiency (Fig. 2n). In Raw264.7 cells (Fig. 2o), stiff NPs maintained their uptake advantages after decoupling two stages, confirming that internalization efficiency governs their uptake preference. In contrast, in PC12 cells (Fig. 2p), uptake of stiff and soft NPs became comparable once binding and internalization were separated, abolishing the original soft NPs preference (Fig. 2c). This demonstrates that PC12 uptake is primarily governed by binding affinity, while immune-cell uptake depends on internalization kinetics. Collectively, these results are consistent with our initial hypothesis and align well with the simulated energetic profiles of NPs with different stiffness.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ. CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm. (n) Schematic diagram of the cells binding and internalization of NPs during uptake process. The MFI within (o) Raw264.7 cells and (p) PC12 cells after binding and internalization, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nFig. S3 CLSM image of Raw264.7 cells and PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=100 μm.\nFig. S4 The MFI within (a) Raw264.7 cells and (b) PC12 cells co-incubated with stiff and soft NPs, respectively under different concentration of CytD, n=3.\n4.In Fig 2l, soft nanoparticle brain targeting enhancement is attributed to hardness difference, but the MCAO model itself has blood-brain barrier damage, and the interference of passive penetration enhancement needs to be ruled out:\nThank you for your suggestion. We would like to clarify our study does not aim to investigate whether soft NPs can actively penetrate an intact blood brain barrier (BBB). Instead, the dual-stiffness NP system was specifically designed to achieve a spatially tiered delivery strategy that coordinated therapeutic modulation at two pathological interfaces, the peripheral immune system and central nervous system (CNS). Soft NPs can passively accumulate in the brain through the compromised BBB caused by stroke (Fig. 3d). In the application of dual-stiffness NP system, stiff NPs prefer to be phagocytosed by immune cells in the periphery, whereas soft NPs, protected from immune clearance, can circulate longer and passively accumulate more in the brain(Fig. 3d). The passive penetration of soft NPs through the damaged BBB is therefore a key mechanism of our delivery strategy, rather than an interference that needs to be excluded.\nFig. 3 (d) The fluorescence signal of the soft NPs was observed in the brain as well as (e) in the heart, liver, spleen, lung, and kidney of the mice, n=3. Soft (Dye) mean fluorescence labeled soft NPs. Stiff + Soft (Dye) mean fluorescence only labeled soft NPs in the combined administration of stiff NPs and soft NPs.\nIschemic stroke is a dynamic and spatially heterogeneous disease involving both central neurons and peripheral immune responses. During the acute phase, ischemia induces neuronal necrosis, oxidative stress, and disruption of the BBB, leading to leakage of damage-associated molecular patterns (DAMPs) and recruitment of peripheral immune cells, which further aggravate BBB damage. BBB disruption is characterized by altered tight junction complexes and increased paracellular permeability, enabling passive exchange between the systemic circulation and CNS compartments (Am J Physiol Cell Physiol, 2018,315:C343–C356). Indeed, neurologists have recognized that disease-induced BBB breakdown provides a therapeutic window for passive delivery of agents such as proteins, peptides, and NPs without additional BBB modulation (Nat Rev Neurol, 2018,14:133–150).\nConsistent with this concept, previous studies have demonstrated that negatively charged or neutral liposomes can passively accumulate in ischemic brain regions, mainly due to increased vascular permeability and local inflammation (Int J Nanomedicine, 2016,11:3035–3048). Moreover, BBB opening during stroke is biphasic, an early reversible BBB hyperpermeability phase at 4–6 h after stroke and a delayed irreversible opening of BBB at 48–72 h after stroke (Transl Stroke Res, 2016,7:89–92). These findings support that passive leakage through damaged vasculature is a predominant route for liposomes entry during stroke progression.\nBuilding upon this pathophysiological basis, our dual-stiffness NP system was intentionally designed to leverage passive accumulation. Stiff NPs are preferentially internalized by circulating immune cells, thereby reducing clearance and prolonging the circulation time of soft NPs. The soft NPs, possessing lower elastic modulus and negative surface charge, exhibit high deformability and barrier-penetrating capacity, which have been previously reported to enhance transport across compromised biological membranes (ACS Nano, 2018,12:2846–2857). Therefore, their effective brain accumulation is primarily attributed to passive diffusion through disrupted BBB regions, rather than receptor-mediated transcytosis.\nFurthermore, when each NP type carried different drugs, we observed that preferential uptake of stiff NPs by peripheral immune cells, along with suppression of systemic immune activation, further enhanced the relative brain accumulation of soft NPs. This reinforces that prolonged circulation combined with BBB disruption jointly facilitates soft NPs passive CNS delivery.\nIn vitro, we also modeled this pathological condition by treating brain endothelial cells with OGD/R to mimic BBB injury and assess the dual-stiffness delivery behavior under such conditions (Fig. 4e-g).\nNevertheless, we agree that further engineering of soft NPs with active targeting ligands could be an interested and valuable direction to enhance CNS delivery efficiency of the dual-stiffness NP system, especially for complex brain diseases with multi-spatially progressive.\nFig. 4 (e) Schematic diagram depicting the construction of an in vitro damaged BBB model under ischemic stroke conditions, and the evaluation of how drug-loaded NPs with varying stiffness influence neutrophils migration and the recovery of underlying neuronal cell damage. (f) The number of neutrophils migrations in the in vitro disease model under different drug administration methods, n=3. (g) The survival rate of underlying damaged PC12 cells after different treatments, n=3.\nMinor concerns:\n1.In Figure 1f, the liposome height is only 7.2 nm, which differs greatly from the DLS-reported hydrodynamic diameter. Please clarify whether AFM measurements were performed in liquid. If not, the morphology and the Young’s modulus data may not accurately reflect the native properties of the liposomes.\nThank you for this valuable comment. We agree that AFM measurements in air can lead to significant deformation of soft liposomes and thus underestimate their true dimensions and stiffness. In our initial test, the height of the liposomes (~7.2 nm) was indeed smaller than their hydrodynamic diameter measured by DLS, which we attribute to probe-induced deformation due to the intrinsically low Young’s modulus of liposomes.\nTo address this, we repeated the AFM measurements under liquid conditions while carefully preventing sample dehydration and minimizing probe indentation by using a low spring-constant cantilever. The resulting topographic profiles (revised Fig. 1f) show that the liposomes maintain a nearly spherical morphology with an average height of ~100 nm, which is consistent with their hydrated size determined by DLS (~120 nm).\nFig. 1 (f) Transmission electron microscopy (TEM, scale bars=100 nm) and Atomic Force Microscope (AFM) images of Liposome.\n2.In the cellular uptake experiment shown in Figure 1h, the authors used Raw264.7 cells. However, as stated on line 111, the stiff nanoparticles are intended to act on neutrophils and monocyte/macrophage populations, while the soft nanoparticles are designed to promote neural repair. Therefore, key cell types involved in the therapeutic pathway—such as neutrophils, brain endothelial cells, and neurons—should be included in the uptake or functional assays to better validate the proposed targeting mechanism.\nThank you for your kind suggestion. We agree that the proposed dual-stiffness NP system realizing cell selectivity uptake preference should be supported by data from representative cell types along the immune-neural axis. In fact, this concept was developed on systematic in vitro, in vivo cellular uptake and functional studies, rather than being a priori assumption.\nAs shown in revised Fig. 2, we compared the uptake behavior of NPs with different stiffness across multiple cell types (Fig.2 a-c). The results revealed a trend that immune-lineage cells (e.g. Raw264.7 cells, BV2 cells) preferentially internalize stiff NPs, whereas neural-lineage cells (e.g. PC12 cells) favor soft NPs. To further verify this cell-selective-uptake, we constructed a two-compartment Transwell co-culture model, in which immune and neural cells were placed in separate layers. The results (Fig. 2f-h) demonstrated that immune cells preferentially uptake stiff NPs, while soft NPs accumulated mainly in neural cells, consistent with our hypothesis.\nFurthermore, as discussed in response to Major Concern 2, we established a mechanistic framework (Fig. 2i-p) in which NP stiffness governs the energetic landscape of membrane wrapping, while cellular cytoskeletal mechanics determine the dominant uptake stage.\nWe also investigated whether combining NPs of different stiffness affects their uptake efficiency by peripheral immune cells in vivo (Fig. 3a). We obtained disease-state leukocytes ex vivo while amplifying the visibility of stiffness-dependent phagocytic disparities. The results demonstrated that the dual-stiffness NPs enhanced the peripheral leukocytes uptake of the stiff NPs while markedly decreasing that of the soft NPs (Fig. 3b,c).\nThese experiments were conducted to validate our statement that “the stiff nanoparticles are intended to act on neutrophils and monocyte/macrophage populations, while the soft nanoparticles are designed to promote neural repair.”\nFig. 2 The fate of NPs with different stiffness in vitro and analysis of cell selective mechanisms. The mean fluorescence intensity (MFI) within (a) Raw264.7 cells (b) BV2 cells (c) PC12 cells after co-incubation 2 h with stiff NPs and soft NPs, respectively, n=3. The effect of cellular uptake of (d) stiff NPs and (e) soft NPs when simultaneously added stiff and soft NPs into Raw264.7 cells, n=3. (f) Schematic diagram of different stiffness NPs distribution in different types of cells in vitro. The upper layer was supplemented with neutrophils extracted from mice, while the lower layer was seeded with PC12 cells to investigate the distribution patterns of dual-stiffness NPs combinations added in this model. After labeling (g) stiff NPs or (h) soft NPs, the changes in the percentage of cell uptake and MFI of upper-layer neutrophils and lower-layer PC12 cells, which indicate the impact of combined administration of stiff NPs and soft NPs on their uptake by different cells, n=3. (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ. CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm. (n) Schematic diagram of the cells binding and internalization of NPs during uptake process. The MFI within (o) Raw264.7 cells and (p) PC12 cells after binding and internalization, n=3. Stiff (Dye) and Soft (Dye) mean fluorescence labeled stiff NPs and soft NPs, respectively. Stiff (Dye) + Soft and Stiff + Soft (Dye) mean fluorescence only labeled stiff NPs or soft NPs respectively in the combined administration of stiff NPs and soft NPs.\nFig. 3 (a) Schematic figure of isolating leukocytes from the peripheral blood of MCAO/R mice in vivo, followed by co-incubation with stiff NPs and soft NPs in vitro. The MFI of different cell populations, testing the effect of combined administration of stiff NPs and soft NPs on the uptake of stiff NPs (b) or soft NPs (c) by leukocytes and neutrophils, n=5. In vivo imaging system was utilized to investigate the biodistribution of soft NPs in MCAO/R mice following the combined administration of stiff NPs and soft NPs. Stiff (Dye) and Soft (Dye) mean fluorescence labeled stiff NPs and soft NPs, respectively. Stiff (Dye) + Soft and Stiff + Soft (Dye) mean fluorescence only labeled stiff NPs or soft NPs respectively in the combined administration of stiff NPs and soft NPs.\n3.In Figures 3c and 3d, the free drug group shows significantly lower efficacy compared to the drug-loaded nanoparticle group. Please clarify this discrepancy. In principle, under in vitro conditions, free drugs should have greater direct accessibility to cells than encapsulated ones. In addition, the manuscript lacks drug release profiles and pharmacokinetic data.\nThank you for your comment. We agree that, under ideal in vitro conditions, free drugs should exert stronger immediate effects due to their diret accessibility. However, both drugs in this study (NBP and Pic) are highly hydrophobic, eihibiting extremely poor aqueous solubility. Under such conditions, the free drug group required some organic solvent for dispersion and the effective concentration of the “free drug” in the culture medium was limited by its dissolution capacity, rather than by its nominal addition dose.\nIn contrast, the NP formulations convert the hydrophobic drugs into well-dispersed, water-compatible colloids after complete removal of organic solvent during NP preparation. As a result, the NP-loaded drugs remain stably dispersed in the aqueous medium, ensuring a higher effective and bioavailable concentration throughout incubation. Additionally, the co-incubation duration in our assays (several hours) is sufficient for the released fraction of drug from NPs to exert biological activity, which explains why NP-loaded formulations can achieve stronger effects relative to nominal free drug groups.\nRegarding your comment on drug release and pharmacokinetic, we have now added the complete in vitro drug release profiles to the revised manuscript (Fig. S11). These data support the sustained release characteristic of NBP@soft and Pic@stiff.\nIn response to this comment, the drug release data has been included and revisions have been made as follow:\nCD18 (integrin β2), a critical subunit of functional integrins LFA-1 and Mac-132, mediates the binding of peripheral immune cells to vascular endothelial adhesion molecules (e.g., ICAM-1) under ischemic stroke conditions33, promoting massive infiltration of neutrophils, monocytes, and other leukocytes into the brain. The stiff NPs were designed to carry Pic34, a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate CD18 expression35, thereby suppressing leukocyte adhesion, migration, and peripheral inflammation in ischemic stroke model mice. The encapsulation efficiency of Pic@stiff reached at 68.52 ± 2.32 % (Fig. S10a). In vitro release experiment showed that Pic@stiff exhibited an early rapid burst, releasing around 70% of Pic in the first 2 h followed by a slower sustained phase (Fig. S11a). Such an early fast-release window aligns well with the acute inflammatory surge and the rapid neutrophil infiltration that occur within hours after ischemic stroke. We employed Pic@stiff to suppress immune cell migration, like neutrophils, into the brain. As outlined in Fig. 4a, the primary neutrophils isolated from mice could penetrate the in vitro BBB model, followed by N-Formyl-Met-Leu-Phe (fMLP) stimulation to induce inflammation. The Pic@stiff treatment significantly reduced CD18 expression on neutrophils, thereby markedly decreasing their transmigration into the lower chamber under inflammatory conditions (Fig. 4b, c). Meanwhile, NBP36,37, a neuroprotectant targeting oxidative stress and apoptosis, was encapsulated in the soft NPs (NBP@soft), achieving an encapsulation efficiency of 77.42 ± 4.89% (Fig. S10b). NBP@soft demonstrated a gradual release pattern, with an initial release of 35% within the first 2 h and a continuous diffusion-dominated release that reached around 100% at 24 h (Fig. S11b). This sustained release pattern matches the prolonged oxidative stress and delayed neuronal injury that persists during the ischemic stroke, thereby supporting the need for continuous neuroprotection. We further investigated whether NBP@soft could enhance the therapeutic effects of NBP on damaged neuronal cells. The results showed that NBP@soft significantly increased the viability of damaged PC12 cells in vitro compared to free NBP, confirming its enhanced neuroprotective role (Fig. 4d).\nFig. S11 Cumulative release profiles of (a) Pic@stiff and (b) NBP@soft in PBS at 37 °C with shaking at 100 rpm, n=3.\nAs for in vivo pharmacokinetic (PK) studies, we respectfully clarify that full PK investigation (plasma drug concentration–time curves, t , AUC, etc.) is beyond the 1/2 scope of the present work. We aim to elucidate the stiffness-regulated biological behavior of NPs, including cellular uptake, biodistribution, spatial drug delivery ability. What’s more, we have already performed comprehensive in vivo fate studies of the NPs, including (i) quantitative biodistribution across major organs and (ii) uptake by peripheral immune cell populations. For NP-based systems, these measurements are considered the physiological relevant indicators of in vivo behavior. We believe these in vivo fate studies support the conclusions drawn regarding the stiffness-dependent behavior of our formulations.\n4.In Figure 5e, several brain sections appear incomplete or partially missing. Providing complete and representative brain slice images is essential to ensure accurate and reliable infarct volume evaluation.\nThank you very much for your comment. We fully agree that complete and representative slice images are essential. As you pointed out, the brain tissues from ischemic stroke mice often exhibit severe edema, which makes them particularly fragile and prone to fragmentation during sectioning immediately after extraction,\nIn response to your suggestion, we optimized the tissue preparation process by approximately extending the post-extraction freezing time to increase tissue rigidity, which facilitated more intact TTC slicing. The updated representative brain slice images have now been provided in the revised Fig. 6e, ensuring their completeness and reliability for infarct volume quantification.\nFig. 6 (e) 2,3,5-Triphenyltetrazolium chloride (TTC) staining (infarcted regions appeared white, while normal tissue stained red) and cerebral infarct volume of different groups, n=5.\n5.Figures 5f and 6 are missing a Sham group, which is necessary for accurately evaluating the therapeutic outcomes. Please include appropriate Sham controls.\nThank you very much for your suggestion regarding the inclusion of Sham group. We fully agree that appropriate Sham controls are essential for accurately assessing the therapeutic efficacy and distinguishing the treatment-specific effects from surgical interference. Therefore, we have supplemented the experiments accordingly.\nSpecifically, we included an additional Sham group, in which C57BL/6 mice underwent the same surgical procedures without vessel occlusion. The animals received identical handling and evaluation processes as the MCAO/R groups.\nAs shown in the revised Fig. 6f (orginal Fig. 5f), TUNEL staining revealed negligible apoptotic signals in the Sham group, confirming the absence of ischemic injury and validating the reliability of our model and treatment comparison.\nFurthermore, in the long-term assessment (revised Fig. 7, orginal Fig. 6), Sham mice were followed for 28 days post-surgery to evaluate body weight, neurological function, and histological outcomes. The results demonstrated stable survival, consistent body weight, and minimal neurological deficits, as indicated by mNSS scores and behavioral tests. At the study endpoint, Nissl staining and immunofluorescence for neurofilament and GFAP showed well-preserved neuronal morphology and negligible glial scar formation, further supporting the validity of our experimental design.\nFig. 6 (f) Tunel staining of mice on Day 3 brain post-treatment, labeling tunel (green signals) and nucleus (blue signals). Scale bars = 1 mm (original) and 200 μm (magnified).\nFig. 7 (a) Schematic of MCAO/R mice long-term treatment experiment. (b) Survival rate curve, n=9. (c) Body weight change curve, n=9. (d) Modified Neurological Severity Score (mNSS) score curve detailed view of the first 7 days, n=9. (e) mNSS score change curve, n=9. (f) Heatmap of mNSS score for individual MCAO/R mice over 28 days depicting neurological functional recovery (‘X’ denotes mortality on the corresponding day, deep red: mNSS=18, white: mNSS=0). (g) Beam balance test and walking test of MCAO/R mice on Day 0, Day 7, Day 14, Day 28. (h) Drug-loading dual stiffness NPs combination effectively reduced neural injury. Nissl staining labeling Nissl bodies, immune fluorescence-stained neurofilaments (green signals) and nucleus (blue signals), immune fluorescence-stained GFAP (green signals) and nucleus (blue signals) of coronal sections of the brain. Scale bars = 1 mm (original) and 200 μm (magnified).\n6.The manuscript states that the soft nanoparticles exhibit immune evasion capabilities. However, the liposomal formulation used is composed of conventional basic components that have been extensively studied. It remains unclear how stiffness alone accounts for the observed immune evasion. Would changing the lipid composition alter the immune interaction profile accordingly? Further clarification and mechanistic explanation are needed to support this key claim.\nThank you for your insightful comment. Our study aimed to isolate and investigate the effect of stiffness on the biological behavior of NPs, while minimizing interference from size, charge or surface chemical differences. Specifically, we discovered that NPs with different stiffness naturally exhibit distinct biodistribution patterns, and by harnessing this stiffness-induced spatial differentiation, we achieved highly efficient, compartment-specific drug delivery in disease models.\nWe deliberately chose a conventional basic and biocompatible liposomal formulation as the baseline carrier. This ensures that the observed immune evasion and biodistribution behaviors originate from physical stiffness differences rather than compositional or chemical variations.\nIn vitro, stiffness influences the energy landscape of NP-membrane interactions and the dominant stage of cellular uptake. Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment. Meanwhile, different cellular cytoskeletal mechanics determine the dominant uptake stage of PC12 cells and Raw264.7 cells. The cross-talk between these two factors underlies the observed in vitro compartment-specific delivery behavior—immune cells preferentially internalize stiff NPs, whereas neural cells favor soft ones (Fig. 2 f-h).\nFig. 2 (f) Schematic diagram of different stiffness NPs distribution in different types of cells in vitro. The upper layer was supplemented with neutrophils extracted from mice, while the lower layer was seeded with PC12 cells to investigate the distribution patterns of dual-stiffness NPs combinations added in this model. After labeling (g) stiff NPs or (h) soft NPs, the changes in the percentage of cell uptake and MFI of upper-layer neutrophils and lower-layer PC12 cells, which indicate the impact of combined administration of stiff NPs and soft NPs on their uptake by different cells, n=3.\nIn vivo, after entering the bloodstream, NPs rapidly adsorbed serum proteins to form a “protein corona”, which dictates their biological fate. Our proteomic analyses revealed that compared with soft NPs, stiff NPs are more enriched in complement-related proteins. Moreover, when dual-stiffness NP were incubated with serum, stiff NPs competitively adsorb complement factors, thereby reducing complement deposition and opsonization on soft NPs. This competitive adsorption leads to a “shielding effect,” attenuating immune recognition of soft nanoparticles and extending their systemic circulation, which subsequently enhances brain accumulation (Fig. 3a-e).\nFig. 3 (a) Schematic figure of isolating leukocytes from the peripheral blood of MCAO/R mice in vivo, followed by co-incubation with stiff NPs and soft NPs in vitro. The MFI of different cell populations, testing the effect of combined administration of stiff NPs and soft NPs on the uptake of stiff NPs (b) or soft NPs (c) by leukocytes and neutrophils, n=5. In vivo imaging system was utilized to investigate the biodistribution of soft NPs in MCAO/R mice following the combined administration of stiff NPs and soft NPs. (d) The fluorescence signal of the soft NPs was observed in the brain as well as (e) in the heart, liver, spleen, lung, and kidney of the mice, n=3. Stiff (Dye) and Soft (Dye) mean fluorescence labeled stiff NPs and soft NPs, respectively. Stiff (Dye) + Soft and Stiff + Soft (Dye) mean fluorescence only labeled stiff NPs or soft NPs respectively in the combined administration of stiff NPs and soft NPs.\nFrom a therapeutic perspective, this dual-stiffness strategy establishes a spatial-delivery paradigm. By loading a peripheral immunomodulatory drug (Pic) into stiff NPs and a neuroprotective drug (NBP) into soft NPs, we achieve dual-compartment delivery: the stiff NPS preferentially engage and modulate peripheral immune system, and soft NPs protected from clearance and and capable of penetrating the compromised BBB, deliver therapeutic agents to the brain (Fig. 4h-k).\nFig. 4 Post-administration of the drug-loaded combination, changes in Pic@stiff biodistribution in the (h) brain tissue and (i) other major organs of MCO/R mice, n=3. Changes in the biodistribution of NBP@soft in the (j) brain tissue and (k) other major organs of MCO/R mice after treatment, n=3.\nIn summary, our work introduces a mechanically guided dual-stiffness therapeutic paradigm, which harnesses stiffness-induced spatial segregation as a fundamental design principle for nanomedicine. Our work provides a versatile and extensible platform: by further integrating disease-specific lipid compositions targeting ligands on the “protected soft NPs”, this system could achieve both passive shielding from peripheral clearance and active navigation toward pathological sites. We believe this concept opens a new dimension in the rational design of nanotherapeutics, where mechanical properties themselves are harnessed as programmable variables for spatially controlled drug delivery.\n7.The observation that dual-stiffness nanoparticles can selectively target different cell types is intriguing and worthy of further investigation. It is recommended that the authors expand the mechanistic discussion of this phenomenon to better support the rationale behind the design and therapeutic outcomes.\nThank you so much for your kind suggestion. The phenomenon of dual-stiffness nanoparticles selectively targeting different cell types is very interesting and need further investigated. After experimental and simulation experiments, we hypothesized that this phenomenon arises from two factors: (i) NP stiffness and (ii) cellular cytoskeletal organization which together determine which uptake stage dominates.\nIn our simulation, we performed coarse-grained molecular dynamics (CGMD) simulations of NP-membrane interactions. The free energy landscape obtained from steered molecular dynamics (SMD) revealed that soft NPs benefit from stronger adhesion but require deeper penetration and nearly twice the total energy costcompared to stiff NPs. Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment (Fig. 2i-k).\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nFrom a cellular perspective, neuronal cells such as PC12 are softer and have a dispersed actin network, whereas macrophages like Raw264.7 cells have dense actin at the plasma membrane to support rapid phagocytosis. Consistently, actin staining revealed strong membrane-associated actin in Raw264.7 cells and a looser network in PC12 cells (Fig. 2l,m, Fig. S3). Inhibition of actin polymerization with CytD reduced stiff NP uptake in Raw264.7 cells but had minimal effect on PC12 cells or soft NPs, suggesting that stiff NP uptake in macrophages relies largely on internalization, while PC12 cell uptake is dominated by surface binding (Fig. S4).\nFig. 2 CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm.\nFig. S3 CLSM image of Raw264.7 cells and PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=100 μm.\nFig. S4 The MFI within (a) Raw264.7 cells and (b) PC12 cells co-incubated with stiff and soft NPs, respectively under different concentration of CytD, n=3.\nFurther, in two-stage experiments decoupling binding and internalization (Fig. 2n), Raw264.7 cells maintained stiffness-dependent uptake (Fig. 2a,o), confirming internalization-driven preference for stiff NPs. In PC12 cells, decoupling removed the apparent soft NP preference (Fig. 2c,p), revealing the intrinsic thermodynamic favorability of stiff NPs, consistent with simulation results. Collectively, these results indicate that cell-type–specific NP preferences arise from the interplay between NP stiffness and cellular characteristics such as cytoskeletal organization.\nFig. 2 (n) Schematic diagram of the cells binding and internalization of NPs during uptake process.\nFig. 2 The MFI within Raw264.7 cells under (a) normal condition or (o) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nFig. 2 The MFI within PC12 cells under (c) normal condition or (p) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nWe further designed a Tranwell model, the upper layer was supplemented with neutrophils extracted from mice, while the lower layer was seeded with PC12 cells to investigate the distribution patterns of dual-stiffness NPs combinations added in this model (Fig. 2f). Dual-stiffness NPs exploit both the shielding effect and cellular selectivity: stiff NPs are preferentially taken up by immune cells in the upper layer, while soft NPs are captured more efficiently by PC12 cells in the lower layer (Fig. 2g,h). This strategy enhances the natural preference observed in each cell type. In vivo, such selectivity could be even more pronounced.\nFig. 2 (f) Schematic diagram of different stiffness NPs distribution in different types of cells in vitro. The upper layer was supplemented with neutrophils extracted from mice, while the lower layer was seeded with PC12 cells to investigate the distribution patterns of dual-stiffness NPs combinations added in this model. After labeling (g) stiff NPs or (h) soft NPs, the changes in the percentage of cell uptake and MFI of upper-layer neutrophils and lower-layer PC12 cells, which indicate the impact of combined administration of stiff NPs and soft NPs on their uptake by different cells, n=3.\nIn the future, soft NPs could be modified to enhance their targeting ability within the dual-stiffness NP system. This would allow suppression of peripheral immune activation while delivering soft NPs efficiently to targeting pathological site. This represents a highly promising paradigm for multi-targeted and spatiotemporally controlled therapeutic interventions.\nReviewer #3 (Remarks to the Author):\nLiu et al. developed a dual-stiffness nanoparticle system for compartment-specific drug delivery in ischemic stroke, combining stiff NPs and soft NPs. The approach demonstrated synergistic therapeutic effects in mice by suppressing systemic inflammation, reducing brain immune infiltration, and improving neuronal recovery. While the animal results are compelling, but the mechanistic concept is not innovative and the study lacks mechanistic depth (molecular drivers of stiffness-dependent uptake). This work is too preliminary to be published on Nature Communications.\nThank you very much for your insightful comment. We understand your concerns regarding the conceptual innovation and insufficient mechanistic depth in the original manuscript. We provide a comprehensice clarification.\nFirstly, regarding conceptual novelty. We agree that our work builds on the fundamental understanding of the stiffness–cell uptake relationship established by previous studies (ACS Nano, 2018,3; Sci. Adv., 2020,6:eaaz4316; Nat. Commun., 2018,9:2607). These pioneering contributions, including some from our own team, provided essential mechanistic insights into how nanoparticle (NP) stiffness influences cellular internalization, tissue penetration, or in vivo distribution. Our current study represents a significant conceptual and functional advance by transitioning from these foundational mechanistic insights to a novel, functional therapeutic concept: leveraging dual-stiffness NPs as a coordinated, compartment-specific drug delivery strategy for ischemic stroke. This is fundamentally different from prior “single-stiffness, single-function” designs. Our system integrates stiffs NP for peripheral immune modulation and soft NPs for central neuroprotection, thereby achieving spatially drug actions that match the multi-compartment nature of ischemic stroke pathology.\nSpecifically, we demonstrate two layers of innovation:\n(1) Realized dual-space biodistribution: our results demonstrates that dual-stiffness NP combination enabled delivery of stiff NPs to the periphery and soft NPs to the brain, achieving spatially tiered drug delivery. This spatial tiering cannot be achieved by a single-NP system or by immune-cell hitchhiking approaches, which inherently couple peripheral and CNS delivery, meaning the same therapeutic payload must act in both compartments.\n(2) Synergistic therapeutic efficacy: aligning NP stiffness with the pathophysiological demands of each space (peripheral immunosuppression vs. central neuroprotection), the system achieves synergistic therapeutic efficacy far beyond what either stiff or soft NPs can accomplish alone.\nSecondly, regarding mechanism depth. We appreciate your comment that the original version of the manuscript did not sufficiently explain the stiffness-dependent cell selectivity and the mechanism underlying the “shielding effect” phenomenon. In response, we have conducted lots of experiments and substantially deepened the investigation.\n(1) Stiffness-dependent cell selectivity: immune cells prefer stiff NPs and neuron cells prefer soft NPs.\nWe hypothesize that this phenomenon arises from two complementary factors: (a) NP stiffness governs the energetic profile of membrane wrapping, and (b) cellular cytoskeletal mechanics determine the dominant uptake stage.\nTo examine the first factor, we developed a coarse-grained molecular dynamics (CGMD) model to simulate the interaction between membranes and NPs of varying stiffness (Fig. 2i-k). The Gibbs free energy change during phagocytosis was computed in the NPT ensemble using the steered molecular dynamics (SMD) module in LAMMPS (Fig. 2j). The results showed that soft NP exhibited a lower free energy benefit during early adhesion, but required deeper penetration (43.6 nm) and higher deformation energy (538.8 𝑘 𝑇) for full engulfment, being nearly double that of rigid NP (274.6 𝑘 𝑇). These results demonstrate that even in a purely membrane system, NP stiffness profoundly influences uptake efficiency (Fig. 2k). Soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nFrom the cellular perspective, neuronal cells such as PC12 are inherently softer than macrophage, like Raw264.7 cells, as reported in previous studies (ACS Omega, 2020,5: 31115-31125; Proc Natl Acad Sci U.S.A., 2010,107:21016-21021). Reflecting their distinct physiological functions, neurons possess diffuse actin networks to enable dynamic extension (Mol biol Cell, 2016,27:3695-3704; Front Cell Neurosci, 2018,12:447), whereas immune cells exhibit dense actin accumulation at the plasma membrane to support rapid phagocytosis (Immunol Rev, 2014,256:1; Front Immunol, 2020,11:1097). Consistent with these features, our actin staining under identical imaging conditions revealed strong membrane-associated actin fluorescence in Raw264.7 cells, whereas PC12 cells displayed a looser and more dispersed actin organization (Fig. 2l,m; Fig. S3). Because actin polymerization mainly drives the internalization step of endocytosis rather than the initial adhesion, we next used cytochalasin D (CytD) to inhibit actin dynamics and assess its effect on NP uptake. CytD reduced stiff NP uptake in Raw264.7, but had smaller effect on soft NPs or on PC12 uptake overall (Fig. S4). This result suggests that stiff NP uptake in Raw264.7 cells is largely dependent on internalization. In contrast, PC12 cells showed negligible changes in NP uptake upon actin inhibition, consistent with their sparse actin networks and limited involvement of internalization.\nFig. 2 CLSM image of (l) Raw264.7 cells and (m) PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=50 μm.\nFig. S3 CLSM image of Raw264.7 cells and PC12 cells, with actin-tracker labeling the cell actin (green) and DAPI labeling nucleus (blue), scale bars=100 μm.\nFig. S4 The MFI within (a) Raw264.7 cells and (b) PC12 cells co-incubated with stiff and soft NPs, respectively under different concentration of CytD, n=3.\nTo verify this, we decoupled binding and internalization in a two-stage experiment (Fig. 2n). We compared the differences in cell uptake of NPs between this decoupling group and normal experimental conditions, examining the effects of binding and internalization on the uptake efficiency of the two types of cells (Raw264.7 cells and PC12 cells). For Raw 264.7 cells (Fig. 2o), stiff NPs showed higher uptake of stiff NPs in the treated group, consistent with normal condition (Fig. 2a), confirming that stiffness preference is driven by internalization efficiency, while binding process between Raw264.7 cells and NPs has almost no effect on their uptake efficiency. In PC 12 cells (Fig. 2p), after we decoupled binding and internalization steps, the amount of stiff and soft NPs taken up by PC12 cells approaches consistency, despite a strong preference under normal conditions (Fig. 2c). This finding indicates that NP binding, rather than internalization efficiency, predominantly determines uptake in PC12 cells. Once the binding advantage of soft NPs at 37 °C is eliminated, the intrinsic thermodynamic favorability of stiff NPs emerges, resulting in comparable overall uptake and abolishing the apparent soft NP preference. Collectively, these results are consistent with our initial hypothesis and align well with the simulated energetic profiles of NPs with different stiffness.\nFig. 2 (n) Schematic diagram of the cells binding and internalization of NPs during uptake process.\nFig. 2 The MFI within Raw264.7 cells under (a) normal condition or (o) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nFig. 2 The MFI within PC12 cells under (c) normal condition or (p) treated condition, n=3. Stiff and Soft mean fluorescence labeled stiff NPs and soft NPs, respectively.\nTogether, these findings indicate that the NP stiffness-dependent membrane energetic profile and the two cell types differ in the dominant uptake phase governing NP uptake leading to the stiffness-dependent cell selectivity: Raw264.7 cells rely primarily on internalization, leading to a preference for stiff NPs, whereas PC12 cells are dominated by surface binding, showing higher association with soft NPs.\nHowever, we also know that there are other factors that contribute to the existence of this selectivity, like the involvement of mechanosensitive membrane receptors (e.g., Piezo1) in this process. We are trying to further explore and investigate the reasons behind it. We believe that such mechanism research will have great value and significance for the practical application of different stiffness NP combination administration in the future.\n(2) Shielding effect mechanism\nRegarding the shielding effect that you also raised later in your comments, we have conducted additional in-depth investigations. We found that this phenomenon is likely driven primarily by two factors: competitive adsorption of complement-related proteins in the protein corona between NPs of different stiffness, and differences in membrane–NP energetic profiles during cellular uptake.\nFirstly, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment analysis indicated that these differences predominantly involve immune responses, complement activation, and extracellular matrix-related processes (Fig. S6). Given the critical role of opsonization and immune clearance in NP fate, we focused on functional categories related to immune recognition, such as complement and immunoglobulin components. The analysis showed that stiff NPs were significantly enriched in complement-related proteins compared with soft NPs, particularly core components of the classical complement pathway (e.g., C1 complex and C6) (Fig. 3f,g). This suggests that NP stiffness is a key determinant of complement adsorption and immune recognition potential.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs.\nWe further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: normal serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This results indicate that stiff NPs more readily adsorb complement components, thereby promoting complement-mediated immune cell uptake.\nFig. 3 The MFI within Raw264.7 cells after co-incubation with (h) stiff NPs and (i) soft NPs under different serum pre-treated conditions.\nAfter that, we compared the protein corona compositions of each NP type when incubated alone or in dual stiffness NP combination. For stiff NPs (Fig. S7, Fig. 3j), co-administration with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. In contrast, soft NPs under co-incubation (Fig. S8, Fig. 3k) exhibited markedly reduced adsorption of complement and immunoglobulin proteins, particularly C3, a key amplifier in the complement cascade. These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs.\nFig. S7 Bubble plot of GO enrichment analysis showing differences in protein corona formed on stiff NPs in dual-stiffness NPs combination vs. stiff NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (j) stiff NPs alone and stiff NPs in dual-stiffness NPs combination.\nFig. S8 Bubble plot of GO enrichment analysis showing differences in protein corona formed on soft NPs in dual-stiffness NPs combination vs. soft NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (k) soft NPs alone and soft NPs in dual-stiffness NPs combination.\nTo further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Fig. 3l). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups diminished this effect. This supports the notion that complement activation contributes critically to the shielding behavior.\nFig. 3 (l) The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled.\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k). This preferential membrane engagement of stiff NPs not only promotes their own uptake but also energetically hinders subsequent deformation required for soft NP internalization, thereby reinforcing the complement-mediated shielding in vivo.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nWe further evaluated the cellular uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Fig. S9).\n1) Stiff NPs-first, then-soft NPs: When stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Fig. S9 a,b).\n2) Soft NPs-first, then-stiff NPs: Pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Fig. S9 c,d).\n3) Stiff NPs-first, then-stiff NPs: Sequential addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Fig. S9 e,f).\n4) Soft NPs-first, then-soft NPs: Sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Fig. S9 g,h).\nFig. S9 The effect of the first stiff NPs and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\nCollectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms. However, we know that this mechanism warrants further validation in larger cohorts, across other NP stiffness combinations, and in different disease models, which will be pursued in future studies to establish a dual-stiffness delivery platform adaptable to disease-specific conditions.\n1.Key Issues: insufficient justification for nanoparticle design and mechanistic elucidation. This study proposes that stiff NPs (engulfed by immune cells) assist soft NPs (evading clearance) in crossing the BBB. However, numerous prior studies have already demonstrated that NPs can efficiently enter the brain via immune cell hitchhiking (e.g., monocyte- or neutrophil-mediated transport), such as: Nat. Nanotechnol. 2017, 12: 692-700; Nat. Comm. 2020, 11: 1126. Why is a dual stiff/soft NP system necessary? The current data fail to demonstrate a clear advantage over existing single-NP approaches.\nThank you very much for your comment and question regarding the necessity for dual-stiffness NP system. As we know, ischemic stroke is a dynamic, compartmentalized disease involving both periphery activation and central neuronal injury. Acute ischemia induces neuronal necrosis, oxidative stress, and blood–brain barrier (BBB) disruption, leading to the leakage of damage-associated molecular patterns (DAMPs) into systemic circulation. These signals activate the peripheral immunity, triggering rapid recruitment of neutrophils and monocytes to the injured brain.\nUnlike in brain tumors, where neutrophils (NEs) are actively recruited and can infiltrate inflamed tumor tissue, enabling strategies in which NEs carry drugs and release them in response to the tumor microenvironment (Nat. Nanotechnol., 2017,12:692-700), whereas ischemic stroke presents a distinct phenomenon. In ischemic stroke, excessive infiltration of immune cells into the brain can exacerbate tissue damage and accelerate disease progression, making uncontrolled immune cell-mediated delivery potentially harmful (J Clin Invest., 2020, 130:2777-2788).\nDespite growing recognition of the spatially compartmentalized pathology of ischemic stroke, most therapeutic strategies have continued to treat the brain and peripheral immune system as a unified target, aiming primarily for systemic immunomodulation or brain-wide neuroprotection. To address this, we developed dual-stiffness NP combination strategy, a spatially resolved therapeutic approach that independently targets i) the periphery and ii) the brain as distinct yet interacting compartments, each requiring tailored intervention.\nOur dual-stiffness NP system design provides several advantages:\n1. Realized dual-space biodistribution:\n• In vitro: immune cells (e.g., Raw 264.7) preferentially engulfed stiff NPs, while neural cells (e.g., PC12) showed a higher uptake of soft NPs (Fig. 2a-c). Under co-culture (Transwell) conditions, immune cells mainly internalized stiff NPs, whereas PC12 cells accumulated more soft NPs through passive trans-compartmental diffusion (Fig2f-h).\n• In vivo, dual-stiffness approach significantly enhanced the accumulation of soft NPs in the brain (Fig. 3a-e). Stiff NPs “shield” soft NPs from peripheral immune clearance and reduce hepatic uptake, thereby improving circulation time and CNS availability. This critical partitioning cannot be achieved with soft NPs alone.\n2. Synergistic therapeutic efficacy\n• The system delivered two different drugs to two different locations: an anti-inflammatory agent (via stiff NPs) to peripheral immune cells and a neuroprotective compound (via soft NPs) to the brain (Fig. 5, 6).\n• This coordinated intervention resulted in synergistic therapeutic effects, including attenuated peripheral inflammation, improved neuronal survival, and enhanced behavioral recovery (Fig. 7).\n• This therapeutic efficacy was significantly greater than that achieved by administering either the stiff NP (anti-inflammatory) or the soft NP (neuroprotective) population alone, providing definitive proof that the dual-stiffness combination is superior to its single-stiffness counterparts (Fig. S16).\n3. Simplicity and efficiency\n• Mechanical modulation can be precisely tuned via polymer composition and crosslink density without additional chemical synthesis or targeting ligands, making the system cost-effective and scalable.\n• Each NP population carried a compartment-specific therapeutic, avoiding dilution of active drug across sites.\nRegarding your comment on mechanism: we further explored the “shielding effect,” which contributes to the dual-stiffness spatial distribution. Our studies indicate that this effect arises from both the energetic profiles of NPs with different stiffness and complement-related mechanisms. This mechanism explains the in vitro stratified distribution of NPs and the in vivo observations that stiff NPs are preferentially taken up by peripheral immune cells, while soft NPs are protected and accumulate in the brain, producing enhanced therapeutic effects. Detailed mechanistic analysis combined with experimental data is presented in our response to Comment 5.\n2.The NPs share identical lipid compositions, but does the PLGA core alter surface properties (e.g., protein corona formation) in vivo? Why can immune evasion be achieved merely by changing the hardness, without taking into account the surface properties?\nThank you for your insightful questions. We agree that surface properties should be consistent across NP formulations. To this end, we coated the PLGA core of stiff NPs with a lipid layer closely matching the main components of the soft NPs (phosphatidylcholine), as shown in Fig. 1e. We also optimized the preparation methods to ensure comparable NP size and surface charge (Fig. 1c, d), thereby ensuring that all major physical properties other than stiffness of NPs were similar.\nFig. 1 (c) Size and (d) zeta potential of Lipo-PLGA and Liposome using dynamic light scattering (DLS) analysis, n=3. Transmission electron microscopy (TEM, scale bars=100 nm) and Atomic Force Microscope (AFM) images of (e) Lipo-PLGA and (f) Liposome. (g) Young’s module of Lipo-PLGA and Liposome using AFM analysis, n=3. (h) The changes in the uptake of Lipo-PLGA and Liposome by Raw264.7 cells, under the influence of inhibitors targeting different cellular uptake pathways (each specifically inhibiting micropinocytosis, caveolae and clathrin), compared to the normal group, n=3.\nAs you mentioned, we further examined the protein corona composition of NPs with different stiffness after serum incubation to understand potential differences in immune recognition. Gene Ontology (GO) enrichment analysis indicated proteins differentially enriched between stiff and soft NPs, and these differences predominantly involve immune responses, complement activation (Fig. S6). Notably, stiff NPs were significantly enriched in complement-related proteins compared with soft NPs, particularly core components of the classical complement pathway (e.g., C1 complex and C6) (Fig. 3f,g). Functional studies further confirmed that serum complement promotes immune cells uptake of stiff NPs: preincubated with serum showed higher uptake by Raw264.7 cells compared with heat-inactivated serum or buffer controls, whereas soft NPs showed a similar but smaller effect (Fig. 3h,i). These results indicate that NP stiffness influences complement adsorption and, consequently, immune cell recognition.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs. Select the top 20 GO Terms with P-value ranking in the enrichment analysis results (sorted from small to large), and draw a bubble chart of the enrichment items.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs. The MFI within Raw264.7 cells after co-incubation with different way pre-treated (h) stiff NPs and (i) soft NPs.\nTo further probe the role of stiffness independent of surface chemistry, we developed a coarse-grained molecular dynamics (CGMD) model to simulate the interaction between cell membranes and NPs of varying stiffness (Fig.2 i-k). In our simulations, the NP structures were modeled to match the experimental designs (Fig. 2i), with lipid coated cores for stiff NPs and lipid bilayer alone for soft NPs. The Gibbs free energy change during phagocytosis was computed in the NPT ensemble using the steered molecular dynamics (SMD) module in LAMMPS (Fig. 2j). The free energy profile was obtained by integrating the spring force over the NP trajectory. Prior to membrane contact, the spring is compressed due to the NP’s free fall, performing positive work. From initial adhesion (~13 nm) to full immersion (~-12 nm), the spring extends against membrane deformation, resulting in a progressive increase in free energy cost. Notably, soft NP exhibited a modest free energy benefit during early adhesion, but full uptake required significantly greater penetration depth (43.6 nm), leading to a total energy cost of 538.8 k T, being nearly double that of stiff NP (274.6 k T). These demonstrated that soft NPs benefit from enhanced adhesion due to deformability, yet their compliance ultimately imposes higher energy penalties during internalization. In contrast, stiff NPs exhibit pronounced thermodynamic advantages after membrane contact, consuming less energy for full engulfment. This mechanical difference provides a rationale for why soft NPs are less efficiently internalized by immune cells, contributing to “immune evasion”.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nTaken together, we proposed that NP stiffness modulates immune cell uptake through two mechanisms: (i) differential adsorption of serum proteins, especially complements, and (ii) stiffness-dependent energetic profile of membrane wrapping efficiency. These findings support our conclusion that stiffness can regulate immune recognition while surface properties are otherwise held constant.\n3.How does drug release correlate with NP stiffness? PLGA typically degrades slowly—does Pic release match the acute inflammation timeline (peaking at 6–24 h post-stroke)?\nThank you for rasing these critical points regarding the relationship between drug release and NPs stiffness, concerning the acute stroke timeline. In general, the drug release behavior of NPs is primarily determined by the NPs compositions, structure, and the interaction between the drug and NPs. Stiffness has not been demonstrated to directely alter the physicochemical drug release kinetics of NPs in current studies. Instead, stiffness mainly affects the biological fate of NPs, such as barrier penetration ability, recoginition and uptake by immune cells, and circulation time, which in the end exerts indirect influences on the overall pharmacodynamic performance.\nWe do acknowledge that Poly(lactic-co-glycolic acid) (PLGA), the core material of our stiff NPs, is known for its slow degradation. However, its drug release profiles included four release mechanisms, containing diffusion through polymerc shells (governing the early stage of release) and degradation (as the particles degrade in the late stage). Its drug release profile is typically characterized by an initial burst release phase, primarily driven by drug diffusion from the particle surface, which occurs rapidly. This phenomenon is well-documented in PLGA related literature (Sci. Rep., 2025,1:4218; Int. J. Pharm., 2006,314:198–206). This early fast-release window is compatible with the acute inflammation peak (6–24 h) following ischemic stroke.\nOur design was guided by the biodistribution tendencies of the stiff and soft NPs. In the dual-stiffness NP system, the stiff Lipo-PLGA NPs prefer to be more efficiently taken up by peripheral immune cells, while the soft Liposome NPs, being less recognized by the immune system, can passively accumulate in the brain.\nAccordingly, stiff NPs were designed to carry Pic, a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate integrin β2 expression and suppress leukocyte adhesion, migration. Pic@stiff NPs achieved immune modulation in the peripheral compartment, where stiff NPs were predominantly distributed and internalized. Meanwhile, NBP, a clinically approved neuroprotective drug for ischemic stroke, was encapsulated in the soft NPs, enabling enhanced brain delivery and exertion of antioxidant and neuroprotective effects within the CNS. This drug design aligns the pharmacological action of the drug with the spatial delivery behavior of NPs with different stiffness, realizing a rational dual-space therapeutic strategy.\nOur dual-stiffness system utilized a core-shell structure for the stiff NPs (Lipo-PLGA NPs), where we desired to leverage the release profiles of PLGA NPs. This feature is highly advantageous for treating acute ischemic stroke, as the inflammation cascade peaks rapidly (typically 6–24 hours post-stroke).\nIn response to this comment, the drug release data has been included and revisions have been made as follow:\nCD18 (integrin β2), a critical subunit of functional integrins LFA-1 and Mac-132, mediates the binding of peripheral immune cells to vascular endothelial adhesion molecules (e.g., ICAM-1) under ischemic stroke conditions33, promoting massive infiltration of neutrophils, monocytes, and other leukocytes into the brain. The stiff NPs were designed to carry Pic34, a Syk kinase inhibitor (a key regulator of integrin signaling) to downregulate CD18 expression35, thereby suppressing leukocyte adhesion, migration, and peripheral inflammation in ischemic stroke model mice. The encapsulation efficiency of Pic@stiff reached at 68.52 ± 2.32 % (Fig. S10a). In vitro release experiment showed that Pic@stiff exhibited an early rapid burst, releasing around 70% of Pic in the first 2 h followed by a slower sustained phase (Fig. S11a). Such an early fast-release window aligns well with the acute inflammatory surge and the rapid neutrophil infiltration that occur within hours after ischemic stroke. We employed Pic@stiff to suppress immune cell migration, like neutrophils, into the brain. As outlined in Fig. 4a, the primary neutrophils isolated from mice could penetrate the in vitro BBB model, followed by N-Formyl-Met-Leu-Phe (fMLP) stimulation to induce inflammation. The Pic@stiff treatment significantly reduced CD18 expression on neutrophils, thereby markedly decreasing their transmigration into the lower chamber under inflammatory conditions (Fig. 4b, c). Meanwhile, NBP36,37, a neuroprotectant targeting oxidative stress and apoptosis, was encapsulated in the soft NPs (NBP@soft), achieving an encapsulation efficiency of 77.42 ± 4.89% (Fig. S10b). NBP@soft demonstrated a gradual release pattern, with an initial release of 35% within the first 2 h and a continuous diffusion-dominated release that reached around 100% at 24 h (Fig. S11b). This sustained release pattern matches the prolonged oxidative stress and delayed neuronal injury that persists during the ischemic stroke, thereby supporting the need for continuous neuroprotection. We further investigated whether NBP@soft could enhance the therapeutic effects of NBP on damaged neuronal cells. The results showed that NBP@soft significantly increased the viability of damaged PC12 cells in vitro compared to free NBP, confirming its enhanced neuroprotective role (Fig. 4d).\nFig. S11 Cumulative release profiles of (a) Pic@stiff and (b) NBP@soft in PBS at 37 °C with shaking at 100 rpm, n=3.\n4.Lack of Mechanistic Explanation for Soft NP BBB Penetration: How do soft NPs cross the damaged BBB? The study only provides passive accumulation data (Fig. 2l) but does not clarify: Whether they rely on transcytosis (e.g., LDL receptor-mediated pathways) or exploit inflammation-induced BBB disruption (e.g., MMP-9 upregulation)? Critical mechanistic experiments are missing, such as BBB model studies with endocytosis inhibitors or immunoelectron microscopy to visualize trans-endothelial transport.\nThank you for your important questions. We agree that understanding how soft NPs cross the BBB is important for interpreting our findings. And the purpose of this study was to exploit, rather than exclude, the BBB permeability changes that naturally occur after ischemic stroke.\nIschemic stroke is a dynamic and spatially heterogeneous disease involving both central neurons and peripheral immune responses. During the acute phase, ischemia induces neuronal necrosis, oxidative stress, and disruption of the BBB, leading to leakage of DAMPs and recruitment of peripheral immune cells, which further aggravate BBB damage. BBB disruption is characterized by altered tight junction complexes and increased paracellular permeability, enabling passive exchange between the systemic circulation and CNS compartments (Am J Physiol Cell Physiol, 2018,315:C343–C356). Indeed, neurologists have recognized that disease-induced BBB breakdown provides a therapeutic window for passive delivery of agents such as proteins, peptides, and NPs without additional BBB modulation (Nat Rev Neurol, 2018,14:133–150).\nConsistent with this concept, previous studies have demonstrated that negatively charged or neutral liposomes can passively accumulate in ischemic brain regions, mainly due to increased vascular permeability in BBB and local inflammation (Int J Nanomedicine, 2016,11:3035–3048). Moreover, BBB opening during stroke is biphasic, an early reversible BBB hyperpermeability phase at 4–6 h after stroke and a delayed irreversible opening of BBB at 48–72 h after stroke (Transl Stroke Res, 2016,7:89–92). These findings support that passive leakage through damaged vasculature is a predominant route for liposomes entry during stroke progression.\nBuilding upon this pathophysiological basis, our dual-stiffness NP system was intentionally designed to leverage passive accumulation. Stiff NPs are preferentially internalized by circulating immune cells, thereby reducing clearance and prolonging the circulation time of soft NPs. The soft NPs, possessing lower elastic modulus and negative surface charge, exhibit high deformability and barrier-penetrating capacity, which have been previously reported to enhance transport across compromised biological membranes (ACS Nano, 2018,12:2846–2857). Therefore, their effective brain accumulation is primarily attributed to passive diffusion through disrupted BBB regions, rather than receptor-mediated transcytosis.\nFurthermore, when each NP type carried different drugs, we observed that preferential uptake of stiff NPs by peripheral immune cells, along with suppression of systemic immune activation, further enhanced the relative brain accumulation of soft NPs. This reinforces that prolonged circulation combined with BBB disruption jointly facilitates soft NPs passive CNS delivery.\nIn vitro, we also modeled this pathological condition by treating brain endothelial cells with OGD/R to mimic BBB injury and assess the dual-stiffness delivery behavior under such conditions (Fig. 4e-g).\nNevertheless, we agree that further engineering of soft NPs with active targeting ligands could be an interested and valuable direction to enhance CNS delivery efficiency of the dual-stiffness NP system, especially for complex brain diseases with multi-spatially progressive.\nFig. 4 (e) Schematic diagram depicting the construction of an in vitro damaged BBB model under ischemic stroke conditions, and the evaluation of how drug-loaded NPs with varying stiffness influence neutrophils migration and the recovery of underlying neuronal cell damage. (f) The number of neutrophils migrations in the in vitro disease model under different drug administration methods, n=3. (g) The survival rate of underlying damaged PC12 cells after different treatments, n=3.\n5.The \"shielding effect\" of stiff NPs on soft NPs is interesting but poorly dissected. The study relies on indirect flow cytometry data rather than real-time imaging (e.g., intravital microscopy) to demonstrate NP-NP or NP-cell interactions. Furthermore, the molecular basis for stiffness-dependent uptake (e.g., involvement of mechanosensors, cytoskeletal remodeling) is unexplored. A deeper mechanistic investigation is needed to justify the novelty beyond prior work on NP elasticity.\nThank you very much for your important questions regarding the molecular basis of the “shielding effect”.\nTo further elucidate this phenomenon, we conducted experiments from the perspectives of protein corona formation and underlying biophysical mechanisms. Specifically, we systemically analyzed the protein corona composition of different stiffness NPs after serum incubation. As detailed in our response to Comment 2, we have systematically analyzed the protein corona composition of NPs with different stiffness, revealing stiffness-dependent complement adsorption and immune recognition (Fig. S6, Fig. 3f-i). Building on these findings, we further explored the molecular basis underlying the “shielding effect” observed when stiff and soft NPs coexist.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs. Select the top 20 GO Terms with P-value ranking in the enrichment analysis results (sorted from small to large), and draw a bubble chart of the enrichment items.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. (g) Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs.\nWe compared protein corona compositions under single versus dual-stiffness co-incubation conditions. Given the critical role of opsonization and immune clearance in NP fate, we focused on functional categories related to immune recognition, such as complement and immunoglobulin components. For stiff NPs (Fig. S7, Fig. 3j), co-incubation with soft NPs significantly enriched classical complement pathway components (C1qa, C1qb, C1s1, C1rb) and reduced complement inhibitory factors (Cfh, Cfhr1, Cfhr2), suggesting enhanced complement activation potential. In contrast, soft NPs under co-incubation (Fig. S8, Fig. 3k) exhibited markedly reduced adsorption of complement and immunoglobulin proteins, particularly C3, a key amplifier in the complement cascade. These findings indicate that stiff NPs preferentially adsorb complement proteins in serum, reducing complement deposition on soft NPs and supporting a competitive complement adsorption mechanism.\nFig. S7 Bubble plot of GO enrichment analysis showing differences in protein corona formed on stiff NPs in dual-stiffness NPs combination vs. stiff NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (j) stiff NPs alone and stiff NPs in dual-stiffness NPs combination.\nFig. S8 Bubble plot of GO enrichment analysis showing differences in protein corona formed on soft NPs in dual-stiffness NPs combination vs. soft NPs alone.\nFig. 3 Differential relative abundance of complement and immunoglobulin components in the protein corona of (k) soft NPs alone and soft NPs in dual-stiffness NPs combination.\nTo directly test this hypothesis, we pre-incubated stiff NPs in serum to allow complement adsorption, then co-incubated them with soft NPs in the presence of immune cells. Under high-serum conditions, pre-treated stiff NPs significantly reduced the uptake of soft NPs compared with heat-inactivated serum or buffer controls (Fig. 3l). This supports the notion that complement activation contributes critically to the shielding behavior.\nFig. 3 (l) The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled.\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k). This preferential membrane engagement of stiff NPs not only promotes their own uptake but also energetically hinders subsequent deformation required for soft NP internalization, thereby reinforcing the complement-mediated shielding in vivo.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nWe further evaluated the cellular uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Fig. S9).\n1) Stiff NPs-first, then-soft NPs: When stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Fig. S9 a,b).\n2) Soft NPs-first, then-stiff NPs: Pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Fig. S9 c,d).\n3) Stiff NPs-first, then-stiff NPs: Sequential addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Fig. S9 e,f).\n4) Soft NPs-first, then-soft NPs: Sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Fig. S9 g,h).\nFig. S9 The effect of the first stiff NPs and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\nCollectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms. However, we know that this mechanism warrants further validation in larger cohorts, across other NP stiffness combinations, and in different disease models, which will be pursued in future studies to establish a dual-stiffness delivery platform adaptable to disease-specific conditions.\nIn response to the comment, more information has been added to the manuscript as below:\nTo understand this phenomenon, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment revealed that stiff NPs were more enriched with complement-related proteins, particularly those involved in the classical pathway (C1 complex, C6), compared with soft NPs (Fig. S6, Fig. 3f–g). This indicates that NP stiffness is a key determinant of complement adsorption and immune recognition potential. We further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that, compared with stiff NPs, soft NPs adsorb fewer complement-related components from serum and therefore experience a weaker complement-mediated increase in immune cell uptake.\nWe then compared the corona composition of each NP type when incubated alone or in dual stiffness NP combination. For stiff NPs (Fig. S7, Fig. 3j), co-administration with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. In contrast, soft NPs in the dual-stiffness formulation exhibited substantially reduced adsorption of complement and immunoglobulin proteins (Fig. S8, Fig. 3k). These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs. To further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Fig. 3l). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups diminished this effect. This supports the notion that complement activation contributes critically to the shielding behavior.\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k, Movie 1). This preferential membrane engagement of stiff NPs not only promotes their own uptake but also energetically hinders subsequent deformation required for soft NP internalization, thereby reinforcing the complement-mediated shielding.\nWe further evaluated the immune cell uptake of NPs under different administration sequences and time intervals, we fluorescently labeled and combined stiff and soft NPs (Fig. S9).\n1) Stiff NPs-first, then-soft NPs: When stiff NPs were added first, immune cells preferentially internalized them due to lower energetic barriers. As a result, subsequently added soft NPs exhibited reduced uptake, with the inhibitory effect becoming more pronounced at shorter dosing intervals (Fig. S9 a,b).\n2) Soft NPs-first, then-stiff NPs: Pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs, consistent with the lack of a mechanical advantage for soft NPs (Fig. S9 c,d).\n3) Stiff NPs-first, then-stiff NPs: Sequential addition of stiff NPs showed reduced uptake of the second batch, likely due to partial energy depletion from the first phagocytosis. However, shorter intervals increased complement activation, which enhanced the second-batch uptake (Fig. S9 e,f).\n4) Soft NPs-first, then-soft NPs: Sequential soft NP dosing led to reduced uptake of the second batch at shorter intervals, reflecting an “energetic occupation effect” rather than complement-mediated stimulation (Fig. S9 g,h).\nCollectively, these results suggest that NP stiffness dictates not only individual uptake efficiency but also interparticle competition and sequence-dependent shielding behavior, mediated by both energetic and complement-related mechanisms.\nFig. 3 (f) Gene Ontology (GO) enrichment analysis of differentially adsorbed proteins on stiff and soft NPs. The horizontal axis represents the enrichment factor (the ratio of the number of differentially expressed proteins enriched in this entry to the number of annotated proteins). The larger the enrichment factor, the higher the degree of enrichment of differentially expressed proteins. The vertical axis represents the name of the GO entry. The color of the dots changes from blue to red, representing the change in P-value from large to small. The smaller the P-value, the more statistically significant it is. The size of the dots represents the number of differentially expressed proteins annotated by the corresponding entries. (g) Differential relative abundance of complement and immunoglobulin components in the protein crown of stiff and soft NPs. The MFI within Raw264.7 cells after co-incubation with different way pre-treated (h) stiff NPs and (i) soft NPs. Differential relative abundance of complement and immunoglobulin components in the protein crown of (j) stiff NPs alone and stiff NPs in dual-stiffness NPs combination or (k) of soft NPs alone and soft NPs in dual-stiffness NPs combination. (l) The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled.\nFig. S6 Bubble plot of gene Ontology (GO) enrichment analysis showing differences in protein corona formed on soft vs. stiff NPs. Select the top 20 GO Terms with P-value ranking in the enrichment analysis results (sorted from small to large), and draw a bubble chart of the enrichment items. The horizontal axis represents the enrichment factor (the ratio of the number of differentially expressed proteins enriched in this entry to the number of annotated proteins). The larger the enrichment factor, the higher the degree of enrichment of differentially expressed proteins. The vertical axis represents the name of the GO entry. The color of the dots changes from blue to red, representing the change in P-value from large to small. The smaller the P-value, the more statistically significant it is. The size of the dots represents the number of differentially expressed proteins annotated by the corresponding entries.\nFig. S7 Bubble plot of GO enrichment analysis showing differences in protein corona formed on stiff NPs in dual-stiffness NPs combination vs. stiff NPs alone.\nFig. S8 Bubble plot of GO enrichment analysis showing differences in protein corona formed on soft NPs in dual-stiffness NPs combination vs. soft NPs alone.\nFig. 2 (i) Schemic figure of stiff NP (left) and soft NP (right). (j) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (k) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nFig. S9 The effect of the first stiff and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\n6.Controls are incomplete in the in vivo experiments. Free drug combo (Pic + NBP) should be included to isolate the contribution of NP delivery vs. drug synergy. Single-drug NPs (e.g., Pic@stiff alone) should also be included to clarify whether dual-compartment targeting is essential or if peripheral immunomodulation suffices.\nWe sincerely appreciate your suggestion. In response to the comment, more information has been added to the manuscript as below:\nTo validate our interpretations and isolate the contribution of NPs delivery versus drug synergy, we conducted long-term control treatment experiments with five designed control groups (Fig. S15, 16): Group E (only-soft NPs: NBP@soft), Group F (only-stiff NPs: Pic@stiff), Group G (dual-stiffness NPs without drug: stiff NPs and soft NPs without loading drug), Group H (drug-swapped dual-stiffness NPs: NBP@stiff + Pic@soft), Group I (free drug without NPs: NBP and Pic) and a Sham Group. Our results demonstrated all of the control treatment experiments showed lower survival rates and worse mNSS scores on Day 28 compared to the final formulation Group A. Group E showed high early-phase mortality, particularly among severely injured animals, indicating failure to provide acute protection. Groups F, G, and H, each containing stiff NPs, modestly delayed early mortality, likely through transient peripheral inflammation suppression via phagocytosis, but lacked durable neuroprotection. In assessing neurological functional recovery, drug-free Group G exhibited an average score of 10 on the mNSS at 28 days, indicating minimal neural repair. Group F which contained Pic@stiff retained high mNSS scores. In contrast, Group E utilized NBP@soft achieving significant attenuation of neurological deficits in surviving mice. As to Group H, swapped drug in dual-stiffness NPs system showed diminished survival rates and failed to rescue profoundly affected subjects, emphasizing the importance of compartment-specific drug allocation. And the free drug group (Group I) performed a survival rate of only 11.1%, likely due to rapid systemic clearance and minimal brain accumulation. These results demonstrated the therapeutic superiority of our stiffness-tiered drug delivery strategy - Pic-loaded stiff NPs combined with NBP-loaded soft NPs.\nFigure S15. (a) Schematic of MCAO/R mice long-term control treatment experiment. (b) Survival rate curve, n=9. (c) Body weight change curve and (d) Modified Neurological Severity Score (mNSS) score curve, n=9. (e) Heatmap of mNSS score for individual MCAO/R mice depicting neurological functional recovery (‘X’ denotes mortality on the corresponding day, deep red: mNSS=18, white: mNSS=0). (f) Beam balance test and walking test of MCAO/R mice on Day 0, Day 1, Day7, Day 14, Day 28.\nFigure S16. Beam balance test and walking test of MCAO/R mice during long-term control treatment experiment.\n7.What is the basis for choosing a treatment window of 6 hours? Survival benefits (77.8% vs. 11.1%) are impressive, but is the therapeutic timing optimized? The 6 h post-MCAO window may not reflect real-world stroke treatment delays.\nThank you for this question. We selected 6 h post-MCAO as the therapeutic time point based on both clinical and mechanistic considerations. Clinically, the efficacy of reperfusion therapy in humans declines sharply within ~3–4.5 hours after stroke onset (N Engl J Med, 2008,359:1317–1329), whereas in animal models with prompt reperfusion, the therapeutic window can extend up to 12 hours, providing a comparatively longer duration for experimental interventions (Drug Des Devel Ther., 2015,52:3445–3454). These considerations define a clinically relevant early-to-delayed intervention range. Mechanistically, previous studies demonstrated that around 6 h after MCAO, the blood–brain barrier (BBB) enters a transiently reversible “open window” phase (Transl Stroke Res, 2016, 7:89–92), coinciding with the onset of accelerated peripheral immune activation and leukocyte infiltration (Nat. Immunol., 2024, 25:357–370). This time frame is therefore optimal for our dual-stiffness nanoparticle strategy: stiff NPs can modulate peripheral immune activation while soft NPs penetrate the brain through the temporarily loosened BBB. In this context, the 6 h time point represents a biologically justified window that balances peripheral regulation and central delivery.\nWe acknowledge that the 6 h window may not encompass all real-world treatment delays. Accordingly, in the future, we may evaluate both earlier (e.g., 1–3 h) and later (e.g., 12 h) interventions studies to fully delineate the time–response relationship.\n8.ANOVA post-hoc tests (e.g., Tukey vs. Sidak) are unspecified.\nThank you for your helpful suggestion. We have now clarified all statistical tests used in the manuscript. Specifically, for comparisons between two groups, an unpaired two-tailed t-test was conducted, such as Fig.2 a-e.\nFig. 2 The mean fluorescence intensity (MFI) within (a) Raw264.7 cells (b) BV2 cells (c) PC12 cells after co-incubation 2 h with stiff NPs and soft NPs, respectively, n=3. The effect of cellular uptake of (d) stiff NPs and (e) soft NPs when simultaneously added stiff and soft NPs into Raw264.7 cells, n=3.\nFor conparisons among more than two groyps, one-way ANOVA followed by Tukey’s multiple post-hoc test was performed, such as Fig.6 a-b.\nFig. 6 (a) Quantification of myeloperoxidase (MPO) and neutrophils elastase (NE) levels in the lesioned cerebral hemisphere of mice at 3 days after treatment, n=5. (b) The contents of TNF-α and IL-1β at 3 days after treatment, n=5.\nTo address whether there were significant differences among the long-term treatment experimental groups, a two-way ANOVA (or mixed-effects model) was performed using the Compare column means (main column effect) approach to evaluate the overall treatment effects across the 28-day experimental period (Fig.7e). The results demonstrated significant intergroup differences.\nFig. 7 (e) Modified Neurological Severity Score (mNSS) score curve, n=9.\nTo further investigate the time-specific differences, a two-way ANOVA (or mixed-effect model) with simple effects within rows was applied to compare the groups at each individual time point (Fig. 7d).\nFig. 7 (d) Detailed view of the first 7 days of Modified Neurological Severity Score (mNSS) score curve, n=9.\nIn response to the comment, more information has been added to the method of manuscript as below:\nStatistical analysis\nAll statistical analyses were performed using GraphPad Prism 10.4.1 software (USA). Data are expressed as mean ± standard deviation (SD). For comparisons between two groups, an unpaired two-tailed t-test was used. For comparisons among multiple groups, onw-way ANOVA followed by Tukey’s multiple comparison post-hoc test was performed. For measurements of recovery scores over the 28-day period, a two-way ANOVA (mixed-effects model) was conducted using the Compare column means (main column effect) approach to assess overall treatment effects across time. To further investigate the time-specific differences, a two-way ANOVA (or mixed-effect model) with simple effects within rows was applied to compare the groups at each individual time point. Statistical significance was determined based on the calculated p-values (p < 0.05)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors fully addressed my comments, with clear logic and reliable data.\nIt meets the journal’s requirements. I recommend its immediate acceptance for publication.", "reviewer_2": "This manuscript has been revised according to previous comments, and I think it can be accepted for publication without change.", "reviewer_3": "While the authors have responded to several points, the mechanistic foundations of the study remain inadequately supported. Key questions are unresolved:\n1. The complement competition hypothesis is intriguing but not conclusively demonstrated. Can the authors provide quantitative data on complement protein adsorption (e.g., C3b deposition via ELISA or western blot) on stiff vs. soft NPs, alone and in combination, to substantiate the competitive adsorption claim? Could the shielding be further validated using complement-deficient serum or complement inhibitors to show the effect is specifically complement-dependent?\n2. Beyond complement, is there evidence that stiff NPs physically deplete phagocytic capacity (e.g., by exhausting actin polymerization or membrane curvature resources)? Are there live-cell imaging data (e.g., time-lapse microscopy) showing real-time competition between stiff and soft NPs at the cell surface?\n3. The authors note that passive leakage through a disrupted BBB is a key route. However, this limits the system's applicability to conditions with significant BBB damage. In a model with intact or minimally disrupted BBB (e.g., early time points or milder injury), does the dual-stiffness strategy still facilitate brain delivery of soft NPs? If not, does this constrain its clinical translation? Has the team performed in vitro BBB model studies with endocytosis inhibitors to rule out active transcytosis pathways? It is suggested to provide the ultrastructural evidence (e.g., TEM) of NPs within brain endothelial cells or traversing the BBB."}, "author_response": "Thank you very much for your comments. We agree that the complement competition hypothesis requires rigorous quantitative support and functional validation. In response to your suggestions, we have now provided comprehensive quantitative proteomic data and complement-dependent functional experiments, and we further quantified C3b deposition by ELISA as an additional validation.\nFirstly, we quantitatively analyzed the serum protein corona formed on stiff and soft nanoparticles (NPs) under identical incubation conditions. As shown in Table1 and Figure1, stiff NPs exhibited significantly higher adsorption of complement-related proteins, including key components such as C1qa and C6, and also showed a clear trend toward increased immunoglobulin binding. After normalization of protein abundance, the results are visualized as a heatmap, which highlights the marked differences in the major protein corona compositions between stiff and soft NPs.\nSoft_vs_Stiff\nProtein Stiff-1 Stiff-2 Stiff-3 Soft-1 Soft-2 Soft-3\n_Regulation\nC1qa 33.79976 30.25097 23.77535 16.19997 13.9125 16.37825 down\nC1qc 27.43094 30.55909 21.73634 10.98995 9.077022 12.02024 down\nC1s1 19.38167 18.80612 15.10309 10.42546 8.494407 9.676113 down\nC6 7.313471 7.739677 5.359026 2.984978 2.57599 3.656468 down\nIghv14-2 3.057189 4.491947 3.410556 0.717746 0.450957 1.106419 down\nIgkv1-117 4.506342 6.582646 4.093686 2.509313 1.311515 2.358502 down\nIglv3 1.247008 1.604509 1.43737 0.929702 0.500419 1.022999 down\nTable1. The relative abundance values of complement-related differentially expressed proteins in the stiff and soft NP groups, n=3. (Original data was presented in resource data files)\nFigure1. The relative abundance values of complement-related differentially expressed proteins in the stiff and soft NP groups, n=3.\nFigure2. Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff and soft NPs.\nWe next investigated how co-administration in a dual-stiffness NP system influences complement-related protein adsorption on stiff NPs. Quantitative proteomic results in Table2 and Figure3 demonstrated that co-added with soft NPs led to an increased enrichment of C1 complex components (C1qa, C1qb, C1s1, C1rb) and decreased levels of complement inhibitory factors (Cfh, Cfhr1, Cfhr2), indicating enhanced complement activation potential. For clarity, the normalized data are presented in Figure4, which enables more intuitive visualization of how the dual-stiffness strategy reshapes the protein corona composition on stiff NPs.\nSoft+Stiff_vs\nSoft+Stiff Soft+Stiff Soft+Stiff\nProtein Stiff-1 Stiff-2 Stiff-3 _Stiff_Regula\n-1 -2 -3\ntion\nC1qa 86.85559 88.68022 92.81933 296.77493 254.37484 262.09183 up\nC1qc 169.0803 148.3202 150.3914 435.27282 378.55320 431.41962 up\nC1rb 27.4984 25.80762 29.94188 81.998779 79.506714 89.494255 up\nC1s1 51.79992 54.18506 62.89648 209.29421 194.89943 208.00826 up\nCfh 232.7222 198.7165 222.9213 63.484402 62.505160 62.469215 down\nCfhr2 5.883765 6.334492 7.09048 3.2476395 2.7610913 2.6334894 down\nCfhr1 5.012414 5.386083 5.748463 1.9359935 1.8436530 2.6323867 down\nTable2. The relative abundance values of complement-related differentially expressed proteins in the protein corona of stiff NPs alone and stiff NPs in dual-stiffness NPs combination, n=3. (Original data was presented in resource data files)\nFigure 3. The relative abundance values of complement-related differentially expressed proteins in the protein corona of stiff NPs alone and stiff NPs in dual-stiffness NPs combination, n=3.\nFigure 4. Differential relative abundance of complement and immunoglobulin components in the protein corona of stiff NPs alone and stiff NPs in dual-stiffness NPs combination.\nUsing the same method, we further examined the protein corona formed on soft NPs within the dual-stiffness NP system. Quantitative proteomic results in Table3 and Figure5 showed that soft NPs in the dual-stiffness system exhibited substantially reduced adsorption of complement-related proteins and immunoglobulins compared with soft NPs alone. These results suggest a competitive adsorption process, where stiff NPs preferentially bind complement proteins, thereby reducing complement deposition on soft NPs.\nStiff+Soft_vs\nStiff+Soft Stiff+Soft Stiff+Soft\nProtein Soft-1 Soft-2 Soft-3 _Soft_Regula\n-1 -2 -3\ntion\nC3 433.413 472.9984 512.8602 221.34047 132.13921 204.11772 down\nC4b 58.39388 76.7405 81.41893 30.934168 17.991998 29.299717 down\nC5 64.09208 70.95448 79.67293 27.854437 21.443405 24.524473 down\nC6 8.861673 9.886698 9.91114 4.4652217 2.6490422 3.9341319 down\nC8a 43.12382 48.75411 54.49778 17.963020 12.553185 18.197218 down\nIghg1 100.295 120.1922 124.8812 40.640928 45.970835 44.390498 down\nIghg2b 266.6189 308.2074 313.3878 134.33710 34.743661 117.49017 down\nIgkc 216.5796 232.2072 264.0544 114.94502 71.798482 121.08666 down\nTable 3. The relative abundance values of complement-related differentially expressed proteins in the protein corona of soft NPs alone and soft NPs in dual-stiffness NPs combination, n=3. (Original data was presented in resource data files)\nFigure 5. The relative abundance values of complement-related differentially expressed proteins in the protein corona of soft NPs alone and soft NPs in dual-stiffness NPs combination, n=3.\nFigure 6. Differential relative abundance of complement and immunoglobulin components in the protein corona of soft NPs alone and soft NPs in dual-stiffness NPs combination.\nWe also validate our conclusion by using complement-deficient serum or complement inhibitors to show the effect is specifically complement-dependent. We examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. As shown in Figure7a, stiff NPs preincubated with serum exhibited significantly higher immune cellular uptake, whereas heat-inactivation largely abolished this enhancement. Soft NPs showed a similar trend (Figure7b), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that soft NPs experience a weaker complement-mediated increase in immune cell uptake.\nTo further confirm this complement-mediated protection, we pre-treated stiff NPs under three serum conditions and combined pre-treated stiff NPs with dye-labeled soft NPs as dual-stiffness NP system. We evaluated immune cell uptake of soft NPs in this system (Figure8). The lowest uptake of soft NPs was observed in serum pre-incubation stiff NPs group, whereas heat-inactivation or untreated stiff NPs groups significantly diminished this shielding effect. This supports the notion that complement activation contributes critically to the shielding behavior.\nFigure 7. The mean fluorescence intensity (MFI) within Raw264.7 cells after co-incubation with (a) stiff NPs and (b) soft NPs under different serum pre-treated conditions, n=3.\nFigure 8. The MFI within Raw264.7 cells after co-incubation with different way pre-treated dual-stiffness NPs combination, soft NPs were labeled, n=3.\nIn addition, motivated by your suggestion, we quantified C3b deposition in NP protein coronas using ELISA. Following serum incubation, NP-bound proteins were isolated and normalized by total protein content. The ELISA results showed higher C3b levels on stiff NPs compared with soft NPs (approximately 0.219 ng vs. 0.078 ng C3b per 200 μg of total corona protein; Figure 9), consistent with the trends observed in the quantitative proteomic analysis. Notably, the absolute amounts of C3b were close to the lower detection limit of the assay (Table4). This low abundance highlights the necessity of employing unbiased, high-sensitivity quantitative proteomics, which allows reliable detection and comparison of subtle yet biologically meaningful differences in corona composition that are difficult to resolve using single-analyte assays alone.\nFigure 9. Quantification of C3b deposition on NPs protein coronas by ELISA. (a) Standard curve for C3b quantification (y = 0.0008x + 0.0323, R² = 0.99). (b) C3b content normalized to total corona protein on stiff and soft NPs, n=3.\nTable 4. Original data of C3b deposition quantification.\nC3b concentration (ng/mL) OD value Standard curve\n3.75 1.427\n1.875 0.83\ny = 0.0008x + 0.0323\n0.9375 0.518\nR² = 0.99\n0.46875 0.337\n0.234375 0.197\nNP type OD value C3b concentration (ng/mL)\n0.248 0.24032825\nStiff NPs 0.239 0.21395076\n0.236 0.20515826\n0.196 0.08792497\nSoft NPs 0.193 0.07913247\n0.189 0.06740914\nTaken together, the consistent trends across quantitative proteomic profiling, complement-dependent functional uptake assays, and ELISA measurements provide convergent support for a complement-mediated competitive opsonization mechanism, in which stiff NPs preferentially bind complement proteins, thereby reducing complement deposition and immune recognition of soft NPs in the dual-stiffness NP system.\nIn response to the comment, more information has been added to the manuscript as below:\nTo understand this phenomenon, we analyzed the protein corona composition of NPs after serum incubation. Gene Ontology (GO) enrichment revealed that stiff NPs were more enriched with complement-related proteins, particularly those involved in the classical pathway (C1 complex, C6), compared with soft NPs (Fig. S6, Fig. 3f–g). This indicates that NP stiffness is a key determinant of complement adsorption and immune recognition potential. We further examined the influence of serum complement on NP uptake by immune cells using three serum conditions: serum, heat-inactivated serum (56 °C, 1 h, to inactivate complement), and untreated buffer. Stiff NPs preincubated with serum exhibited significantly higher immune cellular uptake, whereas heat-inactivation largely abolished this enhancement (Fig. 3h). Soft NPs showed a similar trend (Fig. 3i), but the magnitude of serum-induced enhancement was much smaller than that observed for stiff NPs. This suggests that, compared with stiff NPs, soft NPs adsorb fewer complement-related components from serum and therefore experience a weaker complement-mediated increase in immune cell uptake. This trend was further supported by the quantification of C3b in NP protein coronas using ELISA, which revealed higher C3b levels on stiff NPs than on soft NPs (Fig. S7).\nFig. S7 The amount of C3b adsorbed onto stiff and soft NPs after serum incubation was quantified by ELISA and normalized to total corona protein content, n=3.\n2. Beyond complement, is there evidence that stiff NPs physically deplete phagocytic capacity (e.g., by exhausting actin polymerization or membrane curvature resources)? Are there live-cell imaging data (e.g., time-lapse microscopy) showing real-time competition between stiff and soft NPs at the cell surface?\nThank you for your questions. We agree that, in addition to complement-mediated effects, biophysical constraints on phagocytic capacity may also contribute to the observed stiffness-dependent shielding behavior.\nAs you’ve mentioned, phagocytic capacity included actin polymerization and membrane curvature resources. In our molecular dynamics (MD) simulation, stiff NPs exhibited a clear energetic advantage during phagocytosis, possessing lower membrane-wrapping energy barriers and faster engulfment kinetics compared with soft NPs. This enables stiff NPs to preferentially occupy membrane binding sites and locally consume curvature resources on immune cells, thereby limiting the availability of these resources for subsequently arriving particles (Figure10，Video1).\nFigure 10. (a) Schemic figure of stiff NP (left) and soft NP (right). (b) Free energy profiles for a stiff NP (yellow) and a soft NP (blue) versus NP position relative to the planar membrane. Dashed lines mark the points of complete uptake for each NP type. Inset: Simulation snapshot showing pronounced membrane neck formation induced by the soft NP. (c) Mesoscopic model of nanoparticle endocytosis. CGMD stimulation of stiff NP (left) and soft NP (right) during 15750 τ.\nTo experimentally probe whether such biophysical engagement is associated with cytoskeletal resource allocation, we further examined actin cytoskeleton remodeling in immune cells co-incubated with stiff or soft NPs. Representative confocal images showed actin remodeling in immune cells upon contact with stiff NPs (Figure11a), characterized by early actin enrichment at the cell periphery (10 min), followed by extensive stiff NPs internalization with reduced actin signal (30 min), and partial actin reorganization at later stages (60 min). In contrast, soft NPs induced minimal changes in actin distribution (Figure11b), suggesting that their cellular internalization does not require extensive actin cytoskeleton engagement.\nFigure 11. Confocal laser scanning microscopy (CLSM) images of RAW264.7 cells incubated with (a) stiff NPs or (b) soft NPs for 10, 30, and 60 min. NPs were labeled with DiD (red), actin was stained with Actin-Tracker (green), and nuclei were stained with DAPI (blue), scale bars=50 μm.\nBased on our MD simulations and cytoskeletal observations suggesting a potential depletion of phagocytic resources during stiff NP uptake, we also assessed whether this depletion of phagocytic capacity functionally translates into altered cellular uptake behavior using sequential dosing experiments. When stiff NPs were added first, the uptake of subsequently added soft NPs were reduced, with stronger inhibition observed at shorter dosing intervals (Figure12a, b). In contrast, pre-incubation with soft NPs did not significantly affect the uptake of subsequently added stiff NPs (Figure12c, d), consistent with the lack of a mechanical advantage for soft NPs. Sequential stiff–stiff dosing also resulted in reduced uptake of the second stiff NP batch (Figure12e, f), indicating that phagocytic capacity is finite and can be transiently exhausted following prior engulfment events. Together, these results provide functional evidence that stiff NPs can physically deplete phagocytic capacity in a stiffness- and time-dependent manner.\nFigure 12. The effect of the first stiff and then soft NPs administration method on the cellular uptake of (a) stiff NPs and (b) soft NPs, the effect of the first soft and then stiff NPs administration method on the cellular uptake of (c) soft NPs and (d) stiff NPs, the effect of the first stiff and then stiff NPs administration method on the cellular uptake of (e) the first stiff NPs and (f) the second stiff NPs, the effect of the first soft and then soft NPs administration method on the cellular uptake of (g) the first soft NPs and (h) the second soft NPs, with administration time intervals of 4 h, 2 h and 1 h, n=3.\nThe combination of MD simulations, cytoskeletal remodeling analysis, and sequence- and interval-dependent uptake assays provides converging evidence for interparticle competition occurring during the phagocytic process. Collectively, our results indicate that stiff NPs can transiently deplete phagocytic capacity by preferentially consuming membrane curvature and actin polymerization resources, thereby creating a biophysical basis for interparticle competition that complements the observed complement-mediated shielding effects.\nIn response to the comment, more information has been added to the manuscript as below:\nBeyond the biochemical contribution of complement competition, the shielding effect can also be interpreted from a biophysical perspective. Stiff NPs, which possess lower membrane-wrapping energy barriers and faster engulfment kinetics, are more likely to occupy membrane binding sites and locally deplete curvature resources on immune cells (Fig. 2i-k, Movie 1). In addition, as NP internalization by immune cells involves actin cytoskeleton remodeling, we performed CLSM to observe actin organization during NP-cell interaction (Fig. S10). After co-incubation with stiff NPs, immune cells exhibited noticeable actin redistribution accompanied by progressive stiff NP internalization, whereas soft NPs induced minimal changes in actin organization. These observations suggest that stiff NPs not only benefit from energetic advantages in membrane wrapping but are also associated with increased engagement of the actin cytoskeleton during uptake. Such enhanced cytoskeletal involvement may reduce the availability of actin polymerization capacity, potentially limiting the phagocytic uptake of subsequently encountered NPs and thereby further reinforcing the complement-mediated shielding effect observed in the dual-stiffness NP system.\nFig. S10 CLSM images of RAW264.7 cells incubated with (a) stiff NPs or (b) soft NPs for 10, 30, and 60 min. NPs were labeled with DiD (red), actin was stained with Actin-Tracker (green), and nuclei were stained with DAPI (blue), scale bars=50 μm.\n3. The authors note that passive leakage through a disrupted BBB is a key route. However, this limits the system's applicability to conditions with significant BBB damage. In a model with intact or minimally disrupted BBB (e.g., early time points or milder injury), does the dual-stiffness strategy still facilitate brain delivery of soft NPs? If not, does this constrain its clinical translation? Has the team performed in vitro BBB model studies with endocytosis inhibitors to rule out active transcytosis pathways? It is suggested to provide the ultrastructural evidence (e.g., TEM) of NPs within brain endothelial cells or traversing the BBB.\nThank you for raising this important point regarding blood-brain barrier (BBB) transport mechanism and translational scope of our dual-stiffness NP system. We agree that the efficiency of soft NPs entering the brain is closely related to the pathological state of the BBB.\nIschemic stroke is a dynamic and spatially heterogeneous disease involving both central neurons and peripheral immune responses. During the acute phase, ischemia induces neuronal necrosis, oxidative stress, and endothelial dysfunction, leading to disruption of tight junctions and increased paracellular permeability of the BBB, enabling passive exchange between the systemic circulation and CNS compartments (Am J Physiol Cell Physiol, 2018,315:C343–C356). Importantly, BBB disruption under ischemic is known to involve the concerted upregulation of both paracellular leakage and nonspecific transcellular transport pathways, rather than the activation of a single dominant route (J Clin Med, 2021, 10:3795; Neuron, 2014, 82:603-617).\nIn this context, disease-induced BBB breakdown has been widely recognized as providing a therapeutic window for passive delivery of agents such as proteins, peptides, and NPs without additional BBB modulation (Nat Rev Neurol, 2018,14:133–150). Previous studies have also demonstrated that negatively charged or neutral liposomes can passively accumulate in ischemic brain regions, mainly due to BBB disruption (Int J Nanomedicine, 2016,11:3035–3048). Therefore, within the disease context investigated here, negatively charged soft liposomes are expected to passively accumulate in ischemic brain tissue.\nTo experimentally validate this premise, we further supplemented BBB transport studies using the negatively charged liposomes employed in this work. In vitro, we established an BBB model and compared soft NP transport across normal v.s. OGD/R-treated endothelial monolayers that mimic ischemia-induced BBB disruption. As shown in Figure13, soft NP penetration across an intact BBB remained minimal (~3–5%) and showed little increase over time. In contrast, BBB disruption induced by OGD/R treatment resulted in a time-dependent and significantly enhanced penetration of soft NPs. These results confirm that efficient brain entry of soft NPs primarily occurs under BBB-compromised conditions, which is fully consistent with our expectations.\nFigure 13. The penetration ability of soft NPs under different blood-brain barrier (BBB) conditions, n=3.\nIn vivo, we observed that the dual-stiffness NP system significantly increased the brain accumulation of soft NPs in the MCAO/R model. Soft NPs can passively accumulate in the brain through the compromised BBB caused by stroke (Figure14a). In the application of dual-stiffness NP system, stiff NPs prefer to be phagocytosed by immune cells in the periphery, whereas soft NPs, protected from immune clearance, can circulate longer and passively accumulate more in the brain (Figure14b). The passive penetration of soft NPs through the damaged BBB is therefore a key mechanism of our delivery strategy, rather than an interference that needs to be excluded.\nFigure 14. (a) The fluorescence signal of the soft NPs was observed in the brain as well as (c) in the heart, liver, spleen, lung, and kidney of the mice, n=3. Soft (Dye) mean fluorescence labeled soft NPs. Stiff + Soft (Dye) mean fluorescence only labeled soft NPs in the combined administration of stiff NPs and soft NPs.\nFurthermore, in the drug-loaded dual-stiffness NP system, we found that stiff NPs carrying Pic were extensively taken up by peripheral immune cells, leading to suppression of immune activation. Under these conditions, soft NPs benefited from reduced peripheral immune clearance and showed significantly enhanced brain accumulation compared with administration of soft NPs alone, accompanied by reduced off-target accumulation in peripheral organs such as the liver (Figure15).\nFigure 15. Post-administration of the drug-loaded combination, changes in the biodistribution of NBP@soft in the (a) brain tissue and (b) other major organs of MCO/R mice after treatment, n=3.\nImportantly, the present study does not aim to isolate or attribute soft NP brain entry to a specific endothelial transport mechanism. Instead, it leverages the global increase in BBB permeability induced by disease pathology. Therefore, the use of endocytosis inhibitors to distinguish individual transcytosis pathways was not pursued, as such approaches would not reflect the integrated transport landscape of a pathologically compromised BBB. We agree that mechanistic dissection of specific transport routes may be of interest in future studies, however, it falls beyond the primary objective of the present work, which focuses on disease-context–dependent delivery behavior.\nAs to clinical translation, we agree that under conditions of an intact or minimally disrupted BBB, passive entry of soft NPs would be limited, which represents an important boundary condition of the current system. However, many clinically relevant brain diseases, including ischemic stroke, Alzheimer’s disease (AD), and glioblastoma (GBM), are characterized by disease-stage–dependent BBB dysfunction, rather than a fully intact barrier. In AD, chronic neuroinflammation-driven BBB disruption has been validated by both pathological and neuroimaging studies (Nat Rev Neurol, 2021,3:157-172; Translational Neurodegeneration, 2025,14:16). In GBM, the BBB is partially disrupted with increased permeability due to increased angiogenesis and the release of cytokines and chemical mediators (Int J Mol Sci, 2021,22:12654). Therefore, the clinical relevance of BBB disruption is not an exception but a common feature of many brain disorders.\nIschemic stroke is characterized by acute peripheral immune activation, AD involves a chronic, low-grade systemic inflammation, and GBM displays a profound systemic immunosuppression. Despite their different immune directions, all these conditions involve pathological crosstalk between the periphery and the brain. Therefore, our dual-stiffness NP strategy — in which stiff NPs target peripheral immune cells and soft NPs penetrate the compromised BBB to modulate the CNS microenvironment — could provide a generalizable approach to address such dual-space pathologies.\nWith respect to disease conditions featuring an intact or minimally disrupted BBB, we agree that further engineering of soft NPs with active targeting ligands could be an interesting and valuable direction to enhance CNS delivery efficiency of the dual-stiffness NP system, especially for complex brain diseases with multi-spatially progressive."}]

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