Aging affects reprogramming of pulmonary capillary endothelial cells after lung injury in male mice

Abstract

Aging increases the risk of developing fibrotic diseases by hampering tissue regeneration after injury. Using longitudinal single-cell RNA-seq and spatial transcriptomics, here we compare the transcriptome of bleomycin (BLM) -induced fibrotic lungs of young and aged male mice, at 3 time points corresponding to the peak of fibrosis, regeneration, and resolution. We find that lung injury shifts the transcriptomic profiles of three pulmonary capillary endothelial cells (PCEC) subpopulations. The associated signatures are linked to pro-angiogenic signaling with strong Lrg1 expression and do not progress similarly throughout the resolution process between young and old animals. Moreover, part of this set of resolution-associated markers is also detected in PCEC from samples of patients with idiopathic pulmonary fibrosis. Finally, we find that aging also alters the transcriptome of PCEC, which displays typical pro-fibrotic and pro-inflammatory features. We propose that age-associated alterations in specific PCEC subpopulations may interfere with the process of lung progenitor differentiation, thus contributing to the persistent fibrotic process typical of human pathology.

Fig. 1. Analyses of fibrotic parameters in BLM-injured lungs from young and old mice.

A Experimental design. Young (7 weeks) or old (18 months) mice were treated either with BLM or PBS. Their lungs were collected at the BLM-induced fibrotic peak at day 14 and during lung regeneration (day 28) and fibrosis resolution (day 60). B Hydroxyproline quantification on BLM (n ≥ 6) or PBS (n = 4)-treated young or old mouse samples at indicated time points. Source data are provided as a Source Data file. Boxplot are represented with the median in the center, the whiskers correspond to the interquartile ranges, and the bounds correspond to the minimum and maximum values. * P < 0.05. **P < 0.01. P values were calculated by two-way ANOVA test followed by a multiple comparisons test with Holm-Šídák correction. C Histological sections of control and fibrotic lungs from young and old mice using H&E at indicated time points (representative images, n = 3). D Experimental design of spatial transcriptomics data from histological sections (H&E) of lungs from young (n = 1) and old (n = 2) mice challenged with BLM and collected at fibrotic peak (day 14, D14) and during regeneration (day 28, D28). Spots captured corresponding to tissue area were deconvoluted through ST deconvolve package in topics (n = 14). E Functional annotations of topics by IPA. Enrichment p-values obtained with IPA are calculated by right-tailed Fisher’s Exact Test and Benjamini-Hochberg correction. F Average proportion of each topic across deconvoluted spots in each slice. A, D created in BioRender. MARI, B. (2025) https://BioRender.com/b0jq8np.

Fig. 2. Time-resolved scRNA-seq captures PCEC subpopulations expressing Lrg1 in BLM-injured lungs from young and old mice.

A scRNA-seq experimental design (n = 3, for each experimental condition at each time point). B UMAP of the integrated dataset of the 45,311 sequenced cells. Cells are colored according to the annotated clusters. Prolif. AM = proliferating macrophages, AM = alveolar macrophages, IM = interstitial macrophages, LCM = large-cavity macrophages, Prolif. DC = proliferating dendritic cells, cDC = conventional dendritic cell, Mature DC = mature dendritic cells, pDC = plasmacytoid dendritic cells, cMonocytes/ncMonocytes = conventional/non-conventional monocytes, Prolif. T cells = proliferating T cells, Reg T cells = regulatory T cells, ILC2 = type 2 innate lymphoid cells, NKT = natural killer T cells, NK cells = natural killer, AT1/2 = Alveolar Type 1/2 cells, gCap = general capillary endothelial cells, aCap = aerocytes capillary endothelial cells, Prolif. EC = proliferating endothelial cells. C DEGs between BLM- and PBS-treated cells for indicated populations. The number of DEGs is limited to 1500 for the bar plot. D UMAP of pulmonary EC subpopulations. sCap = systemic capillary endothelial cells, SV EC = systemic venous endothelial cells, PV EC = pulmonary-venous endothelial cells. E Relative proportions of PCEC subpopulations across time points. (F) Normalized expression of Lrg1, Aplnr, Ednrb, Col15a1, Vwa1 and Ackr1 in mouse EC subpopulations. G Level and percentage of expression of capillary, systemic and venous markers in Lrg1^pos PCEC subpopulations. H In situ hybridization of Col15a1 and Aplnr mRNA in BLM or PBS conditions. Analysis of EC subpopulations in IPF (Habermann et al. dataset): UMAP and relative proportion of EC subpopulations (I); Normalized expression of LRG1, APLNR, EDNRB, COL15A1, VWA1 and ACKR1 in EC from IPF and control lungs (J); Level and percentage of expression of capillary, systemic and venous markers in human IPF EC subpopulations (K). L Expression of COL15A1 in lung sections from normal or IPF tissues by Immunofluorescence. Representative immunofluorescent images of COL15A1 (in red) with the pan EC marker CD31 (white) and αSMA (green). Nuclei are counterstained with DAPI. (n = 5).

Fig. 3. Lung injury shift pulmonary endothelial cells towards Lrg1^pos subpopulations associated with lung alveolar niche regeneration.

A Selection of top activated canonical pathways in Lrg1^pos PCEC and venous EC subpopulations. B Selection of top activated functions in Lrg1^pos PCEC and venous EC subpopulations. Enrichment p-values obtained with IPA are calculated by right-tailed Fisher’s Exact Test and Benjamini-Hochberg correction. Level and percentage of expression of hypoxia-regulated (C) and pro-angiogenic (D) genes in Lrg1^pos PCEC and venous EC subpopulations. E RNA FISH acquisition for Hif1a1 (black, top), Sox17 (black, middle), Vegfa (black, down), Aplnr (green) and Lrg1 (red) mRNA in PBS or D14 (BLM) in young mice. Scale bars = 20 µm, n = 3 independent mice and 5 field/mouse were captured. F Interaction potential between the prioritized ligands and their bona fide receptors (left); Level and percentage of expression of each receptor in Lrg1^pos PCEC subpopulations (right). G Circos plot of ligands-receptors interactions predicted by NicheNet analysis. Ligands are colored according to their specific expression. H Comparison of human and mouse pulmonary fibrosis signatures in sCap, SV EC, PV EC, gCap and aCap. Log2FC of a selection of genes that are similarly modulated by fibrosis in murine and human sCap.

Fig. 4. Lrg1^pos PCEC dynamics are delayed in old mice.

A Pulmonary endothelial subpopulations relative frequencies in young and old mice across time points. B BLM-induced signature score (see material and methods section) in gCap (top) and aCap (bottom) in young and old mice across time points. C In situ hybridization of Col15a1 mRNA at indicated time points in young and old mice. Scale bars = 20 µm. D RNA FISH of Lrg1 mRNA with gCap marker Aplnr mRNA or aCap marker Ednrb mRNA at indicated time points in young and old mice. (n = 3 independent mice, 3 counted microscopic fields/mouse, scale bars = 20 µm). Quantification was performed in three different fields for each mouse. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. P-values were calculated by a Two-way ANOVA test followed by a multiple comparisons test with Holm-Sidak correction. Boxplot are represented with the median in the center, the whiskers correspond to the interquartile ranges, and the bounds correspond to the minimum and maximum values. Source data are provided as a Source Data file.

Fig. 5. Aged mouse lung gCap exhibits pro-inflammatory and injury-activated phenotype.

A Differentially expressed genes (DEGs) between old and young gCap in fibrotic condition. B Expression of several genes dysregulated in age-related in gCap under BLM conditions (Cd74, Gbp4, H2-Ab1, Klf2, Klf10 and Peg3). Boxplot of relative expression, each point corresponds to one mouse (n = 3 by condition, ● for D14 ▲ for D28). C In situ hybridization of Cd74 mRNA in BLM or Day 14 after BLM induction in young and old mice. Scale bars = 20 µm. D DEGs between old and young gCap in physiological condition. E Pseudobulk expression of several genes dysregulated by ageing in gCap under PBS conditions (Aplnr, Col15a1, Lrg1, Slc6a2, Ntrk2, Plat, Prss23 and Vwf). Each point corresponds to the aggregated expression in one mouse (n = 3 by condition, ● for D14, ▲ for D28 and ■ for D60). F Function enrichment analysis on DEGs between old and young physiological gCap. G Comparison of the log₂FC obtained by comparing physiological gCap of old and young mice, and the corresponding log₂FC between gCap of BLM and PBS-treated mice. Boxplot are represented with the median in the center, the whiskers correspond to the interquartile ranges, and the bounds correspond to the minimum and maximum values. Source data are provided as a Source Data file. Statistic: P-values were calculated by the Wald test and the Benjamini-Hochberg method for multiple tests correction.

Fig. 6. RNA velocity analysis reveals transcriptional dynamics of PCEC during alveolar regeneration.

Diffusion map computed on Young D14 (A) or Old D28 (B) mouse spliced RNA data (3 BLM and 3 PBS-treated mice), with cells colored according to PCEC subpopulation (up) and RNA velocity latent time (bottom). Heatmap of the top contributing genes to the transcriptional dynamic, with cells ordered by latent time in young D14 (C) or old D28 (D) mice.