Neonatal Hyperoxia Induces Metabolic Reprogramming in Senescent Alveolar Macrophages, Leading to Persistent Lung Injury

Abstract

Background:

Bronchopulmonary dysplasia (BPD) is a chronic lung disease in premature infants. Neonatal hyperoxia induces a BPD-like phenotype and lung cell senescence in rodents. In our 3-day hyperoxia model, senescent cells were predominantly lung macrophages, with their abundance peaking at postnatal day 7 (pnd7). However, the molecular and functional characteristics of these senescent macrophages remain undefined.

Methods:

We reanalyzed a scRNA-seq dataset (GSE207866) generated from senescent lung cells isolated at pnd7 (SD7) following neonatal hyperoxia. Hierarchical clustering combined with manual annotation was used to compare transcriptional profiles with age-matched air-exposed controls (AirD7) and hyperoxia-exposed mice without senescent-cell enrichment (O2D7). Key molecular findings were validated by immunofluorescence. In vivo, neonatal mice received daily injections of the pyruvate dehydrogenase kinase inhibitor, dichloroacetate (DCA) from pnd4 to pnd6, and a senolytic cocktail consisting of quercetin and dasatinib from pnd4 to pnd14, following 3 days of hyperoxia exposure.

Results:

Macrophages accounted for 65.90% of senescent cells in the SD7 group. Seven macrophage clusters were identified, enriched in M1-like and alveolar macrophage phenotypes. Two major clusters (clusters 0 and 1), together representing nearly half of all senescent macrophages, exhibited strong expression of genes associated with innate immunity, inflammation, and DNA damage responses. These clusters also showed a shift toward glycolysis, the pentose phosphate pathway, and glutamine metabolism, with reduced reliance on β-oxidation. Administration of DCA activated pyruvate dehydrogenase and attenuated hyperoxia-induced macrophage senescence and lung injury. Pathway enrichment analyses revealed enhanced metal-handling pathways, immune and stress signaling (including p38 mitogen-activated kinase, ataxia-telangiectasia mutated, and mechanistic target of rapamycin), apoptosis, and RNA regulatory processes. Conversely, genes involved in reactive oxygen species detoxification, DNA repair, phagocytosis, cytoskeletal organization, and cell adhesion were downregulated. Notably, reducing senescent cells by a senolytic cocktail during the alveolar stage mitigated hyperoxia-induced persistent lung injury.

Conclusion:

Neonatal hyperoxia drives the emergence of a heterogeneous population of senescent macrophages characterized by metabolic reprogramming and dysregulated signaling pathways, which contribute to the development and persistence of lung injury.

Graphical abstract

Keywords

bronchopulmonary dysplasia / single-cell gene expression analysis / cellular senescence / macrophages / metabolism

1. Introduction

Bronchopulmonary dysplasia (BPD) is a chronic lung disease of prematurity that arises from a combination of factors, including exposure to supplemental oxygen, mechanical ventilation, and other perinatal insults. In the US, approximately 10,000–15,000 premature infants develop BPD each year, with an estimated first-year medical cost approaching \$380,000 per infant [1, 2, 3]. Despite advances in neonatal care that have improved survival, the incidence of BPD and the burden of long-term respiratory morbidity remain largely unchanged [4]. This underscores a critical need for new therapeutic approaches that directly target the mechanisms driving lung injury in BPD.

Emerging evidence indicates that cellular senescence may contribute to the pathogenesis of BPD. Airway smooth muscle cells from oxygen-exposed infants exhibit increased markers of senescence compared with those from infants who died shortly after birth without oxygen exposure [5]. Similarly, premature infants requiring mechanical ventilation show a notable reduction in nuclear lamin B1 in distal alveoli, consistent with heightened senescence [6]. Additional studies have reported increased senescence-associated biomarkers, including lipofuscin accumulation, phosphorylated p53, and \gamma-H2AX, in the lungs of infants with BPD [7]. Experimental models parallel these clinical findings: neonatal rats and mice exposed to hyperoxia exhibit robust induction of lung cell senescence [6, 7]. In our 3-day neonatal hyperoxia model, senescent cells peak at postnatal day 7 (pnd7), with lung macrophages representing the majority of senescent cell populations [6]. Macrophages in the developing lung encompass diverse functional subsets, yet the specific characteristics of senescent macrophages generated following neonatal hyperoxia remain undefined. Characterizing their phenotypes and molecular pathways is essential for understanding how they contribute to lung damage and for identifying strategies to selectively target these cells.

To address this gap, we reanalyzed a single-cell RNA sequencing dataset (GSE207866) of senescent lung cells isolated from hyperoxia-exposed neonatal mice at pnd7 (SD7). The dataset is limited to senescent cell populations and thus does not include a corresponding hyperoxia-exposed, non-senescent cell-enriched group for comparison. Using hierarchical clustering and comparative transcriptomic analyses [8], we characterized the gene expression profiles and pathway enrichment signatures of senescent macrophages and contrasted them with age-matched air controls (AirD7) and non-senescent cells from hyperoxia-exposed mice (O2D7). Portions of these analyses have been deposited in bioRxiv [9]. Mice were injected a pyruvate dehydrogenase (Pdh) kinase (PDK) inhibitor to determine whether activating Pdh decreases hyperoxia-induced macrophage senescence, alveolar and vascular simplification. Finally, we evaluated whether senolytic treatment during the alveolar stages inhibits neonatal hyperoxia-induced persistent lung injury.

2. Materials and Methods

2.1 Hyperoxic Exposure and Treatment

Newborn C57BL/6J mice (12 h old, both sexes) and their dams were placed in either room air (21% O₂) or 95% O₂ for 3 days using an A-chamber system (BioSpherix, Redfield, NY, USA). Dams were rotated between hyperoxia and room air every 24 hours to prevent maternal injury. Pups were subsequently maintained in room air until pnd7 or pnd60. Each pup was treated as an independent biological replicate. Lungs from 3–5 mice originating from 2 litters were collected, with 3–4 mice assigned to each experimental group. Group assignment, data collection, and analysis were performed with the investigator blinded. Sex-specific effects were not assessed. This is because there is no sex difference in lung cellular senescence in neonatal mice after the 3-day hyperoxia [6].

For a senolytic cocktail treatment, mice received quercetin and dasatinib (2.5 and 5 mg/kg, i.p.) from pnd4 to pnd14 following neonatal exposure to room air or hyperoxia. These doses were selected based on prior reports demonstrating effective senescent-cell clearance without toxicity [6]. In a separate experiment, a PDK kinase inhibitor sodium dichloroacetate (DCA, 15 and 30 mg/kg, i.p.) was injected daily between pnd4 and pnd6. Animals were anesthetized with ketamine (75 mg/kg, i.p.) and xylazine (10 mg/kg, i.p.), prepared from stock solutions of ketamine (10%, w/v) and xylazine (2%, w/v) and diluted in sterile saline to achieve the appropriate injection volume. Mice were then cervical dislocated following confirmation of deep anesthesia and complete loss of reflexes, with subsequent removal of vital organs.

2.2 Evaluating Lung Morphometry

Lung morphometric analyses were performed on hematoxylin and eosin (H&E)-stained mouse lung sections. Non-lavaged lungs were gently inflated with 1% low-melting point agarose at a constant pressure of 25 cm H₂O and subsequently fixed in 4% neutral-buffered paraformaldehyde. After fixation, lung tissues were paraffin-embedded and sectioned at a thickness of 4 µm using a rotary microtome (Leica Biosystems, Deer Park, IL, USA). Radial alveolar count (RAC) was determined by drawing a perpendicular line from the center of a respiratory bronchiole to the distal acinus, defined by the pleural surface or the nearest connective tissue septum, and counting the number of alveolar septa intersected by this line. At least three measurements were obtained per animal to ensure reliable quantification.

2.3 Immunofluorescence

Paraffin-embedded lung sections underwent deparaffinization and heat-induced antigen retrieval prior to immunofluorescence staining. Samples were incubated overnight at 4 ℃ with primary antibodies against Pdh E1 subunit \alpha1 (Pdha1, ab168379, Abcam, Cambridge, MA, USA, 1:100 dilution), NOS2 (PA1-036, ThermoFisher, Waltham, MA, USA, 1:50 dilution), Syk (PA5-96063, ThermoFisher, 1:100 dilution), transferrin receptor (ab84036, Abcam, 1:100 dilution), or CD68 (ab283654, Abcam, 1:100 dilution), co-staining with lamin B1 (MA1-06103, ThermoFisher, 1:100 dilution) and p21 (MA5-31479, ThermoFisher, 1:100 dilution) as senescence biomarkers. After incubation with goat-anti mouse (A-11001, ThermoFisher) or goat-anti-rabbit (A-11012, ThermoFisher) secondary antibodies (1:5000, 1 h, room temperature), sections were mounted in 4^′,6-diamidino-2-phenylindole (DAPI)-containing hard-set medium (Vector Labs, Newark, CA, USA). To assess microvascular density, von Willebrand factor (vWF) immunostaining was performed in the lung. Images were acquired using a Nikon fluorescence microscope (Melville, NY, USA).

2.4 Pdh Activity Assay

Pdh activity was assessed in protein lysates prepared from snap-frozen lung tissue using a commercially available kit (ab287837; Abcam). Approximately 50 mg of frozen lung tissue was homogenized in 300 µL of ice-cold assay buffer and incubated on ice for 10 min. The homogenate was then centrifuged at 10,000 \timesg for 5 min, and the supernatant was collected. Each sample was transferred to a 96-well plate and brought to a final volume of 50 µL per well with assay buffer. For the positive control, 10 µL of the supplied control material was added to the wells and adjusted to 50 µL with the assay buffer. The enzymatic reaction was initiated by adding 50 µL of the reaction mixture to each well, followed by incubation for 30 min at room temperature in the dark. Pdh enzymatic activity was quantified by measuring absorbance at 450 nm using a Cytation 5 imaging microplate reader (BioTek, Winooski, VT, USA).

2.5 Use of Publicly Available scRNA-seq Datasets

Previously published single-cell RNA-seq data (GEO: GSE207866) from senescent lung cells isolated at pnd7 (SD7) were used for analysis [6]. Single-cell datasets from age-matched air-exposed (AirD7) and hyperoxia-exposed (O2D7) mice served as reference controls.

2.6 Reanalysis of Publicly Available scRNA-seq Dataset

Data processing was carried out in Seurat v4.1.1 (Satija Lab, New York, NY, USA) [10]. CellRanger count matrices (10\times Genomics) were imported using Read10X() to create individual Seurat objects. Cells expressing fewer than 200 genes, or genes detected in fewer than three cells, were excluded. Additional filtering removed cells with fewer than 700 or more than 8000 detected genes or >5% mitochondrial transcripts. Quality control was performed as previously described [6].

Normalization and scaling were performed with SCTransform using default parameters. Integration of Seurat objects employed canonical correlation analysis with SelectIntegrationFeatures(), PrepSCTIntegration(), FindIntegrationAnchors(), and IntegrateData(). Principal component analysis (first 50 PCs) was used to build a K-nearest neighbor graph (K = 20), followed by Louvain clustering using FindClusters(). Cluster resolution was guided by clustree v0.5.1 [11]. UMAP visualization was generated using the first 50 CCA embeddings. Cell types were assigned based on established marker genes (Table 1).

The same workflow was repeated iteratively to derive subclusters within each major lineage. Immune cells were isolated from the global dataset, and macrophages were distinguished from monocytes and dendritic cells. This approach yielded seven macrophage subsets (518 cells) for downstream analyses. Marker genes for each macrophage subset were identified using the cosg() function [12]. Figures were generated with SCpubr v2.0.0, scplotter v0.1.1, and fmsb v0.7.6 in R v4.4.1 using RStudio v2023.12.0 (RStudio/Posit, Boston, MA, USA).

2.7 Pathway Analysis Using SCPA

Gene sets representing GO biological processes, KEGG pathways, Reactome, PID, BioCarta, and WikiPathways were obtained from the Molecular Signatures Database (v7) (Broad Institute, Cambridge, MA, USA) [13]. Pathway comparisons were carried out using compare_pathways() in SCPA v1.6.2 (Broad Institute, Cambridge, MA, USA) (parameters: min_genes = 15; max_genes = 500) [14]. Macrophage populations from AirD7, O2D7, and SD7 were analyzed. Visualization and post-processing were performed using Seurat v4.4.0 [10] and ggplot2 v3.5.1 (RStudio/Posit Software, Boston, MA, USA). Pathways with |Fold change| >5 and a Benjamini-Hochberg (BH)-adjusted p-value 0.01 were selected for subsequent visualization.

2.8 Pseudotime Trajectory Analysis

Single-cell trajectory analysis of seven macrophage clusters was performed using Monocle2 (v2.32.0) (Trapnell Lab, University of Washington, Seattle, WA, USA) [15]. Prior to cell ordering, the differentialGeneTest function was applied to identify differentially expressed genes (DEGs) across clusters, and genes with a q value 0.01 were selected for pseudotime ordering. Cell trajectories were then constructed using the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm with default parameters to infer transitional relationships among macrophage clusters. Branch Expression Analysis Modeling (BEAM) was performed on pseudotime-ordered data to identify genes associated with branch fate decisions (Benjamini-Hochberg (BH)-adjusted p value 1 \times 10^-5). Trajectory visualizations and heatmaps were generated using the plot_cell_trajectory, plot_complex_cell_trajectory, and plot_genes_branched_heatmap functions. Upregulated genes from branch-specific BEAM analyses were subsequently subjected to pathway enrichment analysis using the DAVID database (https://davidbioinformatics.nih.gov/), and pathways with a BH-adjusted p value 0.05 were selected for downstream visualization.

2.9 Metabolic Pathway Scoring

AUCell (Version 1.26.0) (Bioconductor, Buffalo, NY, USA) [16] was employed to assign metabolic pathways activity scores in the single-cell RNA data. Initially, a ranking of selected pathways genes was built based on the single-cell expression matrix using the AUCell_buildRankings() function with default parameters. Subsequently, the area under the curve (AUC) was calculated using the top 5% of genes in the ranking using the AUCell_calcAUC() function. Cells expressing a higher proportion of genes within the gene set received higher AUC values. The AUC score for each cell was mapped onto violin and box plots for visualization using the ggplot2 v3.5.1. Finally, the Wilcoxon rank-sum test for multiple comparisons was performed using the stat_compare_means function from the ggpubr v0.6.0 (RStudio/Posit Software, Boston, MA, USA). A p-value 0.05 was considered statistically significant.

2.10 Statistical Analysis

Data are presented as mean \pm SEM. Statistical analyses were performed using GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA) with blinding whenever feasible. Group comparisons were made using unpaired Student’s t-tests with Welch’s correction. For multiple comparisons, the statistical significance of the differences was evaluated by using one-way ANOVA followed by Tukey’s post-test to specifically compare indicated groups. A p-value 0.05 was considered statistically significant.

3. Results

3.1 Macrophages Constitute the Majority of Senescent Cells After Neonatal Hyperoxia

Using canonical marker genes (Table 1), we first assigned all single cells in the AirD7, O2D7, and SD7 datasets to epithelial, endothelial, immune, or mesenchymal lineages (Table 1, level 1). Immune populations corresponded to clusters 5, 6, 12, 14, 16, 18, 19, 21, 25, 26, 30, 32, and 33 (Fig. 1A). Among the 493 SD7 cells, 73.63% were immune cells, with smaller fractions of epithelial (8.11%), endothelial (9.74%), and mesenchymal (8.52%) cells (Table 2). Thus, immune populations constituted the largest senescent pool.

To further resolve these immune cells, all immune clusters were combined and reclassified using lineage-specific markers (Table 1, level 2). Eight immune cell types, such as B cells, NK cells, T cells, mast cells/basophils, neutrophils, monocytes, dendritic cells, and macrophages, were identified. Monocytes, dendritic cells, and macrophages accounted for 63.3% of immune cells overall (within clusters 0, 1, 3, 4, 6, 7, 11, and 15), and 97.5% of immune cells in the SD7 group (Fig. 1B; Table 3). Clusters 3 and 6 expressed overlapping signatures of macrophages with monocytes or dendritic cells. These clusters were combined with other myeloid clusters and refined using additional markers (Table 1, level 3).

This analysis produced eleven initial subclusters (Fig. 2A). Clusters 2, 3, 5, 6, 7, 8, 9, and 10 showed clear macrophage identity, while clusters 1 and 4 contained mixed dendritic cell and macrophage markers. Subclustering of clusters 1 and 4 allowed extraction of macrophages, which were integrated with the eight pre-defined macrophage clusters (Fig. 2B). This produced seven final macrophage subsets (clusters 0–6), totaling 518 macrophages: 116 in AirD7, 77 in O2D7, and 325 in SD7 (Tables 4,5). In SD7, macrophages represented 65.90% of all senescent cells, confirming that macrophages are the dominant senescent population after neonatal hyperoxia. We then used these seven clusters of macrophages for further analyses.

3.2 Senescent Macrophages Predominantly Exhibit an M1 Phenotype

Neonatal hyperoxia drives inflammatory injury with a shift toward M1 polarization [17], but its effects on senescent macrophages are unclear. Polarization analysis of the 325 SD7 macrophages using canonical markers (Table 1) showed that clusters 0, 1, 5, and 6 displayed M1 signatures; cluster 4 aligned with M2; and clusters 2 and 3 exhibited mixed M1/M2 characteristics (Fig. 3A–C). Although total macrophage numbers were reduced in O2D7 compared with AirD7, the distribution of M1, M2, and mixed phenotypes remained similar across groups (Table 6). M1 cells were the dominant subtype in all conditions. Lamin b1 loss is a senescence-associated biomarker [18]. Immunostaining showed that NOS2 (M1 marker) was enriched in lamin b1 negative macrophages at pnd7, whereas the M2-associated protein Syk was reduced in the hyperoxic group compared to air control (Fig. 3D). These results suggest that senescent macrophages formed after hyperoxia predominantly adopt an M1-like inflammatory state.

3.3 Alveolar Macrophages are the Most Common Senescent Macrophage Subtype After Hyperoxia

Because lung macrophages occupy both alveolar and interstitial compartments, we next assessed whether senescence preferentially affects specific subsets. Among the 325 SD7 macrophages, marker-based classification identified alveolar, interstitial, recruited/monocyte-derived, proliferative, and non-classical interstitial macrophages (Table 1). Clusters 0, 1, and 3 corresponded to alveolar macrophages, while clusters 2, 4, 5, and 6 mapped to interstitial, recruited/monocytes-derived, proliferative, and non-classical interstitial macrophages, respectively (Fig. 4).

In O2D7 mice, alveolar macrophage proportions were decreased and interstitial macrophages increased relative to AirD7 (Table 7). The proportions of recruited/monocyte-derived macrophages, proliferative macrophages, and non-classical interstitial macrophages were largely comparable between the AirD7 and O2D7 groups, with no substantial differences observed. In contrast, senescent macrophages in SD7 were predominantly alveolar (62.8%), with reduced representation of interstitial and proliferative subsets compared with the AirD7 group (Table 7). These results indicate that neonatal hyperoxia reshapes the macrophage landscape and that alveolar macrophages are the most susceptible to senescence.

3.4 Monocle2 Pseudotime Analysis Reveals Fate-Associated Transcriptional Programs in Senescent Macrophages

To investigate potential transcriptional state transitions among the seven macrophage clusters, we performed Monocle2 pseudotime trajectory analysis using all clusters (Fig. 5A). The inferred trajectory revealed two major branch points, partitioning cells into five distinct transcriptional states. State 1 mainly comprised clusters 0, 1, 3, and 5; State 2 predominantly consisted of cluster 4; States 3 and 4 were primarily composed of cluster 2; and State 5 mainly included clusters 0, 1, and 5 (Fig. 5B). Based on biological relevance and gene expression characteristics, recMac/Mo-Mac (cluster 4) was annotated as the root state for pseudotime ordering [19, 20]. Accordingly, State 2, which predominantly comprised cluster 4 cells, was designated as the putative early transcriptional state in the inferred trajectory (Fig. 5C).

Cells from the AirD7 and O2D7 groups were primarily distributed toward later pseudotime positions, whereas SD7 cells progressing along branch 2 were enriched at early or intermediate pseudotime stages (Fig. 5D). Fig. 5E depicts the branched trajectory, illustrating the distribution of each cluster across distinct States. The cluster 2 (IMac) cells from the AirD7 and O2D7 groups were predominantly distributed in States 3 and 4, whereas cluster 2 cells from the SD7 group remained in State 4 only along branch 1. The right side of branch 2 corresponds to State 5, which was largely occupied by cells from the SD7 group. State 5 was predominantly composed of clusters 0 and 1 (alvMac) and cluster 6 (ncIMac) in the SD7 group, whereas State 1 was primarily occupied by clusters 0, 1, 3, 5, and 6 in the AirD7 and O2D7 groups. In addition, cluster 5 (pMac) in the SD7 group was mainly distributed at early or intermediate stages of differentiation (i.e., State 2). Together, these distinct cellular distributions indicate divergent fate-associated transcriptional programs in senescent macrophages.

No significant differences in differentiation trajectories along branch 1 were observed between the O2D7 and AirD7 groups (Fig. 5D,E). Therefore, we focused on characterizing differentiation-associated and functional disparities in State 4 (IMac) cells in the SD7 group. To identify genes associated with fate bifurcation, BEAM was applied at branch point 1, revealing genes whose expression dynamics were significantly associated with branch-dependent fate decisions of cluster 2 cells. The C2 gene module (Table 8) was progressively upregulated during differentiation from branch point 1 toward State 4 (Fig. 5F), reflecting gene activation during the transition from cluster 4 (recMac/Mo-Mac) to cluster 2 (IMac) in the SD7 group. In contrast, the C2 module was downregulated during differentiation from cluster 4 to cluster 2 in the AirD7 and O2D7 groups, corresponding to the trajectory from branch point 1 toward State 3. Accordingly, genes in the C2 module were imported into the DAVID database for functional enrichment analysis. Representative significantly enriched pathways associated with differentiation toward State 4 were visualized in a bar plot (Fig. 5G). This includes inflammatory and immune-related pathways such as CCR chemokine receptor binding, chemokine-mediated signaling, TNF, NF-\kappaB, IL-17, and Toll-like receptor signaling, as well as cellular responses to TNF, eosinophil chemotaxis, and MAPK signaling. Collectively, these findings indicate that senescent macrophage-derived interstitial macrophages adopt a pro-inflammatory, fate-associated transcriptional program.

3.5 Upregulated Genes Define Functional Diversity Among Senescent Macrophage Clusters

To identify defining features of each senescent macrophage subset, we used cosg() [12] to extract the top upregulated genes (Table 9). The predominant functional categories for each cluster were as follows: Cluster 0—innate immunity, inflammation, lipid metabolism, pentose phosphate pathway; Cluster 1—inflammatory and DNA repair programs; Cluster 2—lipid-associated macrophage markers; Cluster 3—downregulated immune activity and thermogenic genes; Cluster 4—migration and recruitment signatures; Cluster 5—cell-cycle regulation; Cluster 6—lysosomal pathways (Fig. 6A,B). Clusters 0, 1, and 3 were expanded in SD7 relative to AirD7 and O2D7 (Fig. 6C), indicating that senescent macrophages are enriched for inflammatory, metabolic, and innate immune programs.

3.6 Senescent Macrophages Favor Glycolysis and the Pentose Phosphate Pathway Over Fatty Acid \beta-oxidation

Because clusters 0 and 2 expressed metabolic signatures, we evaluated metabolic gene expression across groups. Clusters 0 and 1 expressed high levels of metabolic genes, whereas cluster 2 showed comparatively lower expression (Fig. 7A). Relative to AirD7 and O2D7, SD7 macrophages displayed higher glycolysis genes (Slc2a1, Pkm, Pfkfb3, Ldha, Hk2, Gapdh), higher pentose phosphate pathway genes (Tkt, Taldo1, Pgd, G6pdx), and lower \beta-oxidation genes (Hadh, Eci1/2, Echs1, Acads/Acadsb, Acaa2) (Fig. 7B). Pathway analysis confirmed enrichment of glycolysis, glutamine metabolism, and TCA cycle-related pathways, and suppression of fatty acid \beta-oxidation (Fig. 7C). Pdha1, a key regulator linking glycolysis to oxidative phosphorylation, was reduced in lamin b1 negative macrophages after hyperoxia compared to the air control (Fig. 7B,D).

Because clusters 0, 1, and 3 constituted 62.8% of SD7 macrophages, we assessed metabolic changes specifically within these clusters. In all three clusters, glycolysis and glutamine metabolism were increased, while \beta-oxidation genes were consistently reduced in the SD7 group compared to the AirD7 and O2D7 groups (Figs. 8,9,10). The TCA genes were unaltered in clusters 1, 2 or 3 among AirD7, O2D7 and SD7 groups (Fig. 10). However, mitochondrial complexes I, II, IV, and V genes were upregulated, whereas the complex III gene Bcs1l was decreased in the SD7 group compared to the AirD7 and O2D7 groups (Fig. 11). Together, these findings suggest that senescent alveolar macrophages undergo a metabolic shift toward glycolysis and glutamine utilization.

3.7 Pathway Enrichment Analyses Reveal Dysregulated Metabolic, Inflammatory, and Repair Pathways in Senescent Macrophages

Using SCPA to compare GO biological processes across groups, we identified pathway alterations characteristic of senescent macrophages. Volcano plots show broad upregulation and downregulation of signal pathways in SD7 relative to AirD7 and O2D7 (Fig. 12A; Tables 10,11,12,13). Upregulated pathways in SD7 group included metal metabolism and homeostasis, amino acid metabolism, interleukin signals, immune effector process, robo receptor signaling, apoptotic signal pathway, heme oxygenase 1 regulation, and RNA process and translation compared to AirD7 (Table 10) and O2D7 (Table 11). Transferrin receptor is a membrane protein which binds to transferrin and mediates cellular iron uptake. This protein is also regulated by intracellular iron levels. Transferrin receptor expression was increased in lamin b1-negative macrophages after neonatal hyperoxia (Fig. 12B), supporting enhanced iron uptake and metabolism in senescent cells.

Upregulated pathways in SD7 group included lung morphogenesis, ROS/H₂O₂ detoxification, and Rho GTPase signals were downregulated compared with both AirD7 and O2D7 groups (Tables 12,13). Pathways including morphogenesis, development, branching, vasculogenesis, response to retinoic acid, insulin-like growth factor, and Runx2 were downregulated in the SD7 group compared with the AirD7 group (Table 12). Compared to O2D7 group, filament, cytoskeleton and cell junction organization, adhesion, phagocytosis, Eph/ephrin signaling, and DNA repair pathway were also downregulated in SD7 group (Table 13).

In addition, p38 mitogen-activated kinase (p38 MAPK), ataxia-telangiectasia mutated (ATM), and mechanistic target of rapamycin (mTOR) pathways were strongly enriched in SD7, whereas JAK/STAT signaling was reduced (Fig. 12C). Because p38 MAPK, ATM, and mTOR are central regulators of the SASP [21, 22], these findings suggest that senescent macrophages activate pathways that promote SASP production.

3.8 Activating Pdh Decreases Hyperoxia-Induced Macrophage Senescence and Lung Injury

PDK phosphorylates the \alpha subunit of E1, e.g., Ser293, leading to Pdh inactivation. To determine whether activating Pdh represses hyperoxia-induced macrophage senescence and lung injury, mice received a prototypical PDK inhibitor DCA (15 and 30 mg/kg, i.p.) daily between pnd4 and pnd6 after hyperoxia. As shown in Fig. 13A, injection of DCA increased lung Pdh activity in mice exposed to hyperoxia at pnd7. The numbers of lamin b1^–/CD68⁺ cells and p21⁺/CD68⁺ were increased by hyperoxia at pnd7, and this was decreased by DCA injection at a dose of 30 mg/kg (Fig. 13B,C). At pnd14, injection of DCA significantly decreased mean linear intercept (Lm) and increased RAC in hyperoxia-exposed mice (Fig. 13D). Furthermore, neonatal hyperoxia-induced reduction of vWF-positive vessels was attenuated by the treatment at pnd14 (Fig. 13E). Altogether, activating Pdh decreases neonatal hyperoxia-induced macrophage senescence and lung injury. The animal experiments presented in Fig. 13 were approved under Protocol 2024-003, while all remaining animal work described in this study was conducted under Protocol 21-08-0003.

3.9 Senolytic Treatment Reduces Neonatal Hyperoxia-Induced Persistent Lung Injury

We previously demonstrated that dasatinib plus quercetin decreases early senescence in hyperoxia-exposed neonatal lungs [6]. To test whether this treatment also mitigates persistent injury, mice received the senolytic cocktail quercetin/dasatinib (2.5 and 5 mg/kg, i.p.) on pnd4 and pnd14 after neonatal hyperoxia. The numbers of lamin b1^–/CD68⁺ cells and p21⁺/CD68⁺ were increased by hyperoxia at pnd7, and this was decreased by injection of quercetin/dasatinib (5 mg/kg) (Fig. 14A,B). At pnd60, hyperoxia increased Lm and decreased RAC, consistent with impaired alveolarization, but both changes were significantly improved by senolytic treatment in a dose-dependent manner (Fig. 14C). Neonatal hyperoxia-induced vascular simplification, measured by reduced vWF-positive vessels, was also attenuated by the treatment (Fig. 14D). These findings indicate that reducing senescent lung cells ameliorates long-term structural lung injury after neonatal hyperoxia.

4. Discussion

In this study, we analyzed senescent lung cells collected at pnd7 from neonatal mice exposed to hyperoxia and identified macrophages as the predominant senescent population. Seven macrophage clusters were resolved, with clusters 0, 1, and 3 comprising nearly two-thirds of all senescent macrophages. These clusters were enriched for alveolar and M1-like phenotypes, indicating that senescent macrophages are highly heterogeneous and occupy distinct niches in the developing lung. The predominance of M1-associated signatures suggests that senescent proinflammatory macrophages may amplify hyperoxia-induced lung injury through secretion of SASP mediators [17].

Although macrophages represented the majority of senescent cells, we also detected senescent epithelial, endothelial, and mesenchymal cells, consistent with earlier findings in a rat hyperoxia model [7]. In this dataset, 65.9% of senescent cells were macrophages, a lower proportion than our earlier estimate of 92.1% [6]. This discrepancy likely reflects methodological differences. Our previous analysis relied on automated reference mapping [6], whereas the current study applied hierarchical clustering followed by manual curation to refine macrophage subtype assignments [8]. This approach reduces misclassification and yields more stringent identification of macrophage subpopulations.

Resident alveolar macrophages are diminished following neonatal hyperoxia [23]. Our data similarly show reduced numbers and proportions of these cells in the O2D7 group compared with air controls. Because resident alveolar macrophages support vascular development and aid in the retention of endothelial progenitor cells partly through CXCL12 and other trophic factors, their loss may impair reparative processes and contribute to lung injury [23, 24, 25]. Interestingly, the present study reveals that alveolar macrophages form the largest pool of senescent cells following hyperoxia, raising the possibility that their transition into senescence may disrupt normal homeostatic functions and hinder endothelial maintenance.

Although senescence is commonly regarded as a terminal state, senescent cells can reenter the cell cycle when the pathways enforcing arrest are discontinued [26, 27, 28]. Such cells do not revert to their pre-senescent identity but instead adopt a distinct “post-senescent” phenotype [29, 30]. We identified a cluster of macrophages (cluster 5) expressing proliferative markers, including Cenpf, Mki67, Stmn1, Top2a, and Cdk1, suggesting the presence of a subset with proliferative potential. As expected, these potentially proliferative macrophages were less abundant in the SD7 group than in controls. This cluster of cells were mainly distributed at early or intermediate stages of differentiation based on the pseudotime analysis. Whether senescent macrophages can proliferate after hyperoxic injury in vivo remains unclear and warrants investigation using lineage-tracing and histological approaches.

Metabolic reprogramming is a hallmark of senescence, with increased reliance on glycolysis and the pentose phosphate pathway to support SASP production [31, 32, 33]. Our analysis indicates that senescent macrophages favor these pathways over fatty acid \beta-oxidation. Pdh is a multi-enzyme complex that converts pyruvate into acetyl-CoA through pyruvate decarboxylation, and its E1 subunit performs the first and rate-limiting step of this reaction. Pdha1 is strongly expressed in neonatal alveolar macrophages [34]. The reduced expression of Pdha1 in senescent macrophages suggests suppressed glucose oxidation in the TCA cycle. These cells may instead rely on glutamine metabolism as an anaplerotic source to sustain their enlarged size and secretory activity. Integrating Seahorse analysis of glycolysis and oxidative phosphorylation with single-cell metabolomics in sorted populations is critical to validate transcriptomic predictions and identify the substrates supporting senescent macrophages during hyperoxic injury.

Pdh influences both replicative and oncogene-induced senescence [35, 36]. This is supported by our findings demonstrating reduced macrophage senescence following Phd activation by DCA injection. DCA upregulates the cellular NAD⁺/NADH ratio by increasing NADH oxidation in mitochondrial complex 1 [37]. Notably, the NAD⁺/NADH ratio and NAD⁺-dependent SIRT1 levels are reduced in peripheral blood mononuclear cells of premature infants with BPD [38, 39, 40]. Thus, DCA may attenuate hyperoxia-induced macrophage senescence by increasing the NAD⁺/NADH ratio, enhancing NAD⁺-dependent SIRT1 activity, or modulating other signaling pathways, such as AMPK and HIF-1\alpha. Macrophage-specific deletion of Pdha1 would provide further evidence for the role of metabolic reprogramming in hyperoxia-induced senescence within this cell population.

Upregulation of electron transport chain genes in senescent macrophages may increase mitochondrial ROS production, and enhanced iron uptake through the transferrin receptor may further augment oxidative stress. Combined with the observed decrease in ROS-detoxifying and DNA-repair pathways, these processes could generate a self-reinforcing cycle of oxidative damage that stabilizes the senescent phenotype [6]. Given that iron overload can drive ferroptosis, it is possible that iron accumulation may mark senescent macrophages for elimination [41, 42]. Examining ferroptosis-related pathways in senescent macrophages may clarify whether this mechanism contributes to their regulation following hyperoxia. Further experiments are warranted to determine whether iron chelation inhibits hyperoxia-induced senescence in macrophages, or whether induction of ferroptosis preferentially eliminates this population.

Key signaling pathways known to govern senescence, including p38 MAPK, ATM, and mTOR, were enriched in senescent macrophages, along with pathways involved in RNA processing and translation, consistent with enhanced SASP gene expression and protein synthesis. Further studies are warranted to determine whether the SASP secretome from senescent macrophages impairs alveolar epithelial wound healing and endothelial tube formation in vitro, as well as alveolarization and vascularization in vivo, and whether these effects can be attenuated by pathway-specific inhibitors. Conversely, pathways associated with phagocytosis were downregulated, in agreement with reports that senescent macrophages exhibit diminished engulfment capacity [43, 44, 45]. Such impairments could further compromise lung repair by reducing clearance of debris, apoptotic cells, and pathogens. This needs further investigation.

This study did not evaluate long-term lung mechanics, including lung compliance and airway resistance, in adult mice following neonatal senolytic clearance of macrophages, which is essential to link alveolar simplification with functional morbidity. Sex-specific responses to senolytic treatment were not assessed and may limit generalizability. Validation of senescent alveolar macrophage accumulation and associated metabolic phenotypes in human BPD lung tissue would facilitate translational relevance. Additionally, the quercetin/dasatinib cocktail may exert off-target toxicity toward non-senescent cells [46]. Future studies should prioritize the development of targeted approaches to selectively eliminate senescent macrophages for BPD therapy in an optimal therapeutic window.

5. Conclusion

Neonatal hyperoxia induces pronounced cellular senescence in the developing lung, with macrophages-particularly alveolar and M1-polarized subsets-constituting the dominant senescent population. These senescent macrophages undergo metabolic reprogramming characterized by increased reliance on glycolysis and the pentose phosphate pathway, accompanied by dysregulation of oxidative stress responses, inflammatory signaling, and tissue repair pathways. Activation of Pdh attenuates hyperoxia-induced macrophage senescence, while selective clearance of this population mitigates the development of chronic lung injury following neonatal hyperoxia. Collectively, these findings identify senescent macrophages as key mediators of persistent lung injury and highlight metabolic and senescence-associated pathways as potential targets for therapeutic intervention of BPD.

Disclosure

The paper is listed as, “Characterization of hyperoxia-induced senescent lung macrophages in neonatal mice” as a preprint on (bioRxiv) at: https://doi.org/10.1101/2025.05.09.652066.

Availability of Data and Materials

All raw data used and analyzed in this study are available from the corresponding author upon reasonable request. Analysis of the scRNA-seq data used publicly available R packages and custom scripts, which are also available from the corresponding author upon reasonable request.

References

[1] Mowitz ME, Ayyagari R, Gao W, Zhao J, Mangili A, Sarda SP. Health Care Burden of Bronchopulmonary Dysplasia Among Extremely Preterm Infants. Frontiers in Pediatrics. 2019; 7: 510. https://doi.org/10.3389/fped.2019.00510.

[2] Álvarez-Fuente M, Arruza L, Muro M, Zozaya C, Avila A, López-Ortego P, et al. The economic impact of prematurity and bronchopulmonary dysplasia. European Journal of Pediatrics. 2017; 176: 1587–1593. https://doi.org/10.1007/s00431-017-3009-6.

[3] Lapcharoensap W, Bennett MV, Xu X, Lee HC, Dukhovny D. Hospitalization costs associated with bronchopulmonary dysplasia in the first year of life. Journal of Perinatology. 2020; 40: 130–137. https://doi.org/10.1038/s41372-019-0548-x.

[4] Bancalari E, Jain D. Bronchopulmonary Dysplasia: 50 Years after the Original Description. Neonatology. 2019; 115: 384–391. https://doi.org/10.1159/000497422.

[5] Parikh P, Britt RD, Jr, Manlove LJ, Wicher SA, Roesler A, Ravix J, et al. Hyperoxia-induced Cellular Senescence in Fetal Airway Smooth Muscle Cells. American Journal of Respiratory Cell and Molecular Biology. 2019; 61: 51–60. https://doi.org/10.1165/rcmb.2018-0176OC.

[6] Yao H, Wallace J, Peterson AL, Scaffa A, Rizal S, Hegarty K, et al. Timing and cell specificity of senescence drives postnatal lung development and injury. Nature Communications. 2023; 14: 273. https://doi.org/10.1038/s41467-023-35985-4.

[7] Jing X, Jia S, Teng M, Day BW, Afolayan AJ, Jarzembowski JA, et al. Cellular Senescence Contributes to the Progression of Hyperoxic Bronchopulmonary Dysplasia. American Journal of Respiratory Cell and Molecular Biology. 2024; 70: 94–109. https://doi.org/10.1165/rcmb.2023-0038OC.

[8] Li X, Mara AB, Musial SC, Kolling FW, Gibbings SL, Gerebtsov N, et al. Coordinated chemokine expression defines macrophage subsets across tissues. Nature Immunology. 2024; 25: 1110–1122. https://doi.org/10.1038/s41590-024-01826-9.

[9] Lin F, Lu W, Dennery PA, Yao H. Characterization of hyperoxia-induced senescent lung macrophages in neonatal mice. bioRxiv. 2025. https://doi.org/10.1101/2025.05.09.652066. (preprint)

[10] Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology. 2018; 36: 411–420. https://doi.org/10.1038/nbt.4096.

[11] Zappia L, Oshlack A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. GigaScience. 2018; 7: giy083. https://doi.org/10.1093/gigascience/giy083.

[12] Dai M, Pei X, Wang XJ. Accurate and fast cell marker gene identification with COSG. Briefings in Bioinformatics. 2022; 23: bbab579. https://doi.org/10.1093/bib/bbab579.

[13] Castanza AS, Recla JM, Eby D, Thorvaldsdóttir H, Bult CJ, Mesirov JP. Extending support for mouse data in the Molecular Signatures Database (MSigDB). Nature Methods. 2023; 20: 1619–1620. https://doi.org/10.1038/s41592-023-02014-7.

[14] Bibby JA, Agarwal D, Freiwald T, Kunz N, Merle NS, West EE, et al. Systematic single-cell pathway analysis to characterize early T cell activation. Cell Reports. 2022; 41: 111697. https://doi.org/10.1016/j.celrep.2022.111697.

[15] Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology. 2014; 32: 381–386. https://doi.org/10.1038/nbt.2859.

[16] Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nature Methods. 2017; 14: 1083–1086. https://doi.org/10.1038/nmeth.4463.

[17] Syed MA, Bhandari V. Hyperoxia exacerbates postnatal inflammation-induced lung injury in neonatal BRP-39 null mutant mice promoting the M1 macrophage phenotype. Mediators of Inflammation. 2013; 2013: 457189. https://doi.org/10.1155/2013/457189.

[18] Freund A, Laberge RM, Demaria M, Campisi J. Lamin B1 loss is a senescence-associated biomarker. Molecular Biology of the Cell. 2012; 23: 2066–2075. https://doi.org/10.1091/mbc.E11-10-0884.

[19] Li F, Piattini F, Pohlmeier L, Feng Q, Rehrauer H, Kopf M. Monocyte-derived alveolar macrophages autonomously determine severe outcome of respiratory viral infection. Science Immunology. 2022; 7: eabj5761. https://doi.org/10.1126/sciimmunol.abj5761.

[20] Li S, Xu H, Liu S, Hou J, Han Y, Li C, et al. Targeting Lp-PLA2 inhibits profibrotic monocyte-derived macrophages in silicosis through restoring cardiolipin-mediated mitophagy. Cellular & Molecular Immunology. 2025; 22: 776–790. https://doi.org/10.1038/s41423-025-01288-5.

[21] Rodier F, Coppé JP, Patil CK, Hoeijmakers WAM, Muñoz DP, Raza SR, et al. Persistent DNA damage signalling triggers senescence-associated inflammatory cytokine secretion. Nature Cell Biology. 2009; 11: 973–979. https://doi.org/10.1038/ncb1909.

[22] Xu M, Tchkonia T, Ding H, Ogrodnik M, Lubbers ER, Pirtskhalava T, et al. JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age. Proceedings of the National Academy of Sciences of the United States of America. 2015; 112: E6301–E6310. https://doi.org/10.1073/pnas.1515386112.

[23] Kalymbetova TV, Selvakumar B, Rodríguez-Castillo JA, Gunjak M, Malainou C, Heindl MR, et al. Resident alveolar macrophages are master regulators of arrested alveolarization in experimental bronchopulmonary dysplasia. The Journal of Pathology. 2018; 245: 153–159. https://doi.org/10.1002/path.5076.

[24] Leek C, Cantu A, Sonti S, Gutierrez MC, Eldredge L, Sajti E, et al. Role of sex as a biological variable in neonatal alveolar macrophages. Redox Biology. 2024; 75: 103296. https://doi.org/10.1016/j.redox.2024.103296.

[25] Mohammed AN, Kohram F, Lan YW, Li E, Kolesnichenko OA, Kalin TV, et al. Transplantation of alveolar macrophages improves the efficacy of endothelial progenitor cell therapy in mouse model of bronchopulmonary dysplasia. American Journal of Physiology. Lung Cellular and Molecular Physiology. 2024; 327: L114–L125. https://doi.org/10.1152/ajplung.00274.2023.

[26] Li Y, Zhao H, Huang X, Tang J, Zhang S, Li Y, et al. Embryonic senescent cells re-enter cell cycle and contribute to tissues after birth. Cell Research. 2018; 28: 775–778. https://doi.org/10.1038/s41422-018-0050-6.

[27] Milanovic M, Fan DNY, Belenki D, Däbritz JHM, Zhao Z, Yu Y, et al. Senescence-associated reprogramming promotes cancer stemness. Nature. 2018; 553: 96–100. https://doi.org/10.1038/nature25167.

[28] Haferkamp S, Borst A, Adam C, Becker TM, Motschenbacher S, Windhövel S, et al. Vemurafenib induces senescence features in melanoma cells. The Journal of Investigative Dermatology. 2013; 133: 1601–1609. https://doi.org/10.1038/jid.2013.6.

[29] Martínez-Zamudio RI, Stefa A, Nabuco Leva Ferreira Freitas JA, Vasilopoulos T, Simpson M, Doré G, et al. Escape from oncogene-induced senescence is controlled by POU2F2 and memorized by chromatin scars. Cell Genomics. 2023; 3: 100293. https://doi.org/10.1016/j.xgen.2023.100293.

[30] Reimann M, Lee S, Schmitt CA. Cellular senescence: Neither irreversible nor reversible. The Journal of Experimental Medicine. 2024; 221: e20232136. https://doi.org/10.1084/jem.20232136.

[31] Wiley CD, Campisi J. From Ancient Pathways to Aging Cells-Connecting Metabolism and Cellular Senescence. Cell Metabolism. 2016; 23: 1013–1021. https://doi.org/10.1016/j.cmet.2016.05.010.

[32] James EL, Michalek RD, Pitiyage GN, de Castro AM, Vignola KS, Jones J, et al. Senescent human fibroblasts show increased glycolysis and redox homeostasis with extracellular metabolomes that overlap with those of irreparable DNA damage, aging, and disease. Journal of Proteome Research. 2015; 14: 1854–1871. https://doi.org/10.1021/pr501221g.

[33] Kondoh H, Lleonart ME, Gil J, Wang J, Degan P, Peters G, et al. Glycolytic enzymes can modulate cellular life span. Cancer Research. 2005; 65: 177–185.

[34] Cantu A, Gutierrez MC, Dong X, Leek C, Sajti E, Lingappan K. Remarkable sex-specific differences at single-cell resolution in neonatal hyperoxic lung injury. American Journal of Physiology. Lung Cellular and Molecular Physiology. 2023; 324: L5–L31. https://doi.org/10.1152/ajplung.00269.2022.

[35] Kaplon J, Zheng L, Meissl K, Chaneton B, Selivanov VA, Mackay G, et al. A key role for mitochondrial gatekeeper pyruvate dehydrogenase in oncogene-induced senescence. Nature. 2013; 498: 109–112. https://doi.org/10.1038/nature12154.

[36] Stabenow LK, Zibrova D, Ender C, Helbing DL, Spengler K, Marx C, et al. Oxidative Glucose Metabolism Promotes Senescence in Vascular Endothelial Cells. Cells. 2022; 11: 2213. https://doi.org/10.3390/cells11142213.

[37] Lin G, Hill DK, Andrejeva G, Boult JKR, Troy H, Fong ACLFWT, et al. Dichloroacetate induces autophagy in colorectal cancer cells and tumours. British Journal of Cancer. 2014; 111: 375–385. https://doi.org/10.1038/bjc.2014.281.

[38] Pavlyshyn H, Sarapuk I, Kozak K, Klishch O. Diagnostic value of markers of oxidative stress and metabolic disorders in preterm infants in the early neonatal period. Paediatria Croatica. 2021; 65: 7–12. https://doi.org/10.13112/PC.2021.2.

[39] Tan F, Dong W, Lei X, Liu X, Li Q, Kang L, et al. Decreased SIRT1 expression is related to bronchopulmonary dysplasia in premature infants after oxygen exposure. Chinese Journal of Cellular and Molecular Immunology. 2016; 32: 1632–1635. (In Chinese)

[40] Du FL, Dong WB, Zhang C, Li QP, Kang L, Lei XP, et al. Budesonide and Poractant Alfa prevent bronchopulmonary dysplasia via triggering SIRT1 signaling pathway. European Review for Medical and Pharmacological Sciences. 2019; 23: 11032–11042. https://doi.org/10.26355/eurrev_201912_19811.

[41] Feng Y, Wei H, Lyu M, Yu Z, Chen J, Lyu X, et al. Iron retardation in lysosomes protects senescent cells from ferroptosis. Aging. 2024; 16: 7683–7703. https://doi.org/10.18632/aging.205777.

[42] Kureel SK, Rasmussen BB. Targeting Ferroptosis to Eliminate Senescent Cells: Mechanisms and Therapeutic Potential. Aging and Disease. 2025; 10.14336/AD.2025.0141. https://doi.org/10.14336/AD.2025.0141.

[43] Holt DJ, Grainger DW. Senescence and quiescence induced compromised function in cultured macrophages. Biomaterials. 2012; 33: 7497–7507. https://doi.org/10.1016/j.biomaterials.2012.06.099.

[44] Rabhi N, Desevin K, Belkina AC, Tilston-Lunel A, Varelas X, Layne MD, et al. Obesity-induced senescent macrophages activate a fibrotic transcriptional program in adipocyte progenitors. Life Science Alliance. 2022; 5: e202101286. https://doi.org/10.26508/lsa.202101286.

[45] Rawji KS, Mishra MK, Michaels NJ, Rivest S, Stys PK, Yong VW. Immunosenescence of microglia and macrophages: impact on the ageing central nervous system. Brain. 2016; 139: 653–661. https://doi.org/10.1093/brain/awv395.

[46] Cai Y, Zhou H, Zhu Y, Sun Q, Ji Y, Xue A, et al. Elimination of senescent cells by β-galactosidase-targeted prodrug attenuates inflammation and restores physical function in aged mice. Cell Research. 2020; 30: 574–589. https://doi.org/10.1038/s41422-020-0314-9.

Funding

NIH(R01HL166327)

Institutional Development Award (IDeA) from the NIGMS of NIH(#P30GM149398)

Warren Alpert Foundation of Brown University