Leveraging engineered mitochondria through intercellular communication network for accelerated transport and delivery

Domain: biology
Published: 2025-12-23
Editorial outcome: major_revision

Abstract
Inspired by the non-transmembrane transfer of mitochondria in cell-to-cell communications, herein, we report an original exploration to accelerate mitochondrial intercellular transport, and its application to exogenous cargo delivery. We discover that deliberate PINK1-targeted mitophagy down-regulation elevates mitochondrial transit capacity via multifaceted drivers—morphological adaptation, metabolic reprogramming, and respiratory enhancement. Capitalizing on this, we engineer high-speed mitochondrial vehicles for photosensitizer hitchhiking, with spatiotemporal tracking elucidating its dynamic intercellular transit and physiological impacts. Through mitochondria’s communication network—tunneling nanotubes (TNTs), the mitochondria-photosensitizer cotransporter achieves reinforced intercellular delivery, thereby inducing deep tumor penetration and enhanced photodynamic killing. Our work establishes a transformative mitochondria-hitchhiking platform for overcoming biological barriers in drug delivery and provides mechanistic insights into manipulating intercellular organelle transport for therapeutic applications.

Introduction
Effective delivery of therapeutics into tumors is the prerequisite for efficient and low-toxic treatment of malignant tumors. However, the abnormal tumor microenvironment—including dense stromal tissue, high interstitial fluid pressure, low blood perfusion, abnormal angiogenesis, and hypoxia—greatly hinders the diffusion distance and extent of therapeutics, forming a ‘blind zone’ in deep tumor where drugs cannot reach1. Currently, multi-functional or ‘smart’ synthetic drug delivery systems aimed at overcoming these obstacles are active and effective, but complex and rigorous, and unable to provide a comprehensive solution to all problems2,3. It has been reported that the delivery efficiency of cancer nanomedicine is less than 1%4, and the limited effect is not conducive to clinical expansion5–7. In contrast, the cell-to-cell transport of substances and signals needed by tumor cells themselves occurs ‘unobstructed’ within tumors. In 2006, Spees et al. first demonstrated that cell-to-cell transfer of mitochondria from mesenchymal stem cells to mitochondria-depleted A549 cells could rescue aerobic respiration8. Studies have shown that mitochondria carry out intercellular transport in a specific manner to improve mitochondrial respiration, restore cell function, and enhance tumor survival9–12. Intercellular mitochondrial transfer depends on biological information communication between donor cells and recipient cells and is mediated by diverse cellular structures, mainly extracellular vesicles (EVs), tunneling nanotubes (TNTs), and gap junctions13. EVs and gap junctions mediate mitochondrial migration between close and adjacent cells, respectively, while TNTs mediate long-distance directional mitochondrial transport14. Rustom et al. first reported TNTs as a new mechanism of intercellular communication in 2004 and termed them ‘Nanotubular highways for intercellular organelle transport'11. Although TNTs are considered to be the major intercellular mitochondrial delivery pathway in solid tumors15–18, the fundamental mechanisms governing TNT-mediated mitochondrial transport remain poor understood.

Changes in cellular energy and metabolism, as well as differences in cell status drive the transport of mitochondria between cells15. Mitochondria precisely regulate themselves through cycles of fusion, fission, biogenesis, mitophagy, and intercellular transport—collectively referred to as ‘mitochondrial dynamics’—to adapt to various physiological states. They frequently alter their morphology or position in response to diverse stressors and energy requirements19,20. However, harnessing mitochondria movement (whether or not carrying cargo) and further enabling it to transport exogenous substances have rarely been explored, highlighting a gap in the understanding of tumor endogenous transport mechanisms that limit their potential to deliver exogenous substances.

Building on these foundations, this study uncovered that suppression of PINK1-mediated mitophagy initiated a compensatory cellular response—engaging cellular homeostatic recalibration and rescue programs. This adaptive rewiring concomitantly potentiated mitochondrial transit velocity/extent through TNTs and mobilized mitophagy-evaded mitochondria as autonomous bio-carriers. Capitalizing on this mechanism, we engineered a ‘mitochondrial autonomous drive’ (MAD) system by spatiotemporally coupling the mitochondria-targeting photodynamic agent IR780 to form ‘passenger-vehicle’ co-transporters. Within solid tumors, MAD orchestrated mitochondrial transport network expansion and high-speed motility through cellular homeostasis and oxidative stress modulation, enabling deep IR780 penetration. Consequently, MAD-mediated photodynamic therapy achieved potent tumor eradication in vitro and in vivo. Collectively, we pioneered a mitophagy-modulated platform leveraging engineered mitochondria for non-transmembrane drug delivery, providing transformative solutions for overcoming penetration barriers in oncology.

Results
Downregulated mitophagy enhances mitochondrial intercellular transport via TNTs
In order to maintain homeostasis, cells undergo autophagy or excretion of mitochondria that have detached from fused filamentous mitochondria during physiological processes. Inhibition of mitophagy may conversely lead to increased mitochondrial transcellular export. PINK1/Parkin pathway is one of the most classic mitophagy pathways, so firstly we verified the effect of the PINK1/Parkin pathway on mitochondrial transport by silencing PINK1.

After 24h culture in the confocal dish, a communication relationship was established between B16F10 cells. Compared with nonsense siRNA control (NC), the fluorescence of mitochondria was significantly enhanced (Fig. 1a), accompanied by a 4.2-fold increase in the number of TNTs, long, thin, membranous structures extending from cells after mitophagy inhibition by siPINK1. Even more fascinating, the images (Fig. 1a) clearly showed that there were mitochondria transferring in the TNTs structure (green mitochondria were present in the red tubular TNT structure). As TNTs are the most important intercellular transport pathway for mitochondria15, the increase in TNTs was closely related to the increase in mitochondrial trafficking.

Afterwards, we designed a donor-recipient cell co-culture system to explore mitochondrial communications between cells after siPINK1 treatment (Fig. 1b). Recipient cells were stained with a living cell tracker CMFDA (green), while the mitochondria of donor cells were stained with MitoTracker (red), respectively. Donor and recipient cells were treated by nonsense siRNA containing medium (abbreviate as NC), or treated by siPINK1 containing medium, respectively. Then, donor cells and recipient cells were co-incubated, and laser scanning confocal microscope (LSCM) was performed to count the number of MitoTracker red spots that were transferred from donor cells in the green recipient cells in four cases, i.e., NC (donor) to NC (recipient), NC (donor) to siPINK1 (recipient), siPINK1 (donor) to NC (recipient) and siPINK1 (donor) to siPINK1 (recipient) (Fig. 1b, c). Images in Fig. 1c showed that almost all green cells contained red mitochondrial signals, indicating that the transfer of mitochondria between cells was ubiquitous. Among them, the red mitochondria of donor cells in recipient cells were significantly increased after siPINK1 treatment (siPINK1 to NC, NC to siPINK1 and siPINK1 to siPINK1) (Fig. 1d). Notably, the mitochondrial transport increased in siPINK1 (donor) to NC (recipient) and NC (donor) to siPINK1 (recipient) may reflect two different mitochondrial transport mechanisms, namely, cells with metabolic changes send dysregulated mitochondria to normal cells to maintain their own quality control, while normal cells transfer healthy mitochondria to unstable cells for rescue21–23. In a separate experiment, conversely, activation of mitophagy using Urolithin A24 resulted in a further reduction in mitochondrial transfer (Supplementary Fig. S1, P<0.0001). This demonstrated that enhanced mitophagy induced compensatory attenuation of the alternative mitochondrial disposal pathway—mitochondrial extrusion—thereby decreasing mitochondrial transfer, in accordance with our hypothesis. Mitochondria traversed between multiple cells (Donor with Donor, Donor with Recipient, Recipient with Recipient cells, respectively) to form a network through TNTs (Fig. 1e), and TNTs acted as a communication bridge between donor and recipient cells, as observed from 3D confocal microscopy image (Fig. 1f). These results highlighted that inhibiting mitophagy enhanced mitochondrial transfer by increasing the number of mitochondria and TNTs.

Blockade of mitophagy arouses transport driving forces
The above research suggests that blockade of mitophagy in the tumor opens up the extensive TNTs transport channels. Replacing siRNA with CRISPR/Cas9 gene editing to knockout PINK1 could modify the short-term inhibition and unpredictable off-target effects of siRNA25,26. The sgRNA targeting PINK1 gene was cloned into pX330 backbone to construct a CRISPR/Cas9 gene knockout plasmid. Then the plasmid was combined with cationic polymer fluorinated highly branched poly(β-amino ester) to form the plasmid delivery nanocomplex27,28 (named FP, the detail were presented in Supplementary Methods and Results and Supplementary Tables 1–2). As a result, the gene editing efficiency was as high as 41.6% after treatment with this CRISPR/Cas9 gene editing system (Fig. 2a, b), with a weak expression in PINK1 protein (about 35.7% reduction) compared with the negative control plasmid (Fig. 2c). Taken together, this CRISPR/Cas9 gene editing system could induce indels at the targeted PINK1 loci (Supplementary Figs. S2–7) and thus efficiently disrupt the expression of PINK1. The Keima and mitochondria co-localization marker plasmid (mKeima-Red-Mito-7)29,30 was performed to evaluate mitophagy after PINK1 gene editing (Fig. 2d). In Fig. 2e, the red/green fluorescence signal ratio (indicating the proportion of mitochondria under acidic conditions) decreased by 51.8% after CRISPR/Cas9 gene editing system treatment in B16F10 cells, revealing the inhibition of mitophagy.

RNA sequencing (RNA-seq) analysis of cells transfected with PINK1 plasmid revealed 2457 upregulated genes and 1699 downregulated genes (including PINK1, Fig. 2f). KEGG pathway enrichment analysis revealed that descending genes were strongly correlated with glycolysis and mitophagy (Fig. 2g). The biological process analysis in GO functional enrichment analysis showed that the transcription-related capabilities were decreased (Fig. 2h). Further analysis of the differentially expressed genes found that genes related to transcription and translation were downregulated, which may be related to the DNA mismatch caused by CRISPR/Cas9 system and the subsequent decrease in PINK1 transcription and translation. The decrease in mRNA related to glycolysis may indicate an increase in aerobic respiration caused by an increase in mitochondria (Fig. 2i). Protein interaction network analysis revealed that the reduced mitophagy-related genes formed a network relationship centered on PINK1 (Fig. 2j), which also underlined the rationality of our inhibition of mitophagy by knocking out PINK1.

Mitochondria are the energy source of cells, and mitochondrial dynamics are closely related to the energy supply within cells31. Mitochondria respiratory capacity reflects metabolic function. To verify that PINK1 knockout improved cell metabolism, respiratory capacity of mitochondria was investigated. Oxygen consumption rate (OCR) was recorded to monitor oxidative phosphorylation (OXPHOS) in real time, and inhibitors of mitochondrial respiration were added successively: oligomycin, which inhibits ATP production in mitochondria; FCCP, an ionophore that uncouples mitochondrial respiration leading to maximal oxygen consumption; and antimycin A & rotenone, inhibitors of electron transport complexes I and III, respectively, which completely block mitochondrial OXPHOS. As Fig. 2k showed, the entire OCR curve was elevated after PINK1 gene editing treatment, but still maintained the same sawtooth shape as the control group. According to statistics, the basal respiration, maximum respiration, and spare respiration capacity of cells were significantly improved by PINK1 blockade (2.1-, 4.1-, 5.0-fold versus control), showing a stronger metabolic activity without inducing hypoxia (Supplementary Fig. S8). Increased cellular metabolism is a powerful driving force behind mitochondrial intercellular transport15,32. This suggested that PINK1 knockout can globally enhance the mitochondrial OXPHOS pathway, and the enhanced metabolic phenotype supported the cell-to-cell transfer of mitochondria.

Inhibition of mitophagy increases mitochondrial motility and bidirectional transport
Mitochondrial morphology and motility at the subcellular level provide more direct visualization of their movement dynamics and behavioral tendencies. From the 3D imaging and reconstruction of mitochondria by structured illumination microscopy (SIM), we found that knockout of PINK1 increased the round mitochondria (the aspect ratio of each mitochondrion in 1.0≤L/W<1.5), and decreased the tubular mitochondria (the aspect ratio of each mitochondrion in 2.0≤L/W<5.0) (Fig. 3a). As we know, mitochondria can fuse or fission from the mitochondrial network as the cell state changes. Mitochondrial fission produces individual mitochondria that are more suitable for transport along the actin or microtubule network33, so the small round, more freely and quickly moving mitochondria that shed from the mitochondrial network may have the potential to be used to increase mitochondrial transport.

Previous experiments have shown that decreased PINK1 expression can induce increased mitochondrial migration along TNTs (Fig. 1). Given the increased transport driving forces (Fig. 2), we speculate that the moving speed of mitochondria increases. So, in the following, we explored the speed and pattern of mitochondrial movements within and between cells (in TNTs) under different treatments, respectively.

Continuous imaging for 3 min at 5 s intervals with SIM and tracking of mitochondrial trajectories (Supplementary Movies 1 and 2) were performed to study the alterations on mitochondrial motility in individual cells. The speed-position diagram of mitochondria migration paths in the cytosol (Fig. 3b) and the displacements normalized into the X–Y coordinates (Fig. 3c) underlined that the number of high-speed mitochondria increased, as well as the mean track speed (1.3-fold versus control) and track length (1.7-fold versus control) increased under the blockade of PINK1.

Next, we conducted an in-depth and intuitive visualization study on the movement of mitochondria between cells via TNTs. First, mitochondria and TNTs (F-actin) were marked with pAcGFP1-Mito and LifeAct-mCherry plasmid, respectively, and long-term SIM images of a fixed field of view for up to 20 min were captured (Supplementary Movies 3 and 4). The movement of mitochondria along TNTs was rendered and tracked (Fig. 3d, e), visually demonstrating that the treated mitochondria were generally faster in cell-to-cell transfer, with more high-speed mitochondria shuttling through TNTs. Similarly, mitochondria in cells with PINK1 knockout had faster trajectories (2.0-fold versus control) and longer displacements (1.6-fold versus control) in TNTs when both directions normalized to X-Y coordinates (Fig. 3e), reflecting their enhanced motility. Ultimately, long-term imaging captured complete visuals of a mitochondrion transporting from one cell to another in the FP group, which directly proved the intercellular transfer of mitochondria (Supplementary Fig. S9, Supplementary Movie 5).

Then we labeled the mitochondria of PINK1 knockout and untreated cells with green and red, respectively, and co-cultured them to observe the mitochondrial movement pattern between the two individual cells connected with TNT. The results intuitively showed that the exchange of mitochondria between the two cells was mutual (Fig. 3f, and Supplementary Movie 6), which was consistent with our previous explanation of the bidirectional mitochondrial transport mechanism, the ‘maintaining homeostasis’ and ‘rescue’. Excitingly, the movement trajectories of the PINK1 knockout cells were faster (1.3-fold versus control) and longer (1.8-fold versus control) than those of untreated cells (Fig. 3g), which may indicate that mitochondria in cells with reduced PINK1 expression are more inclined to move between cells and transfer from donor cells to recipient cells.

Cargo IR780 hitches a ride on mitochondria for efficient transport along TNTs
Based on the previous studies that decrease in mitophagy induced an increase in mitochondrial cell-to-cell transport, we designed and constructed a ‘mitochondrial autonomous drive’ (MAD) system, containing FP and FIS (integrating SPION and IR780) nanoparticles, to exploit accelerated mitochondrial motility for cargo delivery. Superparamagnetic iron oxide nanoparticle (SPION) is an excellent but short-lived oxidative stress generator at subtoxic concentrations34. Skillfully, when SPION was applied under low-mitophagy condition, as the significantly increased DCFH-DA-stained ROS displayed (Supplementary Fig. S10), the oxidative stress was effectively accumulated to provide more sufficient and long-lasting fuel for mitochondria movements, which was not achieved by SPION alone. IR780 has a natural property of attaching to mitochondria, which is attributed to its highly electropositive and lipophilic nature35, and has been widely used in medical fields such as mitochondrial imaging36 and photodynamic tumor therapy37. So IR780 was selected as a model cargo for delivery, in the form of nanoparticles.

The nanoparticle containing SPION and IR780 (named FIS) was prepared with ∼80 nm diameter and ∼50 mV zeta potential (Supplementary Figs. S11–13, the detail in preparation and characteristics of FIS was presented in Supplementary Methods and Results). IR780 exhibited much higher overlay with mitochondria than lysosomes in the B16F10 cells according to Pearson’s coefficients analysis (Supplementary Fig. S14), emphasizing that the IR780 in MAD was highly accumulated in mitochondria.

MAD treatment increased mitochondrial superoxide (19.3-fold versus control, Fig. 4a) and mitochondrial calcium flux (3.8-fold versus control, Fig. 4b), higher than the group treated with FP alone, highlighting MAD created and maintained a stronger oxidative stress environment for mitochondria migration.

The transfer capacity of mitochondria (red fluorescence) and hitchhiker IR780 (magenta fluorescence) from donor cells to recipient cells was investigated employing the donor-recipient system described previously (Fig. 1b). FIS alone served as control. Mitochondrial migration was enhanced when MAD-treated cells served as donor cells (MAD to FIS, MAD to MAD) (Fig. 4c), which was consistent with previous findings (Fig. 1c, d). Excitingly, IR780’s migration was simultaneously enhanced, and the groups of MAD treated donor cells showed the strongest migration of IR780 hitchhiking on mitochondria, exhibiting a stronger outward transport capacity. Conversely, as demonstrated in Supplementary Fig. S15, recipient cells cultured in conditioned medium exhibited no detectable MitoTracker or IR780 staining, indicating that the fluorescent signals in recipient cells can be attributed to mitochondrial transfer rather than dye crossover contamination. Typical pharmacological inhibitors—cytochalasin D (restraining actin polymerization and thus TNT formation38), L-778123 (inhibiting farnesyltransferase and geranylgeranyltransferase type 1 related to nanotube-mediated intercellular communication39) and dynasore (a non-competitive GTPase inhibitor40) blocked the transfer of mitochondria and IR780 from donor cells to recipient cells (Fig. 4d, P<0.0001 compared with MAD group). These results emphasized that under the driving forces caused by MAD system, shed mitochondria employed TNTs to radiate to surrounding cells under the purpose of maintaining homeostasis, and cargo IR780 followed to migrate to other cells; at the same time, rescue signals were sent to obtain mitochondria and IR780 from other healthy cells.

Further, we showed the synchronous movement of mitochondria and IR780. Through 20 min long confocal fluorescence imaging (Supplementary Movie 7), we observed that IR780 and mitochondria were transported together along TNTs (Fig. 4e), representing that IR780 can hitchhike on mitochondria to transfer from one cell to another without crossing membrane. The 3D rendering of mitochondria and IR780 moving in TNTs (Fig. 4f) clearly revealed the spatial positional relationship among TNTs, mitochondria, and IR780, indicating that IR780 and mitochondria shuttled synchronously inside the TNTs formed between cells in a mutually fused manner.

To demonstrate MAD’s versatility in delivery, we used 10-N-nonylacridine orange bromide (NAO), which employed ΔΨm-independent cardiolipin-specific binding41. NAO showed excellent mitochondrial colocalization (PC=0.90, Supplementary Fig. S16a) and transmission efficiency comparable to IR780 (Supplementary Fig. S16b), confirming MAD’s broad applicability. Virtually any attachable molecule/peptide—through linkage to triphenylphosphonium (TPP) or mitochondrial targeting peptides (MTP)—could be mitochondria-targeted using our MAD system. In contrast, ICG (hydrophilic/sulfonated) and IR780 (lipophilic/ammonium-iodide) shared heptamethine NIR cores but diverged in localization: IR780 showed mitochondrial accumulation (PC=0.83) versus ICG’s diffuse distribution (PC=0.08). Correspondingly, ICG exhibited minimal transfer in MAD (Supplementary Fig. S16b), confirming that mitochondrial loading was essential for intercellular delivery.

Key molecules mediating mitochondrial transport along TNTs deliver a reliable, direct window into the mitochondrion-dependent transport behavior of cargo IR780 within cells. M-sec (TNFAIP2) protein functions as a key regulator of membrane nanotube formation by interacting with the Ral-exocyst pathway42. Knockdown of M-sec protein expression by M-sec siRNA suppressed TNT formation, leading to a decrease in both IR780 and mitochondria trafficking (Fig. 4g). M-sec was found colocalized with F-actin, while IR780 colocalized with mitochondria (Fig. 4h) in MAD treated cells. These results suggested that the intercellular transmission of IR780, like mitochondria, depends on the formation of intercellular TNTs.

MAD controls the switch of cell death though NIR
The previous results have verified that MAD can deliver appropriate cargo (IR780) by leveraging mitochondria in tumor. Based on the important role of TNTs in mitochondrial intercellular transmission as above indicated, we focused on the effects of MAD on TNTs. Confocal images showed that TNT counts slightly increased after FP treatment (2.8-fold versus control), and significantly increased after processing with MAD system (13.5-fold versus control) to form a bundle of interconnected networks (Fig. 5a). This phenomenon was very prominent and also emphasized the importance of SPION in MAD system to produce diverse, long-term driving forces for mitochondrial transport.

In addition, since IR780 is an excellent photosensitizer, we used near-infrared light (NIR) to irradiate MAD treated cells (808 nm, 1 W/cm2 for 20 s) and found that TNTs disappeared almost completely (Fig. 5a), and the tumor cells were dead (Fig. 5b). In fact, NIR aroused a lethal photodynamic effect (7377.7-fold versus control, Fig. 5b), as well as an explosive increase in ROS (4775.2-fold versus control. Fig. 5c), highlighting the extensive distribution and phototherapy efficacy of IR780 in the tumor.

As a contrast, without NIR irradiation, ROS levels increased slightly (1.0% of NIR given) and the cells survived (no significant difference from control). Notably, the cell mortality rate was more than 10,000 times higher upon the NIR irradiation than that without NIR irradiation, greatly highlighting the safety of MAD and the effectiveness of the photodynamic therapy. In addition, it cannot be ignored that the complete MAD system without NIR resulted in 49.5-fold more ROS production than the control group but almost no cell death, which supported the concept of activating mitochondrial intercellular transport by causing cellular oxidative stress without affecting their survival. We anticipated that MAD with NIR would lead to better phototherapy effects, and meanwhile destroy the rescue road of mitochondria transferring, bringing a ‘utilize first and then eliminate’ effect.

IR780 achieves deep penetration through TNTs in tumor
Deep and widespread penetration into tumor is a prerequisite for high efficiency and low toxicity of cancer drugs. Based on previous results, we speculated that intercellular transport by piggybacking on mitochondria could bypass the physical barrier and reach deeper tumor sites. So, B16F10 tumor spheroids were constructed to detect the penetration of IR780 in MAD. The IR780 fluorescence in the tumor spheroids was scanned layer by layer with laser confocal scanning from bottom to top. As the slice section came to the middle, the fluorescence of IR780 in MAD became much higher than that of FIS along (Fig. 6a), revealing greater tumor penetration and accumulation of IR780 in whole tumor. The B16F10-bearing tumor mice model was constructed to examine the tumor penetration ability of MAD in vivo (Fig. 6b). Whether at the border or the core of isolated tumor, the fluorescence intensity of IR780 after MAD treatment was significantly higher than that of FIS alone (Fig. 6c). At the same time, we observed strong overlap of IR780 and TNT structures connecting two cells in tumor sections, highlighting that IR780 diffused through TNTs (Fig. 6d). These results indicated that IR780 can achieve excellent penetration when carried on mitochondria through TNTs, which also laid the foundation for the MAD system to achieve better therapeutic effects in vivo.

MAD system enhances anti-tumor effects in vivo
Cancer photodynamic therapy has been widely used in medicine due to their unique advantages such as minimal invasiveness, potential for tumor ablation, and few side effects44. Next, we verified whether this MAD system could enable the photodynamic therapy drug to achieve better therapeutic effects in vivo. B16F10 melanoma cells were implanted into the right flank of immunocompetent C57BL/6J mice. Animals were randomized into six treatment groups, and different preparations were administered peritumorally, as shown in Fig. 7a. The tumors in group ① (control) grew rapidly (Fig. 7b, c, Supplementary Fig. S17). In groups ② (FP), ③ (FIS+NIR(+)) and ④ (MAD+NIR(+)), the growth rate of tumors declined compared with control, indicating that knocking out PINK1 or SPION/IR780 may exert their effects by accumulating oxidative stress damage of tumors (Supplementary Fig. S18). Group ⑥ (MAD+NIR(++)) that received three treatment cycles produced a significantly reduced tumor growth, and three out of seven animals had complete tumor regression (Fig. 7d), highlighting photodynamic therapy drug can achieve tumor destruction by hitchhiking on mitochondria in MAD system plus NIR. During these treatments, no significant changes were observed in mouse body weight (Fig. 7e). H&E staining of major organs (Supplementary Fig. S19), sequencing of target gene loci in major organs (Supplementary Fig. S20), and blood routine parameters (Supplementary Fig. S21) showed no abnormalities, indicating the safety of MAD system. H&E staining, Ki67 staining, and TUNEL assay of tumors (Fig. 7f–h) showed that MAD under NIR irradiation had the best tumor proliferation inhibition and cell apoptosis ability, concomitant with a tendency toward CD8+ T cell activation (Supplementary Fig. S22).

Following, we sought to dissect the reasons for the different therapeutic effects of these regimens, with a particular focus on the mechanistic changes in vivo associated with mitochondrial trafficking. Compared with control, immunofluorescence results of all treatment groups showed that both M-sec and Miro-1 proteins increased in the tumor tissue (Fig. 7i, j), indicating an increase in TNTs and an increase in mitochondrial movement along TNTs. In Supplementary Fig. S23, additional tumor immunofluorescence analyses revealed upregulated KIF5B (microtubule motor for mitochondrial anterograde transport45) and MYO19 (mitochondrial-specific myosin regulator46), confirming coordinated enhancement of mitochondrial trafficking machinery.

After treatment with PINK1 plasmid in group ② (FP), the protein expression of PINK1 decreased further after each administration cycle (Fig. 7k). And in Fig. 7l, the reduction of PINK1 in group ② and ④ inhibited the phosphorylation of Parkin, further inhibited the conversion of LC3-I to LC3-Ⅱ, and reduced the consumption of the substrate p62, whereas this mitophagy process (Fig. 7m) did not change in the absence of FP treatment (group ① and ③). It should be noted that only the PINK1/Parkin pathway is the main pathway of mitophagy19, while the LC3-I/Ⅱ and p62 pathways are common to all autophagy47, and p62 is also related to the apoptosis signaling pathway48, so their changes were not as typical as Parkin phosphorylation. For example, the higher level of p62 in group ③ than in group ② may be related to apoptosis caused by phototherapy. These results highlighted the changes in mitophagy-related pathways in tumors. MAD inhibited the mitophagy pathway49 (Fig. 7m), especially inhibited PINK1 expression and the downstream Parkin phosphorylation, thereby causing MAD to break the homeostasis of cells and promote the movement of cargo in mitochondria between cells to reach the inside of tumor to exert its effects.

Methods
Materials
Dulbecco’s Minimum Essential Medium (DMEM), penicillin-streptomycin, trypsin and 4% paraformaldehyde were obtained from Macgene Technology (Beijing, China). Fetal bovine serum (FBS) was obtained from Gibco (Grand Island, USA). CMFDA, MitoTracker Deep Red FM, MitoTracker Green FM, LysoTracker Red DND-99, Hoechst 33342, Rhod-2 and Calcein-AM/PI were purchased from Yeasen Biotech Co., Ltd (Shanghai, China). iFluorTM 594-wheat germ agglutinin (WGA), iFluorTM 647 phalloidin, and 2′,7′-Dichlorofluorescein diacetate (DCFH-DA) were purchased from AAT Bioquest (CA, USA). DAPI and Goat Anti-Rabbit IgG H&L antibody (bs-0295G-AF488) were purchased from Bioss Biotechnology Inc. (Beijing, China). MitoSOX was purchased from MCE (Shanghai, China). IR780 iodide was purchased from Macklin Biochemical Technology Co., Ltd (Shanghai, China). GAPDH mouse monoclonal antibody (AF2819, Lot No. 070522230105), HRP-labeled goat anti-mouse IgG (H+L) antibody (A0216), HRP-labeled goat anti-rabbit IgG (H+L) antibody (A0208), radioimmunoprecipitation assay (RIPA) lysis buffer, PMSF, and Enhanced BCA Protein Assay Kit were purchased from Beyotime Biotechnology (Shanghai, China). Cytochalasin D, anti-TNFAIP2 (M-sec) antibody (ab196659, Lot No. GR3337259-7), anti-Parkin antibody (ab77924, PRK8, Lot No. 1013226-13), and anti-phos-Parkin antibody (ab315376, MJF-R17-42-4, Lot No. 1077415-1) were purchased from Abcam Plc (Cambridge, UK). Bisphenol A diglycidyl ether diacrylate, trimethylolpropane triacrylate, and anti-RHOT1 (Miro-1) antibody (ZRB1674, 1F4, Lot No. Q3801743) were purchased from Sigma-Aldrich (St. Louis, USA). Anti-CRISPR (CAS9) antibody (844302, 7A9, Lot No. B398790) was purchased from BioLegend (CA, USA). Anti-PINK1 antibody (BC100-494, Lot No. O-3) was purchased from NOVUS Biologicals (CO, USA). Anti-LC3B antibody (#2775, Lot No. 12) and anti-SQSTM1/p62 antibody (#5114, Lot No. 6) were purchased from CST (Boston, USA). L-778123 and dynasore were purchased from TargetMol Chemicals Inc. (Boston, USA). N-(2-hydroxyethyl)-1,3-propanediamine and N-(3-aminopropyl) morpholine were obtained from Energy Chemicals (Shanghai, China). 1H,1H-Undecafluorohexylamine was purchased from TCI (Tokyo, Japan). SacⅡ® and KpnⅠ® restriction enzymes were purchased from New England Biolabs (MA, USA). TaKaRa MiniBEST Agarose Gel DNA Extraction Kit was purchased from TOYOBO (Osaka, Japan). TIANamp Genomic DNA Kit was purchased from TianGen Biotech Co., Ltd. (Beijing, China).

siPINK1 (F: 5′-GCCCUGAAGAACCUGAAAUTT-3′, R: 5′- AUUUCAGGUUCUUCAGGGCTT-3′), siM-sec (F: 5′-GCUUCAAGGUGUCGGGCUUTT-3′, R: 5′-AAGCCCGACACCUUGAAGCTT-3′) and siMiro-1 (F: 5′-CCAGAGCACCUUUGACAAATT-3′, R: 5′-TTTGTCAAAGGTGCTCTGGTT-3′) were synthesized by GenePharma Co., Ltd (Shanghai, China). mKeima-Red-Mito-7 (Plasmid #56018), pAcGFP1-Mito (Plasmid #174180), pDsRed2-Mito (Plasmid #55838) and LifeAct-mCherry (Plasmid #193300) were obtained from MiaoLing Bio (Wuhan, China). pEGFP plasmid was obtained from Shanghai GeneBio Co., Ltd. (Shanghai, China). Primers used for plasmids construction and Sanger sequencing were provided by Tsingke Biotech Co., Ltd. (Beijing, China).

Animals
Female C57BL/6J mice (6–8 weeks) were purchased from Department of Animal Science of Peking University Health Science Center. All animals were acclimated to the facility for one week before experimentation and housed under a 12-h light/12-h dark cycle, at a controlled ambient temperature of 25 ± 1 °C and 55 ± 15% humidity in specified pathogen free (SPF) conditions. Only female mice were used in this study to control for hormonal variability. For technical procedures (e.g., subcutaneous injection), animals were briefly restrained using a soft, non-invasive rodent restrainer. Every effort was made to perform procedures swiftly and calmly to minimize the duration of restraint. Animals were monitored daily for signs of pain, distress, or illness, including but not limited to posture, activity levels, food and water intake, and any visible abnormalities. At the end of the experiment, mice were euthanized by cervical dislocation under anesthesia. All animal experiments were performed strictly with the protocols stipulated by the Guideline for Care and Use of Laboratory Animals of Peking University Health Science Center and approved by the Animal Ethics Committee of Peking University Health Science Center (assigned number: LA2022340 and PUIRB-LA2023263).

Cell culture
B16F10 melanoma cells were originated from American Type Culture Collection and were cultured in DMEM medium with FBS (10%), penicillin (100 U/mL) and streptomycin (0.1 mg/mL). All cells were maintained in a humidified incubator at 37 °C with 5% CO2.

Donor cells and recipient cells system
B16F10 cells (2×105 cells/mL) were seeded and divided into two experimental groups: donor cells (D) and recipient cells (R). siPINK1 (50 nM)/FP (Plasmid: 1 µg/mL) were given in D-siPINK1/FP and R-siPINK1/FP group for 48 h or 72 h, respectively. Then the donor cells were treated with FIS (3.0 µg/mL for IR780) for 4 h and labeled by MitoTracker and Hoechst 33342, while recipient cells were stained with CMFDA and Hoechst 33342. Donor cells were transferred and co-incubated with recipient cells for 4 h. Laser scanning confocal microscope (LAS X 3.7, LSCM, TCS SP8 STED, Leica, Germany) was performed to observe the intercellular communication between donor and recipient cells. Recipient cells were segmented using Cellpose (https://www.cellpose.org), followed by processing of the resultant masks in ImageJ (1.54f) to define cellular boundaries. Individual recipient cells were isolated by subtracting these masks from raw images. Subsequent ‘Analyze Particles’ analysis (with size thresholding and ‘Add to Manager’ enabled) generated discrete regions of interest (ROIs) corresponding to individual cells. Mitochondrial and IR780 fluorescence channels were then imported, converted to 8-bit, thresholded at a fixed value across all experimental groups, and converted to binary single-point representations. These puncta were normalized to unity (=1) using the Divide function, enabling quantification via the Measure tool which recorded RawIntDen values—representing the summed intensity of all pixels within each recipient cell ROI—to determine counts of mitochondrial and IR780 puncta per cell. During quantification, RawIntDen values were normalized against recipient cell area to account for size-dependent variability, thereby yielding mitochondrial/IR780 puncta density per cell as the definitive metric for cargo delivery efficiency to recipient cells.

For inhibition experiments, donor and recipient cells were cultured respectively in DMEM with FP containing different inhibitors, cytochalasin D (0.5 µM), L-778123 (10 µM) and dynasore (80 µM) for 24 h. Then donor and recipient cells were treated with FIS and stained as the same steps as above, and co-incubated in DMEM with inhibitors for 4 h, respectively, followed by imaging.

For M-sec and Miro-1 knockdown experiments, donor and recipient cells were cultured respectively in DMEM with FP, and siM-sec/siMiro-1 (100 nM) were transfected into both cells via PEI 25k for 48 h. Then donor and recipient cells were treated with FIS and stained as the same steps as above, followed by imaging.

Bleed through control for MitoTracker Deep Red and IR780, and cellular autofluorescence control following mitophagy modulation, are provided in Supplementary Figs. S24 and S25, respectively.

Western blot for protein detection
B16F10 cells were lysed with RIPA lysis buffer (including PMSF) and protein concentration was unified with Enhanced BCA Protein Assay Kit. The protein samples were boiled, separated by SDS-PAGE gel electrophoresis and transferred with PVDF membrane. Next, membrane was blocked with 5% skim milk (5% BSA for phosphorylated proteins), and then primary antibody and secondary antibody were incubated in sequence. Finally, Immobilon Western HRP Substrate (Millipore, USA) was added and a gel image processing system (Amersham Imager 600, GE, USA) was used to analyze the gray value of the target band. Protein expression was quantified as the intensity ratio of target protein bands relative to loading control bands. Complete uncropped western blot data were shown in Supplementary Figs. S26–29.

Structure illumination microscopy (SIM) for super-resolution fluorescent imaging
To analyze mitochondrial morphology, Multi-SIM system (NanoInsights-Tech) with a Nikon ×100, 1.49-NA oil objective and imaging system were used for 3D-SIM imaging. After FP treated for 72 h and pAcGFP1-Mito transfected for 48 h, cells were seeded onto confocal glass-bottomed dishes 18–24 h prior to imaging. 3D-SIM images were acquired with 488 nm laser. Z-stack performance was set as 0.27 µm/slice per field. Cells were maintained in 37 °C and 5% CO2 during imaging. Multi-SIM images were analyzed using ImageJ. Following threshold optimization, shape descriptors were enabled in ‘Set Measurements’ (with Limit to threshold selected). ‘Analyze Particles’ was then executed, with the Aspect Ratio (‘AR’) column recording the major/minor axis ratio (‘L/W’) as a metric of morphological elongation.

To observe real-time mitochondrial movement, Polar-SIM system (AiryTechnology) with a Nikon ×100, 1.49-NA oil objective and imaging system were used for 2D-SIM imaging. After FP treated for 72 h and pAcGFP1-Mito/LifeAct-mCherry/pDsRed2-Mito transfected for 48 h, cells were seeded onto confocal glass-bottomed dishes 18–24 h prior to imaging. 2D-SIM images were acquired with 488 nm and 561 nm laser for 36 loops in movement within cells and 120/ 30 loops for movement between cells. Cells were maintained in 37 °C and 5% CO2 during imaging. ND processing in NIS-Elements (Nikon, Japan) was used to correct the jitter of long-term images. Polar-SIM images were analyzed by Imaris software (9.0.0). Surface rendering of mitochondria was generated in consecutive images. Tracking project was used to depict traces of single mitochondrion. Through autoregressive algorithm, tracks with >10 s duration for movement within cells and >20 s duration for movement between cells were analyzed and dynamic parameters were calculated. Quantitative motility parameters—‘Track Speed Mean’ and ‘Track Displacement Length’—were acquired in Imaris (‘Statistics’ panel) and subjected to statistical analysis. Cellular metabolic assays
Cellular OXPHOS were assessed with Seahorse XF Pro extracellular flux analyzer (Agilent Technologies, USA). Briefly, B16F10 cells were cultured as 2×104 cells/well in a XFe96 cell culture microplate. Prior to metabolic measurements, supernatant was removed and replaced with Seahorse assay medium. The cells were then incubated in a CO2-free incubator for 1 h. Oligomycin (1.5 µM), FCCP (1.5 µM), and R&A (0.5 µM) were injected sequentially. Finally, Hoechst 33342 reagent was injected to quantify the cell number using a high-content analyzer (CellVoyager CV8000, Japan) for normalization. The parameters in the assay were analyzed in Seahorse Analytics (Agilent Technologies, USA).

Flow cytometry for in vitro detection
Cells suspension was prepared in PBS after incubating. For samples that required staining, DCFH-DA (10 µM), MitoSOX (5 µM) or Rhod-2 (5 µM) were added to the cells for 20 min, followed by washing with PBS and passing through a 200-mesh cell sieve. Samples were examined using Cytoflex flow cytometer (CytExpert 2.5, Beckman Coulter, USA). All data were processed using FlowJo software (V10). Details of the gating strategy were provided in Supplementary Figs. S30–33. Typically, 10,000 live cell-gated events were acquired for analysis.

RNA-seq for gene expression
RNA sequencing and analysis were performed by Novogene. Specifically, sequencing libraries were generated using NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The raw data were filtered to obtain clean data, and Q20, Q30 and GC content of the clean data were calculated. The reference genome was indexed using HISAT2 v2.0.5, and paired-end clean reads were aligned to the reference genome using HISAT2 v2.0.5. FeatureCounts (1.5.0-p3) was used to count the number of reads mapped to each gene. The FPKM of each gene was then calculated based on the length of the gene and the number of reads mapped to the gene. Differential expression analysis between two comparison combinations was performed using edgeR (3.22.5). Genes with FDR <0.05 and absolute log2 fold change >1 were identified as differentially expressed genes. The DAVID database (https://david.ncifcrf.gov) was used to analyze the GO and KEGG datasets. The STRING database (http://string-db.org) was used for functional protein association network analysis of differentially expressed genes.

Intracellular ROS detection
B16F10 cells (2×105 cells/mL) were seeded and incubated at 37 °C for 24 h. Then the cells were incubated with FP for 72 h and FIS for 4 h, respectively. Then untreated cells were used as control. Then, DCFH-DA (10 µM) was added at 37 °C for 20 min, and the cells were irradiated (808 nm, 1.0 W/cm2, 20 s) after washing. Finally, cell nuclei were stained with Hoechst 33342 (5 µg/mL) and observed by CLSM (ZEN 3.9, LSM880, Zeiss, Germany). All images were processed in ImageJ following a standardized workflow: raw data underwent 8-bit conversion followed by application of identical channel-specific thresholds across experimental groups before intensity measurement. Per-cell ROS generation was quantified as the ratio of ROS channel integrated density (IntDen) to nuclear channel IntDen, establishing a normalized metric for robust intergroup comparative analysis; this protocol was systematically applied to all subsequent analogous experiments to ensure methodological consistency.

Calcein-AM/PI staining
B16F10 cells (2×105 cells/mL) were seeded and incubated at 37 °C for 24 h. Then the cells were incubated with FP for 72 h and FIS for 4 h, respectively. Then, the cells were irradiated (808 nm, 1.0 W/cm2, 20 s) and stained by Calcein-AM (2.0 µM) and PI (4.5 µM) for 20 min, and then observed by CLSM (STELLARIS 8 STED, Leica, Germany), respectively. Viability quantification was performed using the dead/live cell ratio derived from PI channel IntDen normalized to calcein channel IntDen.

3D B16F10 tumor spheroid model for penetration
A three-dimensional cell spheroid model was established using a 96-well plate. 20 g/L agarose dissolved at 80 °C was added to each well, and 3×103 B16F10 cells were inoculated and cultured until the diameter of the cell spheroid was about 100 µm. The cell spheroids were treated with FP for 72 h and FIS for 4 h, then gently rinsed with PBS and fixed with 4% (m/v) paraformaldehyde. Finally, the cell spheroids were scanned layer by layer along the Z-axis to observe the penetration of tumor cell spheroids at different depths using CLSM (TCS SP8 STED, Leica, Germany). IR780 distribution was subsequently quantified by measuring layer-specific mean fluorescence intensity across all optical sections in ImageJ.

B16F10 solid tumor penetration in vivo
B16F10 cells were inoculated into the right flank of C57BL/6J mouse at 1×106 cells each. On the 10th day, tumor volume reached about 200–300 mm3. Mice were randomly divided into 2 groups: FIS and MAD, with one mouse per group for the representative experiment presented. After FP (10 µg plasmid for 20 g mice) was given peritumorally for 72 h and FIS (12 µg IR780 for 20 g mice) was given for 4 h, the tumor was removed and fixed with tissue fixative. After dehydration with 30% sucrose, the tumor was embedded in OCT embedding medium. Then the tumor tissue was sliced into frozen sections using a freezing microtome (Leica CM3050S, Germany), stained with WGA and Hoechst 33342, and observed with CLSM (STELLARIS 8 STED, Leica, Germany).

Anti-cancer therapy in mice bearing B16F10 solid tumor
B16F10 cells were inoculated into the right flank of C57BL/6J mouse at 1×106 cells each. On the 8th day, tumor volume reached about 100 mm3. Mice were randomly divided into 6 groups: ① Control (n=6), ② FP (n=6), ③ FIS+NIR(+) (n=6), ④ MAD+NIR(+) (n=6), ⑤ FIS+NIR(++) (n=7) and ⑥ MAD+NIR(++) (n=7). Animals were treated peritumorally according to schedule. NIR was irradiated with 808 nm laser at 1.0 W/cm2 for 5 min. Tumors were measured every other day using a vernier caliper, and tumor volume (mm3) was calculated as (long diameter × short diameter2)/2. Total body weight was routinely measured as a measure of any gross toxicity. When the tumor volume exceeded 2000 mm3, body weight loss surpassed 15% of the initial weight, or any other signs of distress were observed, mice were euthanized by cervical dislocation under anesthesia in accordance with the protocols approved by the Animal Ethics Committee of Peking University Health Science Center.

When the tumor grew to the 12th day, two mice in each of groups ① and ② were euthanized and the tumors were isolated for western blot experiments; on the 19th day, mice in groups ①, ②, ③ and ④ were euthanized as shown in Supplementary Fig. S17 for H&E staining, Ki67 staining, western blot, immunofluorescence, flow cytometry and blood routine analysis; on the 26th day, the remaining mice in groups ①, ②, ③ and ④ were euthanized for western blot experiments; the survival of mice in groups ⑤ and ⑥ were observed until 60 days. Immunofluorescence data were quantified by normalizing target protein IntDen to nuclear channel IntDen. Western blot expression levels were calculated as target protein band intensity relative to loading control (GAPDH) band intensity, with both analyses performed using ImageJ.

Hematoxylin-eosin, Ki67, and immunofluorescence staining
Tumor tissues and major organs were fixed with 4% paraformaldehyde and prepared into paraffin sections and frozen sections for immunohistochemistry and immunofluorescence analysis, respectively. Immunohistochemistry was performed for H&E and Ki67 staining. Immunofluorescence was used for TUNEL assay and the detection of protein expression. All frozen sections were fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, blocked with 2% BSA, and incubated with primary antibody at 4 °C overnight. After washing with PBS, secondary antibody (AF488) was added for 1 h and DAPI was added for 5 min at RT. H&E and Ki67 images were taken using a digital pathology slide scanner (NanoZoomer, Hamamatsu, Japan). Immunofluorescence results were observed using CLSM (STELLARIS 8 STED, Leica, Germany). Quantitative analysis involved normalization of target protein IntDen to nuclear IntDen in ImageJ.

Statistical analysis
The data were expressed as the mean or mean ± standard deviation (SD). SPSS (IBM SPSS Statistics Version 26) and GraphPad Prism 8 were used for statistical analysis. Comparison between two groups was analyzed by two-tailed unpaired t test. Comparisons among multiple groups were analyzed by one-way ANOVA with Tukey’s/Dunnett’s multiple comparisons test, two-way ANOVA with Sidak’s multiple comparisons test, or repeated-measure ANOVA with Tukey’s multiple comparisons test. P<0.05 was set as a significant difference. All experiments were repeated independently with similar results for the reproducibility of the results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript submitted by Peng et al. concerns the inter-cellular transfer of mitochondria through tunnelling nano-tubes (TNTs), and their application as anti-cancer agents. The mitochondrial transfer field, particularly via TNTs, is an emerging field in cell biology, which has brought about a number of high profile publications relating to their properties, function and exploitation. In spite, of this many questions remain. Therefore, new finding of this hot topic will be very welcome to the TNT and wider mitochondrial community, and thus of interest to the broad readership of Nature Communications.\nThe paper is very interesting from 2 perspectives. First of all, it provides evidence for a link between mitophagy and TNT conducted mitochondrial transfer. Secondly, the work describes how TNTs could be deployed for the formation of extra-cellular networks for the delivery, and deep penetration, of agents for the purposes of imaging and therapy, such as tumor clearance. These two aspects are both very worthy topics in themselves, but feel very much like two different stories, which might be better suited as different papers. The two parts present very interesting fundamental new knowledge and novel applications, which could in principle be suitable for Nature Communications. However, before publication, either as one paper or two, there are a many concerns that need to be addressed.\nA major concern is that the central feature of the paper – inter-cellular mitochondrial transport – has not been irrefutably demonstrated. We really need to see images/ movies of mitochondrial moving fully from one cell to another one. This also goes for other claims, e.g. that mitochondria can move in both directions, and reagents are able to hitch a ride on mitochondria from one cell to another; we need to see this happening. Given the claim of large scale inter-cellular mitochondrial traffic through TNTs this should be achievable.\nOther concerns figure-by-figure:\nFigure 1\nA) Representative panels clearly present very different cell densities. It looks like the TNTs have been quantified by area, with each area containing a different number of cells. So, it might be misleading to state that TNTs are upregulated by siPINK1 treatment. Quantification of N=4 random regions is not really sufficient. Surely, the number of TNTs needs to be quantified per cell over a large number of cells from a suitable number of independent experiments. Furthermore, the assertion in the text that the TNTs span ‘from one cell to another’ is not clear from the images (related to major concern above).\nB) In the co-culture it is not clear how the team avoided cross contamination of dyes and siRNAs. Can we be certain the co-localisation is not the result of residual dye/ siRNA carrying over from the cells used to seed the co-culture?\nC) The combined red/ green channels make it hard to see red mitochondria within green cell. It would be much better to also show the 2 channels separately.\nD) The Y-axis need clarification. What does ‘mitochondrial signalling/ cell area’ mean? The text states that there is an increase in the number of mitochondria and TNTs. The stated increase in mitochondria appears to be on the basis of an increase in mito-tracker intensity, which is not a reasonable assertion. The increase in TNTs needs to be properly quantified (as noted above).\nRelated to this section of the results we need to see some evidence that PINK1 is knocked down by the siRNA treatment (e.g. by Western blotting).\nFigure 2\nC) Why is the ‘knock out’ only 30% reduced in PINK1? Shouldn’t it be 100%.\nE) Again, quantification is based on random regions of cells, rather than according to the number of cells. What does intensity density mean?\nK) Has the Seahorse data been normalised to cell density/ number?\nFigure 3\nC) Please clarify – have the mitochondria in multiple cells of each type been properly quantified – i.e. more than 1 cell per condition from sufficient biological repeats?\nF) Transfer in both directions must be shown (visualised) to claim bidirectionality; see major concern above.\nG) Again – have the mitochondria in multiple cells of each type been adequately quantified?\nFigure 4\nC) Again – Y-axis needs clarification – what does mitochondrial signalling/ cell area mean, and what property does it relate to?\nJ) What was the purpose of Hoechst in this experiment?\nFigure 5\nA) Again – quantification is based on random regions of the slide, rather than the number of cells. Comparing A) versus B) – why do the same conditions looks completely different? There are no tubes in the MAD condition in B) but many in A). Cells in the MAD+NIR condition are alive in A) but dead in B)\nB) Again, what does intensity density mean?\nIn summary, the claim that all of the interesting effects onserved are mediated by inter-cellular mitochondrial and reagent transfer through TNT networks needs to be proven by visualisation and robust quantification.", "reviewer_2": "In this work, the authors constructed the ‘mitochondrial autonomous drive’ (MAD) system, which utilized the down-regulation of mitochondrial autophagy to accelerate mitochondrial intercellular transport and achieve the transportation of exogenous substances for photodynamic therapy of tumors. This provided a new strategy for non-transmembrane drug delivery. The work innovatively utilized mitochondria as drug carriers and achieved deep penetration and delivery of drugs through the tunneling nanotubes (TNTs). Moreover, it was the first to conduct a detailed study on the relationship between mitochondrial autophagy and intercellular transport, and found that inhibiting mitochondrial autophagy could significantly enhance the transport capacity of mitochondria. Overall, this is a well-prepared manuscript. However, there are still several shortcomings. I recommend the acceptance of this work after addressing the following issues.\nMajor issues:\n1. In Fig 2k，FP treatment to knockout PINK1 can enhance the mitochondrial OXPHOS pathway that support the cell-to-cell transfer of mitochondria. Will the increased oxygen consuming lead to tumor hypoxia after FP treatment that limited the photodynamic effect? For this question, experiments that determine tumor oxygen condition is suggested.\n2. In Fig 7b, it is intriguing that FP treatment inhibits tumor growth. The authors believe that knocking out PINK1 may increase oxidative stress that works for tumor inhibition. However, in Fig 5b, c, FP treatment shows negligible effects in killing tumor cells or generating ROS. Additional experiments are suggested to address this discrepancy.\n3. Tumor tissues consist of various cell types, including immune cells and fibroblasts. Do FP or MAD treatments promote mitochondrial transport between these cells, potentially altering the tumor microenvironment? A recent study (doi: 10.1038/s41586-024-08439-0) reports that mitochondrial transfer from cancer cells to tumor-infiltrating lymphocytes can lead to immune evasion. To address potential concerns that MAD might facilitate tumor immune evasion, additional experiments are needed.\n4. The mechanism of mitochondrial autophagy and intercellular transport is very complex. Although the article revealed some mechanisms, many details still need further research. For example, how the inhibition of mitochondrial autophagy specifically affects the formation and function of TNTs still needs in-depth exploration.\n5. Further research is needed to study the long-term impact of inhibiting mitochondrial autophagy on cells and the body, as well as the stability and effectiveness of the MAD system in the complex physiological environment of the living body.\n6. The article only used the photosensitizer IR780 as a model drug. Whether other types of drugs can also be effectively delivered through this system still needs to be verified. Moreover, there is a lack of comparison with other delivery systems to highlight the advantages of the MAD system in anti-tumor in this study.\nMinor issues:\n1. It is suggested to add a description of Fig 7a for the schematic illustration.\n2. Please check the grammar usage and the format issues in the title and the manuscript. For example, in the title, “for accelerate transport” is suggested to change to “for accelerating transport”.", "reviewer_3": "In the manuscript titled \" Leveraging engineered mitochondria through intercellular communication network for accelerate transport and delivery\" by Peng et al. the authors investigate a potential drug delivery method based on mitochondrial transportation. Essentially, the authors showed that inhibition of mitophagy can accelerate mitochondrial transportation, which can be exploited as a cargo-system to deliver photosensitizer into the deep internal part of tumors and thus to achieve a better anti-tumor effect.\nWhile the manuscript presents a novel concept, 1)overall the experimental data do not support the conclusions. In addition, 2) the manuscript is poorly assembled in terms of focused concepts, particularly in the abstract and introduction, making at time difficult to follow the rationale. 3) The experiments need additional explanation, and particularly robust experimentation to manipulate mitophagy and mitochondrial transportation (including proteins involved in transport, e.g. Miro, etc), in both gain- and loss-of-function. 4) A major concern of these studies is also how quantitative are these studies, both in cultured cells or tumors, and unclear how this can be manipulated in tumors.\nAdditional critiques:\n1. Downregulated mitophagy enhances mitochondrial intercellular transport via TNTs. The authors claim that mitochondria transport between cells through TNTs by showing that mitochondria staining in a long protrusion structure and assuming that mitochondria is in the process of intercellular transportation. Is there a marker of TNTs? Since the cells look in a very stress shape with lots of long branches, is it possible the long branches containing mitochondria shed off from the cells and engulfed by other cells? How this phenomena is conserved among cell types.\n2. Blockade of mitophagy arouses transport driving forces. pX330 backbone plasmid is a not good control. It would be better if the author uses pX330 backbone expressing a non-targeting sequence. The author named the PINK1 knockout group as FP group. All the experiments are using siRNA, other methods to manipulate mitophagy proteins are needed in both gain and loss of function. Also, other proteins involved in mitochondrial transportation including Miro, and other should be tested.\n3. The use of IR780 is also problematic. Does IR780 controls mitophagy? what concentrations are localized to the mitochondria. But more importantly, and conceptually, why the compound needs to use the mitochondria intercellular communication to reach different part of the tubules. The existence of TNTs, independently of mitochondrial, should be sufficient for drug diffusion in the tumor.\n4. To experimentally test the previous point, the authors should use IR780 analogs that don't localize to the mitochondria, to test their hipothesis.", "reviewer_4": "Here the authors explore the relationship between mitophagy and mitochondrial transport between cells via tunnel nanotubes (TNTs) as a potential avenue for enhancing drug delivery. Mitochondria exhibit complex inter-and-intracellular dynamics, and the authors propose using them as a vector for delivering exogenous treatments. They find that inhibiting mitophagy increases transport of mitochondria (both in number and speed) between cells via TNTs. The authors use the photosensitive drug IR780 to develop a ‘mitochondrial autonomous drive’ system that enhances traffic of mitochondria deeper into tumor tissues to lead to improved outcomes in vitro and in vivo.\nMy expertise is in advanced light microscopy, so most of my comments concern imaging methodology and interpretation. The cell biology and therapeutic application here seem sound and exciting to me, but there are some flaws in the imaging methodology that need to be corrected for me to have full confidence in the conclusions of those experiments.\nThe biggest concern for me here is that many of the confocal images included in this analysis are saturated. Saturated images (images with pixel values of 255, in an 8-bit image) are incompatible with quantitative fluorescence intensity measurements. Saturation destroys the mostly-linear relationship between fluorophore density in a specimen and grey values in a digital image. Even a few saturated pixels in a region of interest will disrupt the linearity of the measurement and thus the integrity of the data. There are several instances in this study where saturated images are used for intensity measurements:\nFig 4e. The mitochondria and IR780 channels. In addition to breaking the linearity of intensity measurements, saturation can also cause erroneous dilation of objects and so can confound object-based colocalization analyses as well.\nFig 5b. All of the PI images in the MAD + NIR condition are extremely saturated.\nFig 5c. The DCFHDA images in the MAD + NIR condition\nFig S11. The brightest patches in most of the images are saturated. Pearson’s Correlation is particularly sensitive to saturation because it is impossible to accurately measure how two signals vary relative to one another if one or both of them have hit an upper limit.\nI believe that to have full confidence in their image analysis results, the authors will need to re-image the saturated data using appropriate acquisition parameters. Chances are good that most of the major conclusions from these experiments will remain the same (for instance, in Fig 5b and c, saturation may even be causing an underestimation of how much more PI and DCFHDA there is in the MAD + NIR condition relative to the others), but in my experience as a microscopist it is not valid to make quantitative, absolute intensity measurements on saturated data. This is particularly true for sensitive analyses like colocalization and ratiometry.\nAdditionally bleedthrough controls should be included for most of not all multi-color imaging experiments. A notable example is Fig 4a. One of the conclusions of the figure is that IR780 traffics with mitochondria (labeled with MitoTracker), but signal from the MitoTracker channel bleeding into the IR780 channel would create the appearance of coincidence between the two signals. The authors don’t state which MitoTracker is used in this experiment, but if is MitoTracker Deep Red then bleedthrough is a definite possibility given that there is some overlap between the excitation spectra of MTDR (via FPbase.org) and IR780 (via Fig S9).\nAn autofluorescence control should be included to demonstrate that mitochondrial/cellular autofluorescence does not change when mitophagy is altered.\nThe rest of my comments are only minor points.\nThe image processing and analysis could use a bit more fleshing out, perhaps in the supplementary materials. “ND Processing” is a suite of analysis tools that is exclusive to NIS Elements, and so is not very informative to people who use other types of instruments. Indicating only that analysis was done in ImageJ, LAS, or Zen is not enough information to help people understand how the data was analyzed. How were images pre-processed? (background subtraction, etc)How were ROIs selected? (thresholding? If so, how?) How was particle tracking done?\nAvoid displaying images in blue if possible – it is very difficult to see dark blue on a black background.\nOverall, the findings here are exciting and significant as a potential avenue for drug delivery, but in my opinion some of the imaging needs to be redone without saturation in order to be analyzed appropriately, and bleedthrough controls should be included in the supplementary material where relevant."}, "author_response": "Response to reviewer comments\nWe sincerely appreciate the valuable and insightful comments, which have greatly\nhelped us improve the quality of this work. The revised sections in the manuscript are\nhighlighted in yellow, and our point-by-point responses are as follows:\nReviewer #1 (Remarks to the Author)\nThe manuscript submitted by Peng et al. concerns the inter-cellular transfer of\nmitochondria through tunnelling nano-tubes (TNTs), and their application as anti-\ncancer agents. The mitochondrial transfer field, particularly via TNTs, is an emerging\nfield in cell biology, which has brought about a number of high profile publications\nrelating to their properties, function and exploitation. In spite, of this many questions\nremain. Therefore, new finding of this hot topic will be very welcome to the TNT and\nwider mitochondrial community, and thus of interest to the broad readership of Nature\nCommunications.\nThe paper is very interesting from 2 perspectives. First of all, it provides evidence for\na link between mitophagy and TNT conducted mitochondrial transfer. Secondly, the\nwork describes how TNTs could be deployed for the formation of extra-cellular\nnetworks for the delivery, and deep penetration, of agents for the purposes of imaging\nand therapy, such as tumor clearance. These two aspects are both very worthy topics\nin themselves, but feel very much like two different stories, which might be better suited\nas different papers. The two parts present very interesting fundamental new knowledge\nand novel applications, which could in principle be suitable for Nature\nCommunications. However, before publication, either as one paper or two, there are a\nmany concerns that need to be addressed.\nRe: We appreciate the reviewer's recognition of our work. Indeed, the paper can be\n1\ndivided into 'basic research on reducing mitophagy to promote mitochondrial\nmovement along TNTs' and 'novel application of the accelerated mitochondria for\ndelivery, imaging and therapy'. Integrating them allows the narrative to flow naturally,\npresenting a coherent process of discovering phenomena and subsequently applying\nthem to solve practical problems. Additionally, other reviewers did not recommend\nsplitting the manuscript into two separate parts, as doing so would complicate and\nprolong the subsequent submission process. Therefore, the original integrated version\nwas retained.\nA major concern is that the central feature of the paper – inter-cellular mitochondrial\ntransport – has not been irrefutably demonstrated. We really need to see images/ movies\nof mitochondrial moving fully from one cell to another one. This also goes for other\nclaims, e.g. that mitochondria can move in both directions, and reagents are able to\nhitch a ride on mitochondria from one cell to another; we need to see this happening.\nGiven the claim of large scale inter-cellular mitochondrial traffic through TNTs this\nshould be achievable.\nRe: We appreciate your professional insights. The matter you have raised is one that we\nare also closely attending to. In Supplementary Figure S9 and Supplementary Movie 5,\nwe have captured in our images exactly the phenomenon of the transfer of mitochondria\nfrom one cell to another.\nanother. Yellow arrows indicate the transferring mitochondria. Green: mitochondria.\nScale bar: 5 µm.\nIt should be noted that repeated or long-term imaging of the same field of view\ncan cause non-negligible phototoxicity, eventually leading to TNT rupture. Moreover,\nthinner TNT is more prone to rupture. Since the complete transfer of mitochondria from\none cell to another requires capturing the field of view multiple times, we frequently\nobserved TNT breakage after prolonged imaging in the same field—even though we\nused SIM (structured illumination microscopy), which has relatively low phototoxicity.\nTherefore, capturing complete images or videos is not an easy task.\nFurthermore, observing the complete transfer of mitochondria from one cell to\nanother often requires initiating imaging while the mitochondria are still within the\ndonor cell—selecting such fields of view is extremely challenging. Despite this, we\nhave frequently captured instances of mitochondria ultimately entering recipient cells.\nFor instance, as illustrated in the following Figure R1—which serves as a follow-up to\nthe revised Figure 4e (according to Reviewer 4's comments) in the manuscript—\n3\nmitochondria labeled with IR780 can be clearly observed moving through TNTs and\neventually entering the recipient cell. However, during the later stages of imaging\n(particularly beyond 30 minutes), the image quality deteriorated significantly due to\nunavoidable phototoxicity and focal drift.\nFigure R1. IR780 moved in TNTs synchronously with mitochondria (follow-up to\nFigure 4e). Red: TNTs (F-actin). Green: mitochondria. Magenta: IR780. Yellow arrows\nindicate mitochondria and IR780 moving together. Scale bar: 10 µm. This figure is not\nredundantly presented in the main manuscript.\nThis complete transfer from one cell to another could also be demonstrated by\nother results. For example, in Figure 1c and Figure 4c in the main manuscript, the signal\nof donor cell mitochondria in the recipient cell was only detectable when intercellular\nmitochondrial transfer had occurred. We believe this represented the natural movement\nof mitochondria between cells that occurred even without imaging observation.\nMoreover, during our observations, we noted that mitochondrial exchange\nbetween cells may sometimes occur directly within the nanotubes, as we frequently\nobserved mitochondria migrating bidirectionally and fusing at intermediate positions\n(e.g., the following Figure R2, mitochondrial fusion between two cells observed circa\n6:48 in Supplementary Movie 5). This finding further supports that mitochondria within\ncells are not isolated entities, but rather engage in active intercellular exchange.\nYellow arrows indicate the fusing mitochondria. Green: mitochondria. Scale bar: 5 µm.\nThis figure is not redundantly presented in the main manuscript.\nWe have supplemented in the manuscript in Page 15: 'Ultimately, long-term\nimaging captured complete visuals of a mitochondrion transporting from one cell to\nanother in the FP group, which directly proved the intercellular transfer of mitochondria\n(Supplementary Figure S9, Supplementary Movie 5).'\nOther concerns figure-by-figure:\nFigure 1\nA) Representative panels clearly present very different cell densities. It looks like the\nTNTs have been quantified by area, with each area containing a different number of\ncells. So, it might be misleading to state that TNTs are upregulated by siPINK1\ntreatment. Quantification of N=4 random regions is not really sufficient. Surely, the\nnumber of TNTs needs to be quantified per cell over a large number of cells from a\nsuitable number of independent experiments. Furthermore, the assertion in the text that\nthe TNTs span ‘from one cell to another’ is not clear from the images (related to major\nconcern above).\nclearly present the representative panels with similar cell densities. It should be\nemphasized that the original Figure 1a presented are zoomed-in views (to better display\nTNTs), while the quantitative analysis is performed using complete images (revised\nFigure 1a). For TNT quantification, we normalized the counts by cell number (Y-axis:\nTNTs/Nucleus, detailed quantification methods are provided in the Supplementary\nInformation), thus eliminating potential bias from variations in cell density. Across four\nimaging fields, we collected data from at least hundreds of cells for statistical analysis.\nFigure 1a. Confocal images of B16F10 cells after NC or siPINK1 treatment (Scale bar:\n75/25 μm). Blue: nucleus. Red: cell membrane. Green: mitochondria. White arrows\nindicate that mitochondria were transferring between cells along TNT structure.\nYellow frames indicate the original location of the enlarged image.\nWhile Figure 1a clearly showed mitochondrial transport within TNTs, the\nsubsequent transfer 'from one cell to another' was demonstrated in Figures 1c and 4c.\nThe mitochondria were observed to transfer from NC/Control group cells to\nsiPINK1/FP group cells, vice versa, from siPINK1/FP group cells to NC/Control group\ncells. That is to say, whether the cells were normal or had been treated with siPINK1/FP,\ntheir mitochondria were found in other cells that were either normal or treated with\nsiPINK1/FP. These observations clearly demonstrated that the transfer of mitochondria\n6\nbetween cells was ubiquitous and bidirectional.\nB) In the co-culture it is not clear how the team avoided cross contamination of dyes\nand siRNAs. Can we be certain the co-localisation is not the result of residual dye/\nsiRNA carrying over from the cells used to seed the co-culture?\nRe: We sincerely appreciate your valuable question. Following each incubation of cells\nwith dye, the cells were washed thoroughly with PBS at least three times to ensure\nremoval of uninternalized dye. To verify that the transfer of mitochondria and IR780\nfrom donor to recipient cells represents a normal physiological phenomenon rather than\ndye contamination, we conducted the following validation experiment. We collected\nconditioned medium from donor cells labeled with MitoTracker and IR780 after 4 h of\nculture and added this to recipient cells for an additional 4 h of co-culture. Concurrently,\nwe collected, washed, and added the labeled donor cells themselves to parallel sets of\nrecipient cells, also followed by 4 h of co-culture. We then assessed the levels of\nMitoTracker and IR780 in the recipient cells.\nWe have added the Supplementary Figure S15 to illustrate this matter. Recipient\ncells co-cultured with the conditioned medium displayed no significant MitoTracker or\nIR780 fluorescence. In contrast, recipient cells co-cultured directly with donor cells\nexhibited clear MitoTracker and IR780 fluorescence. This demonstrated that\nnonspecific dye leakage did not occur or had no impact on the experimental\nobservations. Moreover, comparable dye crossover effects in co-culture systems were\nobserved in prior studies, aligning with our findings1,2.\nMitotracker and IR780-labeled donor cells, or conditioned medium. (b) Confocal\nimages of recipient cells co-incubated with either dual-labeled (MitoTracker/IR780)\ndonor cells or conditioned medium, and corresponding statistical number of fluorescent\nspots of mitochondria and IR780 entering the recipient cell (Scale bar: 25 μm, n=50\ncells). Data were presented as mean  SD. P values were determined using two-tailed\nunpaired t test. Source data are provided as a 'Source Data' file.\nThe corresponding explanations have also been added in the revised manuscript\nin Page 19: 'Conversely, as demonstrated in Supplementary Figure S15, recipient cells\ncultured in conditioned medium exhibited no detectable MitoTracker or IR780 staining,\nindicating that the fluorescent signals in recipient cells can be attributed to\nmitochondrial transfer rather than dye crossover contamination.'\nC) The combined red/ green channels make it hard to see red mitochondria within green\ncell. It would be much better to also show the 2 channels separately.\nadjacent to the merged image in Figure 1c.\nFigure 1c. Mitochondria transferred between MitoTracker Red-labelled donor cells\n(red) and CMFDA-stained (green) recipient cells. (Scale bar: 50/10 μm).\nD) The Y-axis need clarification. What does ‘mitochondrial signalling/ cell area’ mean?\nThe text states that there is an increase in the number of mitochondria and TNTs. The\nstated increase in mitochondria appears to be on the basis of an increase in mito-\ntracker intensity, which is not a reasonable assertion. The increase in TNTs needs to be\nproperly quantified (as noted above).\nRe: Thank you for raising this point. To clarify, the quantification showing the\nsignificant increase in mitochondrial fluorescence and TNT number after siPINK1\ntreatment, normalized to nuclear fluorescence and cell count (as also detailed in the\nSupplementary Information) respectively, is presented in Figure 1a. In Figure 1d, our\nquantification methodology involved: (1) segmenting recipient cells as individual units,\n(2) counting fluorescent puncta of mitochondria (red channel) within each recipient cell\n9\n(as detailed in both figure legend and Methods section), followed by (3) normalization\nagainst recipient cell area. This yields the infiltration density of donor-derived\nmitochondria in recipient cells, which is rigorously defined by the Y-axis parameter\n'mitochondrial signaling/cell area'. Consequently, these data specifically reflect\nmitochondrial transfer events, independent of any changes in total mitochondrial\ncontent or TNT numbers.\nDetailed data processing and quantification methodologies have been added to the\nMethods section in Page 36-37: 'Recipient cells were segmented using Cellpose\n(https://www.cellpose.org), followed by processing of the resultant masks in ImageJ to\ndefine cellular boundaries. Individual recipient cells were isolated by subtracting these\nmasks from raw images. Subsequent 'Analyze Particles' analysis (with size thresholding\nand 'Add to Manager' enabled) generated discrete regions of interest (ROIs)\ncorresponding to individual cells. Mitochondrial and IR780 fluorescence channels were\nthen imported, converted to 8-bit, thresholded at a fixed value across all experimental\ngroups, and converted to binary single-point representations. These puncta were\nnormalized to unity (=1) using the Divide function, enabling quantification via the\nMeasure tool which recorded RawIntDen values - representing the summed intensity\nof all pixels within each recipient cell ROI - to determine counts of mitochondrial and\nIR780 puncta per cell. During quantification, RawIntDen values were normalized\nagainst recipient cell area to account for size-dependent variability, thereby yielding\nmitochondrial/IR780 puncta density per cell as the definitive metric for cargo delivery\nefficiency to recipient cells.'\nRelated to this section of the results we need to see some evidence that PINK1 is\nknocked down by the siRNA treatment (e.g. by Western blotting).\nverifications for siM-sec and siMiro-1 knockdowns, has been presented in\nSupplementary Figure S31. The knockdown efficiencies for siPINK1, siM-sec and\nsiMiro-1 were 37.6%, 41.5%, and 50.3%, respectively.\nFigure 2\nC) Why is the ‘knock out’ only 30% reduced in PINK1? Shouldn’t it be 100%.\nRe: Generally, only stably transfected cells that undergo several rounds of screening\nand cultivation can achieve 100% knockout efficiency. Such cells are typically used as\nmodel systems for investigating cellular pathway mechanisms. However, this approach\nis nearly impractical for actual therapeutic applications, as in vivo cell transfection\ncannot incorporate selection processes - consequently, untransfected cells persist within\nthe cellular population. In our opinion, a 35.7% gene knockout efficiency is already\nquite high, when compared with similar products. In our subsequent experiments, we\ndemonstrated that this 35.7% gene knockout efficiency is sufficient to enhance\nmitochondrial transport while maintaining cell viability (e.g., 51.8% reduction in\nmitophagy in Figure 1e with almost no cell death observed in Figure 5b).\nE) Again, quantification is based on random regions of cells, rather than according to\nthe number of cells. What does intensity density mean?\nRe: Thank you for raising this point. In this experiment (Figure 2e), a higher\nmagnification was used during imaging to capture clearer pictures of Keima\nfluorescence, unlike the assay shown in Figure 1a. Consequently, in each measurement,\nonly 1-2 cells were present per field of view. Given concerns about sample size, the\nexperiment was repeated with an increased sample size of n=30 fields of view in the\n11\nrevised manuscript. It is important to note that this assay measures the ratio of red-to-\ngreen fluorescence across the entire field of view. This ratio reflects mitophagy levels\nand is independent of the number of cells per field. Therefore, performing statistical\nanalysis per field of view provides a more representative measure of the average\nmitophagy level across the cell population. The modified Figure 2e is align with the\nresults of the original Figure 2e.\nFurthermore, thank you for bringing 'IntDen' to our attention. We would like to\nclarify that 'IntDen' refers to integrated density (the sum of fluorescence intensities),\nnot intensity density. IntDen (Integrated Density) is a parameter in ImageJ that\nmeasures the sum of the intensity values of all pixels within a selected region of interest.\nIt represents the total signal or integrated intensity within that region and serves as a\nkey metric for quantifying the total amount of a substance within discrete objects (e.g.,\ncells, bands). The ratio of IntDen to the number of cells (or nuclear intensity) provides\nthe mean fluorescence intensity per cell. For Figure 2e, where both the field of view\nand the cells within it were fixed, the ratio of total red fluorescence intensity to total\ngreen fluorescence intensity (IntDen of Red/Green) directly reflected the level of\nmitophagy.\nFigure 2e. Mitophagy detection in B16F10 cells by transfection with mito-Keima\nplasmid and corresponding semi-quantitative results. IntDen = integrated density.\nScale bar: 10 µm (n=30 random regions). Data were presented as mean  SD. P values were\n12\ndetermined using two-tailed unpaired t test. Source data are provided as a 'Source Data'\nfile.\nK) Has the Seahorse data been normalised to cell density/ number?\nRe: In the Seahorse assay, cells were stained with Hoechst 33342 during the final\nloading step, followed by cell counting using a high-content analysis system\n(CellVoyager CV8000, Japan) for normalization against cell numbers. This part was\npresented in Methods: 'Cellular metabolic assays'.\nFigure 3\nC) Please clarify – have the mitochondria in multiple cells of each type been properly\nquantified – i.e. more than 1 cell per condition from sufficient biological repeats?\nRe: Thank you for your question. Yes. The results shown in Figure 3c represented\nstatistical analyses of all mitochondria from individual cells presented in Figure 3b.\nAs stated, all experiments were independently repeated with consistent results to ensure\nreproducibility. The following Figure R3 are two representative replicates\ndemonstrating similar outcomes, which are not redundantly presented in the main\nmanuscript.\nFigure R3. Replicates of SIM images and speed-position tracks of mitochondrial\n13\nmovements, and mean track speed-displacement maps in individual cells over a three-\nminute period. Red: cytoskeleton (F-actin). Green: mitochondria. Scale bar: 5 µm.\n(n=498 mitochondria for Control-1, n=885 mitochondria for FP-1, n=855 mitochondria\nfor Control-2, n=1059 mitochondria for FP-2, n=471 mitochondria for Control-3,\nn=567 mitochondria for FP-3). This figure is not redundantly presented in the main\nmanuscript.\nF) Transfer in both directions must be shown (visualised) to claim bidirectionality;\nsee major concern above.\nRe: As shown in Figures 1c and Figure 4c of the main manuscript, mitochondria\ntransferred bidirectionally between NC/Control group cells and siPINK1/FP-treated\ncells. Specifically, mitochondrial movement occurred both from normal cells to treated\ncells and vice versa. These findings indicate that regardless of whether cells were\nnormal or subjected to siPINK1/FP treatment, their mitochondria were capable of\ntransferring to other cells—irrespective of the recipient cell’s treatment status. Together,\nthese results provide clear evidence of bidirectional mitochondrial transfer.\nAs addressed in our response to the Major Concern, capturing complete video\nrecordings of the entire transfer process for both mitochondrial populations proved\ntechnically challenging. Consequently, we were unable to obtain video evidence\nshowing mitochondria fully transferring from one cell type to the other and vice versa\nin a single continuous sequence. However, this does not negate the demonstration of\nbidirectional mitochondrial transfer. Bidirectional mitochondrial transfer between cells\nhas been documented in multiple studies. For instance, Kangmin He et al. demonstrated\nspontaneous bidirectional mitochondrial trafficking via tunneling nanotubes between\nsomatic cells—specifically cardiomyocytes (CMs) and cardiac fibroblasts—\n14\nestablishing structural and functional connectivity essential for myocardial tissue\nhomeostasis2. Egor Y. Plotnikov et al. detected mutual cytoplasmic and mitochondrial\nexchange between renal tubular cells (RTCs) and multipotent mesenchymal stromal\ncells (MMSCs) in co-culture systems, with transport occurring bidirectionally—albeit\npredominantly toward MMSCs3. Krishna C. Vallabhaneni et al. observed equivalent\nbidirectional mitochondrial transfer between human vascular smooth muscle cells\n(VSMCs) and bone marrow-derived mesenchymal stem cells (BM-MSCs) under\nnormal culture conditions4, which promoted MSC proliferation without differentiation.\nTherefore, the bidirectional transfer of mitochondria is well-supported and specifically\ndemonstrated by the existing experimental data in our manuscript.\nG) Again – have the mitochondria in multiple cells of each type been adequately\nquantified?\nRe: Yes. Experimental reproducibility was verified through systematic replication,\nguaranteeing result reliability. To further ensure result robustness, we switched the\nmitochondrial labeling scheme - labeling FP group mitochondria with red fluorescence\nand control group mitochondria with green fluorescence - then repeated the co-culture\nexperiments. The results (Figure R4) consistently showed that FP group mitochondria\nstill exhibited higher motility.\nFigure R4. Replicates of SIM images of mitochondrial movements and mean track\n15\nspeed-displacement maps between untreated and PINK1 knockout cells over a ten-\nminute period. Green: mitochondria in untreated cells (Control). Red: mitochondria in\nPINK1 knockout cells (FP). Scale bar: 5 µm. (n=9 mitochondria for Control and n=27\nmitochondria for FP). This figure is not redundantly presented in the main manuscript.\nFigure 4\nC) Again – Y-axis needs clarification – what does mitochondrial signalling/ cell area\nmean, and what property does it relate to?\nRe: Just as in the previous explanation regarding the Y-axis, mitochondrial\nsignalling/cell area means the normalization of mitochondrial fluorescent puncta (red\nchannel) to recipient cell area. It relates to the density of mitochondria translocated\nfrom donor cells and presented in recipient cells, thereby assessing mitochondrial\ntransfer capability.\nJ) What was the purpose of Hoechst in this experiment?\nRe: Thank you for your question. Hoechst 33342 binds to the minor groove of double-\nstranded DNA, enabling DNA labeling5. In these magnified views, the Hoechst 33342\nsignal (blue) represented intercellular transfer of mitochondrial DNA. We have\naccordingly updated the figure legend: 'DNA (nuclear DNA and mitochondrial DNA)'\nin the revised manuscript (Page 22).\nFigure 5\nA) Again – quantification is based on random regions of the slide, rather than the\nnumber of cells. Comparing A) versus B) – why do the same conditions looks completely\ndifferent? There are no tubes in the MAD condition in B) but many in A). Cells in the\nMAD+NIR condition are alive in A) but dead in B)\nRe: We sincerely appreciate your valuable question. In Figure 5a, precisely, our\nquantified TNT counts were normalized by cell count on random regions of the slide\n(Y-axis: TNTs/nucleus). This represents the average number of TNTs per cell across\ndozens of cells per field of view, thereby directly reflecting the mean TNT count per\ncell.\nAs for the difference between Figure 5a and Figure 5b, given the fragile nature of\nTNTs, we minimized cellular disturbance during TNT quantification by using tissue\nculture-treated plates in Figure 5a that better maintain cell growth conditions for\nimaging, rather than transferring cells to confocal dishes (Figure 5b) that are more\nsuitable for imaging. In the revised manuscript, we modified the figure legend as: '(a)\nThe number and semi-quantitative results of TNTs in B16F10 cells seeded on tissue\nculture-treated cell culture plates after different treatments (Scale: 50 μm for low\nmagnification, 25 μm for high magnification, n=8 random regions). Red: cell\nmembrane'. While this approach compromised image quality, it better preserved cells\nin their natural growth state. Consequently, the cellular states differed somewhat\nbetween Figure 5a and Figures 5b. The reduced TNT counts in the MAD group in\nFigure 5b compared to Figure 5a were related to this methodological choice, though\nthe MAD group still showed significantly more TNTs than the Control group in within-\ngroup comparisons.\nIt should be noted that cells in the MAD+NIR group in Figure 5a were nearly in a\ndeath state, as evidenced by their complete deviation from normal growth morphology\n(see Control group) - they lost their characteristic quasi-spherical shape and became\nflattened on the dish bottom. This observation is fully consistent with the results shown\nin Figures 5b and Figures 5c. This is due to NIR aroused a lethal photodynamic effect.\nRe: As previously explained for Figure 2e, within defined fields of view containing\nfixed cell populations, the ratio of total red fluorescence intensity to total green\nfluorescence intensity (IntDen of Red/Green) serves as a validated indicator of cellular\nviability status. This fluorescence ratio across the entire field of view reflects the\nviability status of multiple cells collectively, independent of the number of cells per\nfield. Consequently, performing statistical analysis at the field-of-view level provides a\nmore representative measure of the mean viability level across the hundreds of cells\ncomprising each experimental group.\nIn summary, the claim that all of the interesting effects onserved are mediated by inter-\ncellular mitochondrial and reagent transfer through TNT networks needs to be proven\nby visualisation and robust quantification.\nRe: We appreciate the reviewer's detailed comments on each figure. We have enhanced\nthe visualization of intercellular mitochondrial transfer, data processing, and\nquantification—key concerns you emphasized. Thus, we hope that our response to your\ncomments and the additional experimental data have now addressed these points.\nIndeed, this paper concerns the mitochondrial transfer field, particularly via TNTs, is\nan emerging field in cell biology. While current technical barriers limit comprehensive\nTNT imaging in biological systems—though verifiable through other approaches—we\nanticipate that emerging imaging technologies and molecular tools will unlock this\nresearch frontier.\nRelevant references\n18\nimmune cells. Nat Nanotechnol 17, 98-106 (2022). https://doi.org:10.1038/s41565-021-01000-\n4\ncardiofibroblasts mediated by membrane nanotubes. Cardiovasc Res 92, 39-47 (2011).\nhttps://doi.org:10.1093/cvr/cvr189\nand organelle transfer between mesenchymal multipotent stromal cells and renal tubular cells\nin co-culture. Exp Cell Res 316, 2447-2455 (2010). https://doi.org:10.1016/j.yexcr.2010.06.009\nof mesenchymal stem cells by mitochondrial transfer via tunneling nanotubes. Stem Cells Dev\n21, 3104-3113 (2012). https://doi.org:10.1089/scd.2011.0691\nmicrofluorometry. J Cell Physiol 102, 175-181 (1980). https://doi.org:10.1002/jcp.1041020208\nReviewer #2 (Remarks to the Author)\nIn this work, the authors constructed the ‘mitochondrial autonomous drive’ (MAD)\nsystem, which utilized the down-regulation of mitochondrial autophagy to accelerate\nmitochondrial intercellular transport and achieve the transportation of exogenous\nsubstances for photodynamic therapy of tumors. This provided a new strategy for non-\ntransmembrane drug delivery. The work innovatively utilized mitochondria as drug\ncarriers and achieved deep penetration and delivery of drugs through the tunneling\nnanotubes (TNTs). Moreover, it was the first to conduct a detailed study on the\nrelationship between mitochondrial autophagy and intercellular transport, and found\nthat inhibiting mitochondrial autophagy could significantly enhance the transport\ncapacity of mitochondria. Overall, this is a well-prepared manuscript. However, there\nare still several shortcomings. I recommend the acceptance of this work after\naddressing the following issues.\nMajor issues:\n1. In Fig 2k，FP treatment to knockout PINK1 can enhance the mitochondrial\nOXPHOS pathway that support the cell-to-cell transfer of mitochondria. Will the\nincreased oxygen consuming lead to tumor hypoxia after FP treatment that limited the\nphotodynamic effect? For this question, experiments that determine tumor oxygen\ncondition is suggested.\nRe: We sincerely appreciate your valuable suggestion. Following this recommendation,\ncellular hypoxia was assessed with Image-iT™ Hypoxia Reagent (Thermo Fisher, Cat#\nI14834). This probe demonstrates oxygen concentration-dependent fluorescence\nactivation, exhibiting high sensitivity at oxygen tensions below 5%. Crucially, any\nhypoxia-induced probe fluorescence would persist and remain detectable even after\nreturning to normoxic conditions, thus ensuring that no potential hypoxia signal is\nmissed. In the experiment, untreated cells served as the negative control and cells\ncultured in the hypoxia incubator chamber as the positive control. After treating all\nthree groups with Image-iT™ Hypoxia Reagent, the negative control and FP group\nwere cultured under normoxic conditions while the positive control in the hypoxia\nincubator chamber for 4 h. Hypoxic conditions during this incubation period were then\nassessed. Image analysis and fluorescence intensity quantification revealed no\nsignificant hypoxic response in the FP group (Supplementary Figure S8). This\nsupplementary experiment has been included in the revised manuscript in Page 11:\n'According to statistics, the basal respiration, maximum respiration, and spare\nrespiration capacity of cells were significantly improved by PINK1 blockade (2.1-,\n4.1-, 5.0-fold versus control), showing a stronger metabolic activity without inducing\n20\nhypoxia (Supplementary Figure S8)'.\nSupplementary Figure S8. Hypoxic status determined by Image-iT™ Hypoxia\nReagent. Untreated cells served as negative control. For positive control, cells were\nsubjected to 4-hour incubation in a hypoxia incubator chamber under standardized\nconditions. Scale bar: 25 µm (n=20 random regions). Data were presented as mean \nSD. P values were determined using one-way ANOVA with Tukey's multiple\ncomparisons test. Source data are provided as a 'Source Data' file.\nConcurrently, our previous RNA-seq results (Figure 2g) demonstrated\nsignificantly downregulated HIF-1α expression in the FP group. As HIF-1α served as a\nhallmark molecular indicator of cellular hypoxia, this finding further validated the\nabsence of hypoxic conditions in tumor cells at this stage. We speculate this\nphenomenon may correlate with increased mitochondrial biogenesis, resulting in\nreduced glycolysis and normalized respiratory function in tumor cells.\nAdmittedly, while FP treatment itself does not induce hypoxia, aberrant tumor\nvasculature during progression inevitably creates hypoxic zones, particularly within the\ncore. Regarding our model agent IR780, its photodynamic therapy (PDT) efficacy—\n21\nbeing oxygen-dependent for ¹O₂ generation—is indeed compromised in such regions.\nHowever, IR780's potent photothermal therapy (PTT) capability remains effective:\nunder 808 nm laser irradiation, it achieves substantial tumor ablation even in\nprofoundly hypoxic areas (e.g., tumor core). Consequently, our therapeutic regimen\nattained a complete response rate of 3/7 (42.9%) in tumor eradication.\n2. In Fig 7b, it is intriguing that FP treatment inhibits tumor growth. The authors\nbelieve that knocking out PINK1 may increase oxidative stress that works for tumor\ninhibition. However, in Fig 5b, c, FP treatment shows negligible effects in killing tumor\ncells or generating ROS. Additional experiments are suggested to address this\ndiscrepancy.\nRe: We are grateful for your keen observation. Figure 5c demonstrated unaltered ROS\nlevels at 72 h post-FP treatment. However, we propose that inhibited mitophagy leads\nto progressive accumulation of ROS due to impaired clearance of compromised\nmitochondria. This ultimately induces sustained oxidative stress, causing tumor cell\ndestruction. In order to verify this conclusion, we quantified cellular ROS levels at day\n3 and day 7 post-FP treatment. Flow cytometric analysis revealed a significant elevation\nin ROS by day 7 compared to day 3 (Supplementary Figure S18, P=0.0091, has been\nadded in the revised manuscript in Page 27), consistent with our theoretical framework.\nSo, once NIR was administered, the released ROS helped IR780 eliminate tumor cells\n(Figure 5b) and inhibited tumor growth (Figure 7b). These findings indicated ROS\naccumulation likely contributed substantially to FP's tumoricidal efficacy.\npost-FP treatment (n=3 biologically independent samples). Data were presented as\nmean  SD. P values were determined using two-tailed unpaired t test. Source data are\nprovided as a 'Source Data' file.\n3. Tumor tissues consist of various cell types, including immune cells and fibroblasts.\nDo FP or MAD treatments promote mitochondrial transport between these cells,\npotentially altering the tumor microenvironment? A recent study (doi: 10.1038/s41586-\n024-08439-0) reports that mitochondrial transfer from cancer cells to tumor-\ninfiltrating lymphocytes can lead to immune evasion. To address potential concerns that\nMAD might facilitate tumor immune evasion, additional experiments are needed.\nRe: Thank you for raising this point. Indeed, recent literature has conclusively\ndemonstrated that tumor cells either transfer mtDNA-mutated mitochondria to T cells1\nor hijack mitochondrial resources from T cells2 to achieve immune evasion, while\nequally robust studies have highlighted supportive mitochondrial donations to T cells\nfrom other cell types (e.g., bone marrow stromal cells3). These findings collectively\nunderscore the complexity of the tumor microenvironment, where T cells experience\nmultifaceted regulatory inputs.\nAccordingly, we performed in vivo phenotypic characterization of T cells\n23\nfollowing MAD treatment. As shown in Supplementary Figure S22, MAD treatment\ncaused no significant changes in CD4+ or Treg populations, but CD8+ T cells showed\nan increasing trend. This specific immune reprogramming indicated that MAD\ntreatment elicited immunostimulatory T cell polarization rather than inducing\nimmunosuppression. We posit that the underlying mechanisms involve both sustained\nT cell resilience via external mitochondrial support preventing tumor-induced\nexhaustion, and immunostimulatory effects from phototherapy-triggered immunogenic\ncell death.\nWe have supplemented in the revised manuscript in Page 27: 'H&E staining, Ki67\nstaining, and TUNEL assay (Figure 7f-h) showed that MAD under NIR irradiation had\nthe best tumor proliferation inhibition and cell apoptosis ability, concomitant with a\ntendency toward CD8+T cell activation (Supplementary Figure S22).'\nSupplementary Figure S22. Flow cytometry analysis of tumor-infiltrating T cell\nproportion changes in vivo and corresponding statistical results (n=3 biologically\nindependent samples). Data were presented as mean  SD. Source data are provided as\na 'Source Data' file.\n24\n4. The mechanism of mitochondrial autophagy and intercellular transport is very\ncomplex. Although the article revealed some mechanisms, many details still need\nfurther research. For example, how the inhibition of mitochondrial autophagy\nspecifically affects the formation and function of TNTs still needs in-depth exploration.\nRe: Your comments profoundly address a pivotal aspect central to our research\nobjectives, and we acknowledge that existing literature has provided foundational\ninsights into related mechanisms. To comprehensively explore this, we first consider\nthe multifaceted consequences of mitophagy inhibition—including the elevated\nintracellular ROS levels, disruption of cellular energy homeostasis, accumulation of\ndysfunctional mitochondria, amplified pro-inflammatory signaling through mtDNA\nleakage and ROS, and potentially heightened apoptotic susceptibility. Furthermore,\ntunneling nanotubes (TNTs) demonstrate defining characteristics: their cytoskeleton-\ndependent (primarily actin-based) dynamic assembly is regulated by diverse cues such\nas cellular stress, damage signals, and specific receptor-ligand interactions.\nFunctionally, TNTs serve as critical conduits for intercellular communication,\ntransporting an extensive array of cargo spanning ions, signaling molecules, organelles\n(notably mitochondria), pathogens, and prion-like aggregates. These structures play\nindispensable roles in tissue homeostasis, immune responses, disease pathogenesis, and\nneuronal signaling, with mitochondria representing a particularly consequential cargo\nfrequently trafficked through TNT networks.\nConsequently, it is analytically evident that mitophagy inhibition may\nsignificantly promote tunneling nanotube (TNT) formation, constituting a fundamental\ncellular adaptive response to accumulated mitochondria and associated stress signaling.\nFirst, amplified cellular stress signals serve as potent TNT inducers, as extensive studies\n25\ndemonstrate that oxidative stress4, DNA damage5, and viral infections6 robustly\nstimulate TNT biogenesis. Second, the mitochondrial crisis potentially triggers 'distress\nsignaling' wherein compromised cells initiate TNT formation as physical conduits to\nsolicit healthy mitochondria (mitochondrial donation) or restorative factors from\nneighboring cells7. Third, although not yet addressed in our current manuscript, the pro-\ninflammatory milieu resulting from mitophagy suppression (e.g., mtDNA leakage\nactivating the cGAS-STING pathway) further amplifies TNT generation8, particularly\nin immune cell communication contexts.\nThese mechanistic insights align with our described findings, as we discussed in\nthe manuscript in Page 6: 'Notably, the mitochondrial transport increased in siPINK1\n(donor) to NC (recipient) and NC (donor) to siPINK1 (recipient) may reflect two\ndifferent mitochondrial transport mechanisms, namely, cells with metabolic changes\nsend dysregulated mitochondria to normal cells to maintain their own quality control,\nwhile normal cells transfer healthy mitochondria to unstable cells for rescue'. In the\nexperiment, we also paid special attention to metabolic homeostasis and oxidative stress\n(Figure 2k, Figure 4a, Supplementary Figure S10)—precisely because they may serve\nas a possible link from inhibited mitophagy to TNT formation/mitochondrial transfer.\nFunctionally, inhibiting mitophagy profoundly alters the role of tunneling\nnanotubes (TNTs), transforming them from potential rescue conduits into pathways that\npropagate mitochondrial and cellular stress. This subverts TNTs' beneficial role in\nmaintaining intercellular mitochondrial quality homeostasis within tumors. In cancer\ncontexts—where hypoxic and metabolically stressed microenvironments prevail—\nmitophagy suppression may promote the intercellular exchange of damaged\nmitochondria through TNTs. While this could represent a survival strategy for cancer\ncells under duress (e.g., disposing dysfunctional components to neighbors or\n26\ndistributing cellular damage), it may simultaneously disseminate pro-apoptotic signals.\nNotably, when leveraged for therapeutic cargo transport, this reprogrammed pathway\ncan be co-opted as a 'Trojan horse' for targeted drug delivery.\nAlthough distinct, the driving forces for mitochondrial transfer highlighted in our\nstudy (morphological adaptation, metabolic reprogramming, and respiratory\nenhancement) largely overlap with those underlying TNT formation. The fundamental\npremise is that when cells fail to recycle damaged mitochondria through mitophagy,\nexpelling these organelles may serve as a compensatory mechanism to prevent their\naccumulation—with TNTs acting as the primary conduit for mitochondrial transfer.\nIn summary, current theoretical frameworks support that mitophagy inhibition\npromotes tunneling nanotube (TNT) formation and mitochondrial transfer. Within our\nexperimental design, suppressing mitophagy serves additional strategic purposes: (1)\nincreasing mitochondrial availability for cargo loading, (2) preventing degradation of\nmitochondrial transport machinery (e.g., Miro19), and (3) ensuring spatial concordance\nbetween photosensitizer targeting and ROS generation at subcellular compartments.\nAs rightly noted, however, critical mechanistic details require further elucidation.\nFuture studies should quantitatively assess—across diverse cell types and disease models—\nhow direct manipulation of mitophagy impacts TNT frequency/stability, transport\nefficiency of functional mitochondria, and ultimate cellular/tissue outcomes.\n5. Further research is needed to study the long-term impact of inhibiting mitochondrial\nautophagy on cells and the body, as well as the stability and effectiveness of the MAD\nsystem in the complex physiological environment of the living body.\nRe: This is a valid point. Based on current research findings, chronic mitophagy\ninhibition effectively cripples a cell's critical mitochondrial quality control mechanism.\npathological cascade: energy crisis, exacerbated oxidative stress, genomic instability,\ninflammatory activation, and elevated cell death rates. Therefore, our MAD system is\ndesigned for sustained retention within tumor microenvironments. Sequencing of heart,\nliver, spleen, lung, and kidney revealed undetectable mutations at the target gene locus\nfollowing MAD therapy at the experimental endpoint (Supplementary Figure S20).\nWhile our current strategy employs peritumoral administration, future iterations may\nincorporate tumor-targeting ligands onto delivery vectors to further enhance tumor-\nspecific accumulation.\nWe have supplemented in the manuscript in Page 27: 'H&E staining of major\norgans (Supplementary Figure S19), sequencing of target gene loci in major organs\n(Supplementary Figure S20), and blood routine parameters (Supplementary Figure S21)\nshowed no abnormalities, indicating the safety of MAD system' and in Page 33:\n'Optimizing future MAD iterations could include functionalizing the delivery carriers\nwith tumor-homing ligands (e.g., antibodies, peptides) to promote enhanced and more\nspecific tumor accumulation, thereby potentially mitigating off-target effects and\nassociated long-term cellular and systemic consequences'.\nexperimental endpoint (highlighted by red boxes).\nIn a parallel study (manuscript in preparation), we rigorously evaluated the\nstability and efficacy of the MAD system in complex physiological environments.\nThe FHPAE-based carrier: (i) effectively shields plasmids from DNase digestion,\n(ii) sustains transfection efficiency in 30% serum, and (iii) shows negligible hemolysis up\nto 200 μg/mL. These findings demonstrate MAD's functional resilience in biologically\nrelevant environments. We will continue to investigate the long-term impact of\ninhibiting mitophagy on cells and the organism, as well as the stability and effectiveness\nof the MAD system in the complex physiological environment of living organisms.\n6. The article only used the photosensitizer IR780 as a model drug. Whether other types\nof drugs can also be effectively delivered through this system still needs to be verified.\nMoreover, there is a lack of comparison with other delivery systems to highlight the\nadvantages of the MAD system in anti-tumor in this study.\nmitochondrial-targeting agent. Its molecular structure contains a lipophilic cationic\nmoiety, enabling selective mitochondrial accumulation. This property not only makes\nIR780 valuable for studying mitochondrial function but also facilitates its application\nin tumor-targeted therapies. Current mitochondrial targeting strategies include: small\nmolecules (e.g., rhodamine derivatives, triphenylphosphonium (TPP)-based\ncompounds), mitochondria-targeting peptides (MTP), etc. Notably, through\nconjugation strategies using TPP cations or MTP sequences, virtually any attachable\nmolecule or peptide can be engineered for mitochondrial targeting - delivered via our\nMAD system.\nHere, we selected 10-N-nonylacridine orange bromide (NAO), a compound with\nfundamentally distinct targeting mechanisms. NAO localizes to mitochondria through\nspecific binding to cardiolipin—its interaction with mitochondrial membranes is\nindependent of mitochondrial membrane potential. We employed the same MAD\nsystem preparation protocol, with the distinction that NAO was dissolved in the\naqueous phase during formulation. As demonstrated in Supplementary Figure S16a,\nNAO exhibited a Pearson's coefficient (PC) of 0.90 with mitochondria, indicating\nexceptional colocalization. Supplementary Figure S16b further revealed that NAO\nachieved intercellular transfer efficiency comparable to IR780. This evidenced the\nsubstantial versatility of the MAD platform.\nIn contrast, both ICG and IR780 belong to the heptamethine cyanine class of near-infrared fluorophores, sharing highly similar core structures. ICG exhibits hydrophilic\nproperties due to its sulfonate groups, whereas IR780 possesses lipophilic character\nimparted by its quaternary ammonium iodide structure. Consequently, IR780\ndemonstrated pronounced mitochondrial targeting at the subcellular level (PC: 0.83),\n30\nwhile ICG showed negligible localization (PC: 0.08). As evidenced in Supplementary\nFigure S16b, ICG—which failed to associate with mitochondria—exhibited minimal\nintercellular fluorescence transfer. This highlighted the MAD system’s critical\ndependence on mitochondrial loading for cargo transport, enabling dramatically\ndivergent intercellular delivery outcomes.\nWe have supplemented in the manuscript in Page 20: 'To demonstrate MAD’s\nversatility in delivery, we used 10-N-nonylacridine orange bromide (NAO), which\nemployed ΔΨm-independent cardiolipin-specific binding. NAO showed excellent\nmitochondrial colocalization (PC=0.90, Supplementary Figure S16a) and transmission\nefficiency comparable to IR780 (Supplementary Figure S16b), confirming MAD’s\nbroad applicability. Virtually any attachable molecule/peptide—through linkage to\ntriphenylphosphonium (TPP) or mitochondrial targeting peptides (MTP)—could be\nmitochondria-targeted using our MAD system. In contrast, ICG\n(hydrophilic/sulfonated) and IR780 (lipophilic/ammonium-iodide) shared\nheptamethine NIR cores but diverged in localization: IR780 showed mitochondrial\naccumulation (PC=0.83) versus ICG's diffuse distribution (PC=0.08). Correspondingly,\nICG exhibited minimal transfer in MAD (Supplementary Figure S16b), confirming that\nmitochondrial loading was essential for intercellular delivery.'\nmitochondria and lysosomes detected by confocal. Blue: nucleus. Green: mitochondria.\nRed: lysosomes. Magenta: IR780, ICG or NAO. Scale bar: 5 µm. (b) IR780, ICG or\nNAO (red) transferred between donor cells and CMFDA-stained (green) recipient cells\nand corresponding statistical number of fluorescent spots entering the recipient cell\n(Scale bar: 25/5 μm, n=50 cells). Data were presented as mean  SD. P values were\n32\ndetermined using one-way ANOVA with Tukey's multiple comparisons test. Source\ndata are provided as a 'Source Data' file.\nMinor issues:\n1. It is suggested to add a description of Fig 7a for the schematic illustration.\nRe: We agree and have supplemented the description for Figure 7a in Page 29: 'Red\ndots indicate B16F10 tumor inoculation. Blue dots indicate peritumoral drug\nadministration and near-infrared (NIR) irradiation. Green dots indicate sample\ncollection and analytical procedures. Black dots indicate the experimental endpoint.\nComprehensive details of the treatment protocol are available in Supplementary Figure\nS17', as we mentioned in the figure legend and Methods section.\n2. Please check the grammar usage and the format issues in the title and the manuscript.\nFor example, in the title, “for accelerate transport” is suggested to change to “for\naccelerating transport”.\nRe: Thank you for your careful review. We have thoroughly re-examined the\nmanuscript and implemented the necessary revisions, all of which have been marked in\nthe updated paper. Regarding the title, we acknowledge the oversight and have\ncorrected 'accelerate' to 'accelerated' to accurately convey the intended meaning of\n'MAD-driven accelerated transport and delivery' (i.e., transport and delivery that are\naccelerated by the MAD mechanism).\nRelevant references\nNature 638, 225-236 (2025). https://doi.org:10.1038/s41586-024-08439-0\n33\nimmune cells. Nat Nanotechnol 17, 98-106 (2022). https://doi.org:10.1038/s41565-021-01000-\n4\nmetabolic fitness and antitumor efficacy. Cell 187, 6614-6630 e6621 (2024).\nhttps://doi.org:10.1016/j.cell.2024.08.029\nbone marrow stromal cells to leukemic blasts. Blood 130, 1649-1660 (2017).\nhttps://doi.org:10.1182/blood-2017-03-772939\nMutation Burden and Rescues Mitochondrial Bioenergetics in Rotenone-Stressed MELAS\nFibroblasts. Oxid Med Cell Longev 2019, 9537504 (2019).\nhttps://doi.org:10.1155/2019/9537504\npresenting a novel route for HIV-1 transmission. Nat Cell Biol 10, 211-219 (2008).\nhttps://doi.org:10.1038/ncb1682\nand death through tunneling nanotubes. Neuron 112, 3106-3125 e3108 (2024).\nhttps://doi.org:10.1016/j.neuron.2024.06.029\nMCF-7 cells through enhanced tunneling nanotube formation and tunneling nanotube mediated\nrelease of viable cytoplasmic fragments. Exp Cell Res 355, 182-193 (2017).\nhttps://doi.org:10.1016/j.yexcr.2017.04.008\nmitochondrial motility. Cell 147, 893-906 (2011). https://doi.org:10.1016/j.cell.2011.10.018\nReviewer #3 (Remarks to the Author):\nIn the manuscript titled \" Leveraging engineered mitochondria through intercellular\ncommunication network for accelerate transport and delivery\" by Peng et al. the\n34\nauthors investigate a potential drug delivery method based on mitochondrial\ntransportation. Essentially, the authors showed that inhibition of mitophagy can\naccelerate mitochondrial transportation, which can be exploited as a cargo-system to\ndeliver photosensitizer into the deep internal part of tumors and thus to achieve a better\nanti-tumor effect.\nWhile the manuscript presents a novel concept, 1)overall the experimental data do not\nsupport the conclusions. In addition, 2) the manuscript is poorly assembled in terms of\nfocused concepts, particularly in the abstract and introduction, making at time difficult\nto follow the rationale. 3) The experiments need additional explanation, and\nparticularly robust experimentation to manipulate mitophagy and mitochondrial\ntransportation (including proteins involved in transport, e.g. Miro, etc), in both gain-\nand loss-of-function. 4) A major concern of these studies is also how quantitative are\nthese studies, both in cultured cells or tumors, and unclear how this can be manipulated\nin tumors.\nRe: 1) We appreciate your recognition of our study's novelty. We firmly assert that our\nexperimental data comprehensively validate the study's core conclusions, beginning\nwith the foundational observation in Figure 1 where siRNA-mediated PINK1\nknockdown significantly enhanced intercellular mitochondrial transfer—quantified\nthrough both tunneling nanotube (TNT) proliferation (the dominant route for\nmitochondrial trafficking) and direct mitochondrial flux measurements between donor-\nrecipient cells. This preliminary confirmation motivated the development of the\n'mitochondrial autonomous drive' (MAD) system, wherein CRISPR/Cas9-loaded\nnanoparticles (FP) achieved efficient PINK1 knockout as rigorously demonstrated in\nFigure 2a-e and Supplementary Figure S2-S7. Concordant results from multiple\ncomplementary assays—Sanger sequencing, TIDE analysis, Western blotting, T7E1\n35\ncleavage, TA cloning, and mito-Keima fluorescence—collectively established robust\ngene editing efficacy (41.6%), significant protein suppression (35.7%), and\ndownregulated mitophagy flux (51.8%).\nCritically, in Figure 2f-k, transcriptomic profiling revealed significant alterations\nin mitochondrial respiratory pathways, while mitochondrial stress tests demonstrated\nenhanced cellular metabolic capacity—evidenced by elevated basal respiration,\nmaximum respiration, and spare respiration capacity (2.1-, 4.1-, 5.0-fold versus control).\nThis synergistic evidence established that mitophagy suppression remodeled\nbioenergetic infrastructure, creating a self-reinforcing cycle wherein augmented\nmitochondrial motility both necessitated and sustained heightened respiratory activity\nto fuel transport demands.\nMorphometric analysis in Figure 3 further demonstrated an increase in transport-\ncompetent round mitochondria (P=0.0027), while live-cell tracking confirmed\naugmented intracellular (1.3-fold in track speed and 1.7-fold in track length) and\nintercellular (2.0-fold in track speed and 1.6-fold in track length) motility and\nbidirectional intercellular transport. This integrated evidence chain—spanning\nmolecular, metabolic, and biophysical dimensions—definitively established that\nmitophagy suppression remodeled mitochondrial dynamics to potentiate intercellular\ntransfer, thereby fully supporting our central thesis and MAD system design rationale.\nBuilding upon these foundational insights, we constructed a comprehensive MAD\nsystem to translate our discoveries into targeted therapeutic delivery. Beyond FP, this\nintegrated platform incorporated SPION-IR780 conjugates that exploit accelerated\nmitochondrial motility for cargo transport. Critically, the IR780 payload directly\nhitchhiked on mitochondria, leveraging mitochondrial intercellular transport pathways\nfor transmembrane-independent cell-to-cell transfer (Supplementary Figure S14).\nfold vs. control) and calcium flux (3.8-fold), creating an oxidative microenvironment\nthat synergized with metabolic reprogramming to potentiate cargo shuttling.\nConvergent experimental evidence definitively established the mechanistic dependency\nof cargo transport on mitochondrial motility: donor-recipient coculture systems\ncombined with TNT inhibition assays demonstrated obligatory coupling between\nmitochondrial and IR780 trafficking (Figure 4c-d). Spatiotemporal synchronization\nwas directly visualized through longitudinal tracking and 3D reconstruction of co-\nmigrating mitochondria and IR780 cargos along TNTs (Figure 4e-f). Genetic\nperturbation studies further confirmed this inextricable linkage—M-sec siRNA-\nmediated suppression of TNT formation significantly diminished both mitochondrial\nand IR780 translocation (Figure 4g-h), while Miro1 siRNA disruption of mitochondrial\nmotor machinery specifically impaired transport along established TNTs, concurrently\nreducing IR780 mobility (Figure 4i-j). This multi-modal validation unambiguously\npositioned mitochondrial hitchhiking as the fundamental driver of intercellular cargo\ndelivery in the MAD system.\nFigure 5 demonstrated the NIR-controllable cytocidal function of the MAD\nsystem. A 13.5-fold increase in TNT density versus control (Figure 5a) reflected\nsubstantially augmented mitochondrial trafficking capacity. Upon near-infrared (NIR)\nirradiation, near-complete cellular ablation was achieved, concurrent with explosive\nROS generation (4775.2-fold increase) and rapid dissolution of intercellular TNT\nnetworks (Figure 5a-c). This tripartite response—comprising cell death induction,\noxidative burst amplification, and TNT disintegration—collectively established MAD's\ncapacity for spatiotemporal control over cellular viability through NIR-triggered\nmechanisms.\nphysiologically relevant tumor models: IR780 cargo fluorescence permeated deep\nregions of 3D tumor spheroids and in vivo solid tumors, confirming mitochondria-\nhitchhiking delivery to otherwise inaccessible compartments. Critically, we observed\nTNT-like structures within tumor tissues, providing the in vivo morphological evidence\nfor mitochondrial shuttle-mediated deep tumor penetration.\nFigure 7 extended our findings to therapeutic efficacy in vivo. In B16F10\nmelanoma-bearing C57BL/6 mice, peritumoral administration of MAD significantly\nsuppressed tumor growth and extended survival (43% long-term survival; Figure 7a-e).\nMolecular analyses revealed concomitant upregulation of TNT-transport machinery\nproteins M-sec and Miro1 (Figure 7i-j), confirming activation of mitochondrial\ntrafficking pathways. Concurrently, suppression of mitophagy was evidenced by:\nPINK1 depletion, attenuated Parkin phosphorylation, reduced LC3-II/LC3-I ratio, and\np62 accumulation (Figure 7k-m). This integrated response—enhanced mitochondrial\ntransport coupled with sustained mitophagy inhibition—validated the therapeutic\nparadigm and demonstrated the superior anti-tumor efficacy of the MAD system design.\nCollectively, Figure 1 provides initial validation that mitophagy inhibition\nenhances mitochondrial transfer. Building on this foundation, Figures 2-3 establish the\nfirst module of the MAD system—the 'Mitophagy Suppression Module'—\ncomprehensively validating both the increased mitochondrial transfer and its\nunderlying drivers through morphological adaptation, metabolic reprogramming, and\nrespiratory enhancement. Ultimately, the fully MAD system in Figures 4-7\ndemonstrates that harnessing accelerated mitochondrial transport potentiates the tumor-\npenetrating capacity of cargo, enabling deep intratumoral penetration and achieving\nexceptional anti-tumor efficacy in both in vitro and in vivo models. Our experimental\n38\nresults definitively support these conclusions. Should there be any further questions or\nneed for clarification, we would be pleased to provide additional information and\ndiscussion.\n2) We sincerely thank you for this critical feedback regarding the conceptual focus\nand logical flow in the Abstract and Introduction. We acknowledge that enhancing the\nclarity of these sections is crucial for reader comprehension. We have undertaken a\nthorough revision of both the abstract and introduction to directly address these\nconcerns.\nWe have restructured the Abstract to begin with a clear and concise statement of\nthe core research problem and its significance as following: Inspired by the non-\ntransmembrane transfer of mitochondria in cell-to-cell communications, herein, we\nreport an original exploration to accelerate mitochondrial intercellular transport, and its\napplication to exogenous cargo delivery. We discover that deliberate PINK1-targeted\nmitophagy downregulation elevates mitochondrial transit capacity via multifaceted\ndrivers—morphological adaptation, metabolic reprogramming, and respiratory\nenhancement. Capitalizing on this, we engineer high-speed mitochondrial vehicles for\nphotosensitizer hitchhiking, with spatiotemporal tracking elucidating its dynamic\nintercellular transit and physiological impacts. Through mitochondria's communication\nnetwork—tunneling nanotubes (TNTs), the mitochondria-photosensitizer cotransporter\nachieves reinforced intercellular delivery, thereby induces deep tumor penetration and\nenhanced photodynamic killing. Our work establishes a transformative mitochondria-\nhitchhiking platform for overcoming biological barriers in drug delivery and provides\nmechanistic insights into manipulating intercellular organelle transport for therapeutic\napplications.\nAlso, the part of this work in Introduction has been significantly revised to\n39\nimprove logical flow and conceptual focus as following (Page 4-5): Building on these\nfoundations, this study uncovered that suppression of PINK1-mediated mitophagy\ninitiated a compensatory cellular response—engaging cellular homeostatic\nrecalibration and rescue programs. This adaptive rewiring concomitantly potentiated\nmitochondrial transit velocity/extent through TNTs and mobilized mitophagy-evaded\nmitochondria as autonomous bio-carriers. Capitalizing on this mechanism, we\nengineered a 'mitochondrial autonomous drive' (MAD) system by spatiotemporally\ncoupling the mitochondria-targeting photodynamic agent IR780 to form 'passenger-\nvehicle' co-transporters. Within solid tumors, MAD orchestrated mitochondrial\ntransport network expansion and high-speed motility through cellular homeostasis and\noxidative stress modulation, enabling deep IR780 penetration. Consequently, MAD-\nmediated photodynamic therapy achieved potent tumor eradication in vitro and in vivo.\nCollectively, we pioneered a mitophagy-modulated platform leveraging engineered\nmitochondria for non-transmembrane drug delivery, providing transformative solutions\nfor overcoming penetration barriers in oncology.\n3) We appreciate your careful review and insightful comments. We have revised\nor supplemented the explanation or discussion for the experiments. For instance, we\nhave refined the objectives for mitochondrial morphology/motility experiments in Page\n13: 'Mitochondrial morphology and motility at the subcellular level provide more direct\nvisualization of their movement dynamics and behavioral tendencies' and the objectives\nfor key molecules related experiments in Page 20: 'Key molecules mediating\nmitochondrial transport along TNTs deliver a reliable, direct window into the\nmitochondrion-dependent transport behavior of cargo IR780 within cells'. We have also\nsupplemented the description of the experimental schematics in Page 29: 'Red dots\nindicate B16F10 tumor inoculation. Blue dots indicate peritumoral drug administration\n40\nand near-infrared (NIR) irradiation. Green dots indicate sample collection and\nanalytical procedures. Black dots indicate the experimental endpoint. Comprehensive\ndetails of the treatment protocol are available in Supplementary Figure S17'.\nAs you mentioned, in most experiments described in the manuscript, we primarily\ninhibited mitophagy by suppressing the expression of PINK1 (achieving consistent\nresults by siRNA in Figure 1 and CRISPR-Cas9 in Figure 2-7). Here, we employed\nUrolithin A as a gain-of-function control to promote mitophagy. Urolithin A is a\nnaturally occurring compound widely recognized in the scientific community for\nenhancing mitophagy through both PINK1/Parkin-dependent and -independent\npathways1. Quantification of mitochondrial transfer from Urolithin A-treated donor\ncells to recipient cells revealed a further reduction in mitochondrial transfer\n(Supplementary Figure S1, P<0.0001). This indicated that enhanced mitophagy led to\na compensatory attenuation of the alternative mitochondrial disposal pathway—\nmitochondrial extrusion—thereby decreasing mitochondrial transfer. These\nobservations align with our hypothesis.\nSupplementary Figure S1. Mitochondria (Red) transferred between donor cells and\nrecipient cells (Green) treated with Urolithin A and corresponding statistical number of\nfluorescent spots of mitochondria entering the recipient cell (Scale bar: 25 μm, n=50\ncells). Data were presented as mean  SD. P values were determined using two-tailed\n41\nunpaired t test. Source data are provided as a 'Source Data' file.\nIt should be clarified that in the manuscript, siRNA knockdown of proteins such\nas Miro-1 primarily served as molecular tools to block the mitochondrial transport\npathway. This was done to verify that cargo transfer under mitochondrial inhibition\nconditions (via the MAD system) is dependent on mitochondrial transfer. In other\nwords, increasing the expression of proteins like Miro-1 would only reveal their\nspecific effects on the mitochondrial transport machinery itself, while providing limited\ninsight when deployed as a gain-of-function strategy. Indeed, your close focus on\nproteins involved in the mitochondrial transport machinery has been both insightful and\nencouraging. Our primary focus lies on the spontaneous and active alterations occurring\nwithin the mitochondrial transport pathway under conditions of mitochondrial\ninhibition. Consequently, we validated the levels of other relevant proteins, including\nKIF5B and MYO19.\nKIF5 (specifically the KIF5B subtype), as a core member of the kinesin family, is\nthe key motor protein mediating the anterograde transport of mitochondria along\nmicrotubules (from the cell center towards the periphery)2. It specifically connects to\nthe mitochondrial outer membrane via the adaptor complex TRAK1/2 (Milton)-Miro:\nThe TRAK proteins bind to the tail domain of KIF5 on one end and anchor to the Miro\nproteins on the mitochondrial membrane on the other, forming the transport complex.\nBy harnessing the mechanical force generated from ATP hydrolysis, KIF5 propels\nmitochondria towards the plus end of microtubules (such as neuronal axon terminals or\ncell migration fronts). This enables the targeted delivery of mitochondria to high-\nenergy-demand sites (like synapses or pseudopods).\nMYO19 is the sole member within the myosin family that directly and specifically\n42\nmediates mitochondrial movement3. As an actin filament-dependent motor protein, it\nharnesses the mechanical force generated by ATP hydrolysis within its motor domain\nto propel mitochondria along actin filaments (microfilaments) toward the plus end. This\ndirected movement thereby regulates the spatial distribution of mitochondria within the\ncell (e.g., targeting them to high-energy-demand sites such as the leading edge of\nmigrating cells or synapses). Functioning as a central node linking the biomechanics of\nthe actin cytoskeleton to mitochondrial function, MYO19 influences cellular\nphysiology and disease progression by coordinating mitochondrial motility with\nmetabolic homeostasis.\nAs shown in Supplementary Figure S23, we detected the expression levels of\nKIF5B and MYO19 proteins in tumors. Both exhibited highly consistent expression\npatterns with Miro1 protein. This provided robust evidence that the MAD system\nenhanced mitochondrial motility.\nSupplementary Figure S23. KIF5B and MYO19 expression by immunofluorescent\nstaining and corresponding quantitative results on Day 19 (n=10 random regions). Scale\nbar: 25 μm. Data were presented as mean  SD. P values were determined using one-\nway ANOVA with Tukey's multiple comparisons test. Source data are provided as a\n'Source Data' file.\nexperiments in the revised manuscript.\nPage 6: In a separate experiment, conversely, activation of mitophagy using\nUrolithin A resulted in a further reduction in mitochondrial transfer (Supplementary\nFigure S1, P<0.0001). This demonstrated that enhanced mitophagy induced\ncompensatory attenuation of the alternative mitochondrial disposal pathway—\nmitochondrial extrusion—thereby decreasing mitochondrial transfer, in accordance\nwith our hypothesis.\nPage 28: In Supplementary Figure S23, additional tumor immunofluorescence\nanalyses revealed upregulated KIF5B (microtubule motor for mitochondrial\nanterograde transport) and MYO19 (mitochondrial-specific myosin regulator),\nconfirming coordinated enhancement of mitochondrial trafficking machinery.\nAdditionally, revisions incorporating feedback from other reviewers are\nhighlighted in yellow throughout the manuscript to enhance the clarity and\ncompleteness of the experimental descriptions.\n4) In response to your inquiry regarding quantitative approaches, we have\nexpanded the Methods section with additional details in the manuscript, placing\nparticular emphasis on quantitative methodologies. The specific enhancements are\noutlined below.\nFor 'Donor cells and recipient cells system' (Page 36-37): Recipient cells were\nsegmented using Cellpose (https://www.cellpose.org), followed by processing of the\nresultant masks in ImageJ to define cellular boundaries. Individual recipient cells were\nisolated by subtracting these masks from raw images. Subsequent 'Analyze Particles'\n44\nanalysis (with size thresholding and 'Add to Manager' enabled) generated discrete\nregions of interest (ROIs) corresponding to individual cells. Mitochondrial and IR780\nfluorescence channels were then imported, converted to 8-bit, thresholded at a fixed\nvalue across all experimental groups, and converted to binary single-point\nrepresentations. These puncta were normalized to unity (=1) using the Divide function,\nenabling quantification via the Measure tool which recorded RawIntDen values -\nrepresenting the summed intensity of all pixels within each recipient cell ROI - to\ndetermine counts of mitochondrial and IR780 puncta per cell. During quantification,\nRawIntDen values were normalized against recipient cell area to account for size-\ndependent variability, thereby yielding mitochondrial/IR780 puncta density per cell as\nthe definitive metric for cargo delivery efficiency to recipient cells.\nFor 'Western blot for protein detection' (Page 38): Protein expression was\nquantified as the intensity ratio of target protein bands relative to loading control bands.\nFor 'Structure illumination microscopy (SIM) for super-resolution fluorescent\nimaging' (Page 38-39): Multi-SIM images were analyzed using ImageJ. Following\nthreshold optimization, shape descriptors were enabled in 'Set Measurements' (with\nLimit to threshold selected). 'Analyze Particles' was then executed, with the Aspect\nRatio ('AR') column recording the major/minor axis ratio ('L/W') as a metric of\nmorphological elongation. Quantitative motility parameters—'Track Speed Mean'\nand 'Track Displacement Length'—were acquired in Imaris ('Statistics' panel) and subjected\nto statistical analysis.\nFor 'Flow cytometry for in vitro detection' (Page 39): Details of the gating strategy\nwere provided in Supplementary Figure S24-27. Typically, 10,000 live cell-gated\nevents were acquired for analysis.\nFor 'Intracellular ROS detection' (Page 40-41): All images were processed in\nfollowed by application of identical channel-specific thresholds across experimental\ngroups before intensity measurement. Per-cell ROS generation was quantified as the\nratio of ROS channel integrated density (IntDen) to nuclear channel IntDen, establishing\na normalized metric for robust intergroup comparative analysis; this protocol was\nsystematically applied to all subsequent analogous experiments to ensure\nmethodological consistency.\nFor 'Calcein-AM/PI staining' (Page 41): Viability quantification was performed\nusing the dead/live cell ratio derived from PI channel IntDen normalized to calcein\nchannel IntDen.\nFor '3D B16F10 cell sphere model for penetration' (Page 41): IR780 distribution\nwas subsequently quantified by measuring layer-specific mean fluorescence intensity\nacross all optical sections in ImageJ.\nFor 'Anti-cancer therapy in mice bearing B16F10 solid tumor' (Page 43):\nImmunofluorescence data were quantified by normalizing target protein IntDen to\nnuclear channel IntDen. Western blot expression levels were calculated as target protein\nband intensity relative to loading control (GAPDH) band intensity, with both analyses\nperformed using ImageJ.\nThe experimental methods mentioned in the Supplemental Information are also\nsupplemented there in yellow.\nRegarding the inquiry about tumor manipulation: Our system operates as a self-\npropelling cascade. Specifically, reduced mitophagy at tumor sites initiates cellular\nstate alterations that consequently enhance mitochondrial transfer. This process\nautonomously delivers therapeutic cargo deep into tumor masses—once triggered,\ntumor cells spontaneously execute the entire sequence. The system requires only the\n46\npreexistence of intercellular mitochondrial transfer pathways within tumors.\nAs reported in Figure 6d, TNT-like structures were observed in murine tumor\nsections, with visible transport of IR780 cargo along these conduits. Critically, multiple\nstudies have documented similar TNT-like structures in primary cells derived directly\nfrom patients. A study from Emil Lou's laboratory in 2012 provided the first evidence\nof TNT-like structures in solid tumors4, describing mitochondria-containing\nconnections in resected mesothelioma tissue from patients. These initial observations\nwere subsequently corroborated by findings in squamous cell carcinoma5, ovarian6 and\npancreatic cancers7, as well as in human glioblastoma (GBM) cells transplanted into\nmouse models8. Collectively, these studies demonstrate the direct translational\nrelevance of our in vitro findings to in vivo contexts. Indeed, Figure 7 directly\ndemonstrates both the therapeutic efficacy of the MAD system in vivo and its capacity\nto enhance mitochondrial trafficking.\nAdditional critiques:\n1. Downregulated mitophagy enhances mitochondrial intercellular transport via TNTs.\nThe authors claim that mitochondria transport between cells through TNTs by showing\nthat mitochondria staining in a long protrusion structure and assuming that\nmitochondria is in the process of intercellular transportation. Is there a marker of TNTs?\nSince the cells look in a very stress shape with lots of long branches, is it possible the\nlong branches containing mitochondria shed off from the cells and engulfed by other\ncells? How this phenomena is conserved among cell types.\nRe: Currently, tunneling nanotubes (TNT) lack widely accepted and universally\napplicable specific molecular markers—a recognized challenge in the field.\nIdentification of TNT primarily relies on their distinctive morphological characteristics,\n47\nincluding: elongated tubular structures, absence of basal lamina association, and\nphysical connections bridging distantly spaced cells. Functional attributes such as\nintercellular cargo transport further support their recognition. Researchers typically\nemploy non-specific markers for auxiliary identification, including: cytoskeletal\ncomponents (e.g., F-actin labeled with phalloidin or LifeAct), plasma membrane dyes\nand tagged transport molecules (e.g., GFP-fused proteins). However, these markers are\nnot exclusive to TNT and are equally present in other cellular structures (e.g., filopodia,\nstress fibers, or the plasma membrane itself). Consequently, contemporary TNT\nresearch remains heavily dependent on morphological assessment, dynamic live-cell\nimaging, and combined application of multiple non-specific markers.\nRegarding your inquiry about potential mitochondrial release from tunneling\nnanotubes (TNTs) and uptake by bystander cells: Current evidence indicates that TNTs\nare indeed fragile structures susceptible to breakage. Upon rupture, mitochondria and\nother luminal contents typically undergo actin-dependent retraction toward the\noriginally connected cells rather than being released into the extracellular space. In our\nexperimental systems (including time-lapse imaging of co-cultures), we have not\nobserved significant uptake of such mitochondria by third-party cells. However,\nintercellular mitochondrial transfer can alternatively occur through extracellular vesicle\n(EV)-mediated pathways—a functionally distinct mechanism wherein depolarized\nmitochondria or mitochondrial fragments are shed, encapsulated within\nmicrovesicles/exosomes, and subsequently internalized by recipient cells via\nendocytosis. This EV-facilitated route operates independently of direct TNT\nconnections and represents a parallel transport system for mitochondrial dissemination.\nThe mechanism of tunneling nanotube (TNT)-mediated intercellular\nmitochondrial transfer exhibits remarkable evolutionary conservation across eukaryotic\n48\nspecies. This phenomenon is ubiquitously observed in diverse cell types, including\nimmune cells (e.g., T cells9 and macrophages10), tumor cells (glioblastoma11,\nmelanoma12), and tissue-repairing cells (mesenchymal stem cells13, cardiomyocytes14).\nIts core molecular machinery demonstrates cross-species and cross-cell-type\ncommonalities: TNT formation relies on conserved F-actin cytoskeletal dynamics\nregulated by Rho GTPases (such as MIRO1) and AKT-mTOR signaling, while\nmitochondrial transfer involves Drp1-mediated fission to generate mobile fragments\nthat undergo directional transport via microtubule-based motor proteins (e.g., kinesins).\nFunctionally, this transfer invariably serves fundamental demands for metabolic\nhomeostasis and stress adaptation—spanning tissue repair in lower organisms (e.g., C.\nelegans) to mammalian immune regulation (mitochondrial hijacking in tumor\nmicroenvironments) and injury recovery (energy replenishment from stem cells to\ndamaged cells). Thus, TNT-facilitated mitochondrial trafficking represents an ancient\nand highly conserved paradigm of intercellular crosstalk, whose molecular foundations\nand physiological rationale are broadly shared among multicellular organisms, with\ncontext-dependent functional specialization (e.g., immune evasion or tissue\nregeneration) emerging only in pathological or microenvironmental settings.\n2. Blockade of mitophagy arouses transport driving forces. pX330 backbone plasmid is\na not good control. It would be better if the author uses pX330 backbone expressing a\nnon-targeting sequence. The author named the PINK1 knockout group as FP group.\nAll the experiments are using siRNA, other methods to manipulate mitophagy proteins\nare needed in both gain and loss of function. Also, other proteins involved in mitochondrial\ntransportation including Miro, and other should be tested.\nRe: We sincerely appreciate your valuable suggestions. Accordingly, we redesigned a\n49\nnon-targeting sgRNA and cloned it into the pX330 vector, designated as the NC control.\nSubsequent reassessment of PINK1 expression levels using this NC control confirmed\nefficient knockout, as presented in Figure 2C that replaced the original Figure 2c of\ncontrol, pX330, and PINK1.\nFigure 2c. PINK1 and Cas9 expression in B16F10 cells detected by western blot.\nComplete uncropped western blot data were shown in Supplementary Figure S28.\nIt needs to be clarified that in Figure 1, we employed siRNA-mediated knockdown\nof PINK1, while in Figures 2-7, we utilized CRISPR plasmid-based PINK1 knockout.\nSpecifically in Figure 4, we performed siRNA knockdown of both M-sec and Miro-1.\nThe efficiency of all siRNA and plasmid constructs was rigorously validated by Western\nblot analysis of protein levels (Figure 2c and Supplementary Figure S31).\nAdditionally, as you recommended, we also examined the levels of additional\nmitochondrial transport-related proteins (KIF5B and MYO19), as discussed earlier in\nour response to Point 3 of your primary concerns. As shown in Supplementary Figure\nS23, we detected the expression levels of KIF5B and MYO19 proteins in tumors. Both\nexhibited highly consistent expression patterns with Miro1 protein. This provided\nrobust evidence that the MAD system enhanced mitochondrial motility.\nstaining and corresponding quantitative results on Day 19 (n=10 random regions). Scale\nbar: 25 μm. Data were presented as mean  SD. P values were determined using one-\nway ANOVA with Tukey's multiple comparisons test. Source data are provided as a\n'Source Data' file.\n3. The use of IR780 is also problematic. Does IR780 controls mitophagy? what\nconcentrations are localized to the mitochondria. But more importantly, and\nconceptually, why the compound needs to use the mitochondria intercellular\ncommunication to reach different part of the tubules. The existence of TNTs,\nindependently of mitochondrial, should be sufficient for drug diffusion in the tumor.\nRe: Thank you for your comments. We have stated in the manuscript that 'IR780 has a\nnatural property of attaching to mitochondria, which is attributed to its highly\nelectropositive and lipophilic nature'. IR780 is a classic lipophilic small molecule that\nintrinsically targets mitochondria. As a near-infrared (NIR) fluorescent dye belonging\nto the cyanine family, IR780 exhibits excellent optical properties and biomedical\npotential. The molecular structure of IR780 contains lipophilic cationic groups that\nfacilitate selective mitochondrial targeting. This characteristic not only makes it a\n51\npowerful tool for studying mitochondrial function but also provides opportunities for\ntumor-targeted therapy.\nBased on the current experimental results, IR780 does not manipulate autophagy;\nits mitochondrial targeting is determined by its inherent structure and remains\nconcentration-independent. In our experimental system, IR780 functions as both a\nmodel drug to demonstrate the feasibility of mitochondrial-mediated intracellular\ntransport and as a photodynamic therapeutic agent. These applications are\nfundamentally distinct from processes related to mitophagy or concentration-dependent\neffects. The mitochondrial-targeting property of IR780 is solely utilized for\ndemonstrating drug delivery mechanisms and photodynamic therapy, without\ninvolvement in autophagy pathways or being influenced by dosage considerations.\nMoreover, IR780 still faces several challenges in practical applications, with its\npoor water solubility being the most critical issue that complicates drug administration.\nTo address this, researchers typically employ chemical modification or nano-\nencapsulation techniques to enhance its performance15,16. When delivered in\nnanoparticle form, it encounters the tumor microenvironmental barriers we previously\ndescribed: 'However, the abnormal tumor microenvironment—including dense stromal\ntissue, high interstitial fluid pressure, low blood perfusion, abnormal angiogenesis, and\nhypoxia—greatly hinders the diffusion distance and extent of therapeutics'. This is\nprecisely why we propose mitochondrial hitchhiking as a delivery strategy to achieve\ndeeper tumor penetration. Indeed, TNTs inherently exist in tumor cells (regardless of\ntheir abundance), but the current impediment to drug penetration deep into tumor\ntissues demonstrates that pre-existing TNTs are insufficient to facilitate internal drug\ndelivery. This necessitates MAD to facilitate the transport of mitochondrial-cargo\ncomplexes.\n52\n4. To experimentally test the previous point, the authors should use IR780 analogs that\ndon't localize to the mitochondria, to test their hipothesis.\nRe: Thank you for raising this point. As you suggested, we selected ICG as an analog\nof IR780 to validate our hypothesis. Both ICG and IR780 belong to the heptamethine\ncyanine class of near-infrared fluorophores, sharing highly similar core structures. ICG\nis hydrophilic due to its sulfonate groups, whereas IR780 is lipophilic owing to its\nquaternary ammonium iodide structure. Consequently, IR780 exhibited pronounced\nmitochondrial targeting at the subcellular level (Pearson's coefficient: 0.83,\nSupplementary Figure S16a), while ICG showed negligible localization (Pearson's\ncoefficient: 0.08). As shown in Supplementary Figure S16b, ICG, which cannot bind to\nmitochondria, demonstrated negligible intercellular fluorescence transfer. This\nunderscored the strong dependence of the MAD system on mitochondrial loading for\ncargo transport, resulting in a dramatic difference in intercellular delivery outcomes.\nThe MAD system facilitates highly efficient intercellular transport for cargoes capable\nof mitochondrial loading, while cargoes incapable of mitochondrial loading fail to\nbenefit from this mechanism.\nWe have supplemented in the manuscript in Page 20: 'In contrast, ICG\n(hydrophilic/sulfonated) and IR780 (lipophilic/ammonium-iodide) shared\nheptamethine NIR cores but diverged in localization: IR780 showed mitochondrial\naccumulation (PC=0.83) versus ICG's diffuse distribution (PC=0.08). Correspondingly,\nICG exhibited minimal transfer in MAD (Supplementary Figure S16b), confirming that\nmitochondrial loading was essential for intercellular delivery.'\nmitochondria and lysosomes detected by confocal. Blue: nucleus. Green: mitochondria.\nRed: lysosomes. Magenta: IR780, ICG or NAO. Scale bar: 5 µm. (b) IR780, ICG or\nNAO (red) transferred between donor cells and CMFDA-stained (green) recipient cells\nand corresponding statistical number of fluorescent spots entering the recipient cell\n(Scale bar: 25/5 μm, n=50 cells). Data were presented as mean  SD. P values were\n54\ndetermined using one-way ANOVA with Tukey's multiple comparisons test. Source\ndata are provided as a 'Source Data' file.\nRelevant references\nmuscle function in rodents. Nat Med 22, 879-888 (2016). https://doi.org:10.1038/nm.4132\nCell Res 25, 1108-1120 (2015). https://doi.org:10.1038/cr.2015.89\ntracks in living cells. J Biol Chem 298, 101883 (2022).\nhttps://doi.org:10.1016/j.jbc.2022.101883\ncontents in human malignant pleural mesothelioma. PLoS One 7, e33093 (2012).\nhttps://doi.org:10.1371/journal.pone.0033093\nautophagosome transfer in squamous cell carcinoma via tunneling nanotubes. Oncotarget 8,\n20939-20960 (2017). https://doi.org:10.18632/oncotarget.15467\ndirect cell-to-cell transfer of oncogenic microRNAs via tunneling nanotubes. Transl Res 164,\n359-365 (2014). https://doi.org:10.1016/j.trsl.2014.05.011\nin Pancreatic Cancer. Sci Rep 8, 9484 (2018). https://doi.org:10.1038/s41598-018-27649-x\n528, 93-98 (2015). https://doi.org:10.1038/nature16071\nimmune cells. Nat Nanotechnol 17, 98-106 (2022). https://doi.org:10.1038/s41565-021-01000-\n4\nMCF-7 cells through enhanced tunneling nanotube formation and tunneling nanotube mediated\nrelease of viable cytoplasmic fragments. Exp Cell Res 355, 182-193 (2017).\n55\nhttps://doi.org:10.1016/j.yexcr.2017.04.008\nof mitochondria. J Neurooncol (2025). https://doi.org:10.1007/s11060-025-05150-6\nmouse melanoma models. Cell Rep 44, 115154 (2025).\nhttps://doi.org:10.1016/j.celrep.2024.115154\nreperfusion model via tunneling nanotube like structure-mediated mitochondrial transfer.\nMicrovasc Res 92, 10-18 (2014). https://doi.org:10.1016/j.mvr.2014.01.008\ncardiofibroblasts mediated by membrane nanotubes. Cardiovasc Res 92, 39-47 (2011).\nhttps://doi.org:10.1093/cvr/cvr189\nnanoparticles for photothermal and photodynamic therapy. Acta Biomater 14, 61-69 (2015).\nhttps://doi.org:10.1016/j.actbio.2014.11.041\nphotothermal agent for in vivo imaging and cancer therapy. Biomaterials 51, 184-193 (2015).\nhttps://doi.org:10.1016/j.biomaterials.2015.01.069\nReviewer #4 (Remarks to the Author)\nHere the authors explore the relationship between mitophagy and mitochondrial\ntransport between cells via tunnel nanotubes (TNTs) as a potential avenue for\nenhancing drug delivery. Mitochondria exhibit complex inter-and-intracellular\ndynamics, and the authors propose using them as a vector for delivering exogenous\ntreatments. They find that inhibiting mitophagy increases transport of mitochondria\n(both in number and speed) between cells via TNTs. The authors use the photosensitive\ndrug IR780 to develop a ‘mitochondrial autonomous drive’ system that enhances traffic\n56\nof mitochondria deeper into tumor tissues to lead to improved outcomes in vitro and in\nvivo.\nMy expertise is in advanced light microscopy, so most of my comments concern imaging\nmethodology and interpretation. The cell biology and therapeutic application here\nseem sound and exciting to me, but there are some flaws in the imaging methodology\nthat need to be corrected for me to have full confidence in the conclusions of those\nexperiments.\nRe: We sincerely appreciate your highly positive feedback on our work and your unique\nexpertise as a microscopist in helping us improve this study. The manuscript has been\ncarefully refined according to your comments, including image re-acquisition and\ncorresponding explanatory updates.\nThe biggest concern for me here is that many of the confocal images included in this\nanalysis are saturated. Saturated images (images with pixel values of 255, in an 8-bit\nimage) are incompatible with quantitative fluorescence intensity measurements.\nSaturation destroys the mostly-linear relationship between fluorophore density in a\nspecimen and grey values in a digital image. Even a few saturated pixels in a region of\ninterest will disrupt the linearity of the measurement and thus the integrity of the data.\nThere are several instances in this study where saturated images are used for intensity\nmeasurements:\nFig 4e. The mitochondria and IR780 channels. In addition to breaking the linearity of\nintensity measurements, saturation can also cause erroneous dilation of objects and so\ncan confound object-based colocalization analyses as well.\nRe: We are deeply grateful for your strong endorsement of our work and the insightful\nrecommendations you provided. Your guidance has brought to our attention critical\n57\naspects we had previously overlooked, significantly aiding us in refining our study. As\nsuggested, we have reacquired the relevant images and updated the manuscript\naccordingly. During image acquisition, we meticulously monitored saturation levels to\nensure that the raw captured images exhibit virtually no saturation.\nFigure 4e. IR780 moved in TNTs synchronously with mitochondria. Red: TNTs (F-\nactin). Green: mitochondria. Magenta: IR780. Arrows indicate mitochondria and IR780\nmoving together. Scale bar: 10 µm.\nFig 5b. All of the PI images in the MAD + NIR condition are extremely saturated.\nRe: We have reacquired the relevant images and updated the manuscript accordingly.\nFigure 5b. Representative fluorescence images of Calcein-AM/PI staining of B16F10\ncells after treatments and corresponding quantitative results (n=5 random regions).\nGreen: Calcein for living cells. Red: PI for dead cells. Scale bar: 25 μm. Data were\npresented as mean  SD. P values were determined using one-way ANOVA with\nTukey's multiple comparisons test. Source data are provided as a 'Source Data' file.\nRe: In the previously presented images, the display range (min/max values) for the\ngreen channel was adjusted primarily to visualize the low-level ROS production in the\nMAD group (relative to the Control group). Here, we have recalibrated the display\nrange using images confirmed to exhibit no significant saturation in their raw state for\nquantitative analysis. While this approach ensures analytical rigor, it consequently\nrenders the faint fluorescence signal in the MAD group visually indistinguishable.\nFigure 5c. Representative fluorescence images of intracellular ROS generation using\nDCFH-DA probe and corresponding quantitative results (n=4 random regions). Blue:\nnucleus. Green: DCF for ROS. Scale bar: 20 μm. Data were presented as mean  SD.\nP values were determined using one-way ANOVA with Tukey's multiple comparisons\ntest. Source data are provided as a 'Source Data' file.\nFig S11. The brightest patches in most of the images are saturated. Pearson’s\nCorrelation is particularly sensitive to saturation because it is impossible to accurately\nmeasure how two signals vary relative to one another if one or both of them have hit an\nupper limit.\nRe: We have reacquired the relevant images and updated the manuscript accordingly in\nthe revised manuscript (now Supplementary Figure S14).\nlysosomes of MAD detected by confocal and corresponding Pearson’s coefficient\nstatistical analysis. Blue: nucleus. Green: mitochondria. Red: lysosomes. Magenta:\nIR780. Scale bar: 5 µm (n=3 random regions). Data were presented as mean  SD. P\nvalues were determined using two-tailed unpaired t test. Source data are provided as a\n'Source Data' file.\nI believe that to have full confidence in their image analysis results, the authors will\nneed to re-image the saturated data using appropriate acquisition parameters. Chances\nare good that most of the major conclusions from these experiments will remain the\nsame (for instance, in Fig 5b and c, saturation may even be causing an underestimation\nof how much more PI and DCFHDA there is in the MAD + NIR condition relative to\nthe others), but in my experience as a microscopist it is not valid to make quantitative,\nabsolute intensity measurements on saturated data. This is particularly true for\nsensitive analyses like colocalization and ratiometry.\nRe: As you noted, the conclusions derived from these re-imaged experiments and their\nstatistical analyses remain largely consistent, but with enhanced reliability in the\nrevised interpretations. We have accordingly updated the reanalyzed results of Figure\n5b and Figure 5c in Page 23-24: 'NIR aroused a lethal photodynamic effect (7377.7-\nfold versus control, Figure 5b), as well as an explosive increase in ROS (4775.2-fold\nversus control. Figure 5c)', 'As a contrast, without NIR irradiation, ROS levels\nincreased slightly (1.0% of NIR given) and cell survival (no significant difference from\n60\ncontrol). Notably, the cell mortality rate was more than 10,000 times higher upon the\nNIR irradiation than that without NIR irradiation, greatly highlighting the safety of\nMAD and the effectiveness of the photodynamic therapy. In addition, it cannot be\nignored that the complete MAD system without NIR resulted in 49.5-fold more ROS\nproduction than the control group but almost no cell death, which strongly supported\nthe novel idea of activating mitochondrial intercellular transport by causing cellular\noxidative stress without affecting their survival'.\nAdditionally bleedthrough controls should be included for most of not all multi-color\nimaging experiments. A notable example is Fig 4a. One of the conclusions of the figure\nis that IR780 traffics with mitochondria (labeled with MitoTracker), but signal from the\nMitoTracker channel bleeding into the IR780 channel would create the appearance of\ncoincidence between the two signals. The authors don’t state which MitoTracker is used\nin this experiment, but if is MitoTracker Deep Red then bleedthrough is a definite\npossibility given that there is some overlap between the excitation spectra of MTDR\n(via FPbase.org) and IR780 (via Fig S9).\nRe: Thank you for your meticulous feedback. We have maintained rigorous attention\nto potential spectral bleed-through between channels, ensuring no detectable\ninterference exists between the fluorophores used. Regarding your specific concern\nabout MTDR and IR780 (indeed, MTDR was used in Figure 4c), we confirmed that\nbleed-through between these dyes was negligible (fluorescence signals were\nundetectable in cross-talk control experiments in Supplementary Figure S32). This is\nattributable to two key factors: IR780 is excited at 730 nm, a wavelength at which\nMTDR exhibits minimal excitation efficiency; IR780's emission spectrum (~750-850\nnm) shows low overlap with MTDR's emission profile. This allows complete avoidance\n61\nof bleed-through via careful selection of emission collection bands. All dyes used in\nour experiments are spectrally distinguishable under defined filter sets, ensuring\nnegligible crossover concerns. We are confident that the experimental results are\nunaffected by spectral bleed-through artifacts.\nSupplementary Figure S32. Representative confocal microscopy images of MTDR\nand IR780 under distinct excitation/emission acquisition conditions. NaN (Not a\nNumber) indicates pixels where no detectable fluorescence signal was measured above\nthe system's detection threshold. Scale bar: 25 µm.\nIt should be clarified that the images in Supplementary Figure S13 in the revised\nmanuscript represent absorption spectra acquired through UV-Vis spectrophotometry.\nFor spectral bleed-through assessment in confocal imaging systems employing fixed-\nexcitation-wavelength filters, priority should be given to evaluating emission spectral\noverlap. Corresponding emission spectra for the FIS fluorophores are provided in the\nsubsequent figure (Figure R5). Figure R5 provides direct evidence that IR780 can be\nreadily spectrally resolved from MTDR and other structural marker dyes used in this\nstudy through appropriate selection of excitation filters and instrument parameter\n62\noptimization.\nFigure R5. Emission spectra spectrum of FIS nanoparticle. This figure is not\nredundantly presented in the main manuscript.\nAn autofluorescence control should be included to demonstrate that\nmitochondrial/cellular autofluorescence does not change when mitophagy is altered.\nRe: Thank you for your suggestion. We added the autofluorescence control test.\nQuantification of autofluorescence across multiple spectral channels confirmed that FP-\ninduced mitophagy modulation produced no detectable shifts in background\nfluorescence intensities (Supplementary Figure S33, P>0.05). This validated that\nobserved signal changes in experimental groups originated specifically from\nfluorophore labeling rather than autofluorescence artifacts.\nSupplementary Figure S33. Cellular autofluorescence across fluorescence channels\nfollowing mitophagy modulation with statistical quantification. Scale bar: 25 µm (n=5\nrandom regions). Data were presented as mean  SD. P values were determined using\n63\ntwo-tailed unpaired t test. Source data are provided as a 'Source Data' file.\nThe rest of my comments are only minor points.\nThe image processing and analysis could use a bit more fleshing out, perhaps in the\nsupplementary materials. “ND Processing” is a suite of analysis tools that is exclusive\nto NIS Elements, and so is not very informative to people who use other types of\ninstruments. Indicating only that analysis was done in ImageJ, LAS, or Zen is not\nenough information to help people understand how the data was analyzed. How were\nimages pre-processed? (background subtraction, etc)How were ROIs selected?\n(thresholding? If so, how?) How was particle tracking done?\nRe: We sincerely appreciate your insightful critique regarding insufficient\nmethodological details in our image processing descriptions. In direct response, we\nhave substantially augmented the Methods section with expanded technical\ndocumentation—particularly focusing on quantitative pipelines—as systematically\noutlined below:\nFor 'Donor cells and recipient cells system' (Page 36-37): Recipient cells were\nsegmented using Cellpose (https://www.cellpose.org), followed by processing of the\nresultant masks in ImageJ to define cellular boundaries. Individual recipient cells were\nisolated by subtracting these masks from raw images. Subsequent 'Analyze Particles'\nanalysis (with size thresholding and 'Add to Manager' enabled) generated discrete\nregions of interest (ROIs) corresponding to individual cells. Mitochondrial and IR780\nfluorescence channels were then imported, converted to 8-bit, thresholded at a fixed\nvalue across all experimental groups, and converted to binary single-point\nrepresentations. These puncta were normalized to unity (=1) using the Divide function,\nenabling quantification via the Measure tool which recorded RawIntDen values -\n64\nrepresenting the summed intensity of all pixels within each recipient cell ROI - to\ndetermine counts of mitochondrial and IR780 puncta per cell. During quantification,\nRawIntDen values were normalized against recipient cell area to account for size-\ndependent variability, thereby yielding mitochondrial/IR780 puncta density per cell as\nthe definitive metric for cargo delivery efficiency to recipient cells.\nFor 'Western blot for protein detection' (Page 38): Protein expression was\nquantified as the intensity ratio of target protein bands relative to loading control bands.\nFor 'Structure illumination microscopy (SIM) for super-resolution fluorescent\nimaging' (Page 38-39): Multi-SIM images were analyzed using ImageJ. Following\nthreshold optimization, shape descriptors were enabled in 'Set Measurements' (with\nLimit to threshold selected). 'Analyze Particles' was then executed, with the Aspect\nRatio ('AR') column recording the major/minor axis ratio ('L/W') as a metric of\nmorphological elongation. Polar-SIM images were analyzed by Imaris software.\nSurface rendering of mitochondria was generated in consecutive images. Tracking\nproject was used to depict traces of single mitochondrion. Through autoregressive\nalgorithm, tracks with >10 s duration for movement within cells and >20 s duration for\nmovement between cells were analyzed and dynamic parameters were calculated.\nQuantitative motility parameters—'Track Speed Mean' and 'Track Displacement\nLength'—were acquired in Imaris ('Statistics' panel) and subjected to statistical analysis.\nFor 'Intracellular ROS detection' (Page 40-41): All images were processed in\nImageJ following a standardized workflow: raw data underwent 8-bit conversion\nfollowed by application of identical channel-specific thresholds across experimental\ngroups before intensity measurement. Per-cell ROS generation was quantified as the\nratio of ROS channel integrated density (IntDen) to nuclear channel IntDen, establishing\na normalized metric for robust intergroup comparative analysis; this protocol was\nsystematically applied to all subsequent analogous experiments to ensure\nmethodological consistency.\nFor 'Calcein-AM/PI staining' (Page 41): Viability quantification was performed\nusing the dead/live cell ratio derived from PI channel IntDen normalized to calcein\nchannel IntDen.\nFor '3D B16F10 cell sphere model for penetration' (Page 41): IR780 distribution\nwas subsequently quantified by measuring layer-specific mean fluorescence intensity\nacross all optical sections in ImageJ.\nFor 'Anti-cancer therapy in mice bearing B16F10 solid tumor' (Page 43):\nImmunofluorescence data were quantified by normalizing target protein IntDen to\nnuclear channel IntDen. Western blot expression levels were calculated as target protein\nband intensity relative to loading control (GAPDH) band intensity, with both analyses\nperformed using ImageJ.\nThe experimental methods mentioned in the Supplemental Information are also\nsupplemented there in yellow.\nInitially, we attempted to perform image stabilization using open-source software\n(e.g., ImageJ's Image Stabilizer plugin), but the results proved unsatisfactory,\nparticularly for multi-channel datasets. Persistent residual jitter and occasional\nmisalignment between channels compromised data integrity. Consequently, we\nultimately implemented ND Processing for superior stabilization performance. This\nplatform maintains compatibility with externally acquired images from diverse\ninstrumentation systems. Critically, as long as processing efficacy is validated—\ndemonstrating elimination of motion artifacts without introducing spatial distortions—\nthe choice of stabilization software should not impact biological conclusions.\na black background.\nRe: We sincerely appreciate your meticulous feedback regarding nuclear labeling. As\nsuggested, we explored alternative fluorophores but encountered practical limitations:\nmultiple experiments already utilize red, green, magenta, and occasionally cyan\nchannels, leaving limited options for distinguishable nuclear counterstains. While\nalternative other blue-related pseudo-colors remain feasible, we prioritize standard\ntrue-toned representation to minimize chromatic identification bias in multi-channel\nimaging—particularly given the nucleus primarily serves as a spatial reference rather\nthan a focal element. Excessive emphasis on nuclear coloring may inadvertently detract\nattention from key experimental signals.\nAdmittedly, we affirm the significance of your feedback. We have reviewed\nimages potentially susceptible to identification difficulty and adjusted the display range,\nmaintaining absolute consistency across experimental groups within the same\nexperiment.\nOverall, the findings here are exciting and significant as a potential avenue for drug\ndelivery, but in my opinion some of the imaging needs to be redone without saturation\nin order to be analyzed appropriately, and bleedthrough controls should be included in\nthe supplementary material where relevant.\nRe: Thank you very much for your affirmation and suggestions. As you rightly noted,\nwe have strictly avoided saturation by reacquiring the specified images and reanalyzed\nthe corresponding data. Bleed-through controls and autofluorescence controls are now\nincluded in the Supplementary Information. Additionally, expanded methodological\ndescriptions of data processing and quantification have been incorporated (in both the\n67\nmain manuscript and Supplementary Information). We hope that our responses to your\nconcerns and the newly submitted experimental data will be satisfactory.\n68"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thanks to the authors for a tremendous effort in the preparation of the revised MS along with the additional experiments and clarifications that have been added to the resubmitted package.\nWe can certainly understand the difficulties in capturing a complete transfer event from one cell to another. Nevertheless the authors have done a great job overall in convincing this reviewer that transfer has indeed occurred.\nAll of the other concerns have also been fully resolved, particularly the area where more suitable quantification were sought.\nCongratulations on the improved manuscript and good luck to the authors with their future studies in important this area of cell biology.", "reviewer_2": "Overall, the authors have performed substantial additional experiments to address the concerns the reviwers raised.\nNonetheless, there is still a lack of direct comparison with other delivery systems to highlight the\nadvantages of the MAD system in anti-tumor in this study. The advantages of the MAD system is not clear so far.", "reviewer_3": "I have now reviewed the revised version of this manuscript, and while some aspects have been addressed with some new experimental data; in my view, the main problems encountered in the first review still remain, and I found the manuscript problematic in the studies and conclusions indicated. Essentially, the mitophagy manipulations need to be extended to additional loss- and importantly, gain of function experiments. The manuscript remains poorly assembled and difficult to follow a rationale.", "reviewer_4": "As I mentioned in my initial review, my expertise is in advanced light microscopy and so for the most part I have limited my critiques to what pertains to the imaging and microscopy image analysis aspects of the study. My primary critique when I made my initial review was that many of the fluorescence microscopy images included in the manuscript’s datasets were saturated, such that meaningful quantitative information could not be drawn from them. The authors have since re-imaged and analyzed all of the previously saturated datasets that were indicated in my review, which addresses the majority of my concerns. Adjusting the display range to emphasize more dimly fluorescent features as in Fig 5c is fine as long as the underlying raw data isn’t saturated.\nAdditionally, bleedthrough and autofluorescence controls have been added and confirm that neither source contributes significantly to the images in the study.\nImage processing and analysis methods have been expanded upon very well and now clearly indicate exactly how the results were obtained.\nI thank the authors for addressing my previous concerns very thoroughly, and I have no further critiques before publication. I believe that the results shown here are novel and fascinating and will be of broad interest to the cell and systems biology community."}, "author_response": "Response to reviewer comments\nWe sincerely appreciate the valuable and insightful comments, which have greatly\nhelped us improve the quality of this work. Our point-by-point responses are as follows:\nReviewer #1 (Remarks to the Author):\nThanks to the authors for a tremendous effort in the preparation of the revised MS\nalong with the additional experiments and clarifications that have been added to the\nresubmitted package.\nWe can certainly understand the difficulties in capturing a complete transfer event\nfrom one cell to another. Nevertheless the authors have done a great job overall in\nconvincing this reviewer that transfer has indeed occurred.\nAll of the other concerns have also been fully resolved, particularly the area where\nmore suitable quantification were sought.\nCongratulations on the improved manuscript and good luck to the authors with their\nfuture studies in important this area of cell biology.\nRe: We are deeply grateful to the reviewer for the exceptionally kind and encouraging\ncomments on our revised manuscript, as well as for their invaluable feedback\nthroughout the review process. The reviewer's time and positive reinforcement are\ngreatly appreciated, and we extend our thanks for the good wishes for our future\nstudies in this field.\nReviewer #2 (Remarks to the Author):\nOverall, the authors have performed substantial additional experiments to address the\nconcerns the reviwers raised.\nNonetheless, there is still a lack of direct comparison with other delivery systems to\nhighlight the advantages of the MAD system in anti-tumor in this study. The\nadvantages of the MAD system is not clear so far.\nRe: We thank the reviewer for this constructive feedback and for acknowledging the\nsubstantial additional work we have undertaken.\nIn our study, the individual advantages of components within the MAD system\nhave been validated through multiple experiments. For instance, Supplementary\nFigure S16 demonstrates that IR780 exhibits superior intercellular transfer capability\ncompared to non-mitochondria-localizing photosensitizers. Furthermore, Figure 6\nshows that the intact MAD system penetrates deeper into tumor tissues than free\nIR780, which is a prerequisite for effective drug action.\nThis study is designed not to assert the superiority of the MAD system over all\nexisting delivery technologies, but rather to introduce and rigorously validate this\nmitochondria-based platform by establishing its functionality and unique mechanism\nof action for delivering certain therapeutic agents. While we acknowledge that direct\ncomparisons with other systems could be valuable, such analysis is complicated by\nthe considerable diversity among delivery platforms—each with distinct strengths and\ncontext-dependent optimized parameters (e.g., API properties, dosage, and\nadministration frequency). The marked improvement in survival of tumor-bearing\nmice, including a 43% complete cure rate achieved by the MAD system, strongly\nunderscores its promising anti-tumor potential. Taken together, we are confident that\nthe evidence presented—from mechanistic merits to outstanding in vivo efficacy—\neffectively validates the MAD platform and underscores its considerable promise.\nReviewer #3 (Remarks to the Author):\nI have now reviewed the revised version of this manuscript, and while some aspects\nhave been addressed with some new experimental data; in my view, the main\nproblems encountered in the first review still remain, and I found the manuscript\nproblematic in the studies and conclusions indicated. Essentially, the mitophagy\nmanipulations need to be extended to additional loss- and importantly, gain of\nfunction experiments. The manuscript remains poorly assembled and difficult to\nfollow a rationale.\nRe: We thank the reviewer for taking the time to review our revised manuscript and\nfor providing their feedback.\nIn response to the reviewer's general comment, we would like to emphasize that\nthe concerns regarding manuscript clarity and rationale were directly addressed in our\nprevious revision. As detailed in our earlier response, we had already undertaken a\nsubstantial reorganization of the manuscript's abstract and introduction, streamlined\nthe narrative, and added more detailed explanations and more in-depth discussion in\nthe results section to better guide the reader.\nRegarding the specific point on mitophagy manipulations, we confirm that a\ncomplete suite of loss- and gain-of-function experiments was established in our prior\nrevision. The original manuscript included extensive loss-of-function data, and we\nhad already supplemented it with new gain-of-function experiments (Supplementary\nFigure S1), which demonstrate that the activation of mitophagy leads to a reduction in\nmitochondrial transfer. The combination of these two approaches provides a robust\nand conclusive functional validation of mitophagy's fundamental role in\nmitochondrial transfer.\nWe are confident that the manuscript presents a complete and compelling story\nthat merits publication, and we thank the reviewer for their input, which has\nstrengthened this work. We believe our study provides a solid foundation for the\nexpanded research efforts this emerging field rightly calls for.\nReviewer #4 (Remarks to the Author):\nAs I mentioned in my initial review, my expertise is in advanced light microscopy and\nso for the most part I have limited my critiques to what pertains to the imaging and\nmicroscopy image analysis aspects of the study. My primary critique when I made my\ninitial review was that many of the fluorescence microscopy images included in the\nmanuscript’s datasets were saturated, such that meaningful quantitative information\ncould not be drawn from them. The authors have since re-imaged and analyzed all of\nthe previously saturated datasets that were indicated in my review, which addresses\nthe majority of my concerns. Adjusting the display range to emphasize more dimly\nfluorescent features as in Fig 5c is fine as long as the underlying raw data isn’t\nsaturated.\nAdditionally, bleedthrough and autofluorescence controls have been added and\nconfirm that neither source contributes significantly to the images in the study.\nImage processing and analysis methods have been expanded upon very well and now\nclearly indicate exactly how the results were obtained.\nI thank the authors for addressing my previous concerns very thoroughly, and I have\nno further critiques before publication. I believe that the results shown here are novel\nand fascinating and will be of broad interest to the cell and systems biology\ncommunity.\nRe: We are deeply grateful to the reviewer for the expert guidance and for revisiting\nour manuscript. The reviewer's background in advanced light microscopy provided\nexceptionally valuable insights that have directly and significantly improved the quality\nand rigor of the imaging data presented in our study.\nWe are pleased that the re-imaging and re-analysis of the previously saturated\ndatasets, along with the addition of the requested controls for bleedthrough and\nautofluorescence, have fully addressed their primary concerns. We also thank the\nreviewer for their note regarding the display adjustments in Fig. 5c.\nIt is highly rewarding to know that our expanded methodology descriptions are\nnow clear and satisfactory. We are truly honored by the reviewer's generous assessment\nof our work's novelty and broad interest. Their positive feedback from an expert\nperspective is immensely encouraging, and we thank them once again for their critical\nrole in strengthening this manuscript."}]

Decision letter