Truncated NTRK2 is induced in CAP1 endothelial cells during mouse lung injury-repair

Celine Shuet Lin Kong; Mitheera V; Jezreel Pantaleón-García; Scott E Evans; Jichao Chen

doi:10.1016/j.isci.2025.112973

. 2025 Jun 24;28(7):112973. doi: 10.1016/j.isci.2025.112973

Truncated NTRK2 is induced in CAP1 endothelial cells during mouse lung injury-repair

Celine Shuet Lin Kong ^1,², Mitheera V ^2,³, Jezreel Pantaleón-García ¹, Scott E Evans ¹, Jichao Chen ^1,^4,^5,^∗

PMCID: PMC12275058 PMID: 40687816

Summary

Pulmonary capillary endothelial cells (ECs) consist of two populations, CAP1 and CAP2; how each population reacts to diverse tissue injury is incompletely understood. Using single-cell multiome and mouse genetics, we characterize the induction and function of a truncated isoform of Ntrk2, Ntrk2-T1, in multiple lung injury models. Upon Sendai parainfluenza infection, Ntrk2-T1 is broadly induced in CAP1s after the initial interferon response, associated with increased intronic chromatin accessibility, and persists for weeks. Ntrk2-T1 ECs arise from CAP1s but not CAP2s—traced by Kit^CreER and Car4^CreER, respectively—and proliferate and give rise to CAP1s but not CAP2s, as traced by Ntrk2^CreER. Although also induced by lipopolysaccharide, H3N2 influenza, and COVID-19 injuries, EC-specific deletion of Ntrk2 has limited molecular and cellular consequences. Individuals with incident and prevalent respiratory diseases have lower plasma NTRK2. Our data identifies Ntrk2-T1 as an EC marker of lung injury-repair and enhances our understanding of EC heterogeneity.

Subject areas: Biochemistry, Molecular physiology, Cell biology

Graphical abstract

Highlights

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CAP1s broadly upregulate NTRK2-T1 in response to multiple lung injury models
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NTRK2+ ECs arise from CAP1s, proliferate, and give rise to only CAP1s
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NTRK2 is an EC marker of lung injury-repair in mice and humans
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Endothelial loss of NTRK2 has limited molecular and cellular consequences

Biochemistry; Molecular physiology; Cell biology

Introduction

Blood and lymphatic endothelial cells (ECs) form a ubiquitous transport system of fluids, gases, and cells essential for complex organs where passive diffusion is insufficient. Recent single-cell profiling has not only confirmed classical recognition of arterial, venous, capillary, and lymphatic ECs, but also revealed diversity of capillary ECs within and across organs, suiting the respective physiological needs.¹^,² The mammalian lung contains both systemic and pulmonary circulations, the latter of which predominates and forms the gas exchange interface. Traditionally considered homogeneous and used as a convenient source of primary ECs, pulmonary capillary ECs consist of two transcriptionally and morphologically distinct populations, CAP1 and CAP2, also known as gCap and aCap, respectively.³^,⁴^,⁵ CAP1s are 4- to 5-fold more abundant than CAP2s and resemble generic capillary ECs elsewhere. CAP2s are more specialized, sharing basement membrane with the alveolar epithelium and constituting the diffusion barrier between the air and blood. However, gas diffusion still occurs sufficiently to be compatible with life even in the absence of CAP2s in an epithelial Vegfa mouse mutant.³ Thus, the functional significance of CAP1 and CAP2 diversification is still unclear.

This baseline CAP1/2 diversity impacts endothelial responses during lung injury-repair. CAP1s, but not CAP2s, become proliferative, consistent with perceived challenges in cytokinesis of cells with convoluted morphology, such as neurons and CAP2s.³^,⁴^,⁶ CAP1s can give rise to CAP2s, analogous to developmental differentiation of CAP2s from endothelial progenitors that molecularly resemble CAP1s.³^,⁴^,⁶ Besides these cell-type-level changes, CAP1s and CAP2s undergo common and distinct transcriptional changes to occupy cellular states that can be considered generically as activated but named more precisely according to predicted and demonstrated functions. Reacting to an influx of immune cells during sterile and non-sterile injuries, resident lung cells including ECs are likely to be in an inflamed state. During the subsequent recovery that could be complete or partial depending on the injury severity and age-related tissue resilience, cells can transition through regenerative or dysplastic states, both attempts to reach molecular equilibrium again. The trajectory from homeostasis to activated, and returning to new or original homeostasis is being re-investigated in the context of the newly found EC diversity and is expected to be injury-specific,⁷^,⁸^,⁹^,¹⁰^,¹¹^,¹² as revealed for the lung epithelium.¹³^,¹⁴^,¹⁵^,¹⁶^,¹⁷

In a methodology paper on optimization of scRNA-seq profiling and analysis, we reported an unexpected upregulation of a neural gene Ntrk2 in pulmonary capillary ECs at 14 days post infection (dpi) by a Sendai parainfluenza virus.¹⁸ NTRK2, also known as TrkB (tropomyosin receptor kinase B), and its paralogs NTRK1/TrkA and NTRK3/TrkC, are transmembrane receptor tyrosine kinases for neurotrophins, such as NGF, BDNF, and NT-3/4, and are critical for neuronal growth and survival.¹⁹^,²⁰ This neural function likely underlies the developmental lung defects in Ntrk2 mutants.²¹^,²² Non-neural functions of NTRK2 have been suggested for cancer cell therapy and angiogenesis upon injury, mostly in cultured cells and using pharmacological tools.²³^,²⁴^,²⁵ Consistent with our publication,¹⁸ more recent studies have also identified Ntrk2 expression in lung ECs, although fibroblast NTRK2 has been suggested to promote lung epithelial injury-repair.⁸^,¹⁰^,¹¹^,¹²^,²⁶ Adding to the complexity, NTRK2 has multiple truncated isoforms that lack the intracellular tyrosine kinase domain and can dominant-negatively inhibit its full-length form or independently signal.²⁷^,²⁸^,²⁹

To gain clarity on Ntrk2 regulation and function in lung ECs, this study expands on our single-time-point analysis¹⁸ to define the spatiotemporal pattern of Ntrk2 activation including its epigenetic and isoform states using single-cell multiome, the origin and fate of Ntrk2 ECs, and the function of Ntrk2 across multiple lung injury models.

Results

Time-course scRNA-seq profiling of common and distinct responses of CAP1s and CAP2s to Sendai virus infection

We used scRNA-seq to unbiasedly profile cellular and molecular dynamics of CAP1s and CAP2s at 7, 14, and 49 dpi by a Sendai parainfluenza virus, corresponding to the acute injury, active repair, and chronic remodeling phases, respectively.³⁰ ECs were computationally separated (Figure S1A) and reclustered into known cell types including CAP1, CAP2, arterial, venous, and lymphatic ECs (Figures 1A and 1B). Cell-type proportions were comparable across conditions except for a Mki67+ population at 14 dpi, suggesting increased EC proliferation during the active repair phase that coincided with alveolar epithelial cell proliferation³⁰^,³¹ (Figures 1A and 1B; S1D).

Comparing CAP1s and CAP2s in their viral responses revealed a shared interferon response at 7 dpi, including genes for antigen presentation (B2m, H2-Q7, Cd74, and H2-K1), anti-viral effectors (Ifit1, Ifit3, and Ifitm3), and interferon pathway transcription factors (Irf1, Irf7) (Figures 1C and 1D; Table S1). Upregulation of Irf7 and Icam1 in both capillary populations were validated with RNAscope (Figure 1E). By comparison, downregulated genes at 7 dpi were cell-type specific, such as Aplnr for CAP1s and Apln and Kdr for CAP2s, suggesting impaired cell identity or function during acute infection, as observed in other models of viral lung injury¹¹^,³² (Figure 1D).

Over time, the interferon response subdued, consistent with clearance of the Sendai virus 8–12 dpi³⁰ (Figure 1D). The remaining viral responses in CAP1s and CAP2s diverged. The Mki67+ population at 14 dpi clustered with CAP1s, suggesting proliferation of CAP1s but not CAP2s (Figures 1A and 1B). Even after excluding the proliferating population, CAP1s and CAP2s shared fewer differentially expressed genes, which included Sparc and Anxa2 with a possible role in angiogenesis,³³^,³⁴ Ldha and Gapdh to potentially increase glycolysis,³⁵ as well as Ankrd37 in stress response³⁶ (Figure 1D). CAP1s uniquely upregulated Cd34, Lrg1, Sparcl1, Adam15, Gja4, Cxcl12, and Ntrk2, several of which remained higher at 49 dpi (Figures 1D; S1B, and S1C). This gene signature implicated CAP1 signaling through extracellular matrix (Sparcl1 and Adam15),³⁷ cell junctions (Gja4),³⁸^,³⁹ and diffusible ligands (Lrg1, Cxcl12, and Ntrk2).¹⁹^,²⁵^,⁴⁰^,⁴¹^,⁴² Taken together, our time course scRNA-seq profiling identifies a common early interferon response in both CAP1s and CAP2s, but a proliferative and possibly angiogenic response specific to CAP1s implicating them in post-Sendai repair.

ScATAC-seq profiling identifies chromatin changes of angiogenic response genes in CAP1s

To unbiasedly profile the chromatin landscape underlying post-viral repair, we performed scATAC-seq on FACS-purified lung ECs 14 days after mock versus Sendai infection, when cell proliferation peaked based on scRNA-seq (Figures 1A and S1D). Computationally separating ECs from mesenchymal cells that were also included in our profiling but not examined in this manuscript, we captured and could distinguish all EC populations including less frequent lymphatic ECs (Figures 2A and S2). Proliferative ECs did not form a separate cluster as in scRNA-seq, suggesting lesser changes in chromatin than transcript of cell cycle genes (Figure 2A).

Differential accessibility analysis of CAP1s and CAP2s revealed limited changes in CAP2s partially due to fewer profiled cells (Table S2). CAP1s showed 723 increased and 286 decreased peaks, associated with SOX and SP1 motifs, respectively (Figure 2B; Table S2). Assigning peaks to their nearest genes showed a high concordance between chromatin accessibility and gene expression. Repressed genes included Eng and S1pr1 implicated in vascular barrier maintenance⁴³^,⁴⁴^,⁴⁵ that was possibly compromised upon Sendai infection (Figure 2C). Activated genes including Cd34, Cdk19, Sparc, and Sparcl were enriched for the angiogenesis pathway, reinforcing a role of CAP1s in lung EC injury-repair (Figures 2C and S2B).

Integration of scRNA-seq and scATAC-seq using ArchR identified CAP1s and CAP2s marked by expression and accessibility of Plvap and Car4 and confirmed SOX motifs among the most enriched in CAP1s upon infection (Figures S2C and 2D). Among all SOX transcription factors, Sox17 and, to a lesser extent, Sox4, were significantly upregulated at 14 and 49 dpi, but not 7 dpi (Table S1). SOX17 motif was specific to CAP1s and was present in differential peaks of Emp1, Slc6a6, Sparc, and Ntrk2, consistent with a prior study implicating SOX17 in lung endothelial regeneration⁴⁶ (Figures 2D and 2C; Table S2).

Intriguingly, Ntrk2 was among the top activated genes by scRNA-seq and scATAC-seq specifically in CAP1s and contained multiple intronic peaks becoming more accessible in response to Sendai infection (Figures 2E and 2C; S2C). Given its established signaling role in neuronal development and emerging role in vascular repair and lung development and regeneration,¹⁹^,²⁵^,⁴⁰ we hereafter defined its molecular and cellular characteristics as well as potential function across multiple lung injury models.

Emergence and persistence of NTRK2-T1 in CAP1s upon Sendai infection

Our scRNA-seq profiling showed minimal Ntrk2 expression in adult lung ECs at baseline and its upregulation specifically in CAP1s including their proliferative fraction at 14 dpi (Figures 3A and S1C). Immunostaining confirmed upregulated NTRK2 membrane signal that overlapped with a CAP1 membrane marker PLVAP and enclosed an endothelial nuclear marker ERG (Figures 3B and 3C). Despite a decrease in PLVAP staining of NTRK2+ cells, possibly reflecting increased permeability from injury,⁴⁷ Ntrk2 expression by CAP1s with lower Plvap was confirmed by RNAscope (Figure 3B). Also consistent with scRNA-seq, Sendai infection led to a 13-fold increase in EC proliferation marked by MKI67 (8421 ECs from 3 control and 5500 ECs from 3 infected mice) (Figure 3C; Table S3). NTRK2 expression was observed in 20% of all ECs and 25% of NTRK2+ ECs were proliferative, as compared to 21% of NTRK2- ECs, suggesting that NTRK2 expression was not preferentially associated with proliferation (Figure 3C; Table S3). The commercial NTRK2 antibody also variably labeled CD45⁺ immune cells in the control lung, likely non-specifically given the absence of Ntrk2 transcripts there based on scRNA-seq and RNAscope results (Figures S3A, S1A, 3B). Curiously, this non-specific immune signal largely disappeared in the infected lungs, possibly as the result of altered immune cell landscape and/or their gene expression upon infection.

A spatiotemporal analysis to correlate NTRK2 upregulation with previously defined virus cycle and epithelial responses³⁰^,³¹ showed minimal NTRK2 during the acute anti-viral stage (7 dpi) and robust NTRK2 at the commencement of virus clearance and tissue repair (10 dpi) and even 6 weeks after infection (Figures 3D and S3B). AT2-less regions and their subsequent dysplastic repair, indicative of severe epithelial injury, were associated with NTRK2 upregulation, albeit not more strongly than surrounding areas, suggesting that the signal activating Ntrk2 was not localized and might arise from immune cells and/or their secreted effectors.

Since the commercial NTRK2 antibody recognized the N-terminal region common to both its full length and truncated isoforms,²⁹ we performed western blots on whole lungs using the brain as a reference and confirmed the upregulation and persistence of NTRK2 in infected lungs (Figure 3E). Intriguingly, the molecular weight of NTRK2 in the lungs seemingly corresponded to a truncated NTRK2 isoform (Figure 3E). We performed protein deglycosylation to assess the true molecular weight of NTRK2²⁷ and determined that the NTRK2-T1 isoform was specifically upregulated in the lung during injury-repair (Figure 3F). Supporting this, sequencing reads for Ntrk2 in scRNA-seq were mapped to exon 13, corresponding to a truncated receptor without the intracellular tyrosine kinase domain (Figure S4). Therefore, CAP1s throughout the lung upregulate NTRK2-T1 at the onset of tissue recovery from Sendai virus infection and persistently afterward.

NTRK2+ ECs arise from CAP1s, but not CAP2s and give rise to CAP1s, but not CAP2s

To determine the origin and fate of NTRK2+ ECs amid EC turnover during Sendai infection, we lineage traced CAP1s, CAP2s, and NTRK2+ ECs with Kit^CreER, Car4^CreER, and Ntrk2^CreER, respectively. For Kit^CreER and Car4^CreER, recombination was induced 2 weeks before infection to allow enough time for tamoxifen clearance and to minimize labeling from infection-induced atypical expression of Kit or Car4. As we published, CAP1s and CAP2s were identified by the nuclear EC marker ERG outlined by membrane PLVAP and CAR4 on optical sections of an image stack (Figure S5A). Kit^CreER labeled CAP1s efficiently (89%) and specifically (100%; 2190 tdT + cells from 2 mice) and, despite the known expression of Kit during hemopoiesis,⁴⁸ did not label any immune cells (CD45⁺) in either control or Sendai infected lungs (4028 tdT + cells from 2 control and 2 infected lungs) (Figures S5B and S5C; Table S4). Kit^CreER labeled a comparable percentage of ECs 21 days after mock or Sendai infection with only 1.5% labeled cells positive for CAP2 markers CAR4 and TBX2, suggesting a limited number of CAP1s became CAP2s in this injury model (7311 tdT + cells from 6 mice; Figures 4A and S5D; Table S4). Specific to infected lungs, Kit^CreER lineage cells included 29% new cells marked by continuous EdU administration from 8 to 21 dpi, suggesting that CAP1s proliferated to replenish CAP1s injured during Sendai infection (Figure 4A).

With a specificity of >90% after either mock or Sendai infection (1335 and 1006 tdT + cells from 3 control and 3 infected lungs), Car4^CreER showed slightly less labeling in infected lungs with only 3% labeled ECs being new, suggesting that CAP2s were a stable population with minimal replenishing influx from or outflow to CAP1s in this model (Figures 4B; S5E and S5F; Table S4). Complementary Kit^CreER and Car4^CreER lineage tracing along with continuous EdU labeling indicated minimal replacement of CAP2s in the Sendai infection model, possibly attributable to the fact that damaged AT2-less regions remained covered by alveolar type 1 cells.³¹

Consistent with co-expression of NTRK2 and PLVAP (Figure 3B), NTRK2+ ECs were traced by Kit^CreER, but not Car4^CreER, and thus arose from CAP1s (Figures 4C and S5F). Also supporting the MKI67 analysis (Figure 3C), NTRK2+ ECs were not more proliferative than average CAP1s labeled by Kit^CreER (Figure 4C).

For Ntrk2^CreER, we injected tamoxifen daily from 10 to 14 dpi to capture potentially asynchronous tissue injury and upregulation of NTRK2 (Figure 4D). Ntrk2^CreER labeled cells were induced by Sendai infection, remained CAP1s, and were not more proliferative than average CAP1s (Figures 4D; S5G, and S5H; Table S4). Taken together, Sendai infection leads to CAP1 proliferation without significant conversion into CAP2s; NTRK2+ ECs are limited to the CAP1 lineage.

Ntrk2 upregulation in CAP1s is a conserved response to diverse lung injuries

To test the generality of Ntrk2 upregulation, we simulated sterile, inflammatory lung injury using an established model of systemic delivery of a bacterial endotoxin, lipopolysaccharide (LPS)⁴⁹ (Figure 5A). Unlike Sendai infection that included an incubation period for viral replication and spreading, LPS injury led to animal weight loss and recovery within a few days (Figure 5A). NTRK2 upregulation was also expedited to as early as day 3 post-LPS, coinciding with elevated EC proliferation (Figure 5A; Table S5).

To extend our findings to human pathogens, we infected mice with H3N2 human influenza virus and observed the expected weight loss kinetics, comparable to those infected with Sendai virus³⁰ (Figure 5B). Likewise, NTRK2 was upregulated at day 14 after H3N2 infection, again along with elevated EC proliferation (Figure 5B; Table S5). Reanalysis of published snRNA-seq profiling of COVID-19-infected human lungs showed also NTRK2 upregulation, albeit in a subset of CAP1s, potentially explained by species difference and/or presumed ongoing infection in these deceased lungs (Figures 5C, S6A, and S6B; Table S5). Intriguingly, mining a recent plasma proteome dataset of >53,000 adults⁵¹ revealed that 7 or 9 out of the top 10 incident (prognosis) or prevalent (diagnosis) diseases with a negative association with NTRK2 were respiratory (Figure S6C; Table S5). If plasma NTRK2 were shed from lung ECs, its decrease might reflect a paucity of lung ECs or their failure to respond to injury in individuals with or at risk of respiratory diseases. Therefore, CAP1s upregulate Ntrk2 in response to inflammatory and infectious damages in mice and humans.

EC-specific deletion of Ntrk2 has limited molecular and cellular consequences in response to Sendai and H3N2 viral infection

To determine the functional consequences of Ntrk2 upregulation, we specifically deleted Ntrk2 from ECs either 2 weeks before infection with Cdh5-CreER or constitutively with Tek-Cre. Endothelial Ntrk2 mutants had no obvious change in the endothelium or epithelium at baseline (Figure S7A). When infected with Sendai or H3N2 virus, they lost NTRK2 upregulation by design, but had no significant changes in EC number, infection-induced proliferation, and vessel volume 14 days after infection (Figures S7B and 6A; Table S6). Even at 21 days after Sendai infection when EC proliferation largely ceased whereas EC number and vessel volume were still reduced, endothelial Ntrk2 deletion had no significant effect (Figure S7C; Table S6). In the Sendai model, EC proliferation peaked at 14 dpi, instead of 21 dpi as recently reported in a longitudinal study of a different viral infection model.¹²

For a more thorough analysis and a comparison between the two viral models, we profiled control and endothelial Ntrk2 mutant lungs 14 days after Sendai or H3N2 infection using scRNA-seq and computationally separated the ECs (Figures 6B and S8A). Unlike the earlier analysis (Figure 1A), including ECs from H3N2 infected lungs led to a separate cluster, within both CAP1s and CAP2s and even in uninfected lungs, of Atf3+ ECs that were reported to participate in EC injury-repair.⁷ All major EC populations including the infection-induced proliferative population and minor EC populations including inflammatory Ifit3+ ECs and systemic Fabp4+ ECs⁵² were present in comparable proportions between control and Ntrk2 mutant lungs (Figures 6B and S8B).

Hence, we examined differentially expressed genes in CAP1s and CAP2s upon infection, comparing the two infection models as well as control versus Ntrk2 mutant (Figures 6C and 6D). Differential gene expression induced by Sendai versus H3N2 viruses was largely concordant (Figure 6C; Table S6). Consistent with minimal cellular phenotypes (Figure 6A), endothelial deletion of Ntrk2 led to few additional molecular changes other than the expected decrease in Ntrk2 in the Sendai model (Figure 6D; Table S6). In the H3N2 model, a few genes were dependent on Ntrk2; most notably, Cyp1a1 was lower in both CAP1s and CAP2s in the Ntrk2 mutant (Figures 6D and S9). Loss of Cyp1a1/1a2/1b1 was recently shown to be protective to ECs during influenza infection,⁵³ although decreased Cyp1a1 here did not affect the EC response to H3N2 (Figure 6A). Furthermore, epithelial responses to either virus, measured by cell proportions including that of dysplastic pods, were unaffected in the Ntrk2 mutant (Figure S10).

Discussion

In this study, we use scRNA-seq, scATAC-seq, and lineage tracing to define the molecular and cellular changes of two lung capillary EC populations mainly in a Sendai viral infection model and in comparison with other injury models. We show that a common injury response is CAP1 lineage-restricted upregulation of a truncated form of a neurotrophic receptor, Ntrk2-T1. However, except for idiosyncratic changes, endothelial deletion of Ntrk2 does not impact transcriptomic and cellular responses of ECs to Sendai and H3N2 viral infection. Therefore, Ntrk2-T1 upregulation serves as a marker of lung EC injury-repair, albeit with unclear function.

It is surprising that despite its robust, conserved upregulation, Ntrk2 is mostly dispensable for EC responses to Sendai and H3N2 infections (Figure 6). Biochemically, the truncated isoforms of NTRK2, missing the intracellular kinase domain, can either dominant-negatively block signaling of the full-length protein or signal independently through calcium signaling.²⁹ Regardless, our western blot and scRNA-seq do not detect the full-length version; our Ntrk2 mutants delete an exon common to all isoforms and have no obvious consequences either at baseline or upon injury (Figure 6). Although its ligand BDNF is expressed by alveolar type 1 cells and ductal myofibroblasts,¹⁸^,⁵⁴ downstream, especially transcriptional, targets of NTRK2 isoforms, are not available for non-neuronal cells, but are needed to link NTRK2 upregulation to its activity. It will also be important to determine which NTRK2 isoforms are present in the lung fibroblasts that have been shown to be functional in viral injury-repair.²⁶ Nevertheless, seemingly non-functional markers can uncover their regulators that are critical, as exemplified by the identification of the Yamanaka factors using the nonessential FBX15 gene as a reporter.⁵⁵ Our scATAC-seq identifies differentially accessible regions of Ntrk2 and an SOX motif enriched in regions of increased accessibility during injury-repair, adding to our knowledge of the associated transcriptional regulatory network.

In the same way normal physiology such as gas exchange relies on cooperation across cell lineages and types, organ injury-repair is a spatiotemporally choreographed process that requires system-level understanding of the diverse constituent cells. During viral pneumonia, host innate and adaptive immunity races to limit virus spread, but harms infected and bystander lung resident cells that are replaced in regenerative and dysplastic ways. Compared to our increasing knowledge of lung epithelial repair involving region- and model-specific progenitor/stem cell populations,¹³^,¹⁴^,¹⁵^,¹⁶^,¹⁷ we are beginning to recognize a parallel with the endothelium. CAP1s are proliferative, analogous to alveolar type 2 cells; CAP2s have elaborate cell morphology, mirroring and juxtaposing alveolar type 1 cells; arterial and venous macrovascular cells correspond to proximal airway epithelial cells. Accordingly, CAP1s regenerate CAP2s and macrovascular cells contribute to capillaries, as observed in airway cells crossing the proximal-distal compartmental boundaries and venous cells sprouting beyond the macrovasculature-capillary boundaries upon severe injury.¹³^,⁵⁶ Moreover, heterogeneity in Wnt signaling activity within AT2 cells may be extensible to CAP1s.⁷^,⁸^,¹⁰^,¹¹^,⁵⁷^,⁵⁸ However, in our models, Ntrk2 upregulation is broad in CAP1s and does not mark a specific population; Atf3 upregulation occurs to all EC types and only drives separate clustering when integrated with the H3N2 data.⁷ Our CAP1 and CAP2 lineage-tracing experiments indicate that most CAP2s survive Sendai virus infection, possibly due to persistence of alveolar type 1 cells in AT2-less regions, and that EC turnover is largely limited to CAP1s (Figure 4). Similar lineage analysis of capillary and macrovascular ECs in other injury models is needed to uncover injury-specific cellular dynamics that may dictate the EC progenitors being mobilized and the associated signaling and intrinsic mechanisms.

This cellular complexity is accompanied by a molecular complexity even within a single cell. We show that CAP1s and CAP2s share an interferon response with alveolar type 1 and type 2 cells,³¹ likely a concerted emergent effort to curb the virus, before gradually transitioning to reparative responses that are cell-type-specific (Figure 1D). Accordingly, a cell undergoes multiple orthogonal processes that overlap temporally and may confound data interpretation. For instance, it is intuitive that proliferation is a discrete, reversible process and algorithms have been developed to regress out cell cycle genes. It is less obvious how to similarly define and subtract the inflammatory genes that dominates at 7 dpi but linger and blend with reparative genes at 14 dpi and onward (Figures 1D and 6C). The reparative genes themselves likely include a mixture of effectors controlling cell migration, differentiation, extracellular matrix remodeling, etc. The magnitude of each process and associated differential gene expression is expected to vary depending on the nature and severity of the tissue injury, as our comparative analysis of LPS, Sendai, H3N2, and COVID-19 injuries has begun to reveal (Figure 5) and consistent with reported EC heterogeneity upon injury.⁷^,⁸^,¹⁰^,⁴⁹ As a result, readouts of physiology alone such as body weight and blood oxygen saturation, although more relatable to clinics, cannot pinpoint gene functions since protective genes could limit initial tissue damage and/or promote subsequent repair. A precise spatiotemporal map of molecular and cellular injury responses in both wild type and specific mutants, along with simulation using emerging transformer models, such as GeneFormer and scGPT,⁵⁹^,⁶⁰ is needed to unravel the underlying complexity that has been perfected over millions of years to ensure organismal survival.

Limitations of the study

Although our scRNA-seq and scATAC-seq have profiled thousands of cells and are complemented by immunostaining and quantification of thousands of cells in at least 3 additional mice per group, they may not be sufficient to account for the spatial heterogeneity including localized infection and zoned responses from the damage center.⁵⁸ In this context, it is conceivable that despite its widespread upregulation (Figures 1 and 3), NTRK2 functions in a specific region and can be revealed via deeper and/or spatial profiling. Additionally, we have not examined isoform-specific knockouts of Ntrk2 nor complementary gain-of-function models to test the possibilities that an antagonistic balance between the isoforms is important and upregulation of the truncated isoform limits the signaling of undetectable full-length NTRK2. Furthermore, persistence upregulation of Ntrk2 may be functional in chronic injury models, such as cigarette smoke exposure. Given the emerging field of EC heterogeneity, complementary, and intersectional genetic drivers will more precisely dissect EC injury-repair.

Resource availability

Lead contact

Any information and/or requests for resources and reagents will be fulfilled by the corresponding author, Jichao Chen (jichao.chen@cchmc.org)

Materials availability

This study did not generate new reagents.

Data and code availability

•
scRNA-seq and scATAC-seq data have been deposited in GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Additional information required to re-analyze the data reported in this paper is available from the lead contact upon request.
•
No custom code was generated in this study.
•
Original uncropped western blot images have been deposited at Mendeley data and is publicly available as of the date of publication. DOI for the Mendeley data are listed in the key resources table.

Acknowledgments

We thank Dr. Dieter Saur for providing the Kit^CreER mice. We thank Dr. Luis Parada and Dr. James McNamara Sr for sharing the Ntrk2^F/F mice. The University of Texas MD Anderson Cancer Center DNA Analysis Facility and Flow Cytometry and Cellular Imaging Core Facility are supported by the Cancer Center support grant (P30CA016672). This work was supported by the University of Texas MD Anderson Cancer Center retention fund, and National Institutes of Health R01HL130129 and R01HL153511 (J.C.).

Author contributions

C..S.L.K. and J.C. designed research; C.S.L.K. and M.V. performed research and analyzed data; J.P.-G. and S.E.E. performed and supported H3N2 influenza infections, respectively; C.S.L.K. and J.C. wrote the paper; all authors read and approved the paper.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Goat polyclonal anti-TrkB	R&D Systems	AF1494; RRID:AB_2155264
Rabbit monoclonal anti-ERG	Abcam	Ab92513; RRID:AB_2630401
Rat monoclonal anti-PLVAP	BD Biosciences	553849; RRID:AB_395086
Goat polyclonal anti-CAR4	R&D Systems	AF2414; RRID:AB_2070332
Rat monoclonal anti-ICAM2	eBioscience	16-1021-82; RRID:AB_2573077
Rat monoclonal anti-KI67	eBioscience	14-5698-80; RRID:AB_10853185
Guinea pig polyclonal anti-LAMP3	Synaptic Systems	391005; RRID:AB_2713987
Rat monoclonal anti-ECAD	Thermo Fisher Scientific	131900; RRID:AB_2533005
Rat monoclonal anti-CD45	eBioscience	14-0451-81; RRID:AB_467250
Rabbit polyclonal anti-TBX2	Sigma Aldrich	HPA008586; RRID:AB_1080222
Vinculin	Sigma Aldrich	V9131; RRID:AB_477629
Alexa Fluor® 488 AffiniPure™ Donkey Anti-Rabbit IgG (H+L)	Jackson ImmunoResearch	711-545-152; RRID:AB_2313584
Alexa Fluor® 647 AffiniPure™ Donkey Anti-Rat IgG (H+L)	Jackson ImmunoResearch	712-605-150; RRID:AB_2340693
Alexa Fluor® 488 AffiniPure™ Donkey Anti-Guinea Pig IgG (H+L)	Jackson ImmunoResearch	706-545-148; RRID:AB_2340472
Alexa Fluor® 488 AffiniPure™ Donkey Anti-Goat IgG (H+L)	Jackson ImmunoResearch	705-545-003; RRID:AB_2340428
Cy™3 AffiniPure™ Donkey Anti-Goat IgG (H+L)	Jackson ImmunoResearch	705-165-147; RRID:AB_2307351
Peroxidase AffiniPure™ Donkey Anti-Goat IgG (H+L)	Jackson ImmunoResearch	705-035-003; RRID:AB_2340390
Anti-rabbit IgG, HRP-linked Antibody	Cell Signaling Technology	7074; RRID:AB_2099233
PE/Cy7 anti-mouse CD45	BioLegend	103114; RRID:AB_312979
PE anti-mouse CD324 (E-Cadherin)	BioLegend	147304; RRID:AB_2563040
ICAM-2 monoclonal antibody (3C4), Alexa Fluor™ 647	Thermo Fisher Scientific	A15452; RRID:AB_2534465
beta Actin recombinant rabbit monoclonal antibody (JF53-10), HRP	Thermo Fisher Scientific	MA5-32540; RRID:AB_2809817

Bacterial and virus strains

Sendai virus strain Sendai/52	ATCC	VR-105
Influenza A H3N2 virus	Wyde et al.⁶¹	A/Hong Kong/8/68
E. coli lipopolysaccharide	Millipore Sigma	L2630

Chemicals, peptides, and recombinant proteins

Avertin	Sigma	T48402
PFA	Sigma	P6148
OCT	Tissue-Tek	4583
Normal donkey serum	Jackson ImmunoResearch	017-000-121
Aquamount	Polysciences	18606
Tamoxifen	Sigma	T5648
EdU	Thermo Fisher	A10044
Corn oil	Sigma	C8267
Collagenase Type I (CLS-1)	Worthington	LS004197
Elastase (ESL)	Worthington	LS002294
DnaseI (D)	Worthington	LS002007
Fetal bovine serum (FBS)	Invitrogen	10082-139
Halt™ Protease and Phosphatase Inhibitor Single-Use Cocktail (100X)	Thermo Fisher	78442
4-20% mini-PROTEAN precast gels	BioRad	4561094
Precision Plus Protein Kaleidoscope ladder	BioRad	1610375
Bovine Serum Albumin (BSA)	Sigma	A3059
Leibovitz’s media	Gibco	21083027
Cell lysis buffer	Invitrogen	FNN0021
Immobilon Crescendo Western HRP substrate	Millipore	WBLUR0100
Restore™ Western Blot Stripping Buffer	Thermo Fisher	21059
4-16% Carbon Dioxide – 10-20% Nitrogen – Balance Helium	Matheson	UN1956
Gelatin	Sigma	G2500-100G
Eagle’s minimal essential medium	Corning	10-010-CM

Critical commercial assays

BCA Protein Assay Kit	Thermo Fisher Scientific	23225
Chromium Single Cell 3’ Library & Gel Bead Kit v2	10X Genomics	PN-120267
Click-iT EdU Cell Proliferation Kit for Imaging, Alexa Fluor 488	Thermo Fisher Scientific	C10337
RNAscope™ Multiplex Fluorescent V2 Assay for smFISH	ACD	323110
Protein Deglycosylation Mix II	NEB	P6044S

Deposited data

scRNA-seq of control and Sendai mouse lungs (14 dpi)	Cain et al.¹⁸	GEO: GSE144678
scRNA-seq of control and Sendai mouse lungs (7 dpi and 49 dpi)	Hernandez et al.³¹	GEO: GSE180950
snRNA-seq of healthy and lethal COVID-19 patients	Melms et al.⁵⁰	https://singlecell.broadinstitute.org/single_cell/study/SCP1219
scRNA-seq of Sendai control and Sendai Ntrk2 mutant mouse lungs (14 dpi)	This paper	GEO: GSE261083
scRNA-seq of H3N2 influenza control and H3N2 influenza Ntrk2 mutant mouse lungs (14 dpi)	This paper	GEO: GSE261083
scATAC-seq of control and Sendai mouse lungs (14 dpi)	This paper	GEO: GSE261083
Raw uncropped Western Blot gels	This paper	Mendeley Data: https://doi.org/10.17632/dv52g35pww.1

Experimental models: Organisms/strains

Mouse: Cdh5-CreER	Sorensen et al.⁶²	CancerTools UK 151520
Mouse: Tek-Cre	Kisanuki et al.⁶³	RRID: IMSR_JAX: 008863
Mouse: Ntrk2^F	Luikart et al.⁶⁴
Mouse: Kit^CreER	Klein et al.⁶⁵
Mouse: Car4^CreER	The Jackson Laboratory	RRID: IMSR_JAX: 038602
Mouse: Ntrk2^CreER	Liu et al.⁶⁶	RRID: IMSR_JAX: 027214
Mouse: Rosa^tdT	Madisen et al.⁶⁷	RRID: IMSR_JAX: 007914
Mouse: Rosa^Sun1GFP	Mo et al.⁶⁸	RRID: IMSR_JAX: 030952

Oligonucleotides

RNAscope™ Probe- Mm-Plvap-C2	ACD	440221-C2
RNAscope™ Probe- Mm-Car4-C3	ACD	468421-C3
RNAscope™ Probe- Mm-Icam1	ACD	438611
RNAscope™ Probe- Mm-Irf7	ACD	534541

Software and algorithms

IMARIS 10.0.0	Bitplane	http://www.bitplane.com/imaris
Prism 10	GraphPad Software	www.graphpad.com
Seurat	Butler et al.⁶⁹
ArchR	Granja et al.⁷⁰	https://www.archrproject.com/
Enhanced Volcano		https://github.com/kevinblighe/EnhancedVolcano
Loupe Cell Browser	10X Genomics
Cell Ranger	10X Genomics
R (v 4.2.2)	The R Foundation	https://www.r-project.org/
Signac (v 1.8.0)	Stuart et al.⁷¹
Sierra	Patrick et al.⁷²
InteractiVenn	Heberle et al.⁷³
dittoSeq (v 1.10.0)	Bunis et al.⁷⁴
SoupX (v 1.6.2)	Young et al.⁷⁵
MACS2	Zhang et al.⁷⁶

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Experimental model and study participant details

Mice (Mus musculus)

The following mouse strains were used in this study: Cdh5-CreER,⁶² Tek-Cre,⁶³ Ntrk2^F,⁶⁴ Kit^CreER,⁶⁵ Ntrk2^CreER,⁶⁶ Rosa^tdT,⁶⁷ Rosa^Sun1GFP,⁶⁸ and Car4^CreER.⁷⁷ Cre-recombination was induced by intraperitoneal injection of tamoxifen (T5648, Sigma) dissolved in corn oil (C8267, Sigma). Dose and timing of tamoxifen delivery, as well as the numbers of control and mutant pairs, were described in figure legends. Both male and female mice were used in all experiments. Control and mutant samples were processed in the same tissue block to minimize experimental variation. All mice were housed under conditions of 12-12h light cycle, 22°C and 45% humidity. Both male and female 8-10 week old mice were used in all experiments, with no observations of sex differences affecting results in this study. All animal experiments were performed in accordance with all federal regulations on animal use in research and have been approved by the Institutional Animal Care and Use Committee at MD Anderson Cancer Center.

Method details

Antibodies

The following antibodies were used for immunofluorescence: goat anti-Tropomyosin receptor kinase B (TrkB/NTRK2, 1:250, AF1494, R&D Systems), rabbit anti-Avian erythroblastosis virus E-26 (v-ets) oncogene related (ERG, 1:5000, ab92513, Abcam), rat anti-Plasmalemma vesicle associated protein (PLVAP, 1:100, 553849, BD Biosciences), goat anti-Carbonic anhydrase IV (CAR4, 1:500, AF2414, R&D Systems), rat anti-Intercellular adhesion molecule 2 (ICAM2, 1:2500, 16-1021-82, eBioscience), rat anti-KI67 (KI67, 1:1000, 14-5698-82, Invitrogen), guinea pig anti-lysosomal associated membrane protein 3 (LAMP3, 1:500, 391005, SySy), rat anti-epithelial cadherin (ECAD, 1:1000, 13190, Invitrogen), rat anti-CD45 (1:2000, 14-0451-81, eBioscience).

The following antibodies were used for Western blotting: goat anti-Tropomyosin receptor kinase B (TrkB/NTRK2, 1:1000, AF1494, R&D Systems), beta actin (B-actin, 1:1000, MA5-32540, Thermo Fisher), and rat anti-Plasmalemma vesicle associated protein (PLVAP, 1:200, AF2414, R&D Systems), Vinculin (dilution 1:1000, V9131, Sigma Aldrich).

The following antibodies were used for FACS: PE/Cy7 rat anti-CD45 (CD45, 1:250, 103114, BioLegend), PE rat anti-epithelial cadherin (ECAD, 1:250, 147304, BioLegend), and AF647 rat anti-Intercellular adhesion molecule 2 (ICAM2, 1:250, A15452, Thermo Fisher).

Section immunofluorescence

Mice were anesthetized with intraperitoneal injection of Avertin (Sigma, T48402) and the right ventricle of the heart was perfused with PBS. The trachea was subsequently cannulated and 0.5% paraformaldehyde (Sigma, P6148) dissolved in PBS was used to inflate the lungs, with a maintained pressure of 25 cm H₂O. PFA-inflated lungs were fixed for 3 hours in room temperature and washed overnight with PBS at 4°C. After overnight washing, fixed lung lobes were cryoprotected overnight in 20% sucrose PBS containing 10% optimal cutting temperature compound (OCT; 4583, Tissue-Tek, Tokyo, Japan) at 4°C. Immunostaining of frozen sections was previously described.³^,¹⁸^,³¹ Briefly, 10-μm thick frozen sections were blocked in PBS with 0.3% Triton X-100 and 5% normal donkey serum (Jackson ImmunoResearch, 017-000-121) and subsequently incubated with primary antibodies diluted in PBS with 0.3% Triton X-100 at 4°C overnight. After primary antibody incubation overnight, the sections were washed with PBS for 30 minutes at room temperature and then incubated with secondary antibodies (Jackson ImmunoResearch) and 4′,6-diamidino-2-phenylindole (DAPI) diluted in PBS with 0.3% Triton X-100 for 1 hour at room temperature. The sections were washed with PBS for 30 minutes at room temperature, mounted with Aquamount mounting medium (Polysciences, 18606) and imaged on an Olympus FV1000 confocal microscope and quantified using Imaris software.

EdU administration

EdU (5-ethynyl-2’-deoxyuridine; Thermo Fisher, A10044) was dissolved in sterile phosphate buffer saline (PBS, pH 7.4) to create a stock of 10 mg/mL that was stored at -20°C. Prior to administration, the stock was diluted to a working solution of 1 mg/mL EdU. Mice were treated with 100 μl of 1 mg/mL EdU (0.1mg final concentration) intraperitoneally. Specific timelines for EdU treatments were described in figure legends. EdU was detected using Click-iT EdU Cell Proliferation Kit for Imaging, Alexa Fluor 488 (Thermo Fischer Scientific, C10337).

RNAscope in situ hybridization

Lungs were harvested as described above and cryopreserved in a mixture of 20% sucrose, 10% optimal cutting temperature compound and 0.5% PFA in PBS overnight. Subsequently, 10-μm thick frozen sections were probed for Plvap, Car4, Icam1 and Irf7 using the Multiplex Fluorescent Detection Kit v2 (Advanced Cell Diagnostics, 323110) and OPAL 520/560/690 reagent packs (Akoya Biosciences, FP1487001KT/ FP1488001KT/FP1497001KT).

Sendai parainfluenza virus infection

Murine Sendai parainfluenza viral infection was performed following previously published procedure.¹⁸^,³¹ Briefly, mice were anesthetized with isoflurane, placed on a 45° platform with their upper incisors held, and subjected to oropharyngeal instillation of approximately 2.1 X 10⁷ plaque-forming units of Sendai virus (ATCC #VR-105, RRID:SCR_001672CSCSSCS) suspended in 40 μL of PBS. The control group received only 40 μL of PBS.

Influenza A/Hong Kong/1968 (H3N2) infection

The murine model of influenza A infection was performed as previously described⁷⁸ with minimal modifications described here. Briefly, a stock solution of H3N2 influenza A at (1e7 PFU/mL) stored in 0.05% gelatin in Eagle’s minimal essential medium (Corning, 10-010-CM) at -80°C was used. A non-lethal working solution of influenza was freshly made before the infection by mixing 1.5 μL (∼15,000 PFU) of thawed influenza stock and 95 μL of 5% liquid gelatin ((Sigma, G2500-100G; pre-heated at 37°C) in 10 mL of 0.22-micron sterile filtered PBS (pH 7.4). Per infectious challenge, mice were placed in a nebulization chamber where the working solution of influenza A H3N2 was delivered via aerosolization for 45 minutes at a flow rate of 10 L/min using an ∼8% CO₂ and ∼92% air gas mixture (Matheson, UN1956). Animals were followed up daily, checked for signs of respiratory distress and humane euthanasia criteria until indicated experimental timepoints.

Lipopolysaccharide treatment

To model systemic inflammatory response, mice were subjected to sublethal 6 mg/kg LPS (Sigma-Aldrich, L2630-10MG) via intraperitoneal injection. Untreated mice served as experimental controls. Lungs were harvested as described above at 1d, 3d, and 7d post-LPS treatment for tissue immunostaining and analysis.

Tissue dissociation and fluorescence-activated cell sorting

Mouse lungs were collected and processed for dissociation and FACS according to our published protocol with modifications.³ Briefly, after removal of connective tissue and trachea, lungs were minced finely with forceps. Next, lungs were digested in 1.35 mL Leibovitz media (Gibco, 21083-027) consisting of 2 mg/mL collagenase type I (Worthington, CLS-1, LS004197), 0.5 mg/mL DNase I (Worthington, D, LS002007), and 2 mg/mL elastase (Worthington, ESL, LS002294) at 37°C for 45 minutes. To stop the enzymatic digestion, 300 μl of fetal bovine serum (FBS, Invitrogen, 10092-139) was added for a final concentration of 20%. The digested tissue was filtered through a 70 μm cell strainer (Falcon, 352350) on ice, in 4°C cold room, transferred into a 2 mL microcentrifuge tube and pelleted at 1537 rcf for 1 min. The supernatant was removed and 1 mL of red blood cell lysis buffer (15 mM NH₄Cl, 12 mM NaHCO₃ 0.1 mM EDTA, pH 8.0) was added to resuspend the pellet and incubated on ice for 3 min. Cells were centrifuged at 1537 rcf for 1 min, washed with Leibovitz + 10% FBS, resuspended in Leibovitz + 10% FBS, and filtered through a 35 μm cell strainer into a 5 mL glass tube. Samples for scRNA- and scATAC-seq were stained with CD45-PE/Cy7, ECAD-PE, and ICAM2-AF647 antibodies, as well as SYTOX Blue for 30 min on ice, in 4°C cold room. Samples were pelleted, washed again in Leibovitz + 10% FBS, filtered through a 35 μm cell strainer into a 5 mL glass tube and sorted using Aria II Cell sorter with a 70 μm nozzle. Single-cell RNA-seq libraries were prepared using the 3’ and 5’ Library and Gel Bead Kit on the Chromium Single-Cell Gene Expression Solution Platform (10X Genomics) and sequenced using NovaSeq6000 S1. Single cell ATAC-seq library was prepared using the Chromium Single Cell ATAC Reagent Kit (10X Genomics) and sequenced using NovaSeq6000 S1.

Western Blot

Left lung lobe was removed after PBS perfusion and flash frozen. Tissue was added to a mixture of cell lysis buffer (Invitrogen, FNN0021) and protease inhibitor cocktail (Thermo Scientific, 78442) and mechanically homogenized with chrome steel beads (BioSpec Products, 11079113c). Samples were placed on ice for 10 min and centrifuged for 15 min at 3,996 rcf in 4°C cold room. Supernatant was collected and Pierce BCA analysis was performed to determine protein content (ThermoFisher,23227). 50 μg of total protein was run on a 4-20% acrylamide gel (BioRad, 4561094) for 1.5 h at 115 V, before transferring to PVDF membrane (Milipore, IPV00010) at 50 V for 2h on ice. Membranes were blocked in 5% bovine serum albumin (BSA) in tris-buffered saline (A3059, Sigma), with 0.1% Tween-20 (TBST, pH 7.4) with constant agitation, for 1h in 4°C cold room. Primary antibodies diluted in blocking solution were added to the membrane for overnight incubation, with constant agitation in 4°C cold room. Next day, membranes were washed 3-5 times with TBST and incubated with secondary antibodies in 2% BSA in TBST. Chemiluminescence was imaged on BioRad ChemiDoc gel imager with 1mL Immobilon Crescendo Western HRP substrate (Milipore, WBLUR0100).

Protein deglycosylation

Deglyosylation was performed under denaturing conditions using NEB Protein Deglycosylation Mix II (Cat# P6044S) according to manufacturer’s guidelines. Briefly, 40ul of whole lung (100ug) or whole brain (10ug) lysates was added to 5ul of Deglycosylation Mix Buffer 2 and subsequently incubated at 75°C for 10 minutes. After cooling down, 5ul of Protein Deglycosylation Mix II was added and gently mixed. The reaction was incubated at 25°C for 30 minutes and transferred to 37°C for 1 hour to complete protein deglycosylation. Deglycosylated protein sample was used for downstream western blot analysis using commercial NTRK2 and Vinculin antibodies listed above.

Vasculature analysis

Immunostained lung sections at 20 μm were used for vasculature quantification. Comparable tissue regions were imaged on a confocal microscope (Olympus FV1000) using the 20x oil objective with a pixel dimension of 1024 x 1024 x 16 (636 μm x 636 μm x 16 μm). A minimum of three randomly-selected confocal Z-stacks per lung were analyzed on Imaris software (Bitplane) to count ECs (ERG⁺), proliferative ECs (ERG⁺ MKI67⁺), proliferative NTRK2⁺ ECs (ERG⁺ NTRK2⁺ MKI67⁺), tdT cells (tdT⁺), and proliferative tdT cells (tdT⁺ EdU⁺). NTRK2 mean fluorescence intensity was obtained by generating a surface rendering of NTRK2 staining and extracting intensity mean values. Vessel volume was measured using surface rendering of ICAM2 vessel staining with an automatic threshold and a size cut-off of 20 voxels for both control and mutant lungs.

Bioinformatics analyses

ScRNA-seq time course data analysis

Published scRNA-seq datasets (GSE144678 ¹⁸ and GSE180950 ³¹) compiled from 7d, 14d and 49d control and Sendai-infected lungs were analyzed using Seurat (version 4.3.0).⁶⁹ Ambient RNA contamination from sequencing was removed using SoupX (version 1.6.2).⁷⁵ Cells with gene count of less than 200 or over 4000 were filtered out. Lineage-specific markers Cdh1, Cdh5, Ptprc and Col3a1 were used to delineate epithelial, endothelial, immune, and mesenchymal cell populations, respectively. Doublets and cells expressing low feature counts were filtered out. Cdh5⁺ endothelial cells (EC) were subset and re-clustered for analysis. Endothelial cell-type specific markers Plvap, Apln, Nr2f2, and Gja5 were used to identify CAP1, CAP2, venous, and arterial EC populations, respectively. Proliferating ECs were identified using Mki67. Seurat’s FindMarkers function was used to perform differential gene expression analysis on CAP1 and CAP2 ECs at 7d, 14d, and 49d post-Sendai viral infection. Differential gene expression analysis was performed with a minimal percentage of 0.01 and a log fold change threshold of 0.01. Differentially expressed genes were filtered using p-value less than 0.05 and log₂ fold-change more than 1 or less than -1 (up or downregulated, respectively) parameters to identify significant genes. EnhancedVolcano package on R and InteractiVenn⁷³ were used to plot volcano plots and venn diagrams of filtered differentially expressed genes, respectively. Dittoseq (version 1.10.0)⁷⁴ was used to plot cell proportion bar plots.

ScRNA-seq analysis of Sendai and H3N2 infected lungs

FACS purified cells of all lineages (endothelial, mesenchymal, epithelial, and immune) from Sendai, Sendai-infected Ntrk2 mutant, H3N2, and H3N2-infected Ntrk2 mutant lungs were processed through the Chromium Single Cell Gene Expression Solution Platform (10X Genomics) using the Chromium Single Cell 3’ and 5’ Library and Gel Bead Kit according to the manufacturer’s user guide. Chromium scRNA-seq output for each sample was processed on Cell Ranger using cellranger count function. Downstream analysis using the filtered_feature_bc_matrix output files for each sample was performed using Seurat version 4.3.0. Briefly, ambient RNA contamination was removed from each sample as described above. Cells with gene count of less than 200 or over 3500 were filtered out. Each sample was normalized, and variable features were identified independently. Features repeatedly variable across all datasets were selected for integration and integration anchors were used to integrate all datasets together, including control 14d dataset from GSE144678. Next, scaling, PCA dimensionality reduction, unsupervised clustering using nonlinear UMAP dimensional algorithm, as well as K-means clustering were performed. Cell lineages were annotated based on known markers, Cdh5 for endothelial, Cdh1 for epithelial, Col3a1 for mesenchymal, and Ptprc for immune. Cdh5+ ECs were subsetted and reclustered for further analysis. ECs were annotated based on established cell-type specific markers. Differential gene expression analysis and filtering of significant genes were performed as described above. EnhancedVolcano package on R was used to plot volcano plots of filtered differentially expressed genes, respectively. Dittoseq (version 1.10.0)⁷⁴ was used to plot cell proportion bar plots. Raw data has been deposited in GEO under the accession number GSE261083.

SnRNA-seq analysis of published dataset on healthy and lethal COVID-19 patients

Processed files and metadata⁵⁰ were downloaded from the Broad Institute Single Cell Portal for analysis on Seurat version 4.3.0. The dataset was normalized, and variable features were identified. Scaling, PCA dimensionality reduction, unsupervised clustering using nonlinear UMAP dimensional algorithm, as well as K-means clustering were performed. Cell lineages were annotated based on known markers, PECAM1 for endothelial, CDH1 for epithelial, PTPRC for immune, and COL3A1 for mesenchymal. PECAM1+ cells were subsetted and reclustered for analysis. Markers for each cluster were identified with the FindMarkers function and filtered for top 30 (Table S5) and subsequently used for annotation of EC subsets in healthy and COVID-19 samples. Dittoseq (version 1.10.0)⁷⁴ was used to plot cell proportion bar plots.

ScATAC-seq data processing and analysis

Control and Sendai-infected (14 dpi) scATAC-seq samples were processed with Cell Ranger ATAC pipeline. Mouse reference genome version GRCm38 (mm10) was used for alignment process. Count output files for each sample was obtained by running cellranger-arc count. An input CSV file consisting of both samples’ count output files were used to generate an aggregated matrix using the command cellranger-arc aggr. The aggregated output files were used for downstream analyses with the Signac workflow (version 1.8.0), a Seurat extension designed for single-cell chromatin analysis.⁷¹ Briefly, the peak count matrix was filtered for peaks detected in a minimum of 10 cells and cells with a minimum of 200 peaks. The genomic ranges and gene information were annotated for each peak. Per-cell quality control metrics for nucleosome banding signal, transcription start site (TSS) enrichment score, total number of fragments in peaks, fraction of fragment in peaks, and ratio of reads in genomic blacklist regions were computed. High-quality cells with nucleosome banding signal less than 3, TSS enrichment score more than 4, the number of fragments in peaks larger than 350 and less than 100,000, the fraction of fragments in peaks larger than 10%, and reads in genomic blacklist regions less than 2% were kept for further analyses.

Latent semantic indexing (LSI) practice was used on the high-quality peak count matrix. Term frequency-inverse document frequency (TF-IDF) normalization was performed, and the top 25% peaks were selected with the FindTopFeatures function singular value decomposition (SVD) dimensional reduction on the TF-IDF matrix. Notably, the first LSI component was removed using the DepthCor function and the other top 30 LSI components were used for downstream FindNeighbors and FindClusters function. The same LSI components were also used for non-linear dimension reduction of UMAP projections for cell cluster visualization in two-dimensional space.

A gene activity matrix was generated to annotate cell clusters identified on scATAC-seq using the GeneActivity function and normalized by total feature counts, scaled with the median feature counts and log-transformed. The cellular identity of each cluster was annotated with their marker gene activities: Cdh5 for endothelial, Col13a1 for mesenchymal, Cdh1 for epithelial, and Ptprc for immune. MACS2 peak calling was performed on all high-quality cells and the counts in each peak was quantified using the FeatureMatrix function on Signac. For this study, Cdh5⁺ endothelial cells were subsetted and processed as described above. Endothelial cell-type specific marker gene activities were used to annotate endothelial subtypes: Plvap for CAP1s, Apln for CAP2s, Vwf for vessels (arterial and venous), and Prox1 for lymphatics. The differentially accessible regions between cell clusters and conditions (control versus Sendai) were detected by a differential accessibility test and annotated with the closest genes using ClosestFeature function. The pseudobulk accessibility tracks were visualized using the CoveragePlot function in Signac.

ScRNA-seq/scATAC-seq concordance analysis

To generate the scRNA/scATAC-seq scatter plots, differentially expressed genes and differentially accessible peaks identified in scRNA- and scATAC-seq analysis, respectively, was combined and plotted using a custom script from our lab.⁷⁹ To highlight significantly expressed genes and accessible peaks, log₂ fold-change less than -0.5 and more than 0.5, as well as log₂ fold-change less than -0.05 and more than 0.05 were used as respective filtering parameters, along with an adjusted p-value of less than 0.05.

Motif analysis and peak annotation

De novo motif analysis was performed using Homer (version 4.11.1) using the commands findMotifsGenome.pl -size 200 -mask with random backgrounds. ChromVAR (version 1.2.0) was used to test overrepresented motifs in differentially accessible peaks by computing per-cell motif activity scores. Seurat’s FeaturePlot function was used to visualize single-cell level motif activities. Include brief methods on using annotatePeaks.pl to identify specific motif instances in differential peaks and closest gene annotations.

Genomic regions enrichment of annotations tool (GREAT) analysis for functional interpretation of cis-regulatory regions

Differentially accessible regions identified in CAP1s were further processed using GREAT (version 4.0.4)⁸⁰^,⁸¹ to predict functions of enriched cis-regulatory regions. GRCm38 (mm10) species assembly and an input BED file consisting of genomic regions with increased or decreased accessibility, filtered by adjusted p-value less than 0.05 and fold change of more than 0.05 or less than 0.05, respectively, was used to identify biological processes active in CAP1s.

Sierra transcript visualization

PBS and Sendai-infected CAP1 barcodes were extracted from Seurat on R. Sinto package was used to filter and subset PBS and Sendai-infected sample BAM files for respective CAP1 barcodes. Sample-specific subsetted BAM files were filtered for Ntrk2 genomic region (chr13:58,955,511-59,281,784). Cell ranger genes.gtf file and BAM files for Ntrk2 genomic region were used as input for Sierra coverage plot transcript visualization.

ArchR scATAC and scRNA-seq integration analysis

Briefly, CAP1-specific fragments for PBS and Sendai samples were extracted scATAC fragment file on R and applied to downstream ArchR chromatin analysis. Default dimensionality reduction method “addIterativeLSI’ in the ArchR package⁷⁰ was used for CAP1 scATAC-seq dimensional reduction. Gene scores and marker genes imputation were performed using default methods in ArchR. Identification of cell cluster identity was performed by integrating CAP1 scATAC-seq and scRNA-seq data, using ‘addGeneIntegrationMatrix’ function. CAP1 peaks were called using MACS2. Differential peak analysis between PBS and Sendai CAP1s were performed using the default method in ArchR, with statistical significance set at p-value < 0.05. Subsequent TF motif enrichment analysis in differential peaks was performed using default parameters in ArchR.

Quantification and statistical analysis

Vasculature quantification and analyses were described above. Data are represented as mean ± SEM with each data point representing one mouse. Differences between groups were determined by unpaired t-test (2 groups), one-way ANOVA (Sidak correction), and two-way ANOVA (Dunnett correction) for multiple group comparisons. A p-value of <0.05 was considered statistically significant.

Published: June 24, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2025.112973.

Supplemental information

Document S1. Figures S1–S10

mmc1.pdf^{(16.9MB, pdf)}

Table S1. Timecourse scRNA-seq differentially expressed genes (DEGs) in CAP1s and CAP2s

(A) CAP1 DEGs at 7dpi. (B) CAP2 DEGs at 7dpi. (C) CAP1 DEGs at 14dpi. (D) CAP2 DEGs at 14dpi. (E) CAP1 DEGs at 49dpi. (F) CAP2 DEGs at 49dpi.

mmc2.xlsx^{(2.6MB, xlsx)}

Table S2. Raw data for scATAC-seq of CAP1s and CAP2s

(A) Differential peaks in CAP1s. (B) Differential peaks in CAP2s. (C) HOMER motifs identified in CAP1s. (D) ClosestFeatures identified in CAP1s. (E) Merged scRNA and scATAC data for gene expression and accessibility scatterplot. (F) GREAT analysis output for increased peaks in CAP1s. (G) ArchR enriched motifs identified in CAP1s. (H) HOMER motif annotation for SOX17 in CAP1s.

mmc3.xlsx^{(7.1MB, xlsx)}

Table S3. Quantifications for Figure 3C

(A) Quantifications of NTRK2 mean fluorescence intensity, NTRK2+ ECs, and proliferating ECs in PBS and Sendai-infected mouse lungs.

mmc4.xlsx^{(15.8KB, xlsx)}

Table S4. Quantifications for Figure 4 and S5

(A) Quantifications of tdT and EdU cells in Kit^CreER reporter mice for Figure 4B. (B) Quantifications of NTRK2 and EdU cells in Kit^CreER reporter mice for Figure 4C. (C) Quantifications of tdT and EdU cells in Ntrk2^CreER reporter mice for Figure 4D. (D) Quantifications of tdT and CAR4 cells in Kit^CreER reporter mice for Figure S5B. (E) Quantifications of tdT and CD45 cells in Kit^CreER reporter mice for Figure S5C. (F) Quantifications of tdT and TBX2 cells in Kit^CreER reporter mice for Figure S5D. (G) Quantifications of tdT and TBX2 cells in Car4^CreER reporter mice for Figure S5E.

mmc5.xlsx^{(34KB, xlsx)}

Table S5. Raw data for Figure 5 and S6

(A) Body weight measurements and quantifications of proliferating ECs and NTRK2 mean fluorescence intensity in LPS injury model. (B) Body weight measurements and quantifications of proliferating ECs and NTRK2 mean fluorescence intensity in H3N2 Influenza A injury model. (C) Top 30 cell cluster markers for snRNA-seq of human COVID-19 dataset. (D) Association of human plasma NTRK2 with incidence (prognosis) of respiratory diseases. (E) Association of human plasma NTRK2 with prevalence (diagnosis) of respiratory diseases.

mmc6.xlsx^{(183.9KB, xlsx)}

Table S6. Raw data for Figure 6 and S7

(A) Quantifications of EC number, proliferating ECs, and vessel volume in PBS, control Sendai/H3N2, and Sendai/H3N2 Ntrk2 mutants at 14dpi. (B) DEGs in PBS vs. Sendai CAP1s. (C) DEGs in PBS vs. H3N2 CAP1s. (D) DEGs in PBS vs. Sendai CAP2s. (E) DEGs in PBS vs. H3N2 CAP2s. (F) DEGs in control Sendai vs. Sendai Ntrk2 mutant CAP1s. (G) DEGs in control Sendai vs. Sendai Ntrk2 mutant CAP2s. (H) DEGs in control H3N2 vs. H3N2 Ntrk2 mutant CAP1s. (I) DEGs in control H3N2 vs. H3N2 Ntrk2 mutant CAP2s. (J) Merged data for scatterplot of Sendai and H3N2 scRNA identified DEGs in CAP1s. (K) Merged data for scatterplot of Sendai and H3N2 scRNA identified DEGs in CAP2s. (L) Quantifications of EC number, proliferating ECs, and vessel volume in PBS, control Sendai, and Sendai Ntrk2 mutants at 21dpi in Figure S7C.

mmc7.xlsx^{(5.9MB, xlsx)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S10

mmc1.pdf^{(16.9MB, pdf)}

Table S1. Timecourse scRNA-seq differentially expressed genes (DEGs) in CAP1s and CAP2s

(A) CAP1 DEGs at 7dpi. (B) CAP2 DEGs at 7dpi. (C) CAP1 DEGs at 14dpi. (D) CAP2 DEGs at 14dpi. (E) CAP1 DEGs at 49dpi. (F) CAP2 DEGs at 49dpi.

mmc2.xlsx^{(2.6MB, xlsx)}

Table S2. Raw data for scATAC-seq of CAP1s and CAP2s

mmc3.xlsx^{(7.1MB, xlsx)}

Table S3. Quantifications for Figure 3C

(A) Quantifications of NTRK2 mean fluorescence intensity, NTRK2+ ECs, and proliferating ECs in PBS and Sendai-infected mouse lungs.

mmc4.xlsx^{(15.8KB, xlsx)}

Table S4. Quantifications for Figure 4 and S5

mmc5.xlsx^{(34KB, xlsx)}

Table S5. Raw data for Figure 5 and S6

mmc6.xlsx^{(183.9KB, xlsx)}

Table S6. Raw data for Figure 6 and S7

mmc7.xlsx^{(5.9MB, xlsx)}

Data Availability Statement

•
scRNA-seq and scATAC-seq data have been deposited in GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Additional information required to re-analyze the data reported in this paper is available from the lead contact upon request.
•
No custom code was generated in this study.
•
Original uncropped western blot images have been deposited at Mendeley data and is publicly available as of the date of publication. DOI for the Mendeley data are listed in the key resources table.

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PERMALINK

Truncated NTRK2 is induced in CAP1 endothelial cells during mouse lung injury-repair

Celine Shuet Lin Kong

Mitheera V

Jezreel Pantaleón-García

Scott E Evans

Jichao Chen

Summary

Graphical abstract

Highlights

Introduction

Results

Time-course scRNA-seq profiling of common and distinct responses of CAP1s and CAP2s to Sendai virus infection

Figure 1.

ScATAC-seq profiling identifies chromatin changes of angiogenic response genes in CAP1s

Figure 2.

Emergence and persistence of NTRK2-T1 in CAP1s upon Sendai infection

Figure 3.

NTRK2+ ECs arise from CAP1s, but not CAP2s and give rise to CAP1s, but not CAP2s

Figure 4.

Ntrk2 upregulation in CAP1s is a conserved response to diverse lung injuries

Figure 5.

EC-specific deletion of Ntrk2 has limited molecular and cellular consequences in response to Sendai and H3N2 viral infection

Figure 6.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Mice (Mus musculus)

Method details

Antibodies

Section immunofluorescence

EdU administration

RNAscope in situ hybridization

Sendai parainfluenza virus infection

Influenza A/Hong Kong/1968 (H3N2) infection

Lipopolysaccharide treatment

Tissue dissociation and fluorescence-activated cell sorting

Western Blot

Protein deglycosylation

Vasculature analysis

Bioinformatics analyses

ScRNA-seq time course data analysis

ScRNA-seq analysis of Sendai and H3N2 infected lungs

SnRNA-seq analysis of published dataset on healthy and lethal COVID-19 patients

ScATAC-seq data processing and analysis

ScRNA-seq/scATAC-seq concordance analysis

Motif analysis and peak annotation

Genomic regions enrichment of annotations tool (GREAT) analysis for functional interpretation of cis-regulatory regions

Sierra transcript visualization

ArchR scATAC and scRNA-seq integration analysis

Quantification and statistical analysis

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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