Structural evolution of carbon frameworks realizes in vitro interfacial transport in metabolically reprogrammed senescent cells for senolysis

Domain: biology
Published: 2026-03-17
Editorial outcome: major_revision

Abstract
Integrating the diagnostic and therapeutic functions of drugs poses challenges due to specific structural design requirements. However, the trial-and-error dilemma in performance-driven structural evolution of nanomaterials is unsatisfactory. Here, we propose a strategy using graphene quantum dots with sp2–sp3 hybridized carbon frameworks for visualized, intelligent targeted clearance of metabolically reprogrammed senescent cells. We establish a structure-activity relationship between the complex nanostructures and photochemical reactivity. We show that property descriptor-based machine learning promotes the evolution of carbon nanostructures, endowing them with exceptionally high photodynamic efficiency. These machine-learning results also guide the design of carbon-based photo/electrocatalytic structures. By examining the interfacial transport properties of in situ carbon nanostructures in excited states, we identify changes in fluorescence and photodynamic activity of C3N quantum dots within metabolically reprogrammed cellular microenvironments. This allows fluorescence detection technology for senescent cells based on C3N quantum dots and an intelligent targeted clearance treatment plan.

Introduction
Achieving integrated diagnosis and therapy for senescent cells is pivotal in addressing aging-related pathologies. A fundamental premise for designing drug structures tailored for dual diagnosis and therapy applications entails the integration of diagnostic and therapeutic functionalities within the drug’s structure based on specific cellular environments 1,2. However, both diagnosis and treatment impose specific structural requirements on drug designs, including different targets 3 and half-lives 4. Meanwhile, the complex interactions between these requirements lead to potential antagonism or synergy 5,6. For example, in the treatment of metabolic reprogramming-related diseases including senescence, when identification and clearance of metabolically reprogrammed cells are required, and when diagnostic and therapeutic functions are based on different targets 7-9, it becomes difficult to achieve an optimal balance between diagnostic accuracy and therapeutic efficacy. Recently, machine learning has demonstrated substantial advantages in elevating the efficiency of drug screening 10,11. However, the black-box dilemma encountered in machine learning (explaining the interfacial physico-chemical mechanisms behind the machine learning structure) makes it difficult to address the need for a comprehensive, holistic understanding of drug design mechanisms 12. Thus, using machine learning to improve the design efficiency of integrated diagnosis and treatment drugs, while simultaneously advancing our understanding of the interfacial physico-chemical mechanisms of drugs in both in vivo and in vitro environments, represents a critical challenge currently confronting scientists 13.

On the other hand, the structurally diversified sp2–sp3 hybrid carbon nanostructures (such as nanodiamonds, carbon dots, graphene quantum dots (QDs), and carbon nanotubes) bring unlimited possibilities for the structural design of integrated diagnosis and treatment drugs targeting metabolic reprogramming-related diseases 14. Whether it is the basic properties of carbon nanostructures (optical properties, electrical properties) or biological effects, extensive discussions have been conducted with the aid of machine learning 11,15. However, it is worth noting that machine learning also faces the common black-box problems in the research into properties of carbon nanostructures 16. More importantly, traditional studies of the structure-activity relationship are insufficient when addressing the complexities of sp2–sp3 hybridized carbon nanostructures 17. Therefore, starting from the structure of integrated diagnosis and treatment drugs, taking carbon nanostructures as the research object explaining the interfacial physico-chemical mechanisms behind machine learning are of significance for simultaneously advancing the understanding of drug action mechanisms 18 and the structure-activity relationship of carbon nanostructures 19.

Here, by combining machine learning with in situ research techniques of solid-liquid interfaces within cells, the selective physicochemical responses of carbon nanostructures are described at interfaces in complex cellular microenvironments. With the assistance of the NMR relaxometry, we elucidate the unique excited-state interfacial transport mechanism in carbon frameworks that underlies the structure-activity relationship. More importantly, we achieve in situ observation of the interfacial transport behaviors of nanostructures within metabolically reprogrammed cellular environments. This research also demonstrates that C3N QDs, characterized by their orderly arranged lattice nitrogen atoms, alter the fluorescence and photodynamic activity across various cell metabolisms. In the microenvironment of metabolically reprogrammed senescent cells, C3N QDs show a low fluorescence intensity and a high photodynamic activity. In contrast, in normal cells, these C3N QDs exhibit increased fluorescence compared to those in aging cells, while their photodynamic activity is suppressed. Such a selective behavior facilitates the targeted elimination of aging cells and enables effective fluorescent labeling of these cells. The reliability of C3N QDs has been confirmed through extensive testing conducted at the cellular, animal, and clinical sample levels.

Results
The structural evolution of a carbon framework
Nitrogen-doped graphene quantum dots (N-GQDs) were prepared through the hydrothermal treatment by using m-phenylenediamine and pyrocatechol as precursors (Supplementary Data 1). The average size of N-GQDs is 6.1 nm (Supplementary Fig. 1). The typical graphene structure of N-GQDs can be observed in Supplementary Fig. 2. Indeed, the X-ray photoelectron spectroscopy results indicate that most N atoms exist as lattice-doped N atoms (N–(C)3: 71.4%, C–NH–C: 22.9%, and NH2: 5.7%, Supplementary Fig. 3). Additionally, the optical band gap of GQDs is 2.34 eV (Supplementary Fig. 4).

The photodynamic activity of N-GQDs was evaluated via radical trapping experiments (experimental details are provided in Methods). As shown in Fig. 1a, the 1O2 generating rate (r) of N-GQDs is 6.55 min–1 (25 °C, under 365 nm laser irradiation at 80 mW cm–2), which is higher than that of protoporphyrin (r = 6.45 min–1). Although the N-GQDs show a lower r than that of zinc protoporphyrin (r = 9.76 min–1, Fig. 1a), the above results demonstrate that N-GQDs can be used as a photosensitizer.

It is worth noting that the complicated sp2–sp3 carbon framework of N-GQDs makes it difficult to elucidate the photodynamic mechanism of N-GQDs 20. Therefore, the structural evolution of carbon frameworks based on rational structure descriptors is necessary. Inspired by a previous report 21, five key structural descriptors were selected for N-GQDs, including: (i) nitrogen dosage concentration, (ii) nitrogen configurations (N–(C)3, C–NH–C, and –NH2), (iii) oxygen content, (iv) oxygen functional groups (–OH, –COOH, and –CHO), and (v) particle size. A series of N-GQDs was prepared and characterized for structural evolution of carbon frameworks (see Supplementary Information for preparation details). Accordingly, three machine learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were then applied to analyze the relationships between these structural features (–NH2, C–NH–C, N–(C)3, –OH, –CHO, –COOH, and particle size) and the target (r). The comparison of the optimal models for grid search under three different machine learning algorithms indicates that the GBDT model exhibits optimal learning capability (Supplementary Fig. 5). Fig. 1b and 1c depict the coefficient of determination (R2) of the GBDT models for varying estimators (number of trees) and max tree depth on the training dataset through cross validation and test dataset, respectively. Usually, the model learns more details from the data set as the closer the value of R2 is to 1 with the augment of estimators. Nevertheless, higher R2 values may suggest the potential for overfitting of the model. Fig. 1d illustrates the Root Mean Square Error (RMSE) score heatmap of the GBDT models performing on the test dataset. The greater accuracy of the model. Therefore, by comparing the model performance on the training and test datasets, the best GBDT model was determined by both estimators = 35 and max tree depth = 4. Typically, as the GBDT model comprises numerous trees, each tree holds a distinct decision-making process for the target solution. Fig. 1e shows a comprehensive visualization of one decision tree derived from the optimal GBDT model, displaying the structure, node-splitting conditions, sample distribution at each node, and the overall decision-making logic for data partitioning. More importantly, the Shapley Additive Explanations (SHAP) values of each feature indicate the contribution of these features to the target of the optimal model (Fig. 1f). Accordingly, the machine learning results thereby reveal that N–(C)3 performs the highest weight of 2.0998, indicating that it is a critical determinant of r. On the contrary, the –NH2 feature (0.0778), C–NH–C feature (0.0423), –OH feature (0.0075), –CHO feature (0.0417), –COOH feature (0.3545), and size feature (0.0415) with comparatively lower weights exert less influence on r. This provides definitive evidence of the relationship between the molecular structure of C3N QDs and their photosensitive activity. The N–(C)3 structure is deemed to be the critical structural factor for enhancing photodynamic catalysis. Therefore, it can be reasonably deduced that enhancement of the N–(C)3 structure within the C3N QDs will result in an improvement of the photodynamic activity of C3N QDs, thus achieving the optimal catalytic effect.

Based on the machine learning-driven structural evolution, the C3N QDs can be identified as the candidate photosensitizer with high photodynamic activity. As shown in Fig. 2a, both 1O2 and·OH were generated when C3N QDs were subjected to light irradiation. The r of C3N QDs is 27.32 min–1 at 25 °C (under 365 nm laser irradiation at 80 mW cm–2, Fig. 2b), which is much higher than that of traditional photosensitizers (protoporphyrin, r = 6.45 min–1; zinc protoporphyrin, r = 9.76 min–1). Moreover, C3N QDs also show a stable photodynamic activity (Δr < ± 2.5 min–1) in simulated cellular environment (include inorganic salts, biomolecules, proteins, and amino acids, Fig. 2c). However, the photodynamic activity of C3N QDs (r = 17.17 min–1 at 25 °C, under 365 nm laser irradiation at 80 mW cm–2) was significantly inhibited in the presence of nicotinamide adenine dinucleotide (NAD+, 1.0 nmol L–1, Fig. 2c and d). Fig. 2d shows the change in r of C3N QDs under different concentrations of NAD+. A jump in photodynamic activity can be observed. The r of C3N QDs reduced from 25.71 min–1 to 18.38 min–1 when the NAD+ concentration rose from 200 to 400 pmol L–1. Such a result indicates the potential interfacial transport between C3N QDs and NAD+. On the other hand, the changes in the photoluminescence intensity of C3N QDs under different concentrations of NAD+ (Supplementary Fig. 6 and 7) also support the above speculation. Importantly, the concentration range of NAD+ (Fig. 2d) in which the jump in photodynamic activity occurs is comparable to the cytoplasmic NAD+ concentration difference between normal cells and metabolically reprogrammed cells (Supplementary Fig. 8). Such a finding makes it possible to exploit the NAD+ concentration gradient in the cytoplasm of metabolically reprogrammed cells for targeted elimination.

To explore the interaction between C3N QDs and NAD+, density functional theory (DFT) calculations were employed to calculate the interaction energy at each step as C3N QDs and NAD+ approach each other. The results indicated that NAD+ was spontaneously adsorbed onto the C3N QDs (Fig. 2e). Furthermore, simulations of the charge transfer between C3N QDs and NAD+ revealed that when NAD+ is adsorbed on the C3N QDs, electron transfer occurs with 1.29 e (Fig. 2f). This indicates that the excited state of C3N QDs facilitates electron transfer to NAD+, which, in turn, hinders the formation of radicals by C3N QDs under light irradiation. Given that DFT calculations have confirmed the selective binding as well as the interfacial electron transfer between C3N QDs and NAD+, it is worth noting that such evidence is insufficient to elucidate the significant reduction in r of C3N QDs with the presence of NAD+. Such a situation possibly results from the focus on electron transport in the interfacial interaction instead of the proton transport during the DFT calculations. Therefore, the NMR relaxometry compatible with a light source 22-25 can be used to study the proton transport behavior of C3N QDs after the binding with NAD+. The longitudinal relaxation time (T1) measured by the 0.5 T NMR relaxometry is sensitive to the proton transport behavior, including the proton dissociation and transport 26,27. As shown in Fig. 2g, C3N QDs aqueous dispersion in a dark environment shows a T1 of 404.88 ± 6.80 ms, which is shorter than that of a 0.05-mmol L–1 Gd3+ aqueous solution 25, indicating the C3N QDs induce minor acceleration of the water proton exchange in a dark environment 28. After the addition of NAD+, the T1 slightly increased to 503.53 ± 23.05 ms, attributable to decreased proton dissociation at the C3N QDs’ interfacial and thus to moderate water proton exchange. When measuring the T1 with light induction (25 °C, under 365 nm laser irradiation at 80 mW cm–2), the T1 values of C3N QDs aqueous dispersion with and without the addition of NAD+ both decreased to 452.46 ± 8.28 and 179.18 ± 8.04 ms, respectively. Such a reduction in T1 after light irradiation on the aqueous sample represents the fact that irradiation by light may induce more dissociated protons at the interface of C3N QDs. Notably, with the addition of NAD+, the C3N QDs aqueous dispersion shows a long T1, originating from the binding of NAD+ with light induction and then suppressing the C3N QDs to produce more exchangeable protons, leading to the decrease in r.

It is worth noticing that in the highly complex intracellular environment, C3N QDs also show the selective interfacial transport behavior towards NAD+ (Fig. 2h). C3N QDs incubated with healthy cells in a dark environment show a T1 of 438.97 ± 15.42 ms. After the incubation with senescent cells, the T1 of C3N QDs slightly decreased to 247.80 ± 5.68 ms, attributing to the increased proton dissociation at the C3N QDs interface and thus the accelerated water proton exchange. When measuring T1 under light induction, the T1 values of C3N QDs incubated with healthy and senescent cells both decreased to 333.45 ± 14.21 and 84.78 ± 7.91 ms, respectively. Such a reduction in T1 after irradiation of the sample represents the fact that the light may induce more dissociated protons at the interface of C3N QDs. Notably, in senescent cells, the C3N QDs show the lowest T1 among the samples, originating from the low concentration of NAD+ in senescent cells under light irradiation, as the C3N QDs produce more exchangeable protons.

Thus, after uncovering the photodynamic activity of N-GQDs, the structural evolution of the carbon frameworks was achieved with the help of machine learning, and C3N QDs were proven to be efficient photosensitizers. Based on this, the selective interfacial transport behavior of C3N QDs towards NAD+ was further studied. After binding with NAD+, the interfacial charge transport characteristics and interfacial proton transport behavior of C3N QDs both changed significantly. This made C3N QDs show lower photosensitivity at high concentrations of NAD+. The interfacial transport behavior of C3N QDs was proven to persist in vitro.

In vitro interfacial transport of C3N QDs
The above results demonstrate the fluorescence response of C3N QDs to NAD+ and their varying photodynamic activities at different NAD+ expression levels. The feasibility of C3N QDs in identifying cells at different metabolic levels was further validated. A typical example is such that the concentration of NAD+ in the cytoplasm of senescent cells is lower than that of normal cells (Supplementary Fig. 8) 29. Indeed, C3N QDs exhibited expected results at both the cell and tissue levels (both in vitro cells and clinical tissue samples). Due to their ultrasmall size and appropriate ζ potential 30, C3N QDs are able to penetrate cells rapidly (3 h, Fig. 3a). It is noteworthy that the process of C3N QDs entering cells is not affected by the intracellular environment 31. This results in the same rate and amount of C3N QDs entering both normal and senescent cells (Fig. 3a). Additionally, C3N QDs can maintain the fluorescence activity within cells for 3–7 days (Fig. 3b and 3c, Supplementary Fig. 9).

Co-localization staining results indicate that after crossing the cell membrane, C3N QDs are mainly located in the cytoplasm and do not bind to specific organelles (Golgi apparatus, lysosomes, endoplasmic reticulum, Supplementary Fig. 10). As shown in Fig. 3d, normal HUVECs transfected with red fluorescent protein (RFP, red dotted line) and senescent HUVECs, which were not transfected with RFP (blue dotted line), were mixed and cultured. In the normal HUVECs, bright cell fluorescence of C3N QDs was visible. In contrast, the fluorescence of C3N QDs in senescent HUVECs, characterized by lower NAD+ levels, was greatly weakened. At 72 h, the normal cells emitted both red and green fluorescence, while no significant green fluorescence was observed in the senescent cells within the same culture system. The sensitivity and specificity of the method with respect to identifying senescent cells were 93.43% and 93.08%, respectively (Fig. 3d, Supplementary Fig. 11).

Since a low level of cellular NAD+ in senescent cells is a common phenomenon 32, C3N QDs showed distinct fluorescence variations between senescent and normal cells in multiple cell type models of senescence induced by H2O2 or etoposide (ETO), including HUVEC, A549, bEND3, HK2, MRC5, and 3T3, as well as in primary cell models of replicative senescence (PEAC, PLF) (Supplementary Fig. 12–14). After C3N QDs were incubated with cells for 48 h, a significant difference in fluorescence intensity was observed between normal and senescent cells. Compared with normal cells, the fluorescence intensity of C3N QDs in senescent cells diminished by 44.6%, 49.9%, 56.3%, 64.3%, 46.9%, 58.6%, and 42.4%, respectively (Fig. 3e). The detection threshold for senescent cells based on the fluorescence intensity of C3N QDs was established at 90.0% (Fig. 3f, Supplementary Fig. 15).

The staining results of C3N QDs at the tissue level are consistent with the cell experiments. Due to their non-selective staining properties, C3N QDs are widely present in tissue samples. When examining the mouse aorta, C3N QDs were able to stain the entire layer of the blood vessel (intima, media, and adventitia) (Fig. 3g). In aorta samples from both young and senescent male mice, C3N QDs were different in the fluorescence intensity (Fig. 3h and 3i). Pulse wave velocity (PWV) can be used to assess arterial stiffness. The PWV of the aorta in senescent mice was significantly higher than that in normal mice (Supplementary Fig. 16). According to the analysis of receiver operating characteristic (ROC) curves, the sensitivity of C3N QDs was 96.0%, and its specificity was 72.0%, while the sensitivity of PWV was 68.8% and its specificity was 87.5%. Furthermore, the area under the curve (AUC) value of C3N QDs was 0.9552, which was significantly higher than the AUC value of PWV (0.7695), indicating that C3N QDs outperform PWV in terms of accuracy in detecting vascular aging (Fig. 3j). Furthermore, C3N QDs exhibited effective fluorescence selectivity across various organs in senescent mice, including the heart, liver, spleen, lungs, and kidneys (Fig. 3k, Supplementary Fig. 17).

C3N QDs can also be used to characterize aging in clinical samples. In clinical aortic vascular samples, the fluorescence intensity of C3N QDs in the aorta of young (20–45y) patients was significantly stronger than that of elderly (≥60y) patients (Fig. 4a and 4b, Supplementary Fig. 18). Similarly, the aortas from elderly patients showed vascular thickening and extensive aging across all layers. Clinical liver, kidney and skin samples demonstrated analogous results (Fig. 4c–4f, Supplementary Fig. 19–21). Notably, when using C3N QDs to stain skin samples from young and elderly patients, the dermis of elderly patients exhibited more obvious aging than the epidermis, suggesting that it is feasible to use C3N QDs to detect differences in aging at the tissue or cell level.

Statistics pertaining to clinical samples verified the negative correlation between C3N QDs intensity and aging (Fig. 4g). Compared to traditional aging assessment technologies that rely on NAD+ level detection (such as high-performance liquid chromatography, mass spectrometry, or enzyme-linked immunosorbent assay) 33, the C3N QDs-based detection technique showed higher accuracy, the correlation analysis revealed a significant linear relationship between C3N QDs and NAD+ concentration (Pearson correlation coefficient = 0.7932, P < 0.0001), indicating that both variables exhibit a consistent trend in aging detection. (Fig. 4h, Supplementary Fig. 22). Furthermore, the AUC value of C3N QDs was 0.9305, significantly higher than that of PWV (0.9033), indicating that C3N QDs outperform PWV in terms of accuracy for aging detection (Fig. 4i).

In vitro interfacial transport for visual therapy
The results show that C3N QDs achieve the recognition of normal cells and senescent cells through the selective fluorescence response to NAD+. Concurrently, recognition of NAD+ by C3N QDs also causes changes in the photodynamic activity of C3N QDs, which makes it possible to eliminate senescent cells through the photodynamic activity of C3N QDs. Previous studies have shown that selective clearance of senescent cells (known as senolysis) in tissues helps to postpone aging 34. To demonstrate the efficacy of C3N QDs in the selective elimination of senescent cells, normal HUVECs and senescent HUVECs were co-incubated with C3N QDs (0.5 µg mL–1) for 3 h, and then irradiated by laser light with a wavelength of 365 nm at 80 mW cm–2 for 15 min, the mortality rate of normal cells was 0.33% ± 0.27% (Fig. 5a). In contrast, the lower NAD+ concentration of senescent cells caused C3N QDs to show higher photodynamic activity. The mortality rate of senescent cells after photodynamic therapy was 90.52% ± 4.23% (Fig. 5a, Supplementary Fig. 23). In addition, control experiments using identical irradiation parameters but without C3N QDs showed no significant impact on the viability of senescent HUVECs (Supplementary Fig. 24), confirming that the observed cytotoxicity arises from the photodynamic activity of C3N QDs rather than laser irradiation itself.

The heatmap can help understand how different treatments affect cellular mechanisms at the genetic level: S (senescence), S+C (senescence + C3N QDs), and S+C+I (senescence + C3N QDs + Irradiation). As depicted in Fig. 5b, within the S+C+I group, the expression levels of several aging-related genes changed notably. Specifically, the expression of genes such as MAPT, FOXO4, EPOR, FADS1, CTF1, and PTEN exhibited an upward trend, indicative of enhanced transcriptional activity or protein synthesis. In contrast, the levels of expression of other genes, namely FOS, NRG1, NOG, CXCL8, IL6, CTGF, CXCL1, SERPINE1, KLF4, MAP3K14, NGF, PLAUR, TRAF1, DDIT3, ARID5A, E2F1, TERT, and CEBPB, were found to be decreased in the S+C+I group. Analysis of molecular function highlights the prominent DNA-binding and transcriptional regulatory activities in the various experimental groups, with distinct changes seen in the S+C+I group (Supplementary Fig. 25–28). GSEA identified significant suppression of the KEGG p53 signaling pathway (NES = –2.16, FDR < 0.05) (Fig. 5c). These findings imply that the combination of C3N QDs and irradiation effectively scavenges senescent cells, leading to an improvement in aging-related conditions.

This reveals that C3N QDs can selectively recognize and clear senescent cells by leveraging variations in photodynamic activity. Indeed, C3N QDs showed consistent photodynamic therapy effects across various cell types, such as A549, bEND3, and HK2 (Supplementary Fig. 29–31). More importantly, the clearance of senescent cells led to significant changes in senescence markers such as p16, p21, and senescence-associated secretory phenotype (SASP). Quantitative analysis of β-gal across different cell lines unveiled that the β-gal levels in senescent cells treated with photodynamic therapy using C3N QDs reduced significantly, by 61.26% to 61.39% (Supplementary Fig. 32). In senescent cells treated with photodynamic therapy using C3N QDCs, a significant reduction in senescence markers was observed, decreasing by 26.07% to 87.43% (Fig. 5d–5f, Supplementary Fig. 33–34). At the same time, the cell proliferation ability of senescent HUVECs also changed accordingly. After the clearance of senescent cells, the proliferative capacity of the remaining cells improved 20.7% (Fig. 5g and 5h).

Experimental results at the cellular level validated the feasibility of using C3N QDs for the specific clearance of senescent cells. Further in vivo experiments, including those on the aorta, organs, and skin repair in induced senescence models, were conducted. The biosafety of C3N QDs was initially confirmed (Supplementary Fig. 35–37). Senescent mice induced by intraperitoneal injection of doxorubicin (10 mg kg–1) 35(Supplementary Fig. 38), followed by the administration of C3N QDs (5 μg mL–1, 100 μL) via the tail vein, and subsequent illumination of the aorta with 365 nm laser irradiation at 80 mW cm–2 for 15 min. The p21 staining revealed that p21 expression in the arteries of these doxorubicin-induced senescent mice significantly lowered after photodynamic therapy (Fig. 6a and 6b). Indeed, compared to the control group, senescent aorta treated with PDT showed 40.78% and 58.27% decreases in p21 and γ-H2AX expression, and an 85.69% increase in the Lamin B1 expression (Supplementary Fig. 39). This result was directly reflected in the reduction of vascular senescence (Supplementary Fig. 40).

Similar outcomes were observed in doxorubicin-induced senescence mouse models of the liver, spleen and kidneys (Fig. 6c–6f, Supplementary Fig. 41–43). In the liver, results were similar to those seen in the senescent vascular model, with β-gal levels reduced by 73.07% and p21 by 34.03%, and a 77.95% decrease in γ-H2AX expression (Fig. 6c and 6d, Supplementary Fig. 41). Importantly, in a liver fibrosis model associated with a high risk of tumorigenesis 36,37, C3N QDs also demonstrated satisfactory therapeutic effects, with reductions in β-gal by 87.6% and p21 by 64.30%, and a 64.2% reduction in γH2AX expression (Fig. 6e and 6f, Supplementary Fig. 44).

Finally, a practical and demonstrative application was conducted based on a diabetic skin injury model (Fig. 6g) 38,39. In this model, diabetic mice received C3N QDs (5 μg mL–1, 100 μL) through tail vein injection, followed by photodynamic treatment of the skin 365 nm laser irradiation at 80 mW cm–2 for 15 min). Similar to the senescence model, the tissue of the diabetic showed a lower NAD+ level 40,41, which made the healing rate of skin defects in the diabetic model significantly slower 42-44. Selectively eliminating cells with a low NAD+ expression in diabetic skin models is beneficial to the repair of skin defect models 43,45. In fact, after photodynamic treatment of C3N QDs, the skin injury model of diabetic mice showed better repair effects compared with the non-intervention experimental group. As illustrated in Fig. 6h, the control group exhibited significant inflammatory edema and scabbing between days 9–12. In contrast, the C3N QDs photodynamic therapy group experienced inflammatory edema from days 3–6 and scabbing from days 6–9. Moreover, wound healing via C3N QDs photodynamic therapy was noticeably faster than that in other groups, with the average relative area percentage of wound closure on day 12 being 84.98%, 85.1%, 84.83%, and 93.9% (Fig. 6i, Supplementary Fig. 45 and 46). HE staining results showed that after C3N QDs photodynamic treatment, the epidermis layer of the defective part of the skin was restored. On the day 12, the distances between the unhealed epidermal wounds were 2.35 mm, 2.28 mm, 2.21 mm, and 1.15 mm, respectively. Additionally, wound contraction and re-epithelialization were observed in the scar tissue (Fig. 6j–6m).

Methods
Ethical statement
All animal experiments were approved by the Animal Research Committee of Xinhua Hospital, Shanghai Jiaotong University School of Medicine (Ethical Approval No. [XHEC-F-2024-064]). All experiments involving human participants were approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Ethical Approval No. [XHEC-C-2024-145-1]). All human participants provided written informed consents, no compensation was provided.

N-GQDs fabrication
N-GQDs were prepared through hydrothermal reaction using m-phenylenediamine (m-PD, 99.5%, Aladdin Co., Ltd.) and pyrocatechol (Py, 99.5%, Aladdin Co., Ltd.). The detailed synthesis parameter of the hydrothermal treatment can be found in Supplementary Data 1. The reaction solution was filtered through a 0.02 µm alumina nano-porous membrane after the reaction was completed. The filtrate was then dialyzed using a 100–500 g mol–1 dialysis bag for 10 days. The dialysate was concentrated by rotary evaporation. When powder N-GQDs were needed, the concentrated dialysate was subjected to freeze-drying. The information of N-GQDs synthesis and characterization can be found in Supplementary Data 1.

C3N QDs fabrication
2,3-Diaminophenazine (DAP, 98%) was purchased from J&K Chemical Technology Co., Ltd (USA). 80.0 mL, 2.0 mmol L–1 of DAP aqueous solution was added to a 100 mL Hastelloy autoclave, heated and kept at 260 °C for 16 h. The product was filtered by a 0.02 µm alumina nano-porous membrane. Next, the obtained filtrate was dialyzed in a 3500 g mol–1 dialysis bag against deionized (DI) water for times to remove any unreacted molecules. The obtained reaction liquid can be directly used for biological experiments.

NMR relaxometry measurement
The T1 of sample was measured using a 0.5 T NMR relaxometry using inversion recovery (IR) sequence with the following parameters: spectrometer frequency = 21 MHz, π/2 pulse length = 6.52 μs, π pulse length = 13.04 μs, spectral width = 250 kHz, receiver gain 1 = 20, digital receiver gain 1 = 2, preamplifier gain = 3, time wait = 20000 ms, number of inversion times = 20, number of scans = 4. For T1 measurement with light induction, a 365 nm light source with an intensity of 80 mW cm–2 was applied to the sample.

Characterization techniques
The transmission electron microscopy (TEM) and high-resolution TEM were captured using JEM-ARM300F (JEOL Ltd.) operating at a voltage of 80 kV. The X-ray photoelectron spectroscopy (XPS) spectra were obtained with an Escalab 250XIi (Thermo Fisher Scientific Inc.). Ultraviolet-visible spectroscopy (UV-Vis, Cary 100 UV-Vis, Agilent Technologies, Inc.) was used to probe the UV-Vis absorbance of C3N QDs.

The dataset, derived from Radical_CAnalysis.xlsx, was preprocessed by selecting chemical features including –NH2, –NH, N–(C)3, OH, –CHO, –COOH, and size to predict the target variable r. To ensure the models mimic real-world predictive applications and strictly avoid data leakage, the data was randomly partitioned into independent training and testing sets prior to any model training or hyperparameter optimization. Predictive performance for the tree-based ensemble models, comprising Random Forest, XGBoost, and gradient boosting decision trees (GBDT), was evaluated using R2 and root mean square error score (RMSE) metrics. Furthermore, model interpretability was rigorously validated through SHAP analysis, Gini-based feature importance rankings, and individual decision tree visualizations to confirm that the underlying algorithmic logic aligns with chemical descriptors.

Machine learning methods
All the proposed methods were implemented using Jupyter Notebook 6.5.7 in Python 3.11.7 on a workstation with Intel® CoreTM i7 10700 CPU @ 2.90 GHz with 16 GB RAM and an NVIDIA GeForce RTX 3060 GPU with 12 GB memory.

Pearson correlation coefficient matrix calculation. The Pearson correlation coefficient measures the strength and direction of a linear relationship between two variables, X and Y. The coefficient value (r) ranges from –1 (perfect negative correlation) to +1 (perfect positive correlation). If 𝑟 is close to 1 or –1, it suggests a strong linear relationship, while values near 0 indicate a weak or no linear relationship. The Pearson correlation matrix is obtained by computing pairwise correlations between all features in a dataset, providing insights into their interrelationships and dependencies:
𝑟 = ∑𝑛𝑖=1(𝑥𝑖−𝑥̅)(𝑦𝑖−𝑦̅) / √∑𝑛𝑖=1(𝑥𝑖−𝑥̅)2 ∙∑𝑛𝑖=1(𝑦𝑖−𝑦̅)2 (1)
where 𝑥𝑖 and 𝑦𝑖 are the individual sample values of X and Y; 𝑥̅ and 𝑦̅ are the mean values of X and Y, respectively; and n is the number of data points in the dataset.

Principal component analysis. Principal component analysis (PCA) is a dimensionality reduction technique that transforms a high-dimensional dataset into a lower-dimensional space while preserving as much variance (information) as possible. Thus, PCA reduces the number of features while maintaining the essential structure of the data, facilitating easier visualization, interpretation, and further analysis. It works by identifying the directions (principal components) along which the data varies the most, then projecting the data onto these directions to reduce its dimensionality. The steps involved in PCA are:
(i) Covariance matrix calculation
The covariance matrix C represents how the variables in the dataset vary with respect to one another. For a dataset with features X1, X2, … , Xp, the covariance matrix is computed as:
C = 1/(n−1) (𝑋−𝑋̅)𝑇(𝑋−𝑋̅) (2)
where X is the data matrix (each column is a feature, and each row is a sample), 𝑋̅ is the mean-centered data (subtract the mean of each feature), n is the number of data samples.
(ii) Eigenvalues and eigenvectors
PCA calculates the eigenvectors and eigenvalues of the covariance matrix. The eigenvectors (principal components) represent the directions of maximum variance, and the eigenvalues indicate the magnitude of variance along those directions.
𝐶𝑣 = 𝜆𝑣 (3)
where v is an eigenvector (principal component), and 𝜆 is the corresponding eigenvalue.
(iii) Projection onto principal components
Once the eigenvectors are obtained, the data is projected onto the space defined by the first few eigenvectors, corresponding to the largest eigenvalues. This reduces the dimensionality of the data while retaining the maximum variance.
Z = X∙W (4)
where Z is the transformed data, and W is the matrix of eigenvectors (principal components).
Thus, PCA reduces the number of features while maintaining the essential structure of the data, facilitating easier visualization, interpretation, and further analysis.

Gradient boosting decision tree (GBDT). GBDT is an ensemble learning method that builds a strong predictive model by combining the outputs of multiple weak learners—typically shallow decision trees46. It employs a gradient descent approach in function space to optimize the model, particularly powerful for handling complex, nonlinear relationships in structured data 47. The steps involved in GBDT are:
(i) Model initialization with a constant prediction:
𝐹(𝑥) = argmin𝛾 ∑𝑛𝑖=1 𝐿(𝑦𝑖,𝛾) (5)
where 𝐹(𝑥) is the function that the goal is to learn, y is the true label, and x is the feature vector.
(ii) Iterative training (for m=1, 2, ..., M):
First, compute the negative gradients (residuals: 𝑟𝑖𝑚) for each sample:
𝑟𝑖𝑚 = −[𝜕𝐿(𝑦𝑖,𝐹(𝑥𝑖)) / 𝜕𝐹(𝑥𝑖)] 𝐹(𝑥)=𝐹𝑚−1 (𝑥) (6)
Second, for each leaf node 𝑅𝑗𝑚 (for j=1, ..., 𝐽𝑚), compute the optimal output value:
𝛾𝑗𝑚 = argmin𝛾 ∑𝑥𝑖∈𝑅𝑗𝑚 𝐿(𝑦𝑖,𝐹𝑚−1 (𝑥𝑖)+𝛾) (7)
Third, update the model by adding the new tree’s contribution:
𝐹𝑚 (𝑥) = 𝐹𝑚−1 (𝑥)+𝜂∑𝐽𝑚𝑗=1 𝛾𝑗𝑚 ⋅1{𝑥∈𝑅𝑗𝑚} (8)
where 𝜂 ∈ (0,1] is the learning rate controlling step size.
(iii) Loss functions:
For regression, mean squared error (MSE) is commonly used:
𝐿(𝑦,𝐹(𝑥)) = 1/2 (𝑦−𝐹(𝑥))2 (9)
For binary classification, logistic loss is frequently applied:
𝐿(𝑦,𝐹(𝑥)) = log(1+𝑒−2𝑦𝐹(𝑥)) (10)
The choice of loss function defines the direction of the gradient and affects how residuals are computed at each iteration.

Shapley additive explanations (SHAP). SHAP is an advanced model-agnostic interpretation framework based on cooperative game theory. It is designed to explain the output of any machine learning model by quantifying the contribution of each feature to an individual prediction 48. SHAP combines the consistency and local accuracy of additive feature attribution methods with the theoretical soundness of Shapley values from game theory.
(i) Mathematical definition:
Given a model f, an input instance x = {𝑥1, 𝑥2, ..., 𝑥𝑛}, and the set of all features N, the SHAP value 𝜙𝑖 for feature i is defined as:
𝜙𝑖 (𝑓,𝑥) = ∑𝑆⊆𝑁∖{𝑖} |𝑆|!(|𝑁|−|𝑆|−1)! / |𝑁|! [𝑓𝑆⋃{𝑖} (𝑥𝑆⋃{𝑖})−𝑓𝑆 (𝑥𝑆)] (11)
where S is a subset of the feature set excluding i, 𝑓𝑆 (𝑥𝑆) is the expected model output using only features in set S. The factorial term is a weighting factor ensuring fair contribution across all permutations.
(ii) Additivity and accuracy:
The sum of all feature contributions equals the difference between the prediction and the average model output:
𝑓(𝑥) = 𝐸[𝑓(𝑥)]+∑𝑛𝑖=1 𝜙𝑖 (12)

Cell culture conditions
All cell lines were cultured at 37 °C in a humidified 5% CO2 incubator. HUVEC (human umbilical vein endothelial cells) and primary lung fibroblasts (PLF, isolated from male rat lung tissues) were maintained in DMEM (Gibco, USA; Cat. No. C11995500BT) supplemented with 10% fetal bovine serum (FBS; Gibco, USA) and 1% penicillin/streptomycin (P/S; Gibco, USA). Primary aortic endothelial cells (PAEC) were isolated from male rat aortic tissues and cultured in ECM medium (ScienCell, USA) with 20% FBS and 1% P/S. HK-2 cells (human renal proximal tubular epithelial cells) were grown in DMEM/F12 medium (Gibco, USA; Cat. No. C11330500BT) containing 10% FBS and 1% P/S. 3T3 (mouse embryonic fibroblasts), bEND3 (mouse brain endothelial cells), and MRC-5 (human lung fibroblasts) were propagated in standard DMEM (Gibco, USA) with 10% FBS and 1% P/S. A549 cells (Adenocarcinoma human alveolar basal epithelial cells) were cultured in F-12K medium (Gibco, USA) supplemented with 10% FBS and 1% P/S. All media were refreshed every 2–3 days, and cells were passaged using 0.25% trypsin-EDTA (Gibco, USA) upon reaching 80%–90% confluence. HUVEC (SCSP-5535), bEND3 (SCSP-5267), A549 (SCSP-503), HK-2 (SCSP-511), 3T3 (SCSP-5038), and MRC-5 (SCSP-5040) were obtained from the Cell Bank of the Chinese Academy of Sciences.

Establishment of a cellular senescence model and identification of senescent cells
Hydrogen peroxide-induced cellular senescence model. The hydrogen peroxide-induced cellular senescence model was established by treating cultured cells with hydrogen peroxide (H2O2). Briefly, cells were cultured to 70%–80% confluence and then exposed to H2O2 (50 µmol L–1) for 4 h in phosphate-buffered saline (PBS). Following treatment, cells were washed with PBS to remove excess H2O2 and allowed to recover in fresh medium for 72 h.

Etoposide-induced cellular senescence model. The etoposide-induced cellular senescence model was established by treating cultured cells with etoposide, a chemotherapeutic agent known to induce DNA damage and promote cellular senescence. Cells were plated at 70%–80% confluence and exposed to etoposide 2.5 µmol L–1 for 48 h in complete growth medium.

Replicative senescence model. To establish replicative senescence, cells were cultured in complete growth medium at 37°C with 5% CO₂ and serially passaged at a 1:4 split ratio upon reaching 80%–90% confluency. Proliferative capacity was monitored by calculating population doublings (PDs) using the formula:
PD = log(Final cell number / Initial cell number) / log2 (13)
Senescence onset was defined as a sustained < 0.5 PDs per passage for three consecutive passages.

Methods for senescent cell identification. Cellular senescence was verified using senescence-associated β-galactosidase (SA-β-gal) staining, with senescent cells showing prominent blue staining under light microscopy. Additionally, changes in cell morphology, such as increased size and flattening, were observed. The expression of senescence markers, including p16, p21, and Senescence-Associated Secretory Phenotype (SASP), was further assessed by qPCR to confirm the senescent phenotype. Core SASP factors, including IL-1β, IL-6, CXCL1, CXCL14, CCL2, MMP-2, and MMP-9, were prioritized for their universal association with senescence-related inflammation and extracellular matrix remodeling.

Gene and protein expression analysis
qPCR-based gene expression analysis. Total RNA was extracted from cultured cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), and cDNA was synthesized using a first-strand cDNA synthesis kit (Takara PrimeScript RT Master Mix, Takara Bio, Otsu, Japan) according to the manufacturer’s protocol. Gene expression was quantified using real-time PCR with Hieff qPCR SYBR Green Master Mix (Yeason, Shanghai, Japan). Relative gene expression levels were calculated using the 2^−ΔΔCt method with GAPDH as the internal control. All primers were designed based on sequences retrieved from the NCBI Gene database and PrimerBank, with specificity verified using NCBI BLAST. Primers were synthesized by Shanghai Biotechnology Co., Ltd., and the sequences are listed in Supplementary Tables 1 and 2.

Protein extraction and western blotting. Total proteins were extracted from cells using RIPA lysis buffer containing protease and phosphatase inhibitors (Beyotime, China). Protein concentrations were determined using a BCA Protein Assay Kit. Equal amounts of protein (20 μg) were loaded on 12% SDS-PAGE, separated at 100 V for 90 min, and then transferred onto 0.22 μm PVDF membranes (Millipore, USA) at 100 V for 60 min. The membranes were blocked with QuickBlock sealing solution (Beyotime, China) for 15 min at room temperature. Subsequently, membranes were incubated with primary antibodies (details and dilutions listed in Supplementary Table 3) overnight at 4°C. After washing with TBST (10 min, three times), the membranes were incubated with HRP-conjugated secondary antibodies (Supplementary Table 3) for 1 h at room temperature. Protein bands were visualized using an ECL chemiluminescence detection system (Bio-Rad, USA) and quantified using ImageJ software. GAPDH was used as the internal loading control.

Co-cultured systems of normal and senescent HUVECs. Fluorescence images of a co-culture system comprising normal and senescent HUVECs. Senescent cells were pre-transfected with RFP prior to mixing with normal cells, and then the co-culture system was incubated with 0.5 μg mL⁻¹ C3N QDs for 72 h in 24-well plates.

Proliferation assays
Cell proliferation was assessed using two commonly employed methods: EdU (5-ethynyl-2’-deoxyuridine) labeling and CCK-8 (Cell Counting Kit-8) assay.

EdU assay. HUVECs were exposed to 10 μmol L–1 5-ethynyl-2’-deoxyuridine (EdU; Beyotime, Shanghai, China) for 2 h at 37 °C. Next, they were fixed, permeabilized, washed, and treated with 1× Apollo reaction cocktail for 30 min. Afterward, they were stained with DAPI (Beyotime, Shanghai, China) for 30 min. The HUVECs’ proliferation (ratio of EdU+ to all cells) was analyzed using images of randomly selected fields obtained on the fluorescence microscope (Olympus, Japan).

CCK-8 assay. Cells were plated in 96-well plates at a density of 5000 cells per well. After the indicated treatments for 24 h, 10 μL of CCK-8 reagent was added to each well and incubated for an additional 4 h at 37°C. The absorbance at 450 nm was then measured using a microplate reader. The relative cell viability was calculated as follows: (ODtreated – ODblank) / (ODcontrol – ODblank), where ODtreated, ODcontrol, and ODblank represent the absorbance of the treated samples, the untreated control samples, and the medium-only blank samples, respectively. For data analysis, the relative cell viability of the control group was set as 1.

NAD+ level detection
NAD+ levels were quantified using the NAD+/NADH Assay Kit (WST-8, Beyotime). Briefly, cells were plated at a density of 1 × 106 cells per well in 96-well plates. After appropriate treatment, 200 μL of lysis buffer was added to each well. Subsequently, 20 μL of the cell supernatant was mixed with 90 μL of alcohol dehydrogenase working solution and incubated at 37°C, shielded from light, for 10 min. Following this, 10 μL of chromogenic solution was added to each well, and the reaction was further incubated at 37 °C for 30 min in the dark. During this incubation, an orange-yellow formazan product was formed, and the absorbance was measured at 450 nm to determine NAD+ levels.

Subcellular localization of C3N QDs by fluorescence microscopy
To evaluate the co-localization of C3N QDs with intracellular organelles, HUVECs were incubated with C3N QDs (0.5 μg mL⁻¹) for 12 h. After washing with PBS, cells were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and blocked with 5% BSA. Subcellular localization was analyzed through immunofluorescence staining as follows: Golgi apparatus: Labeled with rabbit anti-RCAS1 primary antibody (1:1000). Endoplasmic reticulum (ER): Stained with mouse anti-PDI primary antibody (1:400). Mitochondria: Targeted with rabbit anti-TOM20 primary antibody (1:200). Cytoskeleton: Actin filaments were labeled with Actin-Tracker Red-555 for 30 min. Nucleus: Counterstained with DAPI. Confocal imaging was performed using the GFP channel (488 nm excitation) for C3N QDs and the RFP channel (594 nm excitation) for organelle markers. Co-localization analysis was quantified using Pearson’s correlation coefficient in ImageJ.

Fluorescence intensity
Fluorescence intensity of C3N QDs in cell homogenates after incubation for different time points. Two groups of cells, normal and senescent, were cultured separately and incubated with C3N QDs (0.5 μg mL–1) for varying durations (1, 3, 6, 12, 24, and 48 hours). After the specified incubation periods, the cells were washed with PBS and counted to ensure equal cell concentrations. The cells were then homogenized, and the fluorescence intensity of C3N QDs in the homogenates was measured to evaluate the uptake and distribution of the nanoparticles in both normal and senescent cells at each time point. All units displayed as a. u. represent arbitrary units in this work.

PL emission intensity in co-cultured normal and senescent HUVECs. Normal and senescent HUVECs were co-cultured in 96-well plates at 5%, 10%, and 15% senescence ratios (e.g., 5% senescent + 95% normal, 10% senescent + 90% normal, etc.), with a total density of 5,000 cells per well. The co-culture system was incubated with C3N QDs (0.5 μg mL⁻¹) for 72 hours, followed by three PBS washes to remove unbound probes. PL emission intensity was quantified to evaluate senescence-dependent signal dynamics. The measurements were taken using a fluorescence spectrophotometer, with an excitation wavelength of 461 nm and an emission wavelength of 542 nm.

Quantification of PL emission intensity in blood samples using C3N QDs. The PL emission intensity in blood samples was quantified by incubating the samples with C3N QDs at a concentration of 5 μg mL–1 for 24 hours. PL intensity was measured using a microplate luminometer (LB 960, Berthold Technologies, Germany) configured with excitation and emission wavelengths of 461 nm and 542 nm, respectively.

Animal experiments
C57BL/6J male mice (species: Mus musculus) were obtained from Shanghai Jihui Laboratory Animal Care Co., Ltd. and housed at the Animal Facility of Xinhua Hospital. All male mice were 8 weeks old at the start of experiments and housed in a specific-pathogen-free (SPF) facility at Xinhua Hospital under a 12-h light/dark cycle with ad libitum access to standard chow and water. All procedures were performed to minimize animal suffering in compliance with the guidelines for the Care and Use of Laboratory Animals.

Doxorubicin-induced senescence model. Senescence was induced in 8-week-old C57BL/6 mice by intraperitoneal injection of doxorubicin (10 mg kg–1) once daily for two consecutive days, followed by a 3-day observation period. Successful senescence induction was confirmed through staining for established senescence markers, including β-galactosidase, p21, and γ-H2AX.

Induction of liver fibrosis model. Liver fibrosis was induced in male C57BL/6 mice by intraperitoneal injection of 40% carbon tetrachloride (CCl4) solution (prepared by mixing CCl4 with olive oil at 4:6 volume ratio) at a dose of 1.5 mL kg–1 body weight, twice weekly for 7 consecutive weeks. Control mice received vehicle treatment. Successful fibrosis induction was confirmed by histological examination through hematoxylin and eosin (HE) staining and Masson’s trichrome staining.

Induction of a diabetic mouse model. Type 1 diabetes was induced in male C57BL/6 mice by a single intraperitoneal injection of streptozotocin (STZ, 200 mg kg–1 body weight). Control mice received vehicle treatment. After 9 days, fasting blood glucose levels were measured, and mice with glucose levels ≥ 16.7 mmol L–1 (≥ 300 mg dL–1) were considered successfully diabetic. After successful diabetes induction, dorsal hair was removed by shaving and depilatory cream to expose the skin. A full-thickness excisional wound (5 mm diameter) was created using a sterile biopsy punch. Wound healing progression was monitored every 3 days by digital photography, and the wound area was quantified using ImageJ software to calculate the healing rate. The unhealed wound distance was measured using a calibrated scale ruler. On day 12 post-wounding (before complete healing), mice were sacrificed, and skin tissues were harvested for histological analysis. For collagen quantification, sections were stained with Masson. High-resolution images of multiple tissue sections were captured under consistent microscopic conditions and analyzed using ImageJ software to quantify collagen deposition.

Measurement of aortic pulse wave velocity (PWV)
The mice were anesthetized by inserting an anesthetic mask into their mouths and noses, and anesthesia was maintained with isoflurane at a concentration of 1%–2% and a flow rate of 0.6–1.0 L min–1, and the heart rate of the mice was maintained at 400–650 bpm. Waveforms were captured at the aortic arch using a Vevo 2100 high-resolution small animal ultrasound machine, and then immediately measured at the descending aorta 35 mm distal to the aortic arch, and the PWV of mice was calculated using the formula D (m) / t (sec) to calculate aortic PWV in mice.
All measurements were repeated three times for averaging, with doxorubicin-induced senescence models serving as positive controls.

Photodynamic therapy
HUVEC were co-incubated with C3N QDs at a concentration of 0.5 μg mL–1 for 3 h. Following the incubation, the cells were treated with 365 nm laser irradiation at a power density of 80 mW cm–2 for 15 min to induce photodynamic therapy (PDT). After treatment, senescence-related markers, including SASP, β-gal staining, and the expression levels of p16 and p21, were assessed to evaluate the efficacy of the PDT treatment.

Mice were induced by intraperitoneal injection of doxorubicin (10 mg kg–1), followed by intravenous administration of C3N QDs at a concentration of 0.5 μg mL–1 in a volume of 50 μL via the tail vein. After the administration, the aorta was subjected to 365 nm laser irradiation at a power density of 80 mW cm–2 for 15 min to perform PDT.

Immunofluorescence and C3N QDs direct fluorescence staining
Following euthanasia via sodium pentobarbital (70 mg kg–1, intraperitoneal) combined with isoflurane inhalation, murine aortas, heart, liver, spleen, lung, and kidney tissues were harvested. Then, tissues were fixed in 4 % paraformaldehyde (24 h at 4 °C), and paraffin-embedded using standard protocols. Serial sections (5 μm thickness) were prepared for fluorescence-based analysis.

Antibody-based immunofluorescence staining. Standard immunofluorescence procedures were performed strictly according to the manufacturers’ instructions for specific protein markers. Details regarding the primary and secondary antibodies are summarized in Supplementary Table 3.

C3N QDs direct fluorescence staining. To assess the interaction of C3N QDs with tissues, an antibody-independent direct staining protocol was applied. Briefly, after deparaffinization and rehydration, tissue sections were blocked with 5% bovine serum albumin (BSA) for 30 min at room temperature. This blocking step was employed specifically to minimize the non-specific adsorption of the nanoparticles to the tissue matrix and the slide surface, thereby enhancing the signal-to-noise ratio. Subsequently, sections were incubated with C3N QDs (0.5 μg mL–1) for 30 min at room temperature, followed by three gentle washes with PBS to remove unbound QDs.

Image acquisition and quantification. All fluorescence images were acquired using an Olympus microscope. To ensure comparability, identical exposure times and acquisition settings were maintained across all groups. Quantitative analysis of fluorescence intensity was performed using ImageJ.

RNA sequencing
RNA sequencing was performed by Annoroad Gene Technology Company (Beijing, China). Total RNA was extracted from cultured cells (n = 3 biological replicates per group) using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). mRNA was enriched using Oligo(dT) magnetic beads and fragmented prior to cDNA synthesis. Sequencing libraries were constructed following standard protocols and sequenced on an Illumina platform. Differential gene expression analysis was performed using the DESeq2 package, with genes showing an adjusted P value (Benjamini–Hochberg false discovery rate, FDR < 0.05) considered significantly differentially expressed. Heatmaps were generated to visualize expression patterns of differentially expressed genes. Gene Ontology (GO) enrichment analysis was conducted using the clusterProfiler package, with terms showing P < 0.05 considered significantly enriched.

Human sample collection
Peripheral venous blood was collected from 90 hospitalized non-surgical patients using standard venipuncture procedures. Tissue samples (liver, kidney, aorta, and skin) were obtained from independent individuals during clinically indicated surgical procedures. Participants were stratified into two predefined age groups: young (20–45 years) and elderly (≥60 years). For each tissue type, samples were collected from 20 individuals per age group. Aggregated demographic characteristics, including age range, mean age, sex distribution, and sample numbers for each cohort, are provided in Supplementary Table 5. Although sex was recorded for all participants, no sex-stratified analyses were performed because the primary objective of the study was to investigate age-associated biological mechanisms.

Statistics and reproducibility
All statistical analyses were performed using Prism 10 (GraphPad Software Inc., USA). Data are presented as mean ± SD unless otherwise indicated. For comparisons between two groups, a two-tailed Student’s t-test was used. For comparisons between each experimental group and the control group, Dunnett’s multiple comparisons test was applied following one-way ANOVA. For comparisons among three or more groups, one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons post hoc test was applied. For factorial designs involving two independent variables, two-way ANOVA was performed, and simple main effects analysis was conducted to examine the effects of one factor at each level of the other factor. A P value < 0.05 was considered statistically significant. All experiments were repeated independently at least three times with consistent results. The exact number of biological replicates (n) for each experiment is indicated in the corresponding figure legends. Representative images are selected from these independent replicates. No statistical method was used to predetermine sample size. No data were excluded from the analyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript cannot be accepted for publication in any Journal. The quantum dots used for this study have not been characterized to any extent and there are huge flaws in the materials section. In addition, since no structural details are given for these CQDs, it is difficult to understand how the authors can claim the “structural evolution” in the title.\nIn general, the authors try to describe a strategy for the detection and treatment of senescent cells using quantum dots (C3N QDs). The authors used machine learning to define the structural properties of these carbon nanoparticles, specifically designed to have photodynamic activity. The paper explains how the QDs selectively interact with cellular environments based on NAD+ concentration, allowing for fluorescent detection of senescent cells (which have lower NAD+ levels) and targeted elimination through photodynamic therapy. The approach used is validated in vitro and in animal studies, demonstrating some potential for addressing aging-related pathologies. In addition, the interactions between C3N QDs and NAD+ have been studied using density functional theory (DFT) calculations, but there is nothing in the manuscript about how these calculations were performed.\nUnfortunately, this work presents several inconsistencies, mainly concerning the material preparation and characterization. I believe that for the publication in any journal, several points should be carefully addressed and solved before considering the work.\nIn general the text is quite confusing and not scientifically acceptable. Other examples of nanodots or particles for the same scopes should be reported, to better describe the framework of interest for the materials prepared. Furthermore, the verb “decline” is used several times in the text in the sense of “decrease”. I don’t find it fitting the scientific context so I suggest substituting it with another verb. There are several assumptions not justified by the experiments.\nHere is a list of aspects to be answered.\n1) The synthesis reported in the supporting information does not match the one described in the text. Are the dots obtained from m-phenylenediamine and pyrocatechol or from 2,3-Diaminophenazine? Is the reaction done in autoclave? Have the nanoparticles been purified? These aspects should be corrected and clarified. Without the right description of the synthetic procedure, the characterizations reported lose some of their validity.\n2) The authors should explain better the reason behind the choice of the two precursors. Assuming that m-phenylenediamine and pyrocatechol have been selected, as stated in the main text, how the two molecules are supposed to react with each other to form the carbon quantum dots? If the reactivity of the reagents is considered, it does not seem that they are chemically compatible (an aromatic amine and a phenolic compound). Is there any specific reason for the choice of those precursors? The nanoparticles synthesis should be better described to highlight the application sections.\n3) As reported previously, (DOI: 10.1021/acs.jpcc.0c06996) m-phenylenediamine, and its structural isomers, are known for giving fluorescent side products that alter the emission properties of the final dots, ending up in imaginative results. In many cases, the use of m-phenylenediamine affords green emitting mixtures of low fluorescent carbonaceous particles and green emitting molecules. The emitting species absorb in the UV region of the spectra, close to 250 – 280 nm. Similar absorption band can be seen in the UV-spectra of the nanomaterials reported by the authors in Supplementary Figure 4. This aspect has strong implications in the reliability of the materials obtained. The authors should validate their material, by checking its composition, describe the right synthetic procedure and validate the absence of fluorescent impurities that may rule the nanomaterials fluorescence (or general properties). I suggest at least carrying out mass spectroscopy experiments, to evidence any molecular impurities.\n4) In Supplementary Fig. 2 only one particle is shown for the HR-TEM. Anyway, the analysis of only one particle is not enough to define the overall crystallinity of the materials. Furthermore, crystallinity may arise from experimental conditions, such as the exposure of the nanoparticles to high voltage beam during the experiments. In other word, the crystallinity of only one particle cannot reflect the features of the bulk material. HR-TEM should be implemented with images of other nanoparticles and the morphological data should be supported with X-Ray diffraction on powder to confirm the nano- and bulk crystalline feature.\n5) The purification of dots is a current topic in the scientific community (https://doi.org/10.1002/smll.202206714; 10.1039/C9NR01029G). Nuclear magnetic resonance spectroscopy is a helpful technique to assess the purity and composition of the dots. In particular, molecular impurities appear in the spectrum as sharp peaks, peaks, while the protons of the nanoparticles give broad and enlarged signals (DOI: 10.1002/anie.202200038). Before any applications, the nanomaterials should be characterized by means of proton NMR spectroscopy and DOSY analysis to precisely describe their composition and the water diffusion coefficient (that should be lower than that of the precursors or small molecules in general) comparing the results with those of the precursors. The materials used for biological experiments should be exhaustively characterized. This is a golden rule in science.\n6) Fluorescence is exploited for the quantum dots activity in vitro and in vivo. Anyway, only the UV-vis absorption spectrum is shown in the text, and no emission/fluorescence spectra are reported. Is it well known that carbon quantum dots should be characterized by a wavelength dependent fluorescence, otherwise the presence of fluorescent molecular impurities can be inferred. The authors should address this aspect.\n7) For the photodynamic characterization, a laser emitting at 365 nm is selected. Anyway, in clinical applications- or biomedical publications- lasers at lower wavelength are used, since high energy sources can be cause of damage. This decreases a lot the interest for these materials in PDT.", "reviewer_2": "This manuscript entitled “Structural evolution of carbon frameworks realizes in vitro interfacial transport in metabolically reprogrammed senescent cells for senolysis”, reported that nitrogen doping in graphene quantum dots (GQDs) is a factor determining their photodynamic properties, a conclusion supported by a machine learning model. The authors extend this finding to discuss the related photodynamic characteristics of C3N quantum dots (QDs). A key finding is that the fluorescence of C3N QDs taken up by cells changes depending on the intracellular NAD+ concentration, enabling the distinction between normal and senescent cells. Furthermore, the study challenged to reveals that subsequent laser irradiation endows these C3N QDs with senolytic activity.\nWhile C3N QDs show potential as a senolysis, the in vivo experiments presented in Figure 6 lack a comparison between healthy controls and the senescence model. Additionally, the assessment of cellular senescence in the in vivo context is insufficient. Therefore, the establishment and validation of the cellular senescence models used throughout the manuscript require careful verification.\nRegarding the diabetic ulcer model, the specific mouse model used is not described, and it is unclear whether the model was properly established. The lack of appropriate healthy controls precludes interpretation of the results of this study. Finally, the description of the cell lines used in various experiments is often unclear, making it difficult to assess the consistency and coherence of the experiments.\nDetailed points regarding necessary revisions and additional data are listed below.\nMajor Comments\n1. Senescent cells may undergo apoptosis or necrosis due to prolonged laser irradiation, independent of any photodynamic activity. Please provide data on the biosafety of laser irradiation alone on senescent cells to rule out this possibility. While the in vivo data in Figure 6 suggests that irradiation alone does not reduce senescent cells, specific data for the in vitro model in Figure 5a is crucial to confirm this.\n2. In figure 6 and related supplementary figures, the \"Control\" group in these figures appears to be the senescence-induced mouse model. However, data from a healthy, age-matched control group is missing. The authors must show that senescence markers (e.g., β-gal staining, p16/p21 expression) are significantly elevated in the senescence model compared to the healthy controls to validate the model's phenotype. Similarly, for the diabetic mouse model, data demonstrating delayed wound healing compared to healthy control mice is required to establish the model's validity.\n3. Most of the experiment rely on experiments using HUVECs. The induction of senescence in these cells should be confirmed at the protein level, in addition to the currently presented data.\nMinor Comments\n1. Figure 1e, In the top-left panel, the color-coding for the percentages (red and blue) appears to be reversed in the pie chart. Please check and correct it. In the left most graph of the third row, there are no data dots plotted. Please clarify if this is intentional or a plotting error.\n2. Figure 2d and Page 6, Lines 34-36, The figure includes shaded areas representing the NAD+ concentration ranges for \"Senescent cell\" and \"Healthy cell.\" However, the data supporting these specific ranges is not provided or cited. Please provide the evidence for these ranges.\n3. Figure 2h, Figure 3a-c, and Supplementary Figs. 7, 31, Please specify the cell types and the methods used to induce senescence for the experiments shown in these figures.\n4. Page 6, Line 15, The text states, \"Error bars in (g) and (i) indicate the SD of three repetitive tests.\" This appears to be a typo. Should \"(i)\" be \"(h)\"?\n5. Figure 3a, At the 48-hour time point, the PL intensity in the senescent cell homogenate is comparable to that of normal cells. Why does it not show the expected decrease? Is it possible that NAD+ was removed or diluted during the homogenization process, thereby eliminating its fluorescence quenching effect? Please clarify.\n6. Figure 4g, The methodology for measuring the PL intensity in serum samples (e.g., the concentration of C3N QDs added, incubation time) is not described. Please add these experimental details.\n7. Figure 5, The Methods section lacks details regarding the RNA-sequencing experiment. Please provide a comprehensive description of the library preparation, sequencing, and data analysis pipelines.\n8. Page 13, Lines 28 & 30, The text provides numerical values for changes in fluorescence intensity and cell proliferation. Please specify which cell line these numbers refer to.\n9. Page 13, Lines 30 & 32, The figure citations appear to be incorrect. Should \"Fig. 5d–g\" be \"Fig. 5d–f\" and \"Fig. 5h and i\" be \"Fig. 5g and h\"? Please verify.\n10. Terminology on Pages 13-14, The term \"aging marker\" is used frequently. In the context of induced senescence, \"senescence marker\" would be a more precise and appropriate term. Please consider revising the terminology.\n11. Figure 6, The manuscript lacks crucial details about the in vivo experimental procedures. Please add a thorough description of the methods, including: how the aged/senescent mice were generated, the parameters for laser irradiation (e.g., area, duration), the strain of diabetic mice used, the procedure for wound creation, and the exact timing of sacrifice.\n12. Figure 6, The mouse model used in Figure 6 appears to be a model of induced senescence (e.g., doxorubicin-induced) rather than a model of natural aging. Please revise the terminology throughout the manuscript for accuracy.\n13. Page 15, Lines 21 & 23, The term \"swelling\" is used to describe the wound. Please clarify what this refers to. Does it mean inflammation or the formation of granulation tissue?\n14. Figure 6k and 6m, Please provide a description of the methods used for the quantification of the unhealed wound distance and collagen content.\n15. Page 15, Line 29, The manuscript claims, “Additionally, the dermis layer and hair follicles rapidly returned to normal.” This is a strong statement, as it is generally understood that severe dermal injury leads to scar formation, which replaces normal dermal structures, including hair follicles. If true tissue regeneration with de novo hair follicle formation occurred, this is a significant finding that requires substantial evidence. Please provide the evidence supporting this claim. Otherwise, please revise the statement to more accurately describe the observed histology, which likely involves wound contraction and re-epithelialization over a scar.\n16. Supplementary Figures 7, 10-14, 16, 30, 32, 36, Please state the number of biological samples (n) or experimental replicates for each experiment shown in these figures.\n17. Supplementary Figure 14, The figure legend lists series of percentages: \"(a) 98.2%, 81.4%, 70.5%, 63.4%, 51.0%. (b) 99.8%, 93.5%, 91.0%, 81.4%, 79.6%.\" Please clarify what these values represent. Also, please specify if different cell types were used for panels (a) and (b).\n18. Supplementary Figure 32, The label \"A549\" appears to be missing from the top of the corresponding graph. Please correct this."}, "author_response": "Thank you for your overall evaluation.\nWe highly respect the numerous insights you have provided on our manuscript and value your suggestions. However, we feel regrettable that we are unable to fully agree with all of your comments. We believe that some of the misconceptions regarding our paper may stem from a confusion between the synthesis methods of N-GQDs and C N QDs. At the same time, we appreciate the many constructive comments you have raised, which have allowed us to further improve our manuscript during the revision process. Below is our detailed response to your general evaluation.\nFirst and foremost, it is crucial to emphasize that the core reason for your negative assessment of our work lies in the confusion between the preparation methods of N-GQDs and C N QDs. Our study is based on the discovery of the photodynamic activity of N-GQDs. Using machine learning, we guided the efficient structural evolution of N-GQDs, and the results suggested that N-GQDs with ordered atomic arrangement (C N QDs) represent the optimal structure. Therefore, this work involves two types of GQDs, and there is no inconsistency between the synthesis method reported in the Supplementary Information and the description in the main text.\n1. N-GQDs\nThese N-GQDs were synthesized via a hydrothermal method using m-phenylenediamine and pyrocatechol as precursors. By varying reaction time, temperature, and precursor ratios, we modulated the doping concentration and configuration of nitrogen atoms.\n2. C N QDs\nC N QDs were prepared using 2,3-diaminophenazine, as described in section 1.2 of the Supplementary Information. The synthesis of C N QDs and the modulation of larger C N structures can be found in our previous reports [Nat. Electron. 4, 486–494 (2021); Adv. Mater. 29, 1605625 (2017)].\n1. N and O content based on XPS\n2. Functional group ratios from XPS analysis\n3. Average particle size from TEM imaging of no less than 100 N-GQDs particles\n4. Retention factor (R) values from thin-layer chromatography\nf\n5. Major molecular ion peaks (m/z) in MALDI-TOF mass spectrometry\n6. Singlet oxygen (1O ) generation rate (r)\nIn summary, the reviewer’s opinion that this work is scientifically unacceptable and contains unverified assumptions does not align with the facts. Meanwhile, we have made further revisions to the manuscript in response to the reviewer’s other constructive comments. Detailed point-by-point responses are provided below.\nReviewer #1 Comment 1: The synthesis reported in the supporting information does not match the one described in the text. Are the dots obtained from m-phenylenediamine and pyrocatechol or from 2,3-Diaminophenazine? Is the reaction done in autoclave? Have the nanoparticles been purified? These aspects should be corrected and clarified. Without the right description of the synthetic procedure, the characterizations reported lose some of their validity.\nResponse: Thank the reviewer for pointing out the insufficient experimental procedures in this work. We have provided detailed experimental information, including the precursors, synthesis, and purification of nitrogen-doped graphene quantum dots (N-GQDs) and C N quantum dots (QDs).\nThis study is built upon the discovery of the photodynamic activity exhibited by N-GQDs. Leveraging machine learning techniques, we systematically guided the structural optimization of N-GQDs. The computational analysis ultimately identified that ordered nitrogen doping in GQDs, specifically C N QDs, yielded the most favorable performance. Consequently, this work involves two distinct types of GQDs, and there is full consistency between the synthetic procedures detailed in the supporting information and the descriptions provided in the main text. The N-GQDs with different structures were obtained through hydrothermal synthesis using m-phenylenediamine (m-PD) and pyrocatechol (Py), which describes in section 1.1 of the Supplementary Information. We modulated the doping amount and form of N atoms by varying the reaction time, temperature, and precursor ratio during synthesis. C N QDs were prepared from 2,3-diaminophenazin, and the preparation method is described in the section 1.2 of the Supplementary Information. The modulation of C N QDs and large-sized C N structures has been reported previously by us [Nat. Electron. 4, 486–494 (2021); Adv. Mater. 29, 1605625 (2017)].\nMeanwhile, we fully understand the concerns raised by Reviewer 1 regarding the characterization results of N-GQDs. We believe these concerns are justified. Therefore, we have systematically supplemented the detailed preparation information of N-GQDs and the related characterization results in the revised Supplementary Information (Supplementary Table 1).\nFor the preparation of N-GQDs, the typical method is the hydrothermal reaction using m-phenylenediamine and pyrocatechol. The reaction solution was filtered through a 0.02 µm alumina nano-porous membrane after the reaction was completed. The filtrate was then dialyzed using a 100–500 g mol–1 dialysis bag for 10 days. The dialysate was concentrated by rotary evaporation. When powder N-GQDs were needed, the concentrated dialysate was subjected to freeze-drying. To confirm that the purified N-GQDs do not contain other small molecules or oligomers, we used thin-layer chromatography to confirm the purity of the product. No other impurities were found in the purified samples based on the thin-layer chromatography results.\nIt is worth noting that by adjusting the ratio of m-phenylenediamine and pyrocatechol, their concentrations, reaction time, and reaction temperature, the size and groups of N-GQDs can be modulated. The N-GQDs used for machine learning in Fig. 1 were prepared by modulating the above reaction conditions. Supplementary Table 1 lists the N-GQDs in Fig. 1, as well as the preparation conditions and characterization results for the N-GQDs used for machine learning. The characterization results include N and O contents based on XPS, group proportion analysis based on XPS, the average particle size of at least 100 of N-GQDs particles under TEM, the R value (Retention factor value) from thin-layer chromatography, the position of the main molecular ion peak (m/z) in MALDI-TOF mass spectrometry, and the 1O generating rate (r).\nRevision: Supplementary Table 1 in revised Supplementary Information\n[...Table 1 omitted for brevity...]\nReviewer #1 Comment 2: The authors should explain better the reason behind the choice of the two precursors. Assuming that m-phenylenediamine and pyrocatechol have been selected, as stated in the main text, how the two molecules are supposed to react with each other to form the carbon quantum dots? If the reactivity of the reagents is considered, it does not seem that they are chemically compatible (an aromatic amine and a phenolic compound). Is there any specific reason for the choice of those precursors? The nanoparticles synthesis should be better described to highlight the application sections.\nResponse: Similar to the Reviewer #1 Comment 1, it is obvious that the Reviewer #1 misunderstood the preparation method of C N QDs. This led to the incorrect judgment. The preparation of C N QDs was obtained through hydrothermal synthesis from 2,3-diaminophenazine. Different structures of N-GQDs were prepared through hydrothermal synthesis from m-phenylenediamine and pyrocatechol, which are N-GQDs with disordered N atoms.\nThe preparation of C N from 2,3-diaminophenazine was first reported by us in 2017, and the detailed preparation mechanisms have been reported [Nat. Electron. 4, 486–494 (2021); Adv. Mater. 29, 1605625 (2017)].\nMeanwhile, we understand the reviewer’s concern regarding the feasibility of the reaction between aromatic amines and phenols. In organic chemistry research, the coupling of phenols and aromatic amines is an important topic. For the coupling process in constructing N-GQDs, various metal-based catalysts have been developed. In recent studies, the coupling of aromatic amines and phenolic compounds under metal-free catalysis has also been reported [J. Am. Chem. Soc. 144, 1144–1151 (2022)]. It is worth noting that in organic chemistry research, researchers focus on high-yield and structurally clear small molecule coupling products. For example, the direct coupling of phenol and aniline results in diphenylamine. This makes it necessary to avoid the oxidation polymerization of phenols and aromatic amines during the reaction process. In contrast, the process of preparing GQDs by bottom-up synthesis has a basic reaction mechanism of small molecule coupling and carbonization. In fact, during the reaction process, the oligomers formed by the oxidation of phenols (especially in the solvent heating environment with air present) and the oligomers formed by aromatic amines can undergo coupling reactions. More importantly, the quinone structure formed by the oligomers of phenols at high temperatures can further promote the occurrence of the coupling reaction. This process has been clearly demonstrated in previous studies [J. Polym. Res. 27, 295 (2020); Synth. Met. 192, 66 (2014); J. Solid. State. Electrochem. 19, 2653 (2015); Russ. J. Gen. Chem. 87, 145 (2017)]. Fig. R1 presents the reaction mechanism proposed in previous studies.\n[...Fig R1 omitted for brevity...]\nReviewer #1 Comment 3: As reported previously, (DOI: 10.1021/acs.jpcc.0c06996) m-phenylenediamine, and its structural isomers, are known for giving fluorescent side products that alter the emission properties of the final dots, ending up in imaginative results. In many cases, the use of m-phenylenediamine affords green emitting mixtures of low fluorescent carbonaceous particles and green emitting molecules. The emitting species absorb in the UV region of the spectra, close to 250 – 280 nm. Similar absorption band can be seen in the UV-spectra of the nanomaterials reported by the authors in Supplementary Figure 4. This aspect has strong implications in the reliability of the materials obtained. The authors should validate their material, by checking its composition, describe the right synthetic procedure and validate the absence of fluorescent impurities that may rule the nanomaterials fluorescence (or general properties). I suggest at least carrying out mass spectroscopy experiments, to evidence any molecular impurities.\nResponse: We understand the reviewer’s concerns. In fact, we have repeatedly emphasized in our previous works and reviews the potential possibility of small molecule interference during the preparation of GQDs [ACS Appl. Mater. Interfaces 10, 5750 (2018); Small 16, 2004621 (2020); Small 19, 2205957 (2023)]. At the same time, we have also conducted related work on the photoluminescence mechanism of GQDs [Adv. Mater. 29, 1605625 (2017); Adv. Mater. 36, 2313639 (2024); Adv. Funct. Mater. 34, 2401246 (2024)].\nIn this work, the GQDs used throughout are purified through processes including column chromatography and dialysis. Additionally, we have carried out mass spectrometry characterization of the products, confirming that all the quantum dots used in our work (including N-GQDs and C N QDs) do not contain other small molecule by-products. The relevant characterization can be found in the revised Supplementary Information (Supplementary Table 1).\nRevision: Supplementary Table 1 in revised Supplementary Information\n[...Table 1 omitted for brevity...]\nReviewer #1 Comment 4: In Supplementary Fig. 2 only one particle is shown for the HR-TEM. Anyway, the analysis of only one particle is not enough to define the overall crystallinity of the materials. Furthermore, crystallinity may arise from experimental conditions, such as the exposure of the nanoparticles to high voltage beam during the experiments. In other word, the crystallinity of only one particle cannot reflect the features of the bulk material. HR-TEM should be implemented with images of other nanoparticles and the morphological data should be supported with X-Ray diffraction on powder to confirm the nano- and bulk crystalline feature.\nResponse: As outlined in our responses to Reviewer #1 Comments 1–3, we have conducted comprehensive characterizations of both N-GQDs and C N QDs. These characterizations include the synthesis conditions and analytical data for N-GQDs presented in Fig. 1, as well as those for the N-GQDs samples used in machine learning (N-GQDs-1 to N-GQDs-99). The characterization data encompass nitrogen and oxygen content determined by XPS, functional group ratio analysis from XPS, average particle size based on TEM imaging of at least 100 individual N-GQD particles, R values (retention factor) obtained from thin-layer chromatography, the m/z position of the main molecular ion peak in MALDI-TOF mass spectrometry, and the singlet oxygen generation rate (r). In accordance with the reviewer’s recommendation, these characterization results have been incorporated into the revised Supplementary Information.\nWe have carefully considered the reviewer’s suggestion regarding the HR-TEM image in the Supplementary Information and have accordingly updated the figures. As shown in Supplementary Fig. 2, the revised TEM image now includes four high-resolution TEM (HR-TEM) images of N-GQDs. The imaging results clearly demonstrate that the N-GQDs are well-crystallized GQDs.\nRevision: Supplementary Fig. 2 revised supplementary information\n[...Fig 2 description omitted for brevity...]\nReviewer #1 Comment 5: The purification of dots is a current topic in the scientific community (https://doi.org/10.1002/smll.202206714; 10.1039/C9NR01029G). Nuclear magnetic resonance spectroscopy is a helpful technique to assess the purity and composition of the dots. In particular, molecular impurities appear in the spectrum as sharp peaks, while the protons of the nanoparticles give broad and enlarged signals (DOI: 10.1002/anie.202200038). Before any applications, the nanomaterials should be characterized by means of proton NMR spectroscopy and DOSY analysis to precisely describe their composition and the water diffusion coefficient (that should be lower than that of the precursors or small molecules in general) comparing the results with those of the precursors. The materials used for biological experiments should be exhaustively characterized. This is a golden rule in science.\nResponse: As outlined in our responses to Reviewer #1 Comments 1–4, we have conducted comprehensive characterizations of both N-GQDs and C N QDs. These characterizations include the synthesis conditions and analytical data for N-GQDs presented in Fig. 1, as well as those for the N-GQDs samples used in machine learning (N-GQDs-1 to N-GQDs-99). The characterization data encompass nitrogen and oxygen content determined by XPS, functional group ratio analysis from XPS, average particle size based on TEM imaging of at least 100 individual N-GQD particles, R values (retention factor) obtained from thin-layer chromatography, the m/z position of the main molecular ion peak in MALDI-TOF mass spectrometry, and the singlet oxygen generation rate (r). These characterization results are sufficient to prove that the product we prepared is N-GQDs rather than a polymer with fluorescence properties.\nOn the other hand, NMR is a commonly used method for characterizing small molecule organic compounds and simple nanomaterials. However, in this case, NMR is not applicable for the characterization of N-GQDs. As the reviewer mentioned in the comment, the NMR spectra of nanoparticles are usually broad. This does not provide much help in clarifying the chemical environment of H and C atoms. In fact, in previous studies, NMR has shown importance only in the characterization of GQDs based on precise organic synthesis.\nFinally, it should be emphasized that in all the preparation of N-GQDs, in order to confirm that the purified N-GQDs do not contain other small molecules or oligomers, we use thin-layer chromatography to confirm the purity of the product. The results of thin-layer chromatography did not find any other impurities in the purified samples.\nReviewer #1 Comment 6: Fluorescence is exploited for the quantum dots activity in vitro and in vivo. Anyway, only the UV-vis absorption spectrum is shown in the text, and no emission/fluorescence spectra are reported. Is it well known that carbon quantum dots should be characterized by a wavelength dependent fluorescence, otherwise the presence of fluorescent molecular impurities can be inferred. The authors should address this aspect.\nResponse: We understand the reviewer’s concern, but this concern is based on a misunderstanding of the QDs preparation in our work by the reviewer.\nThe reviewer mentioned that carbon dots or GQDs should have excitation wavelength-dependent properties and considered this property as the criterion for identifying carbon dots or GQDs. This is clearly incorrect. Excitation wavelength-dependent properties are characteristics of some carbon dots or GQDs, which are caused by their wide size distribution and rich surface fluorophores. There is still a lot of controversy over the mechanism of excitation wavelength dependence. This situation was well summarized in a review 9 years ago [Nanoscale 8, 7794–7807 (2016)].\nIt is worth noting that there are also carbon dots or GQDs that do not have excitation wavelength dependence. These QDs often have more defined surface structures and more uniform size distributions. Not only in our previous reports, but other researchers have also reported various carbon dots or GQDs that do not have excitation wavelength dependence. Here are some typical reports: Nat. Commun. 9, 2249 (2018); Adv. Mater. 29, 1605625 (2017); Sci. Adv. 6, eabb6772 (2020).\nFurthermore, as we did in our response to Comment 1-5, we provided a series of characterizations for N-GQDs, N-GQDs-1 to N-GQDs-99. These characterizations include the synthesis conditions and analytical data for N-GQDs presented in Fig. 1, as well as those for the N-GQDs samples used in machine learning (N-GQDs-1 to N-GQDs-99). The characterization data encompass nitrogen and oxygen content determined by XPS, functional group ratio analysis from XPS, average particle size based on TEM imaging of at least 100 individual N-GQD particles, R values (retention factor) obtained from thin-layer chromatography, the m/z position of the main molecular ion peak in MALDI-TOF mass spectrometry, and the singlet oxygen generation rate (r). These characterization results are sufficient to prove that the product we prepared is N-GQDs rather than a polymer with fluorescence properties.\nReviewer #1 Comment 7: For the photodynamic characterization, a laser emitting at 365 nm is selected. Anyway, in clinical applications- or biomedical publications- lasers at lower wavelength are used, since high energy sources can be cause of damage. This decreases a lot the interest for these materials in PDT.\nResponse: Our focus lies in the structural evolution driven by machine learning and the interface characteristics of carbon nanomaterials. Although the short excitation wavelengths present the issues the reviewer mentioned, this does not affect the main points of this paper. It is obviously feasible to achieve photodynamic therapy in the red or near-infrared wavelength range in subsequent work.\nResponses to Reviewer #2\nReviewer #2: This manuscript entitled “Structural evolution of carbon frameworks realizes in vitro interfacial transport in metabolically reprogrammed senescent cells for senolysis”, reported that nitrogen doping in graphene quantum dots (GQDs) is a factor determining their photodynamic properties, a conclusion supported by a machine learning model. The authors extend this finding to discuss the related photodynamic characteristics of C N quantum dots (QDs). A key finding is that the fluorescence of C N QDs taken up by cells changes depending on the intracellular NAD+ concentration, enabling the distinction between normal and senescent cells. Furthermore, the study challenged to reveals that subsequent laser irradiation endows these C N QDs with senolytic activity.\nWhile C N QDs show potential as a senolysis, the in vivo experiments presented in Figure 6 lack a comparison between healthy controls and the senescence model. Additionally, the assessment of cellular senescence in the in vivo context is insufficient. Therefore, the establishment and validation of the cellular senescence models used throughout the manuscript require careful verification.\nRegarding the diabetic ulcer model, the specific mouse model used is not described, and it is unclear whether the model was properly established. The lack of appropriate healthy controls precludes interpretation of the results of this study. Finally, the description of the cell lines used in various experiments is often unclear, making it difficult to assess the consistency and coherence of the experiments.\nDetailed points regarding necessary revisions and additional data are listed below.\nResponse: We feel sorry for raising your concerns during your inspection of the initial submission. We have meticulously reviewed and deliberated upon your valuable comments, addressing each comment accordingly. All modifications to the text in both the revised manuscript and the revised supplementary information are highlighted in red.\nWe thank the reviewer for this valuable suggestion to include a healthy control for the in vivo senescence model. In response, we have conducted additional comparative experiments between healthy and senescence model mice. As shown in Supplementary Fig. 45, the data now provide a clear comparison of the healing window, which strengthens the validation of our model and offers a crucial benchmark for the study.\nIn direct response to the reviewer’s comment regarding the validation of cellular senescence, we have supplemented the manuscript with additional characterization data. Please see Supplementary Fig. 35, which now includes results from SA-β-galactosidase staining and analysis of p16 and p21 expression. These data confirm the successful establishment of the senescence phenotype in our in vivo model.\nWe have now supplemented the Methods section (section 1.13.1 in supplementary information) of the manuscript with detailed information regarding the diabetic ulcer model, including the specific mouse strain employed (e.g., C57BL/6J db/db mice), the method used for diabetes induction (e.g., streptozotocin injection), the criteria applied to confirm successful diabetes development (e.g., consecutive measurements of blood glucose level > 16.7 mmol/L), and the specific technique utilized to generate the skin ulcer (e.g., full-thickness excisional wound).\nWe apologize for the lack of clarity in the description of the cell lines used throughout the manuscript, which may have hindered the readers’ understanding of the experimental design. To address this issue, we have systematically reviewed and clarified the identity of all cell lines employed in the study. Specifically, we have ensured that the cell line used in each experiment is clearly indicated upon its first mention in the Results section, including the corresponding figure legends, main text, and supplementary materials. These revisions will enhance the traceability and consistency of our experimental procedures, thereby facilitating a more accurate assessment of the coherence and rigor of our work by readers and reviewers.\nReviewer #2 Major Comment 1: Senescent cells may undergo apoptosis or necrosis due to prolonged laser irradiation, independent of any photodynamic activity. Please provide data on the biosafety of laser irradiation alone on senescent cells to rule out this possibility. While the in vivo data in Figure 6 suggests that irradiation alone does not reduce senescent cells, specific data for the in vitro model in Figure 5a is crucial to confirm this.\nResponse: We have supplemented the experiment as suggested. Data from the CCK-8 assay (Supplementary Fig. 24) under laser irradiation alone (without C N QDs) demonstrate that the laser irradiation parameters used in our study did not significantly affect the viability of senescent cells (P > 0.05).\nRevision: Supplementary Fig. 24 in revised supplementary information\n[...Fig 24 description omitted for brevity...]\nReviewer #2 Major Comment 2: In figure 6 and related supplementary figures, the “Control” group in these figures appears to be the senescence-induced mouse model. However, data from a healthy, age-matched control group is missing. The authors must show that senescence markers (e.g., β-gal staining, p16/p21 expression) are significantly elevated in the senescence model compared to the healthy controls to validate the model’s phenotype. Similarly, for the diabetic mouse model, data demonstrating delayed wound healing compared to healthy control mice is required to establish the model’s validity.\nResponse: We sincerely thank the reviewer for this insightful comment. We have now performed additional experiments including healthy, age-matched control mice to comprehensively validate our models. For the senescence model, we assessed key senescence markers (SA-β-gal, LambinB1, p21 and γ-H2AX) across multiple organs (aorta，liver, kidney, and spleen), with results now presented in Supplementary Fig. 35 demonstrating significant marker elevation compared to healthy controls.\nFor the diabetic model: We have added data showing significantly delayed wound healing in diabetic mice compared to the healthy control group (now in Supplementary Fig. 45). These additions robustly validate our models as suggested.\nRevision: Supplementary Figs. 35 and 45 in revised supplementary information\n[...Fig 35 and 45 descriptions omitted for brevity...]\nReviewer #2 Major Comment 3: Most of the experiment rely on experiments using HUVECs. The induction of senescence in these cells should be confirmed at the protein level, in addition to the currently presented data.\nResponse: We sincerely thank the reviewer for raising this critical point regarding protein-level validation of senescence induction. In direct response, we have now comprehensively supplemented our analysis through SA-β-gal staining, qPCR quantification of p16 and p21 mRNA expression, and Western blot validation at the protein level, which collectively provide multi-layered evidence—encompassing enzymatic activity, transcriptional upregulation, and translational expression—to robustly confirm successful senescence induction and fully address the reviewer's concern.\nRevision: Supplementary Fig. 6 in revised supplementary information\n[...Fig 6 description omitted for brevity...]\nReviewer #2 Minor Comment 1: Figure 1e, In the top-left panel, the color-coding for the percentages (red and blue) appears to be reversed in the pie chart. Please check and correct it. In the left most graph of the third row, there are no data dots plotted. Please clarify if this is intentional or a plotting error.\nResponse: We thank you for pointing this out; the corrections have been implemented in the original figure.\nRevision: Top-left part of Fig. 1e in revised manuscript\n[...Fig 1e description omitted for brevity...]\nReviewer #2 Minor Comment 2: Figure 2d and Page 6, Lines 34–36, The figure includes shaded areas representing the NAD+ concentration ranges for “Senescent cell” and “Healthy cell”. However, the data supporting these specific ranges is not provided or cited. Please provide the evidence for these ranges.\nResponse: The first “concentration” in this sentence refers to the data in Fig. 2d, while the second “concentration” refers to the data in Supplementary Fig. 8. We have supplemented the two citations of the figures in the revised manuscript (page 6, lines 34–36).\nRevision: Lines 34–36 (page 6) in revised manuscript\nImportantly, the concentration range of NAD+ (Fig. 2d) in which the jump in photodynamic activity occurs is comparable to the cytoplasmic NAD+ concentration difference between normal cells and metabolically reprogrammed cells (Supplementary Fig. 8).\nReviewer #2 Minor Comment 3: Figure 2h, Figure 3a–c, and Supplementary Figs. 7, 31, Please specify the cell types and the methods used to induce senescence for the experiments shown in these figures.\nResponse: As suggested, we have added detailed descriptions of the cell types (e.g., HUVECs, HK2) and senescence induction protocols (e.g., H O , etoposide treatment) for Figs. 2h, 3a–c, Supplementary Figs. 8 and 33, and section 1.6 in revised manuscript.\nRevision: Section 1.6 in revised supplementary information\n[...Section 1.6 description omitted for brevity...]\nReviewer #2 Minor Comment 4: Page 6, Line 15, The text states, “Error bars in (g) and (i) indicate the SD of three repetitive tests.” This appears to be a typo. Should “(i)” be “(h)”?\nResponse: We thank the reviewer for catching this typo; we have corrected “(i)” to “(h)” in the referenced sentence in line 15 on page 6.\nReviewer #2 Minor Comment 5: Figure 3a, At the 48-hour time point, the PL intensity in the senescent cell homogenate is comparable to that of normal cells. Why does it not show the expected decrease? Is it possible that NAD+ was removed or diluted during the homogenization process, thereby eliminating its fluorescence quenching effect? Please clarify.\nResponse: We thank the reviewer for raising this important question. In Fig. 3a, the photoluminescence (PL) intensity of senescent cell homogenates at 48 hours appears comparable to that of normal cells. This is likely due to the homogenization process: during cell lysis, the intracellular NAD⁺ concentration is substantially diluted, reducing its fluorescence quenching effect on C N QDs. As a result, the expected difference in PL intensity between senescent and normal cells is minimized in the homogenate. Importantly, this does not reflect the behavior in intact cells, where NAD⁺ levels remain sufficient to modulate C N QD fluorescence, as shown in Figs. 3b–c.\nReviewer #2 Minor Comment 6: Figure 4g, the methodology for measuring the PL intensity in serum samples (e.g., the concentration of C3N QDs added, incubation time) is not described. Please add these experimental details.\nResponse: We thank the reviewer for this suggestion. We have now added the detailed methodology for measuring PL intensity in serum samples in section 1.11.3 of the supplementary information.\nRevision: Section 1.12.3 in revised supplementary information\n[...Section 1.12.3 description omitted for brevity...]\nReviewer #2 Minor Comment 7: Figure 5, The Methods section lacks details regarding the RNA-sequencing experiment. Please provide a comprehensive description of the library preparation, sequencing, and data analysis pipelines.\nResponse: We thank the reviewer for this suggestion. We have now added a comprehensive description of the RNA-sequencing methodology in method section 1.17 of the revised supplementary information.\nRevision: Section 1.17 in revised supplementary information\n[...Section 1.17 description omitted for brevity...]\nReviewer #2 Minor Comment 8: Page 13, Lines 28 & 30, The text provides numerical values for changes in fluorescence intensity and cell proliferation. Please specify which cell line these numbers refer to.\nResponse: In response to this comment, we have now added detailed cell line specifications in the revised supplementary information (Supplementary Figs. 31 and 32).\nReviewer #2 Minor Comment 9: Page 13, Lines 30 & 32, The figure citations appear to be incorrect. Should “Fig. 5d–g” be “Fig. 5d–f” and “Fig. 5h and i” be “Fig. 5g and h”? Please verify.\nResponse: We have corrected the figure citations on page 13 as suggested, with “Fig. 5d–g” now revised to “Fig. 5d–f” and “Fig. 5h and i” to “Fig. 5g and h”.\nReviewer #2 Minor Comment 10: Terminology on Pages 13–14, The term \"aging marker\" is used frequently. In the context of induced senescence, “senescence marker” would be a more precise and appropriate term. Please consider revising the terminology.\nResponse: We thank the reviewer for this precise terminology suggestion. We have replaced “aging marker” with “senescence marker” throughout pages 13–14, and have applied the recommended verbs (decrease/reduce for numerical values; diminish/attenuate for biological functions) where appropriate to improve scientific accuracy.\nReviewer #2 Minor Comment 11: Figure 6, The manuscript lacks crucial details about the in vivo experimental procedures. Please add a thorough description of the methods, including: how the aged/senescent mice were generated, the parameters for laser irradiation (e.g., area, duration), the strain of diabetic mice used, the procedure for wound creation, and the exact timing of sacrifice.\nResponse: We thank the reviewer for highlighting the need for more detailed methodology. We have now added comprehensive descriptions of the in vivo experimental procedures in section 1.13 of the revised supplementary information, including the protocols for generating senescence, fibrosis, and diabetic mouse models, laser irradiation parameters, wound creation, and sacrifice timing. We believe these additions provide the necessary experimental details to ensure reproducibility.\nRevision: Section 1.13 in revised supplementary information\n[...Section 1.13 description omitted for brevity...]\nReviewer #2 Minor Comment 12: Figure 6, The mouse model used in Figure 6 appears to be a model of induced senescence (e.g., doxorubicin-induced) rather than a model of natural aging. Please revise the terminology throughout the manuscript for accuracy.\nResponse: We thank the reviewer for this pertinent observation. We have revised the terminology throughout the manuscript, particularly in the Fig. 6 legend and on pages 14 and 15 (lines 22–24 and 3–10, respectively), replacing “aged”, “natural aging”, “aging” with the more accurate term “induced senescence”, “senescent” to properly reflect the doxorubicin-induced model used in our study.\nReviewer #2 Minor Comment 13: Page 15, Lines 21 & 23, The term “swelling” is used to describe the wound. Please clarify what this refers to. Does it mean inflammation or the formation of granulation tissue?\nResponse: We thank the reviewer for requesting clarification. The term “swelling” refers specifically to inflammatory edema resulting from the wound healing process, not to the formation of granulation tissue. We have revised the text accordingly to avoid ambiguity.\nRevision: Lines 23–24 (page 15) in revised manuscript\nAs illustrated in Fig. 6h, the control group exhibited significant inflammatory edema and scabbing between days 9–12.\nReviewer #2 Minor Comment 14: Figure 6k and 6m, please provide a description of the methods used for the quantification of the unhealed wound distance and collagen content.\nResponse: We thank the reviewer for this suggestion. We have now added detailed quantification methods in section 1.13.3, specifying that unhealed wound distance was measured using a calibrated scale ruler, while collagen content was quantified from Masson-stained sections using ImageJ software under consistent microscopic conditions.\nRevision: Section 1.13.3 in revised supplementary information\n[...Section 1.13.3 description omitted for brevity...]\nReviewer #2 Minor Comment 15: Page 15, Line 29, The manuscript claims, “Additionally, the dermis layer and hair follicles rapidly returned to normal.” This is a strong statement, as it is generally understood that severe dermal injury leads to scar formation, which replaces normal dermal structures, including hair follicles. If true tissue regeneration with de novo hair follicle formation occurred, this is a significant finding that requires substantial evidence. Please provide the evidence supporting this claim. Otherwise, please revise the statement to more accurately describe the observed histology, which likely involves wound contraction and re-epithelialization over a scar.\nResponse: We thank the reviewer for this insightful comment. We have revised the statement on page 15, line 29 (lines 31–32 in revised manuscript) to more accurately reflect the observed histology. The text now reads: “Additionally, wound contraction and re-epithelialization were observed in the scar tissue”. This modification better aligns with the typical wound healing process involving scar formation rather than complete regeneration.\nReviewer #2 Minor Comment 16: Supplementary Figures 7, 10-14, 16, 30, 32, 36, Please state the number of biological samples (n) or experimental replicates for each experiment shown in these figures.\nResponse: We thank the reviewer for this comment and have now updated the figure numbers accordingly. The number of biological replicates (n) has been clearly stated in the legends of Supplementary Figs. 8, 11–15, 17, 32, and 39 (which correspond to the original Supplementary Figs. 7, 10–14, 16, 30, 32, and 36).\nReviewer #2 Minor Comment 17: Supplementary Figure 14, The figure legend lists series of percentages: “(a) 98.2%, 81.4%, 70.5%, 63.4%, 51.0%. (b) 99.8%, 93.5%, 91.0%, 81.4%, 79.6%.” Please clarify what these values represent. Also, please specify if different cell types were used for panels (a) and (b).\nResponse: All experiments were conducted using HUVEC cells under identical conditions. The percentage values represent normalized fluorescence intensity measurements, reflecting the progressive changes in signal intensity corresponding to varying ratios of normal to senescent cells in the co-culture system.\nReviewer #2 Minor Comment 18: Supplementary Figure 32, The label “A549” appears to be missing from the top of the corresponding graph. Please correct this.\nResponse: We thank you for pointing this out, the corrections have been implemented in the original figure."}, {"round": 2, "reviewer_comments": {"reviewer_2": "While the authors have added a substantial amount of data, there are serious flaws regarding their quality and scientific validity. In particular, the unit of cell viability in the CCK-8 assay in Supplementary Fig. 24 (displayed as 1.5%) makes no biological sense and fundamentally undermines the reliability of the data. Furthermore, the Lamin B1 staining in Supplementary Fig. 35 fails to show localization to the nuclear envelope (appearing instead as non-specific cytoplasmic staining), lacking validity as a senescence marker. Similarly, gamma-H2AX, a marker for DNA double-strand breaks, should appear as nuclear foci colocalized with DAPI. However, in the authors' images, the gamma-H2AX signal is diffuse and extends beyond the nuclear region stained with DAPI.\nComplete information for all antibodies used (e.g., source, catalog number, host species, and dilution ratios) must be provided. Furthermore, the protocol for Western blotting is entirely missing. In the quantitative graphs (specifically panels b, d, and f), the axes appear to be unnaturally truncated or omitted. Additionally, the y-axis label \"Mean intensity (a. u.) or positive area (%)\" is ambiguous; it is unclear which metric applies to which dataset.\nThe authors describe the use of C3N QDs in the \"Immunostaining\" section and mention blocking with BSA. However, it is unclear whether this procedure is truly an antibody-based immunostaining method. If the QDs are used for direct staining, this should be clearly distinguished from immunostaining, and the rationale for BSA blocking should be explained.\nThe description of the statistical analysis is also inadequate. The text does not specify which post-hoc tests were performed. Furthermore, there appears to be an inconsistency in the analysis: some datasets seem to be analyzed using only ANOVA, while others include post-hoc comparisons.Finally, the term \"PL emission\" is used in the text but is not defined. Please define this term (presumably Photoluminescence) at its first appearance. There is also a discrepancy between the descriptions in the main text citing the Supplementary Tables and the actual content of those tables.\nConsequently, deep concerns remain regarding the reproducibility and authenticity of this research.", "reviewer_3": "In this work, the authors conducted an in-depth investigation into the photocatalytic activity of N-GQDs. The machine-learning-driven structural evolution of N-GQDs is particularly impressive. More importantly, magnetic resonance techniques enable in situ characterization of the interfacial properties of intracellular nanoparticles in the excited state, which is of great significance for experimentally elucidating the biological mechanisms of nanomaterials. Both the machine-learning-driven structural evolution of N-GQDs and the biological experiments involved a substantial amount of work. In addition, in the revised manuscript, the authors have responded thoroughly and appropriately to all comments raised by the reviewers, and the results are convincing.\nFor the concerns raised by Reviewer #1, the authors have provided detailed responses in the revised version; the synthesis and characterization of N-GQDs or C3N provide convincing results.\nAdmittedly, the manuscript still has several aspects that require further improvement. The comments are as follows:\n1. I’m very interested in the study of water protons through the longitudinal relaxation time (T1) of protons by NMR. However, T1 reflects the longitudinal magnetization recovery process of protons, and I’m curious about how to establish a direct connection between the T1 and proton dissociation/exchange mentioned in the article.\n2. Where multiple senescence induction methods are used, please clarify whether the observed fluorescence and photodynamic responses were qualitatively similar across models or if any variability was noted.\n3. The manuscript presented data derived from human tissue samples; however, a dedicated Methods subsection describing the human tissue experiments was not included. Please add a concise description specifying: Source of human tissues and sample size, inclusion criteria, experimental procedures performed on human samples, ethical approval, and informed consent.\n4. In Fig. 2f, the atoms should be specified. The meaning of the blue dotted lines in Fig. 3j and Fig. 4i should be specified. In Fig. 3b, why are the data for senescent cells only plotted in the first 3 days? The parameters for linear regressions in Fig. 4g and Fig. 4h should be noted."}, "author_response": "Responses to Reviewer #2\nReviewer #2 Comment 1: In particular, the unit of cell viability in the CCK-8 assay in Supplementary Fig. 24 (displayed as 1.5%) makes no biological sense and fundamentally undermines the reliability of the data.\nResponse: We thank the reviewer for pointing out this critical error and sincerely apologize for the confusion caused by the incorrect labeling of the y-axis in Supplementary Fig. 24; the value “1.5” referred to the raw absorbance (OD450) rather than the percentage viability, and we have now corrected this by re-plotting the original OD450 data after normalization relative to the control group (set as 1) using the formula: Viability = (OD – OD ) / (OD – OD ). In addition, the raw OD450 values obtained from the CCK-8 treated blank control blank\nassay are now provided in the Source Data file. After this correction, the data are biologically reasonable and internally consistent, and importantly, this adjustment does not alter the interpretation of the results in cell viability. We have updated Supplementary Fig. 24 and the “Methods 1.9.2” section accordingly to reflect these changes.\nRevision: Methods 1.9.2 in the revised Supplementary Information\n[...Methods 1.9.2 description omitted for brevity...]\nReviewer #2 Comment 2: Furthermore, the Lamin B1 staining in Supplementary Fig. 35 fails to show localization to the nuclear envelope (appearing instead as non-specific cytoplasmic staining), lacking validity as a senescence marker. Similarly, gamma-H2AX, a marker for DNA double-strand breaks, should appear as nuclear foci colocalized with DAPI. However, in the authors' images, the gamma-H2AX signal is diffuse and extends beyond the nuclear region stained with DAPI.\nResponse: We sincerely apologize for the lack of clarity in the original images, which led to the concern regarding the subcellular localization of Lamin B1 and γ-H2AX. We have taken the reviewer’s constructive criticism seriously and have performed the following improvements:\n1) Regarding Lamin B1 (Supplementary Fig. 38): We have performed re-staining for Lamin B1 in the vascular tissues. To clearly demonstrate its localization, we have provided high-magnification images and added enlarged insets. As shown in the revised figure, Lamin B1 is precisely localized to the nuclear envelope, rather than being diffusely distributed in the cytoplasm, which confirms its validity as a senescence marker in our model.\n2) Regarding γ-H2AX (aorta, liver, kidney, and spleen): We agree with the reviewer that γ-H2AX should appear as distinct nuclear foci. In the revised manuscript, we have provided updated images for the vessel, liver, kidney, and spleen tissues, including magnified views to better visualize the signal. The updated images clearly show that the γ-H2AX signals are present as punctuated foci that strictly colocalize with DAPI-stained nuclei. This confirms the presence of DNA double-strand breaks in these senescent cells.\nWith the above clarifications, we have updated Supplementary Fig. 38 and the corresponding main figures with these clearer images to ensure the localization of these markers is unambiguous.\nRevision: Supplementary Fig. 38 in the revised Supplementary Information\n[...Fig 38 description omitted for brevity...]\nReviewer #2 Comment 3: Complete information for all antibodies used (e.g., source, catalog number, host species, and dilution ratios) must be provided. Furthermore, the protocol for Western blotting is entirely missing.\nResponse: We have now provided a comprehensive list of all antibodies, including their source, catalog number, host species, and specific dilution ratios for WB, IF, and IHC in the Supplementary Table 4. Other general reagents and kits used in the experiments are detailed in the newly added Supplementary Table 5. This clear categorization ensures all experimental materials are fully transparent and reproducible.\nWe apologize for the oversight in the original manuscript. As requested, we have now added a detailed protocol for Western blotting in the “Methods 1.7.2” section.\nRevision: Methods 1.7.2 in the revised Supplementary Information\n[...Methods 1.7.2 description omitted for brevity...]\nReviewer #2 Comment 4: In the quantitative graphs (specifically panels b, d, and f), the axes appear to be unnaturally truncated or omitted. Additionally, the y-axis label “Mean intensity (a. u.) or positive area (%)” is ambiguous; it is unclear which metric applies to which dataset.\nResponse: We appreciate the reviewer’s constructive comments regarding the clarity of data visualization. We have revised the figures accordingly. In the updated Supplementary Information, quantitative graphs from immunofluorescence (IF), immunohistochemistry (IHC), and senescence-associated β-galactosidase (β-Gal) staining are now presented separately, with distinct metrics (“Mean fluorescence intensity (a. u.)” for IF, “Positive area (%)” for IHC and β-Gal) clearly indicated in both axis labels and figure legends. In addition, most quantitative graphs (Fig. 6b, 6d, 6f, and related Supplementary Fig. 39, 40, 42, 43, 44) have been re-plotted using continuous, non-truncated y-axes starting from zero, with complete axis lines and tick marks. For certain specific datasets where the numerical disparity between groups spans a large numerical range, a truncated y-axis was selectively retained. This was done to ensure the visibility and interpretability of individual data points in the low-value groups, which would otherwise be compressed against the baseline and rendered invisible on a continuous scale. This approach was adopted to facilitate the visualization of data distribution across all experimental groups. These revisions improve transparency and interpretability without affecting the original data or conclusions.\nRevision: Fig. 6. in the revised manuscript, and Supplementary Fig. 39, 41, 42, 43, 44 in the revised Supplementary Information.\n[...Fig 6 and Supplementary Fig descriptions omitted for brevity...]\nReviewer #2 Comment 5: The authors describe the use of C N QDs in the “Immunostaining” section and mention blocking with BSA. However, it is unclear whether this procedure is truly an antibody-based immunostaining method. If the QDs are used for direct staining, this should be clearly distinguished from immunostaining, and the rationale for BSA blocking should be explained.\nResponse: We appreciate the reviewer’s insightful comment regarding our staining procedures. To address this, we have thoroughly revised the “Methods 1.16” section in the revised Supplementary Information by clearly distinguishing antibody-based immunofluorescence staining from C N QDs direct fluorescence staining, and we have updated the terminology throughout the manuscript to eliminate ambiguity.\nFor the C N QDs staining, we employed an antibody-independent direct staining protocol. The rationale for using 5% BSA blocking was to minimize the non-specific physical adsorption of the nanomaterials onto the tissue components or the glass slides. This step is crucial for reducing background noise and ensuring that the fluorescence signal accurately reflects the specific interaction between the QDs and the target tissue structures. The revised methodological descriptions now explicitly reflect these distinctions as follows.\nRevision: Methods 1.16 in the revised Supplementary Information.\n[...Methods 1.16 description omitted for brevity...]\nReviewer #2 Comment 6: The description of the statistical analysis is also inadequate. The text does not specify which post-hoc tests were performed. Furthermore, there appears to be an inconsistency in the analysis: some datasets seem to be analyzed using only ANOVA, while others include post-hoc comparisons.\nResponse: We appreciate this important comment. The Statistical Analysis section (Methods 1.19 in the revised Supplementary Information) has been thoroughly revised to explicitly state all statistical tests used, including the specific post hoc comparisons applied to datasets involving three or more groups. All analyses are now performed using a consistent strategy across figures: two-group comparisons were analyzed using two-tailed Student’s t-tests, while comparisons among three or more groups were analyzed using one-way ANOVA followed by Tukey’s multiple-comparisons post hoc test. These clarifications improve transparency and ensure consistency throughout the manuscript.\nRevision: Methods 1.19 in the revised Supplementary Information.\n[...Methods 1.19 description omitted for brevity...]\nReviewer #2 Comment 7: Finally, the term “PL emission” is used in the text but is not defined. Please define this term (presumably Photoluminescence) at its first appearance.\nResponse: We thank the reviewer for noting this omission. The term “PL emission” is now defined at its first occurrence in the manuscript as photoluminescence emission (page 10, lines 3–5 in the revised manuscript).\nReviewer #2 Comment 8: There is also a discrepancy between the descriptions in the main text citing the Supplementary Tables and the actual content of those tables.\nResponse: We apologize for this oversight. We have carefully cross-checked all citations of Supplementary Tables in the main text against their actual contents and corrected all inconsistencies. The revised manuscript now ensures that each Supplementary Table is accurately cited and correctly described in the corresponding sections of the text.\nTaken together, we have carefully re-examined all experimental procedures that the reviewer questioned. The revised manuscript now includes corrected data, detailed methodological descriptions, and complete raw datasets. We believe these revisions fully address the concerns regarding data validity and reproducibility.\nResponses to Reviewer #3\nReviewer #3 Comment 1: I’m very interested in the study of water protons through the longitudinal relaxation time (T ) of protons by NMR. However, T reflects the longitudinal magnetization recovery process of protons, and I’m curious about how to establish a direct connection between the T and proton dissociation/exchange mentioned in the article.\nResponse: Thank you for raising your concern. The mechanism for linking the proton concentration and the magnetization recovery process is important for the readers to understand the mechanism study of this work.\nIn NMR relaxation, the equilibrium magnetization (M ) is directly proportional to the local proton density (ρ ), following: [...formula omitted...]\nDuring longitudinal relaxation, the recovery of longitudinal magnetization (M ) follows: [...formula omitted...]\nindicating that the proton concentration and the longitudinal relaxation time (T ) govern the equilibrium magnetization amplitude when returning to the equilibrium [Phys. Rev. 70, 460–474 (1946); Phys. Rev. 73 (7), 679–712 (1948)].\nAccording to the Bloembergen-Purcell-Pound theory, the longitudinal relaxation rate (1/T ) is governed by magnetic dipole-dipole interactions between neighboring nuclear spins. The general form is: [...formula omitted...]\nThe above expression shows that the 1/T is proportional to ρ and τ , since higher proton concentration increases the number of dipolar pairs contributing to relaxation [Phys. Rev. 73 (7), 679–712 (1948)]. In liquid systems, τ is inversely related to proton density ρ , since higher proton concentrations lead to more frequent molecular collisions and faster local field fluctuations. Consequently, samples with more dissociated protons generally exhibit larger ρ and shorter τ , resulting in shorter T values.\nRevision: Page 7, line 8 in the revised manuscript\n[...Revision description omitted for brevity...]\nReviewer #3 Comment 2: Where multiple senescence induction methods are used, please clarify whether the observed fluorescence and photodynamic responses were qualitatively similar across models or if any variability was noted.\nResponse: We appreciate this important point. Across different senescence induction models used in this study, the fluorescence activation and photodynamic responses were qualitatively consistent, demonstrating enhanced intracellular activation in senescent cells compared to controls (Supplementary Fig. 7, 13).\nReviewer #3 Comment 3: The manuscript presented data derived from human tissue samples; however, a dedicated Methods subsection describing the human tissue experiments was not included. Please add a concise description specifying: Source of human tissues and sample size, inclusion criteria, experimental procedures performed on human samples, ethical approval, and informed consent.\nResponse: We thank the reviewer for pointing out the omission of a dedicated description of the human tissue experiments. We have now added a concise Methods subsection titled “Human tissue experiments”, specifying the tissue source, experimental procedures, ethical approval, and informed consent. This addition improves transparency and compliance with ethical reporting standards.\nRevision: Methods 1.18 in the revised Supplementary Information\n[...Methods 1.18 description omitted for brevity...]\nReviewer #3 Comment 4: In Fig. 2f, the atoms should be specified. The meaning of the blue dotted lines in Fig. 3j and Fig. 4i should be specified. In Fig. 3b, why are the data for senescent cells only plotted in the first 3 days? The parameters for linear regressions in Fig. 4g and Fig. 4h should be noted.\nResponse: We have revised the figures and legends to address these points. Specifically, the atom types are now explicitly labeled in Fig. 2f. The blue dotted lines in Fig. 3j and Fig. 4i are now defined in the figure legends. In Fig. 3b, senescent-cell data are shown only for the first 3 days because fluorescence intensity in senescent cells decreased to near-background levels beyond this time point, precluding reliable quantitative analysis. The parameters used for linear regression analyses in Fig. 4g and Fig. 4h, including slope, R2, and significance values, are now reported in the figure legends.\nRevision: Figure legends in the revised manuscript\n[...Figure legends description omitted for brevity...]\nWe are grateful for the reviewer’s constructive comments and positive assessment. All suggested clarifications and additions have been incorporated, improving the mechanistic clarity, methodological transparency, and figure presentation, without altering the main conclusions of the study."}, {"round": 3, "reviewer_comments": {"reviewer_2": "In my view, the authors have addressed all the comments I raised as Reviewer 2.", "reviewer_3": "The authors revised the manuscript with all previous comments addressed properly. I suggest to accept it for publication on this journal."}, "author_response": ""}]

Decision letter