Identification of Novel Conserved Regenerative Endothelial Program During Lung Repair Using Integrated Single-Cell Genomics

Investigator: Sushreesangita Behera, PhD

Postdoctoral Fellow

Ann & Robert H. Lurie Children’s Hospital of Northwestern University

ORCID: 0000-0003-3797-1014

1. Overview

Scientific Rationale and Importance

Severe lung injury, including sepsis-associated acute respiratory distress syndrome (ARDS)and surgical pneumonectomy (PNX), is characterized by substantial loss of pulmonary endothelial cells^1-3,6,7. Endothelial barrier disruption leads to vascular leak and poor gas exchange^2,4. This results in lung edema, prolonged mechanical ventilation, multi-organ failure, and increased mortality^1,3,4. Despite advances in supportive care, no therapies directly enhance endogenous lung regeneration, and mortality in severe ARDS remains as high as 30-40%^1,4,5.

Restoration of a functional pulmonary microvasculature is therefore a central determinant of survival after lung injury^3-4,6,7. However, the cellular source and mechanisms that drive endothelial regeneration remain incompletely defined.

Pulmonary endothelial cells are heterogeneous and include arterial, venous, general capillary (gCap), and aerocyte capillary (aCap) subtypes^8-9,11-12. While these studies have advanced our understanding of endothelial identity under homeostasis^9-10, they largely emphasize static subtypes rather than dynamic, injury-induced regenerative programs.

A fundamental question remains unresolved:

Is lung repair mediated by expansion of pre-existing endothelial subsets, or by activation of a transient, injury-induced regenerative endothelial state?

To address this clinically relevant question, I performed unbiased re-analysis of single-cell RNA-seq datasets across two biologically distinct lung injury models:

1) Pneumonectomy (PNX): surgical resection model for compensatory lung growth - regenerative phase at Day 7 (GSE272372)¹⁴

2) Sepsis-induced inflammatory lung injury: recovery phase at Day 3 (GSE148499)¹⁰

Across both models, I identified a novel endothelial subpopulation (Figure 1) that selectively emerges during the regenerative phase. This population is:

a) Minimal under basal or sham conditions (Figure 1 A)

b) Absent during early inflammatory injury (Figure 1 C)

c) Robustly expanded only during recovery phase (Figure 1 A & C)

Transcriptomic profiling demonstrates preserved endothelial identity (Cdh5) together with coordinated enrichment of proliferative and cell-cycle genes (Mki67, Top2a, Ccnb1) (Figure 1 B &D), consistent with activation of a bona fide regenerative endothelial transcriptional program rather than nonspecific cell cycling.

Importantly, this regenerative endothelial population appears in both sterile surgical resectionand inflammatory sepsis model, suggesting convergence on a conserved endothelial regeneration program across mechanistically distinct lung injuries.

These findings support the existence of a transient, injury-activated regenerative endothelial state that may represent a critical regulatory bottleneck in effective lung repair.

2. Hypothesis and Project Objective

I hypothesize that lung repair is driven by activation of a conserved regenerative endothelial program within the endothelial lineage that is shared across injury models and detectable in human lung injury datasets.

This project aims to define a conserved regenerative endothelial population that emerges during lung repair using integrated single-cell genomic analysis. Specifically, I will

Define the molecular signature of the regenerative endothelial population identified in murine pneumonectomy and sepsis-induced lung injury models.
Characterize its temporal dynamics during the recovery phase of lung injury using trajectory and gene module analyses.
Quantify transcriptional conservation of this regenerative program across mechanistically distinct lung injury models.
Evaluate the presence of this regenerative endothelial signature in publicly available human COVID-19 lung injury single-cell RNA sequencing datasets.

3. Methods - Computational Genomic Analysis

Datasets: Murine datasets:

à Pneumonectomy (PNX): Sham vs Day 7

à Sepsis model: Basal, acute injury, recovery phase

Human datasets:

à Publicly available single-cell RNA sequencing datasets from human lung injury or ARDS cohorts

4. Planned Analysis

To define and characterize the conserved regenerative endothelial population across lung injury models:

1) Identification of the regenerative endothelial population

I will perform endothelial subsetting and re-clustering of murine pneumonectomy and sepsis single-cell RNA-seq datasets using Seurat. Differential expression analysis will be used to identify EC subpopulations selectively enriched during the recovery phase. A regenerative endothelial signature will be defined based on coordinated upregulation of cell cycle and mitotic regulators (e.g., Mki67, Top2a, Ccnb1, Foxm1, Ube2c).

Gene module scoring will quantify the enrichment of this regenerative program across endothelial subsets and timepoints.

2) Trajectory and lineage transition analysis

To reconstruct transcriptional transitions within the endothelial lineage, I will apply pseudotime and trajectory inference methods (Monocle3 or Slingshot). This will allow inference of whether the regenerative population represents:

à A transition from specific capillary subsets

à A distinct transcriptional state emerging during repair

This will determine whether regeneration follows a structured transcriptional progression.

3) Cross-model conservation analysis

To quantify transcriptional conservation, I will:

à Compute overlap of differentially expressed genes between models

à Perform pathway enrichment analysis

à Use signature scoring and correlation-based approaches to measure similarity between regenerative EC states across pneumonectomy and sepsis datasets

This will determine whether regeneration reflects a shared endothelial program across injury types.

4) Projection onto human lung injury datasets

The murine regenerative endothelial gene signature will be projected onto publicly available human lung injury single-cell RNA-seq datasets. Signature scoring methods will assess whether a comparable endothelial transcriptional program is enriched in human injury samples.

This cross-species analysis will evaluate translational relevance.

5. Pilot Data (Optional)

Figure 1. Identification of a conserved regenerative endothelial cell population across distinct lung injury models. (A-B) Pneumonectomy (PNX) model- compensatory lung growth (Day 7). (A) UMAP visualization of endothelial cells (ECs) from sham and Day 7 post-PNX lungs demonstrates selective expansion of a discrete EC subpopulation (Cluster 5, black arrow) during the regenerative phase. This cluster is minimally represented in sham lungs but markedly enriched at Day 7, corresponding to peak compensatory lung growth.

(B) Feature plots confirm preserved endothelial identity (Cdh5) and coordinated upregulation of proliferation-associated genes (Mki67, Top2a, Ccnb1), consistent with activation of a regenerative endothelial transcriptional program.

(C-D) Sepsis-induced lung injury - recovery phase (Day 3). (C) UMAP visualization across Basal, Day 1 (acute injury phase), and Day 3 (recovery) time points identifies a distinct EC subcluster (Cluster 9, black arrow) that expands selectively at Day 3. This population is minimal at Basal and Day 1 of peak injury but increases during the recovery phase. (D) Similar to the PNX model, this EC population retains endothelial identity (Cdh5) and demonstrates coordinated enrichment of proliferative markers (Mki67, Top2a, Ccnb1), indicating convergence on a shared regenerative EC state.

(E-F) Quantitative expansion of regenerative ECs.(E) Fold enrichment of Cluster 5 in Day 7 PNX relative to sham lungs. (F) Percentage of total ECs represented by Cluster 9 across sepsis time points, demonstrating robust expansion at Day 3.

6. Anticipated Results

I anticipate identifying a reproducible endothelial subpopulation enriched during the recovery phase of lung injury in both pneumonectomy and sepsis models.

This population is expected to:

à Exhibit coordinated upregulation of regenerative and mitotic regulators

à Follow a structured trajectory within the endothelial lineage

à Show significant transcriptional overlap across injury models

Projection into human lung injury datasets is expected to reveal enrichment of a similar regenerative endothelial signature, supporting cross-species conservation.

7. Potential Pitfalls and Alternative Approaches

1) Limited overlap between injury models

If transcriptional overlap is modest, I will perform pathway-level enrichment analysis rather than relying solely on gene-level overlap. This approach captures functional conservation even when individual genes differ.

2) Variability across human datasets

Human lung injury datasets may differ in sampling depth, disease severity, or technical platform. To address this:

à I will use standardized normalization and integration pipelines

à Apply robust gene module scoring instead of direct clustering comparisons

à Focus on pathway-level enrichment rather than strict cell-type matching

3) Weak regenerative signal in human datasets

If the regenerative signature is subtle, I will use enrichment scoring across endothelial subsets rather than expecting a clearly defined cluster. This reduces dependence on discrete clustering structures.

8. Future Directions

If successful, this study will establish a conserved regenerative endothelial transcriptional program across lung injury models and species.

Future studies will focus on:

à Prioritizing candidate genes and transcriptional regulators enriched within the conserved regenerative program for in vivo validation in experimental models of lung injury

à Determining whether modulation of this regenerative program enhances endothelial recovery and vascular integrity following injury.

9. Significance

Current therapeutic strategies in ARDS have primarily focused on targeting individual inflammatory mediators or signaling pathways. However, meaningful recovery ultimately depends on restoration of the endothelial compartment and re-establishment of vascular integrity. By identifying a conserved regenerative endothelial transcriptional program across injury models and species, this study aims to provide a clearer framework for understanding how endogenous vascular repair occurs. This insight may help guide future strategies that support coordinated regenerative responses rather than focusing on isolated molecular targets.

10. Timeline (6 Months)

Month 1-2: Dataset curation, preprocessing, endothelial subsetting

Month 3: Identification of regenerative EC population and gene signature

Month 4: Trajectory and cross-model conservation analysis

Month 5: Human dataset projection and validation

Month 6: Integration, figure generation, manuscript preparation, public data deposition

11. Budget ($5,000)

Computational Resources and Data Storage. Bioinformatics software, cloud computing, and high-performance analysis support.

12. Data Sharing Statement

All processed single-cell objects, regenerative endothelial gene signatures, module scores, and analysis scripts will be deposited in a publicly accessible repository. Code will be made available to ensure reproducibility and transparency.

13. Reference:

Rubenfeld GD, Caldwell E, Peabody E, Weaver J, Martin DP, Neff M, Stern EJ, Hudson LD. Incidence and outcomes of acute lung injury. N Engl J Med. 2005;353(16):1685–1693. doi:10.1056/NEJMoa050333
Lee WL, Slutsky AS. Sepsis and endothelial permeability. N Engl J Med. 2010;363(7):689–691.
Minamino T, Komuro I. Regeneration of the endothelium as a novel therapeutic strategy for acute lung injury. J Clin Invest. 2006;116(9):2316–2319.
Aird WC. The role of the endothelium in severe sepsis and multiple organ dysfunction syndrome. Blood. 2003;101(10):3765–3777.
Niethamer TK, Stabler CT, Leach JP, Zepp JA, Morley MP, Babu A, et al. Defining the role of pulmonary endothelial cell heterogeneity in the response to acute lung injury. eLife. 2020;9:e53072.
Rafii S, Cao Z, Lis R, Siempos II, Chavez D, Shido K, Rabbany SY, Ding BS. Platelet-derived SDF-1 primes the pulmonary capillary vascular niche to drive lung alveolar regeneration. Nat Cell Biol. 2015;17(2):123–136. doi:10.1038/ncb3096
Ding BS, Nolan DJ, Guo P, et al. Endothelial-derived angiocrine signals induce and sustain regenerative lung alveolarization. Cell. 2011;147(3):539–553.
Liao JK. Linking endothelial dysfunction with endothelial cell activation. J Clin Invest. 2013;123(2):540–541. doi:10.1172/JCI66843
Gillich A, Zhang F, Farmer CG, et al. Capillary cell-type specialization in the alveolus. Nature. 2020;586:785–789. doi:10.1038/s41586-020-2822-7
Zhang L, Gao S, White Z, Dai Y, Malik AB, Rehman J. Single-cell transcriptomic profiling of lung endothelial cells identifies dynamic inflammatory and regenerative subpopulations. JCI Insight. 2022;7(11):e158079. doi:10.1172/jci.insight.158079
Evans CE, Behera SP, et al. The unexpected protective role of thrombosis in lung injury via endothelial Alox15. Circ Res. 2026;138(1):e326357.
Jambusaria A, Sandy AR, et al. Endothelial heterogeneity across distinct vascular beds during homeostasis and inflammation. eLife. 2020;9:e51413. doi:10.7554/eLife.51413
Liu M, Zhang L, Marsboom G, et al. Sox17 is required for endothelial regeneration following inflammation-induced vascular injury. Nat Commun. 2019;10(1):2126. doi:10.1038/s41467-019-10134-y
Ma J, Zhang L, Zhang X, et al. Inhibiting endothelial Rhoj blocks profibrotic vascular intussusception and angiocrine factors to sustain lung regeneration. Sci Transl Med. 2024;16(762):eado5266. doi:10.1126/scitranslmed.ado5266

Identification of Novel Conserved Regenerative Endothelial Program During Lung Repair Using Integrated Single-Cell Genomics

Identification of Novel Conserved Regenerative Endothelial Program During Lung Repair Using Integrated Single-Cell Genomics

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Request for Proposals - Genomics and Proteomics

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