Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis

Domain: biology
Published: 2026-03-20
Editorial outcome: minor_revision

Abstract
Pulmonary fibrosis represents a progressive interstitial lung disease marked by excessive extracellular matrix deposition and architectural distortion. Vascular endothelial cells critically contribute to fibrogenesis through paracrine secretion of pro-fibrotic mediators, yet their mechanobiological regulation remains elusive. Using integrated single-cell multi-omics profiling of human pulmonary fibrosis specimens and experimental fibrosis models induced by bleomycin or silica, we identify mechanosensitive Piezo1 upregulation in Endothelial cells as a hallmark of fibrotic progression. Endothelial-specific Piezo1 knockout significantly attenuates Bleomycin-induced fibrotic remodeling in male mice, establishing its pathogenic necessity. Mechanistically, PIEZO1 activation promotes pulmonary fibrosis development via CAPN2-mediated STAT3 phosphorylation, which may regulate the secretion of the pro-fibrotic molecule interleukin-33. These findings suggest that the endothelial PIEZO1-CAPN2-STAT3-IL33 axis is a potential therapeutic target for PF intervention.

Introduction
Pulmonary fibrosis (PF) represents a spectrum of chronic progressive interstitial lung disorders characterized by excessive extracellular matrix deposition, culminating in substantial morbidity and mortality. Idiopathic pulmonary fibrosis (IPF), the most prevalent and clinically significant subtype, presents particular therapeutic challenges due to its indeterminate pathogenesis and restricted treatment modalities. While two antifibrotic agents (pirfenidone and nintedanib) have received clinical approval, these palliative therapies only modestly decelerate disease progression without achieving fibrosis resolution1. This therapeutic limitation underscores the critical need for elucidating pathogenic mechanisms and developing targeted interventions. Current research implicates dysregulated repair mechanisms in PF pathogenesis, particularly abnormal wound healing responses characterized by progressive fibrotic extracellular matrix accumulation2. This complex process involves coordinated interactions among pulmonary structural cells (epithelial, endothelial) and recruited effector cells (immune populations, activated fibroblasts), mediated through an evolving network of soluble mediators (e.g., cytokines, chemokines) that regulate intercellular communication3. Vascular endothelial cells (ECs), constituting the luminal interface of blood vessels, are ubiquitously distributed across tissues and critically regulate diverse pathophysiological processes4. Within the pulmonary system, these cells extend their functional repertoire beyond vascular permeability maintenance to actively modulate tissue homeostasis5. Emerging evidence indicates spatial adjacency between pulmonary fibroblasts and vascular cells, with dysregulated intercellular communication driving progressive fibrotic remodeling6. Experimental models demonstrate that bleomycin (BLM)-induced repetitive lung injury triggers pathological ligand presentation from pulmonary vascular endothelial cells to perivascular fibroblasts, initiating maladaptive repair cascades that culminate in fibrosis6. Furthermore, the NRP1-HIF2\alpha-EPCR signaling axis has been implicated in aging-associated endothelial reprogramming, fostering aberrant macrophage-endothelial crosstalk that impairs regenerative capacity and accelerates fibrotic progression7. These findings collectively highlight pulmonary EC-derived molecular pathways as novel therapeutic targets for PF intervention.

ECs employ mechanosensors to transduce mechanical stimuli through a process termed mechanotransduction8. This biological mechanism converts mechanical forces (e.g., shear stress, cyclic stretch) into intracellular biochemical signals, thereby inducing chromatin remodeling and transcriptional reprogramming8. Biomechanical stresses orchestrate critical endothelial functions spanning angiogenesis, apoptosis regulation, inflammatory responses, and vasoactive mediator production, with dysregulation implicated in both physiological adaptation and pathological progression9–11. Cellular traction forces transmitted through the extracellular matrix (ECM) establish reciprocal mechanical feedback, wherein ECM stiffening perturbs mechanosensitive signaling cascades, ultimately compromising EC functional plasticity in disease states12. Pathological ECM accumulation—a hallmark of fibrotic remodeling—likely disrupts mechanical homeostasis during alveolar morphogenesis, consequently altering pulmonary vascular mechanobiology. Nevertheless, the precise molecular mechanisms linking aberrant mechanical signaling to pulmonary vascular dysfunction remain in completely defined.

In this study, through single-nucleus RNA sequencing (snRNA-seq) profiling of IPF patient lung tissues, we identified mechanosensitive transcriptional signatures in ECs correlating with fibrotic progression. Leveraging multi-omics analysis across human IPF specimens and preclinical PF models (BLM/SiO2-induced), we demonstrated pathological upregulation of PIEZO1 mechanotransduction pathways within the pulmonary vasculature. Conditional Piezo1 knockout in ECs (Cdh5-CreERT2) significantly attenuated fibrotic remodeling in BLM-challenged mice. Pharmacological modulation using PIEZO1 antagonist GsMTx4 or agonist Yoda1 reciprocally mitigated or exacerbated fibrotic outcomes, respectively. Mechanistic investigations revealed that PIEZO1 activation induces endothelial IL33 secretion through CAPN2/STAT3-mediated transcriptional activation, establishing this mechanochemical axis as a driver of fibroblast activation. Our findings establish endothelial mechanotransduction via PIEZO1-IL33 signaling as a druggable target for pulmonary fibrosis intervention.

Results
Mechanical Stress in Pulmonary ECs Associated with Deterioration of Lung Function
To understand pulmonary fibrosis (PF) through clinical prognostic indicators, such as the relationship between lung function and cell subpopulations, we collected lung tissues from four idiopathic pulmonary fibrosis (IPF) lung transplant recipients and five non-IPF normal controls at West China Hospital of Sichuan University and Shenzhen People’s Hospital. These samples were processed using 10x Genomics for single-nucleus RNA sequencing (snRNA-seq) (Fig. 1A, Supplementary Data S1-S3), and 35,310 cells from IPF patients and 35,997 cells from normal controls were obtained for subsequent analysis after quality control. Uniform manifold approximation and projection (UMAP) was performed for dimensionality reduction. Fifteen cell subpopulations were characterized based on marker gene expression, including alveolar type I cells (AT1), alveolar type II cells (AT2), ciliated cells, club cells, basal cells, macrophages, fibroblasts, smooth muscle cells (SMC), lymphoid cells, arterial endothelial cells (arterial ECs), general capillary ECs, aerocyte ECs, systemic venous ECs, pulmonary venous ECs, lymphatic ECs (Fig. 1B, Supplementary Fig. 1A-C). We then employed Scissor, a method that identifies cell subpopulations from single-cell data associated with lung function phenotypes13.

The clinical trajectory of idiopathic pulmonary fibrosis is variable, with a decline in forceful lung volume (FVC) being the main indicator of progression14. Given that an FVC \le 50% is indicative of severe pulmonary dysfunction and significant physiological impairment15,16, we established 50% of FVC as the diagnostic cut-off value. Subjects were stratified into two phenotypes: those with FVC \le 50% were defined as the FVC-low phenotype (FLP, designated Scissor+), while those with FVC > 50% comprised the FVC-high phenotype (FHP, designated Scissor–). We then integrated the bulk data (GSE47460) with the snRNA-seq data using Scissor, identifying 18,272 Scissor+ (FVC% \le 50%) and 29,897 Scissor- cells associated with FLP and FHP, respectively (Fig. 1C). After calculating the proportion of FLP cells of each cell type, we found that the proportion of FLP ECs within the total population of ECs was the most predominant compared with other types of cells (Fig. 1D). To delineate aberrant signaling pathways in ECs associated with severe lung function impairment during pulmonary fibrosis progression, we performed Scissor-based analysis on ECs from IPF patients and normal controls. Endothelial cells were further partitioned into six subtypes (Fig. 1E). FLP cells from IPF patients and controls were then selected using the Scissor method and subjected to pseudobulk and differential expression analysis (Fig. 1F-G). Gene set scoring revealed significant upregulation of the mechanical stress (MS) signaling pathway in the IPF group (Fig. 1H-I).

To further validate our findings, we expanded our sample size by collecting scRNA-seq data from 66 IPF patients and 59 normal lung tissues from five public scRNA-seq datasets in the GEO database17–21. Pearson correlation analysis on collected samples demonstrated a positive correlation between mechanical stress score of ECs and fibrosis score of samples (Fig. 1J). We then identified 34,898 ECs (Supplementary Fig. 2A-B) and used the scissor method to identify FLP and FHP cells (Fig. 1K). We also focused on whether mechanical stress was responsible for the progression of IPF and found widespread increases in mechanical stress in the lung cells of IPF patients (Fig. 1L-M and Supplementary Fig. 2C). Additionally, a meta-analysis combining the five GEO datasets validated that ECs from IPF patients exhibited relatively high mechanical stress (Fig. 1N). We performed Pearson correlation analysis on a randomly selected dataset (GSE136831). This analysis revealed a positive correlation between mechanical stress scores and both the myofibroblast percentage in total fibroblasts and fibroblast markers (Fig. 1O and Supplementary Fig. 2D). These results suggest that abnormally elevated mechanical stress in ECs is associated with the development of PF.

Mechanical Stress of ECs Elevated in SiO2 Induced Silicosis
Silicosis is a type of occupational PF caused by the inhalation of large amounts of SiO2 dust. To determine whether ECs in PF exhibit elevated mechanical stress, we established a mouse model of silicosis by tracheal instillation of SiO2 over five months. We collected single-cell suspensions from lung tissues to prepare scRNA-seq and scATAC-seq libraries using 10X Genomics kits to measure RNA and chromatin accessibility (Fig. 2A). The H&E and Masson staining results demonstrated significant interstitial lung fibrosis in the PF mouse model induced by SiO2 (Fig. 2B).

After quality assessment and screening of SiO2-treated and saline-treated mice (Supplementary Data S4-S6), scRNA-seq clustered 39,048 cells and identified major cell types in the lung tissues based on marker genes expression. As a result, UMAP plots showed lung cell subpopulations, including endothelial cells, dendritic cells, B cells, AT1 cells, AT2 cells, macrophages, neutrophils, T cells, pericytes, clara cells, natural killer (NK) cells, monocytes, ciliated cells, basal cells, mast cells, smooth muscle cells (SMC), and fibroblasts (Fig. 2C, Supplementary Fig. 3A). We also analyzed the scATAC-seq dataset including 26,575 cells for quality control and batch correction using the R package “Seurat” and used our annotated scRNA-seq dataset to predict cell types via scATAC-seq (Fig. 2D, Supplementary Fig. 3B). Additionally, we identified markers for cellular subtypes in mouse lung tissues post-SiO2 treatment using scATAC-seq and scRNA-seq (Fig. 2E-F).

To investigate differentially regulated signaling pathways in ECs, we performed GO analysis and MS scoring on ECs of scRNA-seq dataset. We found that ECs from SiO2-treated mice showed elevated mechanotransduction signals (Fig. 2G-I and Supplementary Fig 3C-D). We also analyzed the different peaks of ECs and performed MS scoring on ECs in the scATAC-seq dataset of SiO2-treated and saline-treated mice, revealing an increase in mechanical stress scores in the SiO2-treated group (Fig. 2J-L Supplementary Fig. 3E).

Finally, we analyzed the enrichment of the mechanical stress-related genes in ECs for motifs (Fig. 2M) and observed that the central motifs included Sox9, Stat1, Ets1, Fosl1, Fos, Etv1, Rela, Neurog1, Meis2, Pparg, Jun, Gata4, Foxp2, Nfkb1, and Irf1 (Fig. 2N). These results confirm the critical role of aberrant mechanosensitive signaling pathways in ECs in the pathogenesis of occupational PF.

Mechanosensitive PIEZO1 is Relevant to the Development of PF
BLM-induced pulmonary fibrosis (PF) mice are widely used as an animal model for studying PF due to their ability to mimic human lung fibrosis. To further validate the above findings, we harvested lung tissues from BLM-treated mice for single-cell multi-omics studies (Fig. 3A). H&E and Masson staining revealed marked interstitial fibrosis in the lungs (Fig. 3B), confirming the successful establishment of the PF model. Using the same bioinformatics analysis approaches, scRNA-seq and scATAC-seq clustered 41,522 and 35,791 cells, respectively (Supplementary Data S7-9). Next, we visualized the major lung cell subpopulations using UMAP analysis (Fig. 3C-D) and identified clustering-specific markers for each subpopulation (Supplementary Fig. 4A-F).

In order to identify by which specific gene of mechanosensitive pathway of EC might attribute to PF development, we then used Venn diagram analysis to integrate bulk RNA-seq of ECs from BLM-induced PF of mice with a single multi-omics dataset from above-analyzed human and mice samples, and we identify mechanosensitive Piezo1 and Piezo2 might serve as the candidate genes to involve in the pathogenesis of PF (Fig. 3E). PIEZOs are calcium-permeable non-selective cation channels that sense mechanical stimuli in multicellular organisms and mediate various biological processes. Subsequently, we analyzed the peaks of all mechanical stress-related genes in ECs and found that the increased peak activities of ECs mainly included Piezo1 and Piezo2 (Fig. 3F and Supplementary Fig. 4G). Additionally, motif enrichment analysis identifies motifs associated with mechanical stress in ECs, including Neurog1, Stat1, Nfkb1, Gata4, Rela, Sox9, Foxp2, Etv1, Fos, Fosl1, Meis2, Irf1, Jun, Ets1, and Pparg (Supplementary Fig. 4H). Interestingly, heatmap analysis of bulk-RNA-seq showed the expression of piezo1 increased in ECs from BLM-treated mice, while Piezo2 exhibited an opposite expression pattern (Fig. 3G). Consistent with these findings, immunofluorescence analysis of murine models demonstrated that Piezo1 expression was significantly upregulated in pulmonary ECs of BLM-treated mice (Fig. 3H). To further verify our finding in human samples, we firstly investigated the subpopulation distribution of PIEZO s in ECs, we quantified the proportions of PIEZO1+ and PIEZO2+ ECs, respectively. The analysis revealed that PIEZO1 is predominantly expressed in vascular ECs, whereas PIEZO2 shows preferential expression in lymphatic ECs (Supplementary Fig. 4I-J). Quantitative analysis of PIEZO1+ and PIEZO2+ EC proportions compared with control revealed a significant increase of PIEZO1+ cells in the IPF group, whereas PIEZO2+ cells showed no significant change (Fig. 3I-J). Subsequent immunofluorescence validation using IPF patient and normal control confirmed that PIEZO1 expression was significantly upregulated in IPF vascular ECs compared to control (Fig. 3K). A meta-analysis of the GEO Human IPF Public Database also showed that PIEZO1 was significantly upregulated in ECs (Fig. 3L). Therefore, we selected PIEZO1 in ECs as the promising intervention target of PF for the functional validation in following study.

Gene Deletion and Pharmacological Intervention of Piezo1 Affect PF Development
To determine the functional role of PIEZO1 in pulmonary vascular cell-mediated fibrosis, we systematically knocked out Piezo1 in vascular cells of adult mice using a tamoxifen-inducible EC-specific CreERT2 system. We developed mice with loxP site-flanking Piezo1 crossed with VE-Cad-CreERT2 mice. Tamoxifen was used to specifically activate CreERT2 activity in ECs, inducing EC-specific deletion of Piezo1 (Piezo1\DeltaEC) in VE-cad-CreERT2 Piezo1loxP/loxP mice. Piezo1-haploinsufficient adult mice (Piezo1WT) served as controls to monitor Cre toxicity. We observed significant knockdown of PIEZO1 in lung vascular endothelial cells (Fig. 4A-B), confirming the successful generation of EC-specific Piezo1-deficient mice. Subsequently, Masson, picrosirius red (PSR) and H&E staining revealed significant reduction of collagen deposition and inflammation in Piezo1\DeltaEC mice compared to controls (Fig. 4C, Supplementary Fig. 5A-B). Alpha-smooth muscle actin (\alphaSMA) immunofluorescence staining further verified the inhibition of lung fibrosis by Piezo1 deletion in ECs (Fig. 4D). We induced pulmonary fibrosis in these Piezo1\DeltaEC mice using BLM and observed a significant reduction in hydroxyproline in lung tissue (Fig. 4E).

To further investigate the role of PIEZO1 in modulating pulmonary EC function and lung injury, we administered the PIEZO1 agonist Yoda1 via both intratracheal and intraperitoneal routes. We found that, compared to the saline control group, both intratracheal and intraperitoneal administration of Yoda1 led to an increased inflammatory cells and fibrosis in the lungs of mice revealed by the hydroxyproline assay, Masson, picrosirius red (PSR) and H&E (Supplementary Fig. 5C-F). Additionally, we applied antagonist and agonist to explore if PIEZO1 is a potential therapeutic target for PF (Fig. 4F). Histopathological staining and hydroxyproline assay revealed that the antagonist GsMTx4 reduced the production of hydroxyproline in fibrotic lung tissue as well as deposition of ECM and collagen, indicating GsMTx4 can effectively alleviate PF, whereas the agonist Yoda1 exacerbated the fibrotic response in the BLM-induced PF mouse model (Fig. 4G-I, Supplementary Fig. 5G-H). Importantly, Yoda1 failed to worsen fibrosis in endothelial-specific Piezo1-knockout mice subjected to BLM, confirming that the pro-fibrotic effect of Yoda1 is mediated through endothelial Piezo1 (Supplementary Fig. 6A-D). In conclusion, our findings demonstrate that targeting PIEZO1 in ECs inhibits the development of pulmonary fibrosis, highlighting PIEZO1 as a promising target for the development of therapeutic interventions for PF.

PIEZO1 Activation Coupling IL-33 to Regulate the Development of PF
Recent studies demonstrate that ECs play a crucial role in the pathogenesis of pulmonary fibrosis through both juxtacrine and paracrine mechanisms, mediated by the secretion of various bioactive molecules, particularly cytokines and chemokines4,6,12,22. Notably, emerging evidence suggests that aberrant mechanical stress can significantly influence the release of these mediators from ECs during pathological processes. Building upon these findings, we propose a novel hypothesis that aberrant mechanical stress contributes to the progression of PF by modulating the secretion of pro-fibrotic factors from ECs. Firstly, we conducted CellChat analysis on our snRNA-seq dataset, which revealed that PIEZO1+ ECs had significantly higher interaction numbers and strengths with other lung cells compared to PIEZO1- ECs (Fig. 5A). To further explore potential downstream pro-fibrotic factors sensitive to mechanical stress and regulated by PIEZO1 signaling, we analyzed publicly available scRNA-seq data and identified 47 differentially regulated pro-fibrotic genes through Venn diagram analysis (Fig. 5B). Heatmap analysis showed that IGFBP5, IL33, MGP, SPRY1, and ACKR1 were specifically expressed in ECs (Fig. 5C), and these genes exhibited a significant correlation with mechanical stress scores (Fig. 5D). However, PIEZO1 expression were only positively correlated with the expression of IL33 and ACKR1 (Fig. 5E). Given that ACKR1 is not classified as a cytokine, whereas IL-33 represents a canonical cytokine, we therefore selected IL33 as the definitive therapeutic target in this study. Feature and violin plot indicated that IL33 was specifically and highly expressed in ECs (Fig. 5F), and IL33 was significantly upregulated in the IPF group compared to normal control (Fig. 5G). The violin plot of our snRNA-seq dataset revealed that IL33 expression was significantly higher in PIEZO1+ cells compared to PIEZO1- cells, with PIEZO1+ ECs in the IPF group exhibiting a higher IL33 expression level than those in the control group (Fig. 5H). We further analyzed bulk RNA-seq data from sorted ECs, observing upregulation of Il33 expression in ECs from BLM-treated mice compared to control, the sequencing results were also validated (Fig. 5I-J), which were further verified by immunofluorescence staining of IL-33 in the lung ECs of fibrosis patients (Fig. 5K). We then examined the relationship between IL33 and PIEZO1. To our expectation, immunofluorescence staining showed that administration of Yoda1 enhanced IL-33 expression, while GsMTx4 had the opposite effect (Fig. 5L). Immunofluorescence analysis further confirmed that specific deletion of Piezo1 in ECs markedly reduced IL-33 expression, reinforcing the hypothesis that PIEZO1 activation regulates IL33 secretion in ECs (Fig. 5M).

Although IL33 is a well-documented profibrogenic cytokine known to drive fibrosis in multiple organs, the specific role of EC-derived IL33 in PF development remains largely unclear. To determine whether IL33 in ECs is essential for PF development, we specifically deleted Il33 in ECs by crossing VE-cad-CreERT2 mice with Il33loxP/loxP mice and applied BLM to create a pulmonary fibrosis mouse model (Fig. 6A). mRNA of Il33 and protein of IL-33 were efficiently deleted in ECs via Vecad-Cre/LoxP (Fig. 6B). Subsequent analyses with Masson, PSR, H&E and \alphaSMA staining, hydroxyproline assay demonstrated that Il33\DeltaEC mice had remarkably lower fibrotic responses than those of Il33WT mice in bleomycin induced PF mouse model (Fig. 6C-E, Supplementary Fig. 6E). Moreover, we constructed a mouse model with EC-specific Il33 overexpression (OE-Il33) using an adeno-associated virus (AAV) delivery system to determine if Piezo1-regulated PF development is dependent on Il33. We generated a lung EC-selective adeno-associated virus (AAV) system expressing Il33 under the EC-selective Tie1 promoter (AAV-Tie1-Il33), which was specifically overexpressed in the lung of Piezo1\DeltaEC mice. Mice were initially pretreated with AAV through the trachea for four weeks, followed by bleomycin treatment. They were then euthanized in the third week after bleomycin administration (Fig. 6F). mRNA of Il33 and protein of IL-33 were confirmed the endothelial-specific overexpression of Il33 in lung tissue. (Fig. 6G). As expected, specific deletion of Piezo1 in ECs alleviated pulmonary fibrosis, but this effect was significantly reversed by the specific overexpression of Il33 in ECs achieved by AAV-Il33 (Fig. 6H-J, Fig Supplementary Fig. 6F). Collectively, these in vivo functional rescue experiments indicate that the endothelial PIEZO1 activation promoted PF development in a IL33 dependent manner.

CAPN2-STAT3 Axis Regulates IL-33 Expression Upon the PIEZO1 Activation
We investigated the regulatory mechanism of PIEZO1 on IL33 expression in vitro using primary human umbilical vein endothelial cells (HUVECs). Since both cellular tension and changes in matrix stiffness can activate the PIEZO1 channel, we applied tension to the monolayer of HUVECs using a cell stretching device and cultured HUVECs on substrates of varying stiffness to simulate the spontaneous activation of PIEZO1. We found that when subjected to 20% strain, the secretion and mRNA transcription levels of IL33 in HUVECs initially increased at 6 hours and then decreased at 24 hours, and meanwhile we observed that in monolayers cultured on a 25 kPa substrate, HUVECs exhibited higher secretion and transcriptional expression levels of IL-33 (Fig. 7A-B). Notably, silencing PIEZO1 expression with shRNA in HUVECs inhibited the secretion and transcription of IL-33 after cells were subjected to 20% strain for 6 hours or cultured on a 25 kPa substrate (Fig. 7C-E).

CAPN2 functions as a key downstream effector in the PIEZO1-mediated mechanotransduction pathway, participating in multiple mechanoresponsive biological processes. Interestingly, both the activity and protein level of Calpain2 significantly increased after 20% strain stretching and 25 kPa substrate culturing with a similar trend to IL-33 (Fig. 7F, G), which were inhibited by PIEZO1 knockdown (Fig. 7H, I). Thus, we speculated PIEZO1 activation induced IL33 is dependent on CAPN2. Consistent with our hypothesis, CAPN2 knockdown (shCAPN2) markedly decreased IL-33 expression and secretion upon mechanical stimulation (Fig. 7J, K). Transcription factors (TFs) are necessary to bind to the promoter region and directly initiate the expression of target genes. To explore which TFs act as downstream molecules of the PIEZO1-CAPN2 axis to regulate IL-33 expression (Fig. 7L), we first utilized the Cistrome (http://dbtoolkit.cistrome.org/) public transcription factor database and predicted the top 10 transcription factors that might regulate IL33 expression (Fig. 7M). Additionally, we performed motif enrichment analysis of differential peaks in ECs using scATAC-seq datasets of the fibrotic mouse model and identified the top 100 motifs highly expressed in the endothelium (Fig. 7N). Venn diagram analysis suggested that STAT3 is a key TF regulating IL33 expression upon mechanical force (Fig. 7N). Importantly, 6 hours of mechanical stretch clearly promoted the activation of STAT3 as revealed by p-STAT3 immunoblotting (Fig. 7O), and this stretch-induced activation was blocked by PIEZO1 and CAPN2 silencing (Fig. 7P). These results suggest that STAT3 might acts as a downstream TF of PIEZO1 that is involved in the regulation of IL33 expression. As demonstrated in Fig. 7Q, R, STAT3 knockdown (shSTAT3) in HUVECs cultured under mechanostimulatory conditions (cyclic 20% tensile strain and 25 kPa substrate stiffness) significantly attenuated both IL33 transcript levels and protein secretion. In conclusion, endothelial PIEZO1 activation induces the downstream profibrotic molecule IL-33 to regulate the development of PF, might potentially through the CAPN2-STAT3 axis.

Methods
Human Patient Samples
In this study, we enrolled four patients (male: 3; female: 1; age range: 45–64 years, mean \pm standard deviation: 57.7 \pm 4.3 years) with idiopathic pulmonary fibrosis (IPF) who underwent lung transplantation surgery and five normal individuals (male: 3; female: 2; age range: 38–69 years, mean \pm standard deviation: 51.0 \pm 6.1 years) whose lung tissue specimens were obtained from surgical resections for other indications. Designed to explore the central role of endothelial cells in pulmonary fibrosis, this study had a limited sample size and was not prospectively stratified or analyzed by sex. Information on participants’ sex was obtained from their clinical medical records. This study was approved by the Medical Ethics Committees of West China Hospital, Sichuan University and Shenzhen People’s Hospital (Approval No.: LL-KY-2021651) in accordance with the guidelines of the Declaration of Helsinki. All participants (or their legal guardians) provided written informed consent for participation in this study, the publication of indirect identifiers mentioned in Supplementary Data S1 and future related research. No financial compensation was provided to participants. For future studies, the collected samples were preserved by freezing or embedding in optimal cutting temperature (OCT) compounds. Detailed demographic and clinical characteristics of the patients are shown in Supplementary Data S1. No individual-level data are presented in a manner that could compromise anonymity, and all data sharing complied with the ethical approvals granted by the Institutional Review Boards of West China Hospital of Sichuan University and Shenzhen People’s Hospital.

Mice
C57BL/6J mice were obtained from the Model Animal Research Center of Nanjing University. Piezo1loxP/loxP (STOCK Piezo1tm2.1Apat/J; 029213) and Il33loxP/loxP (B6(129S4)-Il33tm1.1Bryc/J; 030619) mice were obtained from GemPharmatech, Nanjing, China. Mice expressing EC specific Cdh5-(PAC)-CreERT2 were provided by R.H. Adam. Cdh5-(PAC)-CreERT2 mouse was crossed with floxed Piezo1 mouse to generate CreERT2 Piezo1loxP/loxP mice and CreERT2 Il33loxP/loxP. CreERT2 Piezo1loxP/loxP and CreERT2 Il33loxP/loxP mice were intraperitoneally treated with tamoxifen (100 mg/kg) 2 months after birth for 6 days (interrupted for 3 days after the third dose), leading to endothelial-specific deletion of Piezo1 (Piezo1\DeltaEC) and Il33 (Il33\DeltaEC). Tamoxifen-treated Cre-negative littermates (Piezo1WT and Il33WT) were used as control for all experiments. All experiments used 8–10-week-old male specific pathogen-free (SPF) mice. The animals were housed in a temperature-controlled (22 \pm 2°C), humidity-regulated (50% \pm 10% relative humidity) environment under a standard 12-hour light/dark cycle at the Experimental Center of West China Second University Hospital, Sichuan University. The facility maintains strict SPF conditions with continuous air filtration and regular monitoring of environmental parameters. Mice were group-housed in individually ventilated cages with sterile bedding and had free access to autoclaved standard rodent chow and water ad libitum. Bedding was changed weekly, and health status was monitored regularly. All animal experimental protocols were approved by the Laboratory Animal Ethics Committee of West China Second University Hospital, Sichuan University (approval number: 20220303070), and conducted in strict accordance with the institutional guidelines for the care and use of laboratory animals.

Cell culture
HEK293T (ATCC, #CRL-3216,) cells were purchased from the American Tissue Collection Center (ATCC, Manassas, VA) with validated cell identity and eliminated of mycoplasma contamination. HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (C11995500BT, Gibco) with high glucose supplemented with 10% fetal bovine serum (FBS) (10099141, Gibco) and penicillin-streptomycin (Gibco). Human umbilical vein endothelial cells (HUVECs) were isolated from neonatal umbilical cord as described and cultured in endothelial Cell Medium (1001, ScienCell) at 37°C in a humidified atmosphere of 5% CO2.

Public data
Single-cell RNA sequencing (scRNA) data were obtained from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). We retrieved and utilized scRNA sequencing data from IPF patients and healthy individuals (by GSE13683117, GSE13277118, GSE12803319, GSE15935420 and GSE12296021). In addition, we collected extensive RNA sequencing data from IPF patients and normal lung tissue (courtesy of GSE47460)47.

Method specifics
Endothelial cell isolation. Immunomagnetic bead cell sorting is based on the ability of cell surface antigens to bind to specific antibodies attached to magnetic beads. Briefly, the Sheep anti Rat magnetic beads were pre-coated with CD31 (BD, #550274, 1:20 dilution) and CD45 (BD, #550539, 1:20 dilution) respectively. Harvested mouse lungs were washed with pre-cooled DPBS, cut into pieces, digested with digestion DMEM containing 1 mg/mL type I collagenase (Solarbio, #C8140), 1 mg/mL type IV collagenase (Biosharp, C#8160), 1 mg/mL Dispase II (Solarbio, #D6430), and 0.2 mg/mL DNAase (Solarbio, #BS165) in 37°C for 40 min. After erythrocytes were lysed, cells were resuspended with MACS Buffer (Miltenyi Biotec, #130-091-376) and incubated on ice for 15 minutes with anti-mouse CD31 MicroBeads (for endothelial cell separation, Miltenyi Biotec, #130-097-418), anti-mouse CD45 MicroBeads (for immune cell separation, Miltenyi Biotec, #130-052-301), anti-mouse CD326 MicroBeads (for epithelial cell separation, Miltenyi Biotec, #130-105-958), and anti-mouse CD140a MicroBeads (for fibroblast separation, Miltenyi Biotec, #130-101-502). The MACS MultiStand separation rack (Miltenyi Biotec, #130-042-302), MidiMACS Separator (Miltenyi Biotec, #130-042-302), and LS Separation columns (Miltenyi Biotec, #130-042-401) were assembled and used to separate the cells, the isolated endothelial cells, epithelial cells, immune cells, and fibroblasts are used for Western blot and qPCR experiments.

AAV treatment. Mice were anesthetized by inhalation at 6–8 weeks of age, and after general anesthesia, the limbs of the mice were fixed with sterile tape, the skin of neck was scraped off and disinfected with alcohol, and the trachea was carefully cut open to expose the trachea. 2 \times 1011 Vector Genomes (v.g) of AAVENT-Tie1-NC (Negative control), AAVENT-TIE1-OE-Il33 (GeneChem, Shanghai), were injected into the trachea, respectively. The neck skin was carefully sutured and the efficiency of target gene expression was verified after 4 weeks.

Lung fibrosis model. An intratracheal BLM (Selleck) injection model was used to induce lung fibrosis. BLM sulfate powder was suspended and dissolved in sterile PBS and a dose of 1 unit/kg (50 \muL/20g) was intratracheally injected into mice at 8 weeks of age. During the injection process, mice were anesthetized with Isoflurane. Mice were euthanized 21 days after injection, and the lungs were harvested for the analysis of morphology and fibrosis.

Measurement of hydroxyproline. The hydroxyproline content in lung tissues was measured using Hydroxyproline (HYP) Assay Kit (Solarbio, #BC0255). Briefly, tissues of equal weight were cut into as many pieces as possible. Then, tissues are baked overnight in 12N hydrochloric acid at 110°C and neutralized with sodium hydroxide. Next, the samples were added to 1.4% chloramine T in 0.5M sodium acetate/10% isopropanol and incubated for 20 min at room temperature. Ehrlich’s solution was added into samples for 30 min incubation at 65°C. Finally, the absorbance values of all samples were measured at 560 nm on a SpectraMax Plus 384 full-spectrum microplate reader (Molecular Devices), and the Hydroxyproline content was calculated based on the fresh weight of tissues and the standard curve.

Histological analysis. The right lung was fixed in 4% paraformaldehyde for 24 h and then sent to Servicebio (Wuhan, China) for dehydration, paraffin embedding, and sectioning at 4 \mu m. H&E, Sirius Red and Masson staining were performed by Servicebio according to standard protocols. Whole-slide digital images were captured under identical settings using an Olympus VS120-FL-6 slide scanner. Quantitative analysis of H&E staining, Sirius red staining, and Masson staining was performed by evaluating five random fields in each section. The severity of pulmonary inflammation was graded based on the following criteria: 0, normal; 1, inflammatory cells occupying 0–10% of the entire tissue; 2, inflammatory cells occupying 10–40% of the entire tissue; 3, inflammatory cells occupying 40–70% of the entire tissue; 4, inflammatory cells occupying 70–100% of the entire tissue. For Sirius red staining or Masson staining, ImageJ software was used to define the red areas (in Sirius red staining) or blue areas (in Masson staining) in the tissue as positive areas. These areas were then outlined and their ratios to the entire field of view were calculated.

Immunofluorescence staining. Lung tissues from humans or mice were dehydrated after fixation with 4% paraformaldehyde (PFA) and subsequently embedded, cut into 4 \mu m sections. And then deparaffinized, rehydrated, sections were washed with PBS for a few minutes, then placed in sodium citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) for antigen retrieval (These sections were microwaved on high for 10 min until they reached a slight boil, then the power was reduced to low for an additional 15 min). After cooling to room temperature, the sections were washed again with PBS and blocked with 10% donkey serum in PBS for one hour at room temperature. Then incubated with primary antibodies at 4°C overnight. These sections were washed and then incubated with fluorochrome-conjugated secondary antibodies. Finally, the sections were counterstained with DAPI (Roche, #10236276001) and observed under an Olympus VS120-FL-6 slide scanner. The primary antibodies used were CD31 (Abcam, #ab9498; Servicebio, #GB13063), VE-cadherin (R&D Systems, #AF938), IL-33 (R&D Systems, #AF3626), PIEZO1 (proteintech, #15939-1-AP) and \alphaSMA (Abcam, #ab7817) antibodies.

Cytokine measurement by ELISA. The amounts of IL-33 in HUVECs supernatant were quantified using the Human IL-33 DuoSet ELISA kit (R&D Systems, #D3300B) according to the manufacturer’s instructions. Absorbance was read on a SpectraMax Plus 384 full-spectrum microplate reader (Molecular Devices) and the data were analyzed with OriginPro 9.1.

Protein extraction and western blotting. Isolated cells or tissues were kept on ice and lysed in RIPA buffer (Beyotime, #P0013B) supplemented with protease and phosphatase inhibitor cocktails (MCE). Protein concentration was determined with the BCA assay (beyotime, # P0010). 30–60 \mu g of protein was loaded per well on 4–20% SDS-PAGE gels (ACE, #ET15420L Gel), transferred to ethanol-activated PVDF membranes (Thermo Fisher Scientific, #88518), blocked with 5% skim milk in TBST, and incubated overnight at 4°C with primary antibodies diluted in TBST: IL-33 (R&D Systems, #AF3626, 1:1000), PIEZO1 (Proteintech, #15939-1-AP, 1:1000), CALPAIN2 (Labome, #A03492, 1:1000), P-STAT3 (CST, #9134s, 1:1000), STAT3 (Abcam, #ab68153, 1:2 000), P-STAT1 (CST, #9171, 1:1000), STAT1 (Proteintech, #10144-2-AP, 1:1000), GAPDH (Servicebio, #GB12002, 1:5000), \beta-actin (Proteintech, #66009-1, 1:5000), \beta-tubulin (CST, #2146, 1:5000). Membranes were then incubated with HRP-conjugated goat anti-rabbit or anti-goat IgG for 1.5 h at room temperature and developed with ECL Prime. Chemiluminescence was captured using a ChemiDoc MP Imaging System (Bio-Rad). Uncropped Western blot images are provided in the Source Data file.

Real-time quantitative PCR (qPCR). Purified total RNA from sorted mouse lung cells (include endothelial cell) or HUVECs was extracted using TRIzol (Thermo Fisher, #15596026) and reverse transcribed with PrimeScript RT reagent Kit (RR037A, Takara Bio). Gene expression was analyzed by qPCR with SYBR green chemistry (RR420A, Takara Bio). Relative expression level of mRNA was determined by normalizing each gene’s expression level to that of the internal reference gene GAPDH. A CFX96 Connect Real-Time PCR Detection System (Bio-Rad) was used for amplification and data acquisition. Relative expression was calculated by comparing the cycle threshold values using the 2-\Delta\Delta Cycle threshold method.

Cell mechanical experiment. Matrigel (356234, Corning BD) was diluted with serum-free medium or PBS. 2-3 ml of mixed matrix gel was added to the chambers (Naturethink) and incubated for 1 h at 37°C. The unbound Matrigel was aspirated from the chambers and rinsed with serum-free medium or PBS. 1 \times 105 Cells were seeded in the Matrigel-coated chambers. The chambers were gently shaken to distribute the cells evenly and then placed in the cell culture incubator for 24 h. When the fusion of cells in the chambers reached 80, chambers were attached to the manual retractor (Naturethink) and pull the chambers to the corresponding scale of the manual retractor. After pulling the chambers by 20%, cells were subjected to the pulling time for 0, 6 and 24 h, the cells were then digested from the silica gel chambers with trypsin for further experiments.

Transfection with shRNA plasmid. shRNA oligos were annealed and ligated into AgeI/EcoRI (NEB, #R3552; #R3101)-digested pLKO.1-puro, transformed into competent cells (Tsingke, #DLC101), and sequence-verified. For lentivirus production, 2.5 \mu g of the pLKO.1-shRNA (or scramble) vector, psPAX2 and pMD2G (5:3:2) were co-transfected into 293T cells using Lip6000 (Beyotime, #C0526). Viral supernatants were harvested at 48 h, used to infect HUVECs, followed by 1 \mu g/mL puromycin selection for 2 days. Knockdown efficiency was confirmed by qPCR or Western blot. Primer sequences: shPIEZO1 F 5′-CTCACCAAGAAGTACAATCAT-3′ R 5′-ATGATTGTACTTCTTGGTGAG-3′; shCAPN2 F 5′-TCACCTTGAATGAAGAAATTT-3′ R 5′-AAATTTCTTCATTCAAGGTGA-3′; shSTAT3 F 5′-CTCAGAGGATCCCGGAAATTT-3′ R 5′-AAATTTCCGGGATCCTCTGAG-3′.

Calpain activity. A fluorometric calpain activity assay kit (Abcam, #ab65308) was used to quantify calpain activity in the lysates of HUVECs by fluorescent substrate N-succinyl-LLVY-AMC. All samples were analyzed in triplicate with a multilabel reader (excitation, 400 nm; emission, 505 nm, Thermo, America) and expressed as relative fluorescent units (RFU).

scRNA/snRNA-seq: building libraries with the 10x Genomics platform. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) were performed using the 10X Chromium 3′ v3 kit (10X Genomics, Pleasanton, CA) according to the manufacturer’s instructions. Data processing included quality assessment using FASTQ and quality control using Cell Ranger and was aligned to the Homo sapiens GRCh38.p13 reference genome. The number of unique molecular identifiers (UMIs) and other data are shown in Supplementary Data S2.

scRNA/snRNA-seq data analysis workflow. The scRNA sequencing data were analyzed using the Seurat software package (v4.0)48. The detailed analysis workflow is available on the website (https://satijalab.org/seurat/). Any single cell with less than 200 genes or more than 5000 genes, and cells with more than 25% mitochondrial genes were regarded as low quality cells and excluded. Possible doublets were detected using DoubletFinder. Each dataset was log-normalized using the NormalizeData function. Genes with large differences in expression between cells were identified using the FindVariableFeatures function, and the data was scaled using ScaleData. Integrate each sample and correct batch effects using the Harmony49. Culusters were annotated using reported markers. the Subset function was used to extract endothelial cells etc. for further analysis. Intercellular communication and cellular function orchestration were explored using CellChat25. The Meta and metafor R packages were used for formal meta-analysis and to create forest plots.

Enrichment analysis and gene set scoring. Differential genes were enriched using Gene Ontology (GO) enrichment analysis. Mechanical stress (MS) scoring was performed using AddModuleScore function and the genes for scoring were the pseudobulk DEGs of snRNA-seq data enriched in “response to mechanical stimulus” (GO:0009612). Fibrosis scoring was performed using AddModuleScore function with the genes in GO pathway “collagen fibril organization” (GO:0030199). The gene lists are available as Supplementary Data S10.

“Scissors” identifying cell subpopulations associated with lung function. The “scissors method” integrates single-cell data with phenotypically relevant bulk sequencing data by first quantifying the similarity between each bulk sample and each cell sample. The “scissors method” can then optimize a regression model on a matrix associated with the sample phenotype to identify relevant subpopulations. A detailed description is given in the literature13.

scATAC-seq analysis using the 10x Chromium platform. We use the 10X Genomics Chromium single-cell ATAC-seq solution to prepare single cells for sequencing and the 10X Genomics single-cell ATAC-seq solution technology to prepare scATAC-seq libraries for sequencing. Sample and library preparation as well as instrument and sequencing parameters for generating scATAC-seq data on the 10x Chromium platform are detailed below and also available at https://support.10xgenomics.com/single-cell-atc.

Data processing of scATAC-seq. Data processing included quality assessment using FASTQ and quality control was performed using Cell Ranger and sequencing reads was aligned to the Homo sapiens GRCh38.p13 reference genome. Cells were filtered using the following criteria: (1) 3000 < nCount_peaks < 50000; (2) pct_reads_in_peaks > 15; (3) blacklist_ratio < 0.05; (4) Nucleosome signal < 4; (5) transcription start site (TSS) enrichment score > 1. Peaks calling was performed using MACS2.

Combined analysis of scRNA-seq and scATAC-Seq. To perform an integrated analysis of the scATAC-seq dataset and the scRNA-seq dataset, we utilized the anchoring strategies provided by the R packages Seurat and Signac. Initially, we identified a set of cell pairs or “anchors” between the datasets through the FindTransferAnchors function using typical correlation analysis (CCA) and mutual nearest neighbors (MNNs) methods, which are hypothesized to represent similar biological states. These anchors were the basis for integrating the two datasets. Subsequently, we used the TransferData function to project the clustering labels from the scRNA-seq data onto the scATAC-seq data, which allowed us to evaluate single-cell measurements in different data modalities.

Motif analysis. Motif analysis was performed using Signac package mainly. Transcription factor (TF) binding motifs were downloaded from JASPAR database. Then TF binding motifs were mapped to scATAC-seq peaks using AddMotifs function. RunChromVAR function was used for calculating motif deviation score. We performed differential accessibility analysis with FindMarkers function and obtained corresponding motifs using FindMotifs function. MotifPlot function was used to visualize motifs of peaks with different accessibility between groups.

Bulk RNA sequencing and gene expression analysis. In our study, we performed bulk RNA sequencing of endothelial cells isolated from human and mouse lung tissue. Cells were lysed with RLT Plus buffer immediately after sorting. Total RNA extraction was performed using Qiagen’s RNeasy Plus Mini Kit. We then prepared full-length RNA sequencing libraries and performed 2 \times 75 bp paired-end sequencing using a 150-cycle Nextseq 500 High Output V2 kit. The RNA-seq data compilation included at least three independent biological replicates to ensure robust and reproducible results.

Statistical Analysis
All statistical analyses were performed using Prism 10 and R software by investigators who were not blinded to group allocation during sample processing (genotype/treatment identity was required), but all data analyses were subsequently conducted by independent researchers using identical automated scripts to minimize subjective bias; unpaired two-tailed Student’s t-tests were used for two-group comparisons and one- or two-way ANOVA followed by Tukey’s or Šídák’s multiple-comparison test for multiple groups, with exact tests and p-values reported in the legends, p < 0.05 considered significant, sample sizes (n) stated, and data presented as mean \pm s.e.m.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an original manuscript from Zhang and colleagues exploring the role of PIEZO1 on endothelial cells in mouse models of pulmonary fibrosis. Reporting a small snRNA-seq dataset and analyzing other public resources, they suggest that there are increased endothelial cells in IPF lungs with evidence of mechanical stress, and in several preclinical fibrosis models, suggest that endothelial-cell Piezo1 is a potential driver of this. Several conditional deletion models and ex-vivo studies suggest that endothelial cell specific Piezo1 activation promotes fibrosis in an IL33 dependent manner. There has been limited study of the endothelium in pulmonary fibrosis pathogenesis and these studies offer potential new insights. There are a number of substantive analytical issues the authors need to address.\nMajor comments:\n1) Figure 1: The assertion that there are increased endothelial cells in IPF lungs is at odds with considerable prior work and assessment of histopathology. These results are likely an artifact of small numbers, sampling methods, and statistical treatment. The resolution of annotation of this new snRNA-seq dataset is very low, and could be substantially improved using any of a number of public datasets as a reference. This influences much of the downstream analysis. First, snRNA-seq of IPF and control lungs – lumping all endothelial cells for analysis of cell composition is not really meaningful (capillaries, venules, arterioles, etc are anatomically and functionally distinct). Second, assessment of cell composition from n of 3 samples is not robust (particularly as the authors could have assessed this from the meta-analyzed dataset presented later in the figure). Third, since composition is a “zero-sum” scenario, Bayesian methods (i.e. propeller or others) are more appropriate.\n2) Figure 1: FVC-based classification as utilized by the authors reflects disease stage (not progression). The authors are not reporting an FVC rate of change (which would be an indicator of progression rate), but are reporting FVC which at one point in time is a measure of disease severity. Those with an FVC <50 do not necessarily have more rapidly progressive disease, they simply have had disease longer than those with and FVC >50%. This should be clarified in the text and figure legend.\n3) Figure 1F: Numerous manuscripts have described COL15A1+/PLVAP+ “systemic-venous-like” endothelial cells as markedly more abundant in IPF lungs yet are not described in this figure – are these being misclassified as “capillary cells”?\n4) Lacking code availability or reporting of the genes which were used in the “mechanical stress” gene score, it is difficult to assess the robustness and reliability of this finding.\n5) Figures 2 and 3 – similar to the above comment, lumping all endothelial cells into a single population risks conflating changes in relative abundance of certain anatomically distinct populations with changes in chromatin accessibility and gene expression within a population. There are well-established marker genes that can discriminate endothelial cell subtypes into aCap, gCap, venule, arteriole and lymphatic populations at minimum to report these data.\n6) Figure 3 – PIEZO2 is essentially not expressed in the vascular endothelium, so it is unclear what a ratio of Piezo1+/Piezo2+ cells would mean\n7) Figure 4: Fibrosis assessments are reported as an “Inflammation Score” and “Relative Collagen Content” but no methods for these assessments are reported. Standard measures of fibrosis assessment (quantification of fibrotic area or Ashcroft score) in addition to hydroxyproline should be reported.\n8) No quantitative measurements of fibrosis are reported for the GsMTx4 or Yoda1 treatment studies in Figure 4I – any assertions of effects on fibrosis need to be supported by appropriate quantitative measures and statistical treatment.\n9) Figure 5 – cell communication analyses are really only informative at cell-type resolution – the finding that Piezo1 is correlated with Ackr1 (a specific marker of veins/venules) suggests that these findings overall reflect compositional differences within the endothelial cell population (rather than changes in cell-type specific expression related to Piezo1 expression).\n10) Figure 5J – the image quality is very low and not interpretable\n11) Figure 6 – same comment as #7 – results regarding fibrosis need to be supported by standard, validated, methods for quantification of fibrosis.\nMinor comments:\n1) Figure 2E: ILC’s should not express Cd3e, the authors should check this annotation and/or evaluate ambient RNA contamination\n2) Results for Figure 3: “To investigate the subcellular distribution of PIEZOs in ECs, we analyzed human lung ECs using scRNA-seq bubble plots” – semanic point, but “bubble plots” are a visualization tool, not an analysis, and they report cellular (not “subcellular”) expression – the text for this sentence should be edited to clarify this", "reviewer_2": "This paper by Zhang, Zhang, Gui, Xia et al., looks at the role of endothelial PIEZO1 channels in the development of pulmonary fibrosis in two animal models. Using a Cre-lox strategy to specifically delete Piezo1 from endothelial cells they link PIEZO1 to the fibrotic cascade and attempt to link these effects to IL-33. Despite the raft of bioinformatics approaches the attempts to link this directly to mechanical stress are poorly fleshed out, for example a reader is still left with the question of exactly what cue is stimulating ECs in response to bleomycin or SiO2 to drive pulmonary fibrosis? This seems like a key question that the authors do a poor job of discussing. In addition, the use of PIEZO1 pharmacology is also rudimentary and there is no discussion of the off-target effects of GsMTx-4 or the fact that Yoda-1 drives cellular Ca2+ signalling that is spatio-temporally different from the mechanical activation of PIEZO1 (this is especially important for the in vitro experiments). Despite this, the data from KO mice and human datasets is compelling that PIEZO1 in ECs plays a role in the development of pulmonary fibrosis. The effects on IL-33 are strong and PIEZO1-dependent providing high level confidence that a PIEZO1-IL-33 pathway is at play in these disease models. I have a number of points below that require addressing prior to publication especially related to the in vitro experiments in HUVEC.\nJust a general point the lack of line numbering makes reviewing arduous!\n1) Why is the control haplo-insufficient – what’s its genotype? I assume this is a typo.\n2) PIEZO1 vs Piezo1 needs uniformity.\n3) Figure 4B comes from isolated ECs or whole tissue? If whole tissue how is the reduction so profound given all other cell types express PIEZO1. Figure 4I lacks quantification shown in Figure 4D.\n4) For Figure 7 a calpain assay would be much better than mRNA levels. It’s not clear why Yoda1 would increase the mRNA levels of calpain2(or STAT3 – STAT3 also doesn’t come up earlier in the omics sections). The pathway should be dictated by Ca2+ induced activation of calpain – in this scenario as a homeostatic mechanism one might expect a reduction in calpain levels under a sustained mechanical cue not an increase. Using Yoda1 as a surrogate for mechanical stress is fraught with danger as it induces spatio-temporal Ca2+ signalling that is wholly different to mechanically induced PIEZO1 signalling. In a mechanical response PIEZO1 signalling is localized to certain regions like focal adhesions (Chen et al., 2018 Neuron; Yao et al., 2022 Science advances) and is transient. This is very different from when yoda1 is added – which causes a tonic sustained elevation in intracellular Ca2+. It is necessary to show the same pathway in response to a relevant mechanical cue. The reason for this is that if you use Yoda1 in an in vitro system you can end up suggesting a pathway like seen in cardiac PIEZOs (Zhang et al., 2021 Hypertension) that ends up being incorrect (Yu et al., 2022 Nature Cardiovascular Research). At least a mechanical cue should be used to validate these findings (stretch, stiffness, shear stress etc). This links back to the major question of what cue is driving this signalling in these disease models!!\n5) There's also no data that IL-33 is actually released. An ELISA showing PIEZO1-mediated release of IL-33 is really essential as generally speaking the paper is too reliant on mRNA levels to suggest mechansim.", "reviewer_3": "Zhang et al. present a study “Single-cell multiomics reveals endothelial mechanosensitive PIEZO1 coupling IL33 regulates the development of pulmonary fibrosis” that uncovers a molecular mechanism of pulmonary fibrosis (PF). PF is a lethal disease with limited treatment options. By analyzing sc/nRNA-seq and scATAC-seq data from human specimens and two mouse models (silica and Bleomycin induced) the study authors identify upregulation of genes linked to mechanosensation in endothelial cells. They identify PIEZO1 as top candidate and validate its role using genetic deletion in a mouse model and small molecule inhibition or activation. They could show that blocking or deleting PIEZO1 decreases fibrosis and activation resulted in increased fibrosis. Finally, they show that PIEZO1 couples to IL33 and activates a CAPN2-STAT3 signalling cascade.\nThis is an exciting, comprehensive and well executed study covering hypothesis generating single cell data and follow-up analysis using genetic mouse models and small molecules to validate predictions for the role of PIEZO1 and related signaling in PF.\nI have a few comments that would need to be addressed:\n1) According to Table S1 and S2 only 5 donors were profiled? Where does the sixth datapoint in the figures come from? Please clarify.\n2) What genes are in the MS Score? How was the statistic calculated in 1N, 2H, 2J. Per individual or per cell?\n3) MS Score analysis shows an increase in PF in the silica model (2H, 2J), but in the Bleomycine model mostly Piezo1/2 are robustly regulated (3K). How can this be explained?\n4) The scATAC-seq data lacks analytical details, e.g. how were cells filtered? How was peak calling performed? How was transcription factor motif enrichment performed? How was batch correction performed and doublets removed (also for scRNA data)?\n5) Fig 2L “different thematic patterns of ECs” -> TF motif enrichment analysis? How was this analysis performed, which tool was used? Is this analysis performed on all ECs or differential accessible sites (same for Bleomyin analysis)? What about all the other motifs except STAT motif that were identified from the scATAC analysis?\n6) 3G what exactly is displayed: Figure panel states motif and legend states an AUC score of mechanical stress genes.\n7) 7J there is no binding of STAT3 at the promoter region of IL33 on the tracks visible. The signal of STAT3 at most sites is lower after YODA1 treatment despite the model would suggest otherwise. Do all peaks have a STAT3 binding site?\n8) 7H why top 100 and top 10 TFs (text states top 7)? How was this determined? What are the “top 10 transcription factors”? How was it concluded that it must be STAT3 and not for example STAT1 which was labeled as enriched motif in 2L and S5L. Motif analysis usually cannot pin down the exact factor so both could be options.\nMinor:\n1) From the writing it is unclear if the processing and annotation of scATAC-seq of Silica and BLM datasets (one mentions annotation based on prediction from scRNA and the latter looks at promoter accessibility). Please clarify.\n2) S4 panel legend F and G is switched. S5 description for panel I is incorrect."}, "author_response": "RE: Point-by-point response (NCOMMS-24-47968A)\nContents\nPoint-by-point response to reviewers’comments:\nReviewer #1: .................................................................................1\nReviewer #2: ...............................................................................18\nReviewer #3: ...............................................................................29\nThis is an original manuscript from Zhang and colleagues exploring the role of\nPIEZO1 on endothelial cells in mouse models of pulmonary fibrosis. Reporting a\nsmall snRNA-seq dataset and analyzing other public resources, they suggest that there\nare increased endothelial cells in IPF lungs with evidence of mechanical stress, and in\nseveral preclinical fibrosis models, suggest that endothelial-cell Piezo1 is a potential\ndriver of this. Several conditional deletion models and ex-vivo studies suggest that\nendothelial cell specific Piezo1 activation promotes fibrosis in an IL33 dependent\nmanner. There has been limited study of the endothelium in pulmonary fibrosis\npathogenesis and these studies offer potential new insights. There are a number of\nsubstantiveanalytical issues the authors need to address.\nMajorcomments:\n1) Figure 1: The assertion that there are increased endothelial cells in IPF lungs is at\nodds with considerable prior work and assessment of histopathology.These results are\nlikely an artifact of small numbers, sampling methods, and statistical treatment.The\nresolution of annotation of this new snRNA-seq dataset is very low, and could be\nsubstantiallyimproved usingany ofanumber ofpublicdatasets as areference.\nA)The assertion that there are increased endothelial cells in IPF lungs is at odds with\nconsiderableprior work and assessment ofhistopathology.\nAnswer: We thank the reviewer for raising this important point. While traditional\nhistopathological assessments may not always highlight endothelial cell proliferation,\nrecent advances in single-cell RNA sequencing (scRNA-seq) have provided robust\nevidence for endothelial cell alterations in IPF. Below, we summarize key findings\nfrom three independent studiesthat support our assertion:\n1. As showed in (Figure 8J), Caporarello, Nunzia et al. identified a significant\nincrease in pulmonary venous and systemic venous endothelial cells in IPF lungs\nthrough scRNA-seq analysis.The authors attributed thisphenomenon to dysfunctional\nERGsignaling, which drives vascular aging and fibrosis.\nReference: Caporarello, Nunzia et al. Dysfunctional ERG signaling drives pulmonary\nvascular aging and persistent fibrosis. Nature communications vol. 13,1 4170. 25 Jul.\n2022.\n2.As showed in (Figure 1C), Zhang et al. demonstrated a marked increase in the\nproportion of endothelial cells in IPF lungs compared to normal controls. Their\nscRNA-seq data further revealed distinct endothelial subpopulations contributing to\nfibroticprogression.\nRelevant figure from thestudy:\nReference: Zhang T, et al. Single Cell RNA-Seq Identifies Cell Subpopulations\nContributing to Idiopathic Pulmonary Fibrosis in Humans. J Cell Mol Med.\n2025;29(3):e70402.\n3.As showed in (Fig. 1G), Jin et al. also reported elevated endothelial cell\npopulations in IPF lungs via scRNA-seq. Their work emphasized the role of\nendothelial-fibroblast crosstalk in fibrosis.\nReference：Jin Chengji et al. Single-cell RNAsequencing reveals special basal cells\nand fibroblasts in idiopathic pulmonary fibrosis. Scientific reports vol. 14,1 15778. 9\nJul. 2024.\nIn conclusion: These studies underscore the dynamic changes in endothelial cells\nduring IPF pathogenesis, as revealed by high-resolution scRNA-seq. While\nhistopathology remains a cornerstone of IPF diagnosis, emerging technologies like\nscRNA-seq offerdeeper insights into cellularheterogeneity and disease mechanisms.\nB) The resolution of annotation of thisnew snRNA-seq dataset is very low, and could\nbesubstantially improved using any ofa numberofpublicdatasets as a reference.\nAnswer: Thank you for your important comments. We have followed your very\nconstructive advice to collect more patient samples for both control and IPF groups in\nall analyses, including single nucleus RNA-Seq (snRNA-Seq).We have referred a\nnumber of papers and re-annotated clusters using the published marker genes\n(Sikkema L et al., 2023 Nature medicine; Schupp JC, et al., 2021 Circulation). The\nrevised results are shownbelow.\nRNAsequencing (snRNA-Seq) of IPF patients and control subjects, Data are shown\ninrevised Fig.1B and Fig.S1C.\nReferences:\n[1]. Schupp JC, et al. Integrated Single-Cell Atlas of Endothelial Cells of the Human\nLung. Circulation. 2021Jul27;144(4):286-302.\n[2]. Adams TS, et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident\ncell populations in idiopathicpulmonary fibrosis. SciAdv.2020Jul8;6(28):eaba1983.\nC)This influences much of the downstream analysis. First, snRNA-seq of IPF and\ncontrol lungs – lumping all endothelial cells for analysis of cell composition is not\nreally meaningful (capillaries, venules, arterioles, etc are anatomically and\nfunctionally distinct).\nAnswer: Thanks so much for pointing out this very important point. We have\nreanalyzed the endothelial cells and devided into six subpopulations. The UMAP plot\nand dotplotof marker genes are shown.\nsubpopulations including general capillary EC, aerocyte EC, arterial EC, systemic\nvenous EC, pulmonary venous EC, and lymphatic EC, Data are shown in revised\nFig.S1BandFig.1E.\nD)Second, assessment of cell composition from n of 3 samples is not robust\n(particularly as the authors could have assessed this from the meta-analyzed dataset\npresented laterinthefigure).\nAnswer: Thank you for this very important comment, and we have collected\nadditional samples in response to your very constructive instructions. We have\nincreased the numberofnormal samples to 5and IPF samples to 4(Fig.1B).\nFigure to Reviewer 1: Subpopulation analysis was performed after single nucleus\nRNAsequencing (snRNA-Seq) of IPF patients (n=4) and control subjects (n=5), Data\nareshowninrevised Fig.1B.\nE) Third, since composition is a “zero-sum” scenario, Bayesian methods (i.e.\npropeller orothers) are moreappropriate.\nmethod to determine the proportions of cell subpopulations and validated the increase\ninendothelial cells.\nFigure to Reviewer 1: the increase of endothelial cells was verified by propeller\nmethod.\n2) Figure 1: FVC-based classification as utilized by the authors reflects disease stage\n(not progression). The authors are not reporting an FVC rate of change (which would\nbe an indicator of progression rate), but are reporting FVC which at one point in time\nis ameasure ofdisease severity.Those with an FVC <50 donot necessarily have more\nrapidly progressive disease, they simply have had disease longer than those with and\nFVC >50%.This should beclarified in thetextandfigure legend.\nAnswer: Thank you for this insightful comment. Indeed, single time-point FVC\nmeasurements solely reflect disease severity rather than the annual rate of FVC\ndecline — the gold-standard metric for evaluating disease progression. Our adoption\nofFVC ≤ 50% as thethreshold is grounded in two key rationales:\nGuideline Compliance: Primarily based on the 2022 clinical practice guidelines for\nadult IPF and progressive pulmonary fibrosis (PPF) jointly issued by\nATS/ERS/JRS/ALAT, which classify FVC ≤ 50% as indicative of severe pulmonary\ndysfunction.\nProgram-Specific Criteria: Additionally guided by the diagnostic framework of the\nInova Advanced Lung Disease Program, defining FVC ≤50% of predicted values as\nthethreshold for severe physiological impairment.\nThese methodological justifications have been explicitly detailed inboth the main text\nand figure legends(Raghu G et al., 2022Am J Respir Crit Care Med; Pastre, J. et al.,\n2021RespirRes ).\n[1]. Raghu, G. et al. Idiopathic Pulmonary Fibrosis (an Update) and Progressive\nPulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice\nGuideline.Am JRespir CritCare Med. 2022May 1;205(9):e18-e47.\n[2]. Pastre, J. et al. Idiopathic pulmonary fibrosis patients with severe physiologic\nimpairment: characteristicsand outcomes. RespirRes. 2021Jan 6;22(1):5.\n3) Figure 1F: Numerous manuscripts have described COL15A1+/PLVAP+ “systemic-\nvenous-like” endothelial cells as markedly more abundant in IPF lungs yet are not\ndescribed in thisfigure –are these being misclassified as “capillary cells”?\nAnswer: We are sorry for the annotation in a low resolution. In our previously\nsubmitted manuscript, all the pulmonary and systemic venous ECs were annotated as\nvenous ECs. We have re-annotated the endothelial cells using published marker genes\nincluding COL15A1 and PLVAP for systemic venous endothelial cells. The updated\numap plotand dotplotof marker genes are shownbelow.\nFigure to Reviewer 1: ECs were further subdivided into subpopulations including\ngeneral capillary EC, aerocyte EC, arterial EC, systemic venous EC, pulmonary\nvenous EC, and lymphatic EC,Data areshown inrevised Fig.S1BandFig.1E.\n4) Lacking code availability or reporting of the genes which were used in the\n“mechanical stress” gene score, it is difficult to assess the robustness and reliability of\nthisfinding.\nAnswer: Thank you for this very important comment, and we have followed your\nhighly constructive instruction and have uploaded relevant code which can be found\nat https://github.com/houruijiejerry/RuijieHou-Code. To obtain the mechanical gene\nsets for scoring, we performed enrichment analysis using the differential expressed\ngenes between ECs of IPF samples and normal samples.\nIGF1R, BMP6, IRF1, CASP8AP2, CD40, MAP2K4, MAPK8, BCL10, TMEM150C,\nTNFRSF1A, FAS, BAG3, CRADD, DAG1, PIEZO1, TNFRSF10A, CASP1, CASP8,\nF11R, TNFRSF8, SLC9A1, TLR3, CASP2, TLR4, GCLC, CHEK1, PTGER4, FADD,\nCASP5 GO:0071260, cellular responsetomechanical stimulus.\nThe mechanical gene set for human endothelial cell scoring (public dataset): PIEZO1,\nCCL2, HSP90B1, PIEZO2, SDC2, CTSL, GSTA1, MMP2, MMP9, CTNNB1, SDC1,\nMGST1, ITGAV, ACTG1, IL1R1, ASS1, NFKB1, CALM3, THBD, CALM2, GSTP1,\nRHOA,ACTB\nThemechanical gene set for endothelial cell of silica-induced mousemodel scoring:\nscRNA-seq data:\nCxcl12,Slc12a2,Gsn,Myc,Kit,Abl2,Htt,Itga2,Nfkbia,Cnn2,Thbs1,Slc9a1,Nfkb1,Ptn,Res\nt,Stra6,Pkd1,Tmem120a,Aqp1,Cav1,Atr,Tuba1a,Slc8a1,Nos3,Dag1,F11r,Atp8a2,Jup,A\tp1a1,Got1,Rac1,Ptpn11,Mapk8,Pawr,Pkd2,Strbp,Shank3,Col6a1,Large1,Nrxn2,Tme\nm87a,Abhd12,Lck,Dmd,AU040320,Bace1,Piezo1,Piezo2,Fos,Ednra,Tlr4,Ripor2,Hab\np4,Smpd2,Mmp2,Raf1,Cxcr4,Serpine2,Cacnb3,Jun,Nrxn1,Ankrd2,Clcn6,\nGO:0009612,response to mechanical stimulus.\nscATAC-seq data:\nTmc1,Cradd,Kit,Slc12a2,Plec,Nfkbia,Got1,Gsn,Nos3,Nrxn2,P2rx3,F11r,Foxp2,Fos,Nf\nkb1,Pde2a,Pkd2l2,Gata4,Bag3,Raf1,Asic2,Htr2a,Shank3,Abhd12,Cd36,Slc9a1,Pawr,\nStrbp,Serpine2,Tlr4,Piezo2,Lck,Itga2,Fyn,Kcnq1,Cav1,Rac1,Cxcr4,Gsk3b,Ptprq,AU0\n40320,Ryr2,Piezo1,Pkd1,Dag1,Etv1,Slc8a1,Abl2,Jun, GO:0009612,response to\nmechanical stimulus.\nThe mechanical gene set for endothelial cell of bleomycin-induced mouse model\nscoring: Piezo1, Piezo2,Atp1a1, Kit, F11r, Cxcl12, Cav1, Itga2, Pawr, Bag3, Hpn,\nNos3,Pkd1, Jup, Shank3.\n5) Figures 2 and 3– similar to the above comment, lumping all endothelial cells into a\nsingle population risks conflating changes in relative abundance of certain\nanatomically distinct populations with changes in chromatin accessibility and gene\nexpression within a population. There are well-established marker genes that can\ndiscriminate endothelial cell subtypes into aCap, gCap, venule, arteriole and\nlymphatic populationsat minimumto report these data.\nendothelial cell subtypes with reported marker genes.\n1)scRNA-seq dataof silicamousemodel:\n2)scRNA-seq dataof bleomycin mousemodel:\nFigure to Reviewer 1: Endothelial cells (EC) were further subdivided into\nsubpopulations including general capillary EC, aerocyte EC, arterial EC, systemic\nvenous EC, pulmonary venous EC, and lymphatic EC in silica mouse model and\nbleomycin mouse model, Data are shown in revised Fig.S3G, Fig.2G, Fig.S4J and\nFig.S4I.\n6) Figure 3 – PIEZO2 is essentially not expressed in the vascular endothelium, so it is\nunclear what a ratio ofPiezo1+/Piezo2+cells would mean.\nAnswer: Thank you for highlighting this concern. We would like to clarify a\nterminological inaccuracy in our previous description. The intended meaning was to\nreport the individual proportions of PIEZO1+ and PIEZO2+ cells within the\nendothelial cell population, rather than indicating a ratio between PIEZO1 and\nPIEZO2 expression levels.\n7) Figure 4: Fibrosis assessments are reported as an “Inflammation Score” and\n“Relative Collagen Content” but no methods for these assessments are reported.\nStandard measures of fibrosis assessment (quantification of fibrotic area or Ashcroft\nscore) in addition tohydroxyproline should bereported.\nyour instruction to usenewfibrosis assessment:\n1)The severity of pulmonary inflammation was graded based on the following\ncriteria:0, normal;1, inflammatory cells occupying 0%–10% of the entire tissue;2,\ninflammatory cells occupying 10%–40% of the entire tissue;3, inflammatory cells\noccupying 40%–70% of the entire tissue;4, inflammatory cells occupying 70%–100%\noftheentire tissue.\n(2) For Sirius red staining or masson staining, Image J software was used todefine the\nred areas (in Sirius red staining) or blue areas (in masson staining) in the tissue as\npositive areas. These areas were then outlined and their ratios to the entire field of\nviewwere calculated.\nWe also measured hydroxyproline levels, and assessed the protein levels of αSMAin\nlungtissue.Theresults are shownin thefigure below.\nFigure to Reviewer 1: EC-specific deletion of Piezo1 in mice attenuated the fibrotic\nresponse in the BLM-induced mouse lung fibrosis model. Data are shown in revised\nFig.4C-F.\n11\n(A)Representative images of Masson and Picro Sirius Red (PSR) staining in lungs of\nPiezo1WT and Piezo1ΔEC mice in BLM-induced fibrotic lungs. Quantification is shown\non the right. N=6 mice per group. (B)The plot shows hydroxyproline amounts in lung\ntissues from Piezo1WT and Piezo1ΔECmice in BLM-induced fibrotic lungs. N = 5 mice\nper group. (C) Immunofluorescent staining of αSMA(red) in lungs of Piezo1WT and\nPiezo1ΔEC mice in BLM-induced fibrotic lungs. Representative co-staining image of\nindicated mouse lungs is shown, and quantification (percentage of αSMA+ integrated\ndensity relative to DAPI +) is shown at the right (n=4). Error bars represent mean ±\nSEM.*P< 0.05,**P< 0.01,***P<0.001and ****P<0.0001.\n8) No quantitative measurements of fibrosis are reported for the GsMTx4 or Yoda1\ntreatment studies in Figure 4I – any assertions of effects on fibrosis need to be\nsupported byappropriate quantitativemeasures andstatistical treatment.\nAnswer: We appreciate your advice and followed your constructive suggestion to\nanalyze GsMTx4 or Yoda1 treatment studies, as shown in Fig.4H-4L to reviewer 1\nbelow.\nFigure to Reviewer 1: YODA1 and Gsmtx4 affect the fibrotic response in the\nbleomycin-induced mouse lung fibrosis model. Data are shown in revised Fig.4G-\n4K.\n(A) The schematic illustrates the treatment of BLM-induced pulmonary fibrosis with\nthe PIEZO1 agonistYoda1 and the antagonist GsMTx4. Mice were euthanized after 3\nweeks. (B) The plot shows hydroxyproline amounts in lung tissues from mice in\nYoda1 or GsMTx4-induced via intraperitoneal injection (n = 5). (C)\nImmunofluorescent staining of αSMA(red) in fibrotic lungs of mice in Yoda1 or\nGsMTx4-induced via intraperitoneal. Representative co-staining image of indicated\n12\nmouse lungs is shown, and quantification (percentage of αSMA + integrated density\nrelative to DAPI +) is shown at right (n = 4). (D-E) Representative images of Masson\nand PSR staining in fibrotic lungs of mice in Yoda1 or GsMTx4-induced via\nintraperitoneal. Quantification is shown at right (n = 5). Error bars represent mean ±\nSEM.*P< 0.05,**P< 0.01,***P<0.001and ****P<0.0001.\n9) Figure 5 –cell communication analyses are really only informative at cell-type\nresolution –the finding that Piezo1 is correlated with Ackr1 (a specific marker of\nveins/venules) suggests that these findings overall reflect compositional differences\nwithintheendothelial cell populationrather than cell-type specific expressionchanges\nrelated toPiezo1 expression.\nAnswer: Thank you for this insightful comment. We fully agree with the reviewer's\nperspective that cell communication analyses are most informative at the cell-type\nresolution. Regarding the observed correlation between Piezo1 and Ackr1 (a specific\nmarker of veins/venules), we concur that this likely reflects compositional differences\nwithintheendothelial cell populationrather than cell-type specific expressionchanges\nrelated toPiezo1 expression.Wehave revised it inthe manuscript.\n10)Figure 5J –theimage qualityis very lowand not interpretable.\nAnswer: Thank you very much for your suggestions. We have re-stained the lung\ntissues from pulmonary fibrosis patients and fibrotic mice for IL-33.The results are\nshown in thefigure below:\nFigure to Reviewer 1: Expression of IL-33 in endothelial cells of fibrotic patient\nlungs and BLM-induced mouse fibrotic lungs. A-B Data are shown in revised\nFig.5H.,whileC-D arepresented hereexclusively.\n(A-B) Immunofluorescent staining showing the expression of IL-33 (green) in IPF\n13\nandnormal lungs. Co-staining of endothelial marker CD31 (red) was conducted.\nQuantification (percentage of IL-33+ integrated density relative to CD31+) is shown\nat the right (n=10). (C-D) Immunofluorescent staining of IL-33(green) in mouse\nfibrotic lungs. Co-staining of endothelial marker CD31(red) was conducted.\nRepresentative co-staining image of indicated mouse lungs is shown, and\nquantification (percentage of IL-33+ integrated density relative to CD31 +) is shown\nat theleft (n=3).Error bars represent mean ± SEM. *P< 0.05and ***P<0.001.\n11) Figure 6 – same comment as #7 – results regarding fibrosis need to be supported\nbystandard, validated, methods forquantification offibrosis.\nAnswer: We have followed your constructive comments to use new fibrosis\nassessment and revised it in themanuscript.Theresults are shown in thefigure below:\nresponse in the BLM-induced mouse lung fibrosis model. Data are shown in revised\nFig.6A-K\n15\n(A) The schematic illustrates a BLM-induced lung fibrosis model established via\ntracheal administration in Il-33WT and Il-33ΔEC mice. (B) Quantification of Il-33\nmRNAin lung ECs of control Il-33WT and Il-33ΔEC mice (n = 4). (C) The plot shows\nhydroxyproline amounts in lung tissues from Il-33WT and Il-33ΔEC mice in BLM-\ninduced fibrotic lungs(n = 5). (D-E) Representative images of Masson and PSR\nstainings in lungs of Il-33WT and Il-33ΔEC mice in BLM-induced fibrotic lungs(D).\nQuantification is shown on figure E (n = 5). (F) Immunofluorescent staining of\nαSMA(red) in lungs of Il-33wt and Il-33ΔEC mice in BLM-induced fibrotic lungs.\nRepresentative staining image of indicated mouse lungs is shown, and quantification\n(percentage of αSMA+ integrated density relative to DAPI+) is shown at right (n = 4).\n(G) The schematic illustrates an EC-specific Il-33 overexpression model was created\nusing an adenoassociated virus (AAV) delivery system. The experimental protocol\nincluded: delivering AAV via trachea to Piezo1ΔEC and Piezo1WT mice for 4 weeks of\npretreatment, followed by bleomycin administration in the 4th week, and euthanizing\nmice in the 7th week for lung tissue collection and further analysis. (H) The mRNA\nlevels of Il-33 in mouse ECs after injection ofAAV-OE-Il-33. Il-33 overexpression in\nlung ECs was achieved using Adeno-associated virus (AAV) system by EC-selective\nTie1 promoter (n = 3). (I) The plot shows hydroxyproline amounts in lung tissues\nfrom Piezo1WT and Piezo1ΔECinjected withAAV-control(AAV-NC)andAAV-OE Il-33\n(n = 5). (J) Immunofluorescent staining of αSMA(red) in fibrotic lungs of Piezo1WT\nand Piezo1ΔEC injected with AAV-control(AAV-NC) and AAV-OE Il-33.\nRepresentative staining image of indicated mouse lungs is shown, and quantification\n(percentage of αSMA+ integrated density relative to DAPI+) is shown at right (n = 4).\n(K) Representative images of Masson and PSR stainings in fibrotic lungs of Piezo1WT\nand Piezo1ΔEC injected with AAV-control(AAV-Ctrl) and AAV-OE-Il-33.\nQuantification is shown at right (n = 5). Error bars represent mean ± SEM. *P< 0.05,\n**P<0.01,***P<0.001and ****P< 0.0001\nMinorcomments:\n1) Figure 2E: ILC’s should not express Cd3e, the authors should check this annotation\nand/orevaluate ambient RNAcontamination\nAnswer: We are sorry for the inaccurate annotation. We have re-annotated the data,\nand cells annotated as “ILC”,are actuallyTcells.\nPFModel. Data are showninrevised Fig.2C-F.\n(A) SiO2 UMAP plots of lung tissue scRNA-seq data from SiO2 model and control\nmice, colored by cell type. The UMAP plots depict various lung cell subpopulations,\nincluding Dendritic cells, B cells, AT2 cells, Macrophages, Neutrophils, T cells,\nEndothelial cells, Pericytes, Clara cells, NK cells, Monocytes, Fibroblasts, Ciliated\ncells, Basal cells, Mast cells, Matrix, and Smooth Muscle cells. (B) UMAP plot of\nscATAC-seq cell populations with with subpopulations annotated based on scRNA-\nseq analysis. (C)Violin plots showing lung cell type labeling of scRNA-seq data from\nSiO2 model and control mice. (D) In scATAC-seq data from SiO2 model and control\nmice, peaks for genes such as Ager, Sftpb, Scgb1a1, Col1a2, Cd79a, Mrc1, Cd14,\nTrpc6, Ms4a2, Flt3, Acta2, Hcst, S100a9, Cd3e, Cldn5, Cpa3, and Foxj1 were\nidentified in genomicregions typically foreach subcelltype.\n2) Results for Figure 3: “Toinvestigate the subcellular distribution of PIEZOs in ECs,\nwe analyzed human lung ECs using scRNA-seq bubble plots” – semanic point, but\n“bubble plots” are a visualization tool, not an analysis, and they report cellular (not\n“subcellular”)expression.\nAnswer: Thank you for raising this important point. Bubble plots are indeed a\nvisualization tool rather than a statistical analysis method. We have revised the\nwording and additionally performed t-tests for statistical analysis, illustrating the\nproportions ofPIEZO1 and PIEZO2 separately through barcharts.\nThis paper by Zhang, Zhang, Gui, Xia et al., looks at the role of endothelial PIEZO1\nchannels in the development of pulmonary fibrosis in two animal models. Using a\nCre-lox strategy to specifically delete Piezo1 from endothelial cells they link PIEZO1\nto the fibrotic cascade and attempt to link these effects to IL-33. Despite the raft of\nbioinformatics approaches the attempts to link this directly to mechanical stress are\npoorly fleshed out, for example a reader is still left with the question of exactly what\ncue is stimulating ECs in response to bleomycin or SiO2 to drive pulmonary fibrosis?\nThis seems likea key question that theauthors do apoor job ofdiscussing. In addition,\nthe use of PIEZO1 pharmacology is also rudimentary and there is no discussion of the\noff-target effects of GsMTx-4 or the fact that Yoda-1 drives cellular Ca2+ signalling\nthat is spatio-temporally different from the mechanical activation of PIEZO1 (this is\nespecially important for theinvitro experiments). Despite this, the data from KO\nmice and human datasets is compelling that PIEZO1 in ECs plays a role in the\ndevelopment of pulmonary fibrosis. The effects on IL-33 are strong and PIEZO1-\ndependent providing high level confidence that a PIEZO1-IL-33 pathway is at play in\nthese disease models. I have a number of points below that require addressing prior to\npublication especially related tothe invitro experiments in HUVEC.\nJusta general point thelack of linenumbering makes reviewing arduous!\nA)Despite the raft of bioinformatics approaches the attempts to link this directly to\nmechanical stress are poorly fleshed out, for example a reader is still left with the\nquestion of exactly what cue is stimulating ECs in response to bleomycin or SiO2 to\ndrive pulmonary fibrosis? This seems like a key question that the authors do a poor\njobof discussing.\nAnswer：Thank you for your insightful comment. We appreciate the opportunity to\nclarify the mechanisms by which bleomycin and SiO2 induce fibrosis through\nmechanical disruption. Below, we provide a detailed explanation addressing the\nspecific pointsraised:\nNotably, in the acute phase, these stimuli firstly cause a direct damage onAT2 cells,\nwhich are pivotal for surfactant production and alveolar tension maintenance, leading\nto surfactant dysfunction and aberrant mechanical forces27. Concurrently, epithelial\ncell death and alveolar collapse further disrupt local mechanical equilibrium,\nexacerbating parenchymal stress(Snijder J et al., 2019 Expert Rev Respir Med).\nMoreover, these stimuli induce the acute inflammatory response and alveolar edema,\nwhich further cause the epithelial cell and endothelial cell injury and dysfunction and\n18\namplifies mechanical dysregulation(Moeller A et al., 2008 Int J Biochem Cell Biol).\nAdditionally, it have been reported pro-inflammatory cytokines such as IL-1β can\ndirectly induce expression of mechanosensitive genes (e.g., PIEZO1)(Chen W et al.,\ntension that leads to spatially regulated fibrosis, initiating a new chapter in\nunderstanding PF)(Sainz deAja J et al., 2020 Cell) .An interesting study showed that\nmechanical stretch activates PIEZO1 in caveolae of AT1 cell to trigger ATP release,\nand which in turn stimulate the surfactant paracrine secretion fromAT2 cells(Diem K\net al., 2020FASEBJ).\nDuring the fibrotic phase, excessive ECM deposition increases tissue stiffness,\ninitiating a self-amplifying cycle of mechanical stress, which resulting in the\nactivation of mechanosensitive pathways in ECs, fibroblasts and other resident cells\nand thereby further accelerating ECM remodeling and fibrosis(Nho RS et al., 2022\nAm J Pathol).The resulting pulmonary fibrosis, combined with chronic hypoxia,\ndrives pathological vascular changes including pulmonary hypertension(Ruffenach G\net al., 2020 Respir Res). This aligns with findings by Chen et al. showing Piezo1\nupregulation mediates high shear stress-induced pulmonary hypertension(Chen J et\nal.,2022 Thromb Res). Ultimately, chronic fibrosis leads to lung function decline,\ncausing respiratory rhythm disruption and perpetuating abnormal mechanical forces.\nWe have revised the “Manuscript Discussion” section to include further elaboration\n(Lines 407-418).The flowchart is as follows:\nReferences:\n[1]. Snijder J et al. Pulmonary fibrosis: a disease of alveolar collapse and collagen\ndeposition.Expert Rev RespirMed.2019Jul;13(7):615-619.\n19\n[2]. Moeller A, et al. The bleomycin animal model: a useful tool to investigate\ntreatment options for idiopathic pulmonary fibrosis? Int J Biochem Cell Biol.\n2008;40(3):362-82.\n[3]. Chen W, et al. Elucidating the mechanism of IL-1β-Mediated Piezo1 expression\nregulation of chondrocyte autophagy and apoptosis via the PI3K/AKT/mTOR\nsignalingPathway.TissueCell. 2024Feb;86:102291.\n[4]. Sainz de Aja J, et al. May the (Mechanical) Force Be with AT2. Cell. 2020 Jan\n9;180(1):20-22.\n[5]. Diem K,et al, Winokurow N, Schumacher S, Kranz C, Frick M. Mechanical\nstretch activates piezo1 in caveolae of alveolar type I cells to trigger ATP release and\nparacrine stimulation of surfactant secretion from alveolar type II cells. FASEB J.\n2020Sep;34(9):12785-12804.\n[6]. Nho RS, et al. Biomechanical Force and Cellular Stiffness in Lung Fibrosis.Am J\nPathol.2022May;192(5):750-761.\n[7]. Ruffenach G, et al. Pulmonary hypertension secondary to pulmonary fibrosis:\nclinical data, histopathology and molecular insights. Respir Res. 2020 Nov\n18;21(1):303.\n[8]. Chen J, et al. Upregulation of mechanosensitive channel Piezo1 involved in high\nshear stress-induced pulmonary hypertension.Thromb Res. 2022Oct;218:52-63.\nB) In addition, the use of PIEZO1 pharmacology is also rudimentary and there is no\ndiscussion of the off-target effects of GsMTx-4 or the fact that Yoda-1 drives cellular\nCa2+ signalling that is spatio-temporally different from the mechanical activation of\nPIEZO1 (this isespecially important for theinvitro experiments).\nAnswer: Thank you for your valuable feedback on the pharmacological approaches\ntargeting PIEZO1 in our study. We fully acknowledge the reviewer’s concerns\nregarding the potential off-target effects of GsMTx-4 and the limitations of using\nYoda-1, particularly its spatiotemporal specificity in mimicking mechanical activation\nofPIEZO1.\nWe agree that employing Yoda-1 to simulate mechanical activation of PIEZO1\nrepresents a preliminary methodological approach. To address this limitation, we\nconducted additional experiments to directly evaluate the role of mechanical forces in\n20\nactivating the PIEZO1 pathway. Specifically, the following experiments were added\ninFigure 7:\nStiffness modulation assays: By altering extracellular matrix stiffness, we modeled\nthe mechanically rigid microenvironment observed during pulmonary fibrosis\nprogression.\nEndothelial cell stretch assays: Cyclic mechanical stretching devices were applied to\nsimulateperiodicstress generated byalveolar respiratory motion.\nThese experiments provide physiologically relevant models of mechanical forces and\ndemonstrate that mechanically activated PIEZO1—independent of pharmacological\nagents—directly induces the expression of pro-fibrotic factors such as IL-33.\nThese results complement the pharmacological data and offer robust evidence supporting the\nrole of mechanical forces in pulmonary fibrosis.We discussed it in manuscript from\nline447-464.\nCAPN2-STAT3 axis. Data areshown inrevised Fig.7A-S.\n(A-B) The levels of IL-33 in HUVECs supernatants detected by ELISA(A), and the\nmRNAlevels of IL-33 in HUVECs detected by qPCR(B). The HUVECs treated with\nstretch (20%) for 0, 6 and 24 hours and were cultured on substrates of 2 kPa ,12 kPa\nand 25 kPa for 24 hours (n = 3). (C) Protein levels of PIEZO1 in HUVECs treated\n22\nwith shRNA targeting PIEZO1 (shPIEZO1) (n = 3). (D-E) The levels of IL-33 in\nHUVECs supernatants detected by ELISA(D), and the mRNA levels of IL-33 in\nHUVECs detected by qPCR(E). The HUVECs were treated with stretch (20%) for 6\nhours and cultured on substrates of 2 kPa and 25 kPa for 24 hours after transduced\nwith shPIEZO1 for 48 hours (n = 3). (F-G) The HUVECs treated with stretch (20%)\nfor 0 ,6 and 24 hours and were cultured on substrates of 2 kPa ,12 kPa and 25 kPa for\n24 hours. The calpain activity(F) and the protein levels of CAPN2(G) in HUVECs\nwere detected (n = 3). (H-I) The HUVECs were treated with stretch (20%) for 6 hours\nand cultured on substrates of 2 kPa and 25 kPa for 24 hours after transduced with\nshPIEZO1 for 48 hours. The Calpain activity(H) and the protein levels of CAPN2(I)\nwere detected (n = 3). (J)The protein levels of CAPN2 in HUVECs were treated with\nshRNA targeting CAPN2 (shCAPN2) (n = 3). (K) The levels of IL-33 in HUVECs\nsupernatants detected by ELISA(left), and the mRNA levels of IL-33 in HUVECs\ndetected by qPCR(right). The HUVECs were treated with stretch (20%) for 6 hours\nand cultured on substrates of 2 kPa and 25 kPa for 24 hours after transduced with\nshCAPN2 for 48 hours (n = 3). (L) Schematic showing the proposed mechanisms\nregulating paracrine expression of IL-33. (M) Cistrome database analysis predicts\nSTAT3 as a key transcription factor in the regulation of IL-33 expression. (N) Venn\ndiagrams indicating that the top100 motifs in ECsof BLM-treated mice intersect with\nthetop 10 transcription factors. (O)TheHUVECs treated with stretch (20%) for 0and\n6hours.Theprotein levels of P-STAT3 and STAT3in HUVECs were detected (n = 3).\n(P) The protein levels of P-STAT3 and STAT3 in HUVECs were treated with stretch\n(20%) for 6 hours after transduced with shPIEZO1(up) and shCAPN2(down) for 48\nhours (n = 3). (Q) The protein levels of STAT3 in HUVECs treated with shRNA\ntargeting STAT3 (shSTAT3) (n = 3). (R)The levels of IL-33 in HUVECs supernatants\ndetected by ELISA (left), and the mRNA levels of IL-33 in HUVECs detected by\nqPCR (right).The HUVECs were treated with stretch (20%) for 12hours and cultured\non substrates of 2 kPa and 25 kPa for 24 hours after transduced with shSTAT3 for 48\nhours (n = 3). (S) Fold enrichment of P-STAT3 binding to the promoter region in\nHUVECs, as determined by Cut&Tag-qPCR. HUVECs were subjected to 20% stretch\nfor 6 hours. Data are presented as fold enrichment relative to unstretched controls,\nnormalized to IgG (n = 3). Error bars represent mean ± SEM. *P< 0.05, **P< 0.01,\n***P<0.001and ****P<0.0001.\n1)Whyisthe control haplo-insufficient –what’s itsgenotype? I assumethisis a typo.\nAnswer: Thank you for your suggestion. This was a typographical error on our part.\nIn the animal experiments, we used Piezo1 loxP/loxP (Piezo1WT)and Il33 loxP/loxP (Il33 WT)\n23\nas the control groups, and CreERT2 Piezo1loxP/loxP (Piezo1ΔEC) and CreERT2 Il33\nloxP/loxP (Il33ΔEC)as theexperimental groups.\n2)PIEZO1 vs Piezo1needs uniformity.\nAnswer: Thank you for your suggestion.Wehave corrected all thePiezo1formats.\n3) Figure 4B comes from isolated ECs or whole tissue? If whole tissue how is the\nreduction so profound given all other cell types express PIEZO1. Figure 4I lacks\nquantification shown in Figure 4D.\nAnswer: Thank you for your suggestion, Figure 4B illustrates the use of magnetic\nbeads to sort endothelial cells from mice.Wehave recalculated Figure 4I\nFigure to Reviewer 2: Yoda1 and Gsmtx4 affect the fibrotic response in the BLM-\ninduced mouselung fibrosis model. Data areshown inrevised Fig.4G-K.\n(A) The schematic illustrates the treatment of BLM-induced pulmonary fibrosis with\nthe PIEZO1 agonistYoda1 and the antagonist GsMTx4. Mice were euthanized after 3\nweeks. (B) The plot shows hydroxyproline amounts in lung tissues from mice in\nYoda1 or GsMTx4-induced via intraperitoneal injection (n = 5). (C)\nImmunofluorescent staining of αSMA(red) in fibrotic lungs of mice in Yoda1 or\nGsMTx4-induced via intraperitoneal. Representative co-staining image of indicated\nmouse lungs is shown, and quantification (percentage of αSMA + integrated density\nrelative to DAPI +) is shown at right (n = 4).(D-E) Representative images of Masson\nand PSR staining in fibrotic lungs of mice in Yoda1 or GsMTx4-induced via\nintraperitoneal. Quantification is shown at right (n = 5). Error bars represent mean ±\nSEM.*P< 0.05,**P< 0.01,***P<0.001and ****P<0.0001.\n24\n4) For Figure 7 a calpain assay would be much better than mRNAlevels. It’s not clear\nwhy Yoda1 would increase the mRNA levels of calpain2(or STAT3 – STAT3 also\ndoesn’tcome upearlier in theomics sections). The pathway should be dictated by\nCa2+ induced activation of calpain – in this scenario as a homeostatic mechanism one\nmight expect a reduction in calpain levels under a sustained mechanical cue not an\nincrease. UsingYoda1 as a surrogate for mechanical stress is fraught with danger as it\ninduces spatio-temporal Ca2+ signalling that is wholly different to mechanically\ninduced PIEZO1 signalling. In a mechanical response PIEZO1 signalling is localized\nto certain regions like focal adhesions (Chen et al., 2018 Neuron; Yao et al., 2022\nScience advances) and is transient. This is very different from when yoda1 is added –\nwhich causes a tonic sustained elevation in intracellular Ca2+. It is necessary to show\nthe same pathway in response to a relevant mechanical cue. The reason for this is that\nif you useYoda1 in an in vitro system you can end up suggesting a pathway like seen\nin cardiac PIEZOs (Zhang et al., 2021 Hypertension) that ends up being incorrect (Yu\net al., 2022 Nature Cardiovascular Research). At least a mechanical cue should be\nused to validate these findings (stretch, stiffness, shear stress etc). This links back to\nthemajorquestion ofwhat cueis driving thissignalling inthesedisease models!!\nA) It’s not clear whyYoda1 would increase the mRNAlevels of calpain2 (or STAT3 –\nSTAT3 also doesn’tcome upearlier in theomics sections).\nAnswer: We thank the reviewer for their insightful comment. Notably, Calpain\nactivity is known to be regulated by Piezo1 in endothelial cells and macrophages ( Li,\nJ. et al., 2014 Nature; He Y et al., 2022 Hypertension; Luo S et al.,\n2025Theranostics ). Our observation of upregulated CAPN2 and STAT3 aligns with\nprior reports; for example, Xiaodong Zhao et al. demonstrated that Yoda1 treatment\nenhances both enzymatic activity and protein abundance of Calpain2 in human kidney\nepithelial cells (Zhao X et al., 2022 JCI Insight). We hypothesize that sustained\ncalcium signaling downstream of PIEZO1 activation drives this transcriptional\nupregulation, creating a potential positive feedback loop within the mechanosensitive\npathway. Specifically, PIEZO1-mediated calcium influx may directly activate CAPN2\nwhile simultaneously inducing its mRNA expression—a mechanism consistent with\nbroader calcium-dependent regulation. Given these implications, we think it deserve a\nfurther study.\nReferences:\n25\n[1]. Li J, et al. Piezo1 integration of vascular architecture with physiological force.\nNature. 2014Nov 13;515(7526):279-282.\n[2]. He Y, et al. Myeloid Piezo1 Deletion Protects Renal Fibrosis by Restraining\nMacrophage Infiltration and Activation. Hypertension. 2022May;79(5):918-931.\n[3]. Luo S, et al. Piezo1 specific deletion in macrophage protects the progression of\nliverfibrosis in mice. Theranostics. 2023Oct 2;13(15):5418-5434.\n[4]. Zhao X, et al. Mechanosensitive Piezo1 channels mediate renal fibrosis. JCI\nInsight , (2022).\nB) At least a mechanical cue should be used to validate these findings (stretch,\nstiffness,shear stress etc).\nAnswer: Thank you for your valuable suggestions. We apologize for not including\ndata induced by tension, stiffness, and shear stress. We have analyzed the Calpain\nactivity and the protein levels of CAPN2, STAT3, and P-STAT3 in cells subjected to\ntension and cultured on substrates of varying stiffness. The results show that tension\nand culture on substrates of different stiffness stimulate the expression of CAPN2 and\nP-STAT3.Theresults are shown in thefigure below.\nFigure to Reviewer 2: The effects of different mechanical stimuli on the expression\nofCAPN2 inHUVECs. Data areshown inrevised Fig.7F-G,7O andFig.S7D.\n(A) HUVECs were treated with 0 µM, 0.5 µM and 5 µM PIEOZ1 agonist Yoda1 for\n24 hours, and Calpain activity were detected. N=3 per group. (B-C) The HUVECs\ntreated with stretch (20%) for 0,6 and 24 hours and were cultured on substrates of 2\nkPa and 25 kPa for 24 hours. The calpain activity(A) and the protein levels of\nCAPN2(B) in HUVECs were detected. N=3 per group. (D-E) The HUVECs treated with\nstretch (20%) for 0 and 6 hours and were cultured on substrates of 2 kPa and 25\nkPa for 24 hours. The protein levels of P-STAT3 and STAT3 in HUVECs were\ndetected. N=3 per group. Error bars represent mean ± SEM. *P < 0.05, **P < 0.01,\n***P<0.001and ****P<0.0001.\n26\n5) There's also no data that IL-33 is actually released. An ELISA showing PIEZO1-\nmediated release of IL-33 is really essential as generally speaking the paper is too\nreliant onmRNAlevels to suggest mechanism.\nAnswer: Thank you for your valuable suggestions. We apologize for not including\ndata from IL-33 release ELISA kits. We have re-analyzed the secretion and\ntranscription of IL-33 in cells subjected to tension and cultured on substrates of\ndifferent stiffness.Theresults are shown in thefigurebelow.\nFigure to Reviewer 2: Different mechanical stimuli influence the expression of\nCAPN2 in HUVECs via the PIEZO1-CAPN2-STAT3 axis. A are presented here\nexclusively;B-E areshownin revisedFig.7A-B,7D-E,7K,7R.\n(A) The levels of IL-33 in HUVECs supernatants detected by ELISA and the mRNA\nlevels of IL-33 in HUVECs detected by qPCR (right). The HUVECs treated with 0\nµM, 0.5 µM and 5 µM PIEOZ1 agonistYoda1 for 24 hours. N = 3 per group. (B) The\n27\nlevels of IL-33 in HUVECs supernatants detected by ELISA (left), and the mRNA\nlevels of IL-33 in HUVECs detected by qPCR (right). The HUVECs treated with\nstretch (20%) for 0, 6 and 24 hours and were cultured on substrates of 2 kPa ,12 kPa\nand 25 kPa for 24 hours. N = 3 per group. (C-E) The levels of IL-33 in HUVECs\nsupernatants detected by ELISA (left), and the mRNA levels of IL-33 in HUVECs\ndetected by qPCR(right). The HUVECs were treated with stretch (20%) for 6 hours\nand cultured on substrates of 2 kPa and 25 kPa for 24 hours after transduced with\nshPIEZO1, shCAPN2 and shSTAT3 for 48 hours. N=3 per group. Error bars represent\nmean ± SEM. *P<0.05,**P<0.01,***P< 0.001and ****P< 0.0001.\nZhang et al. present a study “Single-cell multiomics reveals endothelial\nmechanosensitive PIEZO1 coupling IL33 regulates the development of pulmonary\nfibrosis” that uncovers a molecular mechanism of pulmonary fibrosis (PF). PF is a\nlethal disease with limited treatment options. By analyzing sc/nRNA-seq and\nscATAC-seq data from human specimens and two mouse models (silica and\nBleomycin induced) the study authors identify upregulation of genes linked to\nmechanosensation in endothelial cells. They identify PIEZO1 as top candidate and\nvalidate its role using genetic deletion in a mouse model and small molecule\ninhibition or activation. They could show that blocking or deleting PIEZO1 decreases\nfibrosis and activation resulted in increased fibrosis. Finally, they show that PIEZO1\ncouples toIL33 and activates aCAPN2-STAT3 signallingcascade.\nThis is an exciting, comprehensive and well executed study covering hypothesis\ngenerating single cell data and follow-up analysis using genetic mouse models and\nsmall molecules to validate predictions for therole ofPIEZO1 andrelated signaling in\nPF.\nIhave afew comments that would need to beaddressed:\n1) According to Table S1 and S2 only 5 donors were profiled? Where does the sixth\ndatapoint in thefigures comefrom? Pleaseclarify.\nAnswer:We are sorry for the missing information of the sixth donor. We have\nincluded all the information, including the missing part and the new information.\nData areshown inrevisedTableS1.\n2) What genes are in the MS Score? How was the statistic calculated in 1N, 2H, 2J.\nPerindividual orper cell?\nAnswer: To obtain the mechanical gene sets for scoring of per cell, we performed\nenrichment analysis using the differential expressed genes between endothelial cells\nofIPF samples and normal samples.\nThe mechanical gene set for human endothelial cell scoring: EGFR, IL33, ITGA2,\nIGF1R, BMP6, IRF1, CASP8AP2, CD40, MAP2K4, MAPK8, BCL10, TMEM150C,\nTNFRSF1A, FAS, BAG3, CRADD, DAG1, PIEZO1, TNFRSF10A, CASP1, CASP8,\nF11R, TNFRSF8, SLC9A1, TLR3, CASP2, TLR4, GCLC, CHEK1, PTGER4, FADD,\nCASP5 GO:0071260, cellular responsetomechanical stimulus.\nCCL2, HSP90B1, PIEZO2, SDC2, CTSL, GSTA1, MMP2, MMP9, CTNNB1, SDC1,\nMGST1, ITGAV, ACTG1, IL1R1, ASS1, NFKB1, CALM3, THBD, CALM2, GSTP1,\nRHOA,ACTB\nThemechanical gene set for endothelial cell of silica-induced mousemodel scoring:\nscRNA-seq data:\nCxcl12,Slc12a2,Gsn,Myc,Kit,Abl2,Htt,Itga2,Nfkbia,Cnn2,Thbs1,Slc9a1,Nfkb1,Ptn,Res\nt,Stra6,Pkd1,Tmem120a,Aqp1,Cav1,Atr,Tuba1a,Slc8a1,Nos3,Dag1,F11r,Atp8a2,Jup,A\tp1a1,Got1,Rac1,Ptpn11,Mapk8,Pawr,Pkd2,Strbp,Shank3,Col6a1,Large1,Nrxn2,Tme\nm87a,Abhd12,Lck,Dmd,AU040320,Bace1,Piezo1,Piezo2,Fos,Ednra,Tlr4,Ripor2,Hab\np4,Smpd2,Mmp2,Raf1,Cxcr4,Serpine2,Cacnb3,Jun,Nrxn1,Ankrd2,Clcn6,\nGO:0009612,response to mechanical stimulus.\nscATAC-seq data:\nTmc1,Cradd,Kit,Slc12a2,Plec,Nfkbia,Got1,Gsn,Nos3,Nrxn2,P2rx3,F11r,Foxp2,Fos,Nf\nkb1,Pde2a,Pkd2l2,Gata4,Bag3,Raf1,Asic2,Htr2a,Shank3,Abhd12,Cd36,Slc9a1,Pawr,\nStrbp,Serpine2,Tlr4,Piezo2,Lck,Itga2,Fyn,Kcnq1,Cav1,Rac1,Cxcr4,Gsk3b,Ptprq,AU0\n40320,Ryr2,Piezo1,Pkd1,Dag1,Etv1,Slc8a1,Abl2,Jun, GO:0009612,response to\nmechanical stimulus.\nThe mechanical gene set for endothelial cell of bleomycin-induced mouse model\nscoring: Piezo1, Piezo2,Atp1a1, Kit, F11r, Cxcl12, Cav1, Itga2, Pawr, Bag3, Hpn,\nNos3,Pkd1, Jup, Shank3.\n3) MS Score analysis shows an increase in PF in the silica model (2H, 2J), but in the\nBleomycin model mostly Piezo1/2 are robustly regulated (3K). How can this be\nexplained?\nAnswer: We sincerely apologize for the oversight in not listing the mechanosensitive\ndifferentially expressed genes (DEGs) in endothelial cells (ECs) between the silica-\nand bleomycin-induced fibrosis models, which may have caused confusion.Toclarify,\nbased on snATAC-seq analysis, the mechanosensitive gene set significantly enriched\nin the silica model includes: Tmc1, Cradd, Kit, Slc12a2, Plec, Nfkbia, Got1, Gsn,\nNos3, Nrxn2, P2rx3, F11r, Foxp2, Fos, Nfkb1, Pde2a, Pkd2l2, Gata4, Bag3, Raf1,\nAsic2, Htr2a, Shank3, Abhd12, Cd36, Slc9a1, Pawr, Strbp, Serpine2, Tlr4, Piezo2,\nLck, Itga2, Fyn, Kcnq1, Cav1, Rac1, Cxcr4, Gsk3b, Ptprq, AU040320, Ryr2, Piezo1,\nPkd1,Dag1,Etv1,Slc8a1,Abl2,and Jun.\nAtp1a1, Kit, F11r, Cxcl12, Cav1, Itga2, Pawr, Bag3, Hon, Nos3, Pkd1, Jup, and\nShank3. Importantly, the BLM-associated mechanosensitive gene profile is not\nlimited to Piezo1 and Piezo2. The Piezo1 and Piezo2 highlighted in Figure 3K of the\noriginal manuscript represent the core consensus genes identified across multiple\nindependent datasets (including the bleomycin mechanosensitivegene set), rather than\nbeing exclusive to a single model. We then used Venn diagram analysis to integrate\nbulk RNA-seq with a single multi-omics dataset, confirming the involvement of\nPiezo1and Piezo2in thepathogenesis ofPF (Figure3J).\nFigure to Reviewer 3: Venn diagram showing that Piezo1 and Piezo2 are co-\nregulated genes across bulk sequencing, scRNA-seq, and scATAC-seq data from the\nSilica-induced and BLM-induced mouse model.Data areshowninrevised Fig.3J.\n4) The scATAC-seq data lacks analytical details, e.g. how were cells filtered? How\nwas peak calling performed? How was transcription factor motif enrichment\nperformed? How was batch correction performed and doublets removed (also for\nscRNAdata)?\nAnswer：We appreciate the reviewer’s comment. We have provided a more detailed\nmethod toexplain theanalytical details.\nFor scATAC-seq data, cells were filtered using the following criteria:\n1)3000<nCount_peaks< 50000; 2) pct_reads_in_peaks>15; 3) blacklist_ratio<0.05;\n4)Nucleosome signal<4; 5) transcription start site (TSS) enrichment score>1. Batch\neffects were corrected using the Harmony. Peaks calling was performed using\nMACS2. Motif analysis was performed using Signac package mainly. Transcription\nfactor (TF) binding motifs were downloaded from JASPAR database. Then TF\n31\nbinding motifs were mapped to scATAC-seq peaks using AddMotifs function.\nRunChromVAR function was used for calculating motif deviation score. We\nperformed GO enrichment analysis after differential accessibility analysis with\nFindMarkers function and obtained corresponding motifs using FindMotifs function.\nMotifPlot function was used to visualize motifs of peaks with different accessibility\nbetween groups.\nFor scRNA/snRNA-seq data, any single cell with less than 200 genes or more than\n5000 genes, and cells with more than 25% mitochondrial genes were regarded as low\nquality cells and excluded. Possible doublets were detected using DoubletFinder and\nremoved. Each dataset was log-normalized using the NormalizeData function. Genes\nwith large differences in expression between cells were identified using the\nFindVariableFeatures function, and the data was scaled using ScaleData. Integrate\neachsampleand correct batch effects using theHarmony.\n5) Fig 2L“different thematic patterns of ECs” -> TF motif enrichment analysis?\nHow was this analysis performed, which tool was used? Is this analysis performed on\nall ECs or differential accessible sites (same for Bleomyin analysis)? What about all\ntheothermotifs except STATmotifthat were identified from the scATACanalysis?\nAnswer: We appreciate the reviewer’s comment. We will address each of your\nquestionspoint-by-point below.\n1.The incorrect description “different thematic patterns of ECs” has been replaced by\n“TF motifenrichment analysis”.\n2.To perform motif analysis, transcription factor (TF) binding motifs were\ndownloaded from JASPAR database. Then TF binding motifs were mapped to\nscATAC-seq peaks. Motifanalysis was performed using Signac package.\n3.Only the differential accessible sites between IPF and control samples were\nanalyzed.\n4 .We also identified other motifs from the scATAC analysis, but only the motifs of\nmechanical stress-related genes are shown.\n6) 3G what exactly is displayed: Figure panel states motif and legend states an AUC\nscore ofmechanical stress genes.\nbeen corrected in the revised version. Figure 3G in the previous version was the result\nofAUCell scoring, which has been deleted in thelatest version.\n7)7J there is nobinding ofSTAT3 at thepromoter region ofIL33 onthe tracks visible.\nThe signal of STAT3 at most sites is lower after YODA1 treatment despite the model\nwould suggest otherwise. Do all peaks have a STAT3binding site?\nAnswer: We appreciate the reviewer’s thoughtful comment and are grateful for the\nobservations regarding the CUT&Tag seqencing data. Specifically, the result does not\nshow STAT3 binding at the IL33 promoter region, and it is hard to distinguish the\ndifference of binding signaling intensity between YODA1-treated and control groups.\nThe challenge arises because these signals identified by CUT&Tag sequencing\nanalysis were relatively weak, making it difficult to conclusively determine their\nvalidity.\nTo address this limitation, we conducted further validation using CUT&Tag-qPCR.\nWe selected the promoter region and two additional enhancer sites annotated in the\nUCSC database, along with two putative STAT3 binding peaks identified from our\nCUT&Tag sequencing data. By performing CUT&Tag-qPCR on these sites, we\naimed to providemore robust evidence for STAT3 binding..\nRegion GenomicCoordinates (chr9)SourceValidationResult\nPeak1 6,149,365–6,149,765 Novel Positive\nPeak2 6,184,615–6,185,015 Novel Positive\nEnhancer16,185,024–6,185,424 UCSC Positive\nEnhancer26,195,974–6,196,374 UCSC Positive\nPromoter 6,215,585–6,215,985 UCSC Positive\nAdditionally,we acknowledge the reviewer's valid concern regarding YODA1 as an\nimperfect mimic of pathological PIEZO1 activation. To address this limitation, we\nimplemented complementary in vitro systems incorporating substrate stiffness\nmodulation and mechanical stretch. These parallel approaches allow for more\nrigorous examination of STAT3's capacity to directly bind IL-33 regulatory regions\nunderdiverse mechanosensitiveconditions.\nPeak2, Enhancer1, and Enhancer2, with a similar increasing trend observed in the\npromoter region following activation of mechanosensitive pathways by stiffness and\nstretch stimuli. These findings collectively suggest that mechanical stimuli promote\nSTAT3 bindingacross multiple regulatory elements oftheIL-33 gene.\nWeappreciate thereviewer's insightful comment and willincorporate thisclarification\ninthe revised manuscript.\nFigure to Reviewer 3: Cut&Tag-qPCR shows that mechanical stretch promotes the\nbinding of P-STAT3 to the IL-33 promoter region in HUVECs. Data are shown in\nrevisedFig.7S.\nA) Fold enrichment of P-STAT3 binding to the IL-33 promoter region in HUVECs, as\ndetermined by Cut&Tag-qPCR. HUVECs were subjected to 20% stretch for 6 hours.\nData are presented as fold enrichment relative to unstretched controls, normalized to\nIgG. N=3 per group.\n8) 7H why top 100 and top 10 TFs (text states top 7)? How was this determined?\nWhat are the “top 10 transcription factors”? How was it concluded that it must be\nSTAT3 and not for example STAT1 which was labeled as enriched motif in 2L and\nS5L. Motifanalysis usually cannot pin down theexact factorso both could beoptions.\nAnswer: We apologize for not clarifying this in the text. Wewill address each of your\nquestionspoint-by-point below.\nA)7H why top 100 and top 10 TFs (text states top 7)? How was this determined?\nWhatare the“top10transcription factors”?\nAnswer: We sincerely apologize for the inadvertent error in the manuscript where\n\"top 10\" was mistakenly written as \"top 7.\" Actually, the top 10 TFs were selected\n34\nfrom the old manuscript Figure 7I. In this figure, we utilized the Cistrome\n(http://dbtoolkit.cistrome.org/) public transcription factor database and predicted the\ntop 20 transcription factors that might regulate IL-33 expression, we selected the top\n10 transcription factors include SUMO2, JUN, STAT3, CHAT, EP300, CEBPD,\nCEBPB, FOS, BRD4, RELA, which have been presented in revised Figure 7 M. We\nare truly sorry for the missing description of the top 10 TFs. Here is the Cistrome\nDatabase-Based Screening Criteria:\n1)Sorting metric: Transcriptional regulators were ranked by their regulatory potential\nscore (RPS),a quantitativemeasure ofpredicted regulatory activity.\n2)Selection threshold: The top 10 transcription factors (top 10) with the highest RPS\nvalues were prioritized.\n3)Rationale: This approach ensures the inclusion of candidates demonstrating\nstatisticallysignificant regulatory effects onIL-33 expression.\nAdditionally, we performed motif enrichment analysis of differential peaks in ECs\nusing scATAC-seq datasets of fibrotic mouse model and identified the top 100 motifs\nhighly expressed in the endothelium (Figure 7N, Supplement table S13). Venn\ndiagram analysis suggested that STAT3 is a key TF regulating IL-33 expression upon\nmechanical force (Figure 7N). Hereby we selected STAT3 as the downstream target of\nPIEZO1 to regulate IL-33.\nFigure to Reviewer 3: Screening process of the transcription factor STAT3 to\nregulate IL-33. (A) Cistrome database analysis predicts STAT3 as a key transcription\nfactor in the regulation of IL-33 expression from Top 10 TFs. (B) Venn diagrams\n35\nindicating that the top 100 motifs in ECs of BLM-treated mice intersect with the top\n10transcriptionfactors. Data areshowninrevised Figure.7M-N.\nB) How was it concluded that it must be STAT3 and not for example STAT1 which\nwas labeled as enriched motif in 2Land S5L. Motif analysis usually cannot pin down\ntheexact factor so bothcould beoptions.\nAnswer: We regret this oversight in our study. Our experiments revealed that both\nSTAT3 and STAT1 were upregulated in cells exposed to mechanical tension or\ncultured on stiff substrates. However, PIEZO1 knockdown (via shPIEZO1)\nspecifically suppressed STAT3 expression and phosphorylation, while STAT1 levels\nremained unaffected. These results suggest that mechanical regulation of STAT1\noccurs through PIEZO1-independent pathways. Given thismechanistic distinction, we\nfocused oursubsequent investigations onSTAT3.\nFigure to Reviewer3: PIEZO1 can attenuate mechanical stretch-induced STAT3\nphosphorylation, butnot STAT1 phosphorylation. B-DData areshownin revised\nFig.7O-P.;A-Carepresented hereexclusively.\n(A) The HUVECs treated with stretch (20%) for 0 and 6 hours. The protein levels of\nP-STAT1 and STAT1 in HUVECs were detected. N=3 per group.IgG. N=3 per group.\n(B) The HUVECs treated with stretch (20%) for 0 and 6 hours. The protein levels of\nP-STAT3 and STAT3 in HUVECs were detected. N=3 per group.(C) The protein\nlevels of P-STAT1 and STAT1 in HUVECs were treated with stretch (20%) for 6\nhours after transduced with shPIEZO1 for 48 hours. N=3 per group. (D) The protein\nlevels of P-STAT3 and STAT3 in HUVECs were treated with stretch (20%) for 6\nhours after transduced with shPIEZO1 for 48hours. N=3 pergroup.\nMinor:\n36\n1) From the writing it is unclear if the processing and annotation of scATAC-seq of\nSilica and BLM datasets (one mentions annotation based on prediction from scRNA\nand thelatter looksat promoteraccessibility). Please clarify.\nAnswer: We appreciate the reviewer's comment. Both scATAC-seq data of Silica and\nBLM data were annotated using scRNA-seq data of corresponding mouse model as\nreferences according to Seurat official guidance1. After identification of anchors\nbetween scRNA-seq and scATAC-seq data with FindTransferAnchors function using\na CCAand MNNs, scRNA-seq and scATAC-seq data were integrated according tothe\nanchors. Then the annotation of scRNA-seq data was projected onto the scATAC-seq\ndataviatheTransferData function.\nReference:\n[1].Stuart, Tim et al. “Comprehensive Integration of Single-Cell Data.” Cell, vol.\n177,7(2019): 1888-1902.e21. doi:10.1016/j.cell.2019.05.031\n2)S4 panel legend Fand Gis switched. S5 description for panel Iis incorrect.\nAnswer:Weappreciate thereviewer’s comment.Wehave corrected themistakes.\n37"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made extensive revisions to the manuscript in response to critiques from the reviewers. While several responses have addressed my prior comments and have improved the manuscript significantly, there are a number of issues that have not be adequately resolved and in places the revised results raise points that should be addressed.\n1) The authors have removed textual assertions of increased endothelial cells in IPF lungs, although Figure 1D legend continues to state this. In the response letter, they support this assertion by citing three small scRNA-seq datasets. Because of issues related to cellular/nuclear disaggregation (particularly from fibrotic lungs), the assertion that sc/snRNA-seq more precisely quantifies cellular composition compared to histologic/tissue-based methods is not accurate. Specifically, relatively increased proportions of endothelial cells can be recovered when there is poor liberation or viability of alveolar epithelial cells and fibroblasts.\na. The plot from the Caporarello Nature Communications manuscript is plotting subpopulations of endothelial cells using total endothelial cells as the denominator – this is not quantifying endothelial cells in relation to total cells. Further, the cited reference for the primary date underlying that plot appears potentially incorrect – the reference refers to GSE136831 which included 32 IPF and 27 control lungs, yet only 3 dots per group are shown in this plot. Altogether, these results simply do not support the authors assertion that endothelial cells are increased in IPF lungs.\nb. The other referenced examples presented in the response letter (none of which report statistical differences in endothelial cells using appropriate methods including multiple testing correction), also suffer from the same “zero-sum” issue previously raised – if one population is reduced in scRNA-seq (for example, AT1 and AT2 cells), one must recover a larger proportion of other cell populations, even if absolute numbers are decreased – this is simply not a reliable method for compositional analyses.\nc. Further, the largest IPF/ILD scRNA-seq datasets (GSE136831, GSE227136) show the opposite finding (while there are increased COL15A1+ systemic-venous type endothelial cells, dramatic reductions in capillary endothelial cells result in an overall decrease). In addition, three recent tissue-based spatial transcriptomic studies (PMID 38951642, 39121212, 39901013) also show, if anything, reduced endothelial cells – these methods, which are more accurate for compositional assessment than any disaggregation-based scRNA-seq method, should be considered much closer to a “gold standard.”\nd. The overall assertion of increased endothelial cells in IPF lungs is not ultimately very relevant to the authors proposed (and better supported) mechanistic studies, so I would recommend the compositional analyses and claims related to them be removed.\n2) The revised cell annotation appears much improved and more useful for readers to compare across publications and interpret these results\n3) The increased number of samples (now 4-5 per group) represents an improvement, though this is still a small dataset compared to the 100+ public IPF samples available\n4) The revised language clarifying that single-timepoint FVC reflects severity, not progression rate, is acceptable\n5) It is appreciated that the authors report the genes used for the “mechanical stress” score, but shouldn’t the same set of genes be used for each of the analyses? It isn’t clear why a different set of genes was used for human vs. house and was not consistent within human or within mouse experiments.\n6) The revised fibrosis measures are improved, although in some experiments (Figure 4H-K, non-bleomycin treated controls are not reported.\n7) Revised Figure 1G: this still appears to be plotting differentially expressed genes across the entire endothelial cell group, which will reflect compositional differences. Subject-level, cell-type specific pseudobulk differential expression would be the rigorous and robust means of testing for differentially expressed genes among IPF subjects.\n8) In line with comments from Reviewer 3, the Yoda-studies lack specificity; to have high confidence that these studies are mediating their effects through endothelial Piezo 1, it would be necessary to test whether Yoda-treatment of endothelial Piezo1-deficient animals did or did not augment fibrosis.\n9) The revised calpain studies add some strength to the overall body of evidence\n10) In several panels where there are many groups in bar-plots(for example, figures 4 and 6), the statistical methods need to be included in the figure legends - it is difficult to assess these lacking details as to multiple testing adjustments, etc.\nMinor comments:\n1) For many figure legends, there is a space missing between Figure and the number.\n2) In numerous places, the revised figure legends are interpretative of the results rather than descriptive of the study – the legends should describe the experiment, and any interpretation belongs in the main text (1D, 1H-I, 1O, 2I-J, 2L, 5G, 5I, others).", "reviewer_2": "The authors have addressed all my comments fully.", "reviewer_3": "The authors have satisfactorily addressed most comments in the revision.\nThe regulation of IL33 locus by STAT-3 is not yet convincing. The authors acknowledge that YODA1 might not be the ideal system and have performed CUT&Tag-qPCR with 0h and 6h stretch. Why not replace the YODA1 data (Fig S7) with genome wide data using 0h and 6h stretch? In addition, a genome browser track of the location of the regions selected for CUT&Tag-qPCR would be very helpful. Finally, I am wondering how qPCR could show regulation if in the sequencing data there is no signal e.g. at the promoter?"}, "author_response": "RE: Point-by-point response (NCOMMS-24-47968B)\nContents\nPoint-by-point response to reviewers’ comments:\nReviewer #1: .................................................................................1\nReviewer #2: ...............................................................................11\nReviewer #3: ...............................................................................12\nReviewer #1 (Remarks to the Author):\nThe authors have made extensive revisions to the manuscript in response to critiques\nfrom the reviewers. While several responses have addressed my prior comments and\nhave improved the manuscript significantly, there are a number of issues that have not\nbe adequately resolved and in places the revised results raise points that should be\naddressed.\n1)The authors have removed textual assertions of increased endothelial cells in IPF\nlungs, although Figure 1D legend continues to state this. In the response letter, they\nsupport this assertion by citing three small scRNA-seq datasets. Because of issues\nrelated to cellular/nuclear disaggregation (particularly from fibrotic lungs), the\nassertion that sc/snRNA-seq more precisely quantifies cellular composition compared\nto histologic/tissue-based methods is not accurate. Specifically, relatively increased\nproportions of endothelial cells can be recovered when there is poor liberation or\nviability of alveolar epithelial cells and fibroblasts.\nAnswer: 1) We are sorry for the possible misunderstandings caused by our results. Here,\nwe were trying to explain that the highest proportion of Scissor+ cells (FVC-low\nphenotype (FLP) cells) was found in endothelial cells (Figure 1D of previous version)\ninstead of the increase of endothelial cells in the lung tissues. We adjusted the\nvisualization of Figure 1D from the previous version to make it more accessible.\nFigure1 to Reviewer 1: Change in display format of proportion of Scissor cell types.\n2) We fully support your view that the numbers of endothelial cells are indeed not\nincreased in fibrotic lungs. To further support your perspective, we analyzed public\n1\ndatasets and classified the data according to different types of fibrotic lung disease. This\nanalysis also confirmed that the total number of endothelial cells shows no significant\nchange trend . Only venous endothelial cells were found to be upregulated. Thus, we\ncontend that an exclusive focus on quantitative shifts in target cell populations during\ndisease progression is inadequate, as such an approach risk neglecting functionally\nsignificant genes with critical ties to clinical markers. To bridge this gap, we introduce\nscissors single-cell analysis to systematically map relationships between clinical\nphenotypes and disease-associated cell subpopulations. Our analytical framework may\nfurther offer a methodological blueprint for future research in the field of respiratory\ndiseases.\nFigure2 to Reviewer 1: Schematic (Left)representation of the analysis of single-cell RNA\nsequencing data from lung tissues of patients with IPF and non-IPF-PF (chronic\nhypersensitivity pneumonitis (cHP), sarcoidosis, systemic sclerosis, myositis-ILD) in a\nmulticenter public database, aiming to reveal new mechanisms and potential therapeutic targets\nof pulmonary fibrosis. Table (Right) shows a meta-analysis of the differences in the proportions\nof each endothelial cell subset (Lymphatic ECs, Artery ECs, Vein ECs, Capillary ECs) in\npatients with different types of IPF and non-IPF-PF compared to healthy controls. scRNA-seq\ndatasets: GSE122960, GSE128033, GSE128169, GSE132771, GSE135893, GSE136831 and\nGSE159354.\na. The plot from the Caporarello Nature Communications manuscript is plotting\nsubpopulations of endothelial cells using total endothelial cells as the denominator –\nthis is not quantifying endothelial cells in relation to total cells. Further, the cited\nreference for the primary date underlying that plot appears potentially incorrect – the\nreference refers to GSE136831 which included 32 IPF and 27 control lungs, yet only\n3 dots per group are shown in this plot. Altogether, these results simply do not support\nthe authors assertion that endothelial cells are increased in IPF lungs.\nAnswer: After carefully re-examining Caporarello et al.'s Nature Communications\narticle, we concur with your perspective. The findings of Caporarello et al. indeed do\n2\nnot support an increase in the total number of pulmonary endothelial cells in IPF\npatients.\nb. The other referenced examples presented in the response letter (none of which\nreport statistical differences in endothelial cells using appropriate methods including\nmultiple testing correction), also suffer from the same “zero-sum” issue previously\nraised – if one population is reduced in scRNA-seq (for example, AT1 and AT2 cells),\none must recover a larger proportion of other cell populations, even if absolute\nnumbers are decreased – this is simply not a reliable method for compositional\nanalyses.\nAnswer: We quite agree with you. The “zero-sum” issue is also presented in our\nanalysis of new scRNA-seq data. Our analysis similarly revealed that scRNA-seq data\nfrom collected lung tissue samples showed greater abundance of immune cells, while\nparenchymal cells (including endothelial cells) were underrepresented.\nFigure3 to Reviewer 1: Analysis of newly collected scRNA-seq data from IPF and normal\nhuman samples revealed the “zero-sum” issue of scRNA-seq data.\nc (1) Further, the largest IPF/ILD scRNA-seq datasets (GSE136831, GSE227136)\nshow the opposite finding (while there are increased COL15A1+ systemic-venous\ntype endothelial cells, dramatic reductions in capillary endothelial cells result in an\noverall decrease).\nAnswer: Upon re-analysis of scRNA-seq datasets (GSE122960, GSE128033,\nGSE128169, GSE132771, GSE135893, GSE136831, and GSE159354) stratified by\nIPF and non-IPF-PF categories-including chronic hypersensitivity pneumonitis (cHP),\nsarcoidosis, systemic sclerosis, and myositis-ILD-we consistently observed venous\n3\nendothelial upregulation and capillary downregulation in IPF group.\nFigure4 to Reviewer 1: Schematic (Left) representation of the analysis of single-cell RNA\nsequencing data from lung tissues of patients with IPF and non-IPF-PF (chronic\nhypersensitivity pneumonitis (cHP), sarcoidosis, systemic sclerosis, myositis-ILD) in a\nmulticenter public database, aiming to reveal new mechanisms and potential therapeutic targets\nof pulmonary fibrosis. Table (Right) shows a meta-analysis of the differences in the proportions\nof each endothelial cell subset (Lymphatic ECs, Artery ECs, Vein ECs, Capillary ECs) in\npatients with different types of IPF and non-IPF-PF compared to healthy controls. scRNA-seq\ndatasets: GSE122960, GSE128033, GSE128169, GSE132771, GSE135893, GSE136831 and\nGSE159354.\nc (2) In addition, three recent tissue-based spatial transcriptomic studies\n(PMID 38951642, 39121212, 39901013) also show, if anything, reduced endothelial\ncells – these methods, which are more accurate for compositional assessment than any\ndisaggregation-based scRNA-seq method, should be considered much closer to a\n“gold standard.”\nAnswer: To evaluate the proportion of endothelial cells more accurately, we reanalyzed\nthe public Visium data (PMID: 39121212), and the result demonstrated that there is no\nsignificant change in endothelial cell proportion.\nFigure5 to Reviewer 1: Proportions of ECs in HC and IPF groups of public data (PMID:\n39121212).\nspecimen cohort (2 normal control and 2 IPF) confirmed unaltered endothelial cell\nfrequencies .\nFigure 6 to Reviewer 1: Preliminary analysis of spatial transcriptomic sequencing data from\nour newly collected samples-including 2 idiopathic pulmonary fibrosis (IPF) and 2 normal\ncontrols revealed no statistically significant difference in endothelial cell abundance between\nIPF and normal groups.\nd. The overall assertion of increased endothelial cells in IPF lungs is not ultimately\nvery relevant to the authors proposed (and better supported) mechanistic studies, so I\nwould recommend the compositional analyses and claims related to them be removed.\nAnswer: As you noted, we strongly agree with your point and have removed the\ncompositional analyses from the manuscript.\n2) The revised cell annotation appears much improved and more useful for readers to\ncompare across publications and interpret these results.\nAnswer: We are deeply grateful for your constructive critique, which significantly\nstrengthened our study.\n3) The increased number of samples (now 4-5 per group) represents an improvement,\nthough this is still a small dataset compared to the 100+ public IPF samples available\nand 7 Control human samples for scRNA-seq and the same analytical process was\nperformed. The results of scRNA-seq data are consistent with those of snRNA-seq data\nand GEO public data. However, the proportion of endothelial cells is low. On account\nof “zero-sum” issue, we did not choose to present the relevant results.\nFigure7 to Reviewer 1: Reviewer 1: Analysis of new scRNA-seq data of IPF and Normal\nhuman samples.\n4) The revised language clarifying that single-timepoint FVC reflects severity, not\nprogression rate, is acceptable.\nAnswer: We are truly grateful for your suggestion and affirmation.\n5) It is appreciated that the authors report the genes used for the “mechanical stress”\nscore, but shouldn’t the same set of genes be used for each of the analyses? It isn’t\nclear why a different set of genes was used for human vs. house and was not\nconsistent within human or within mouse experiments.\nAnswer: We quite agree with your comments. We unified the mechanical stress gene\nset of scoring for both human and mouse samples. Here, all the scoring for both human\ndata and mouse data is corrected and based on the unified gene set “response to\nmechanical stress stimulus” obtained through enrichment analysis based on DEG\nanalysis of pseudobulk-snRNA-data. The MS scoring was performed on human snRNA-seq\ndata (Figure 1H-I), GEO public data (Figure 1L-M, Supplementary Figure 2C), Silica-\ninduced mouse data (Figure 2G-L).\n6\n6)The revised fibrosis measures are improved, although in some experiments (Figure\n4H-K, non-bleomycin treated controls are not reported.\nAnswer: We appreciate your advice and followed your important suggestions and\nrepeated the experiments with the saline control group that was not treated with\nbleomycin, as well as the Yoda1 and GsmTx4 control groups.\nFigure8 to Reviewer 1: YODA1 and Gsmtx4 affect the fibrotic response in the mouse lung\nfibrosis model induced by bleomycin and saline control. Data are shown in revised Figure\n4H-I and Supplementary Figure 5G-H.\n(A) The schematic illustrates the treatment of BLM-induced pulmonary fibrosis with the\nPIEZO1 agonist Yoda1 and the antagonist GsMTx4. Mice were euthanized after 3 weeks. (B)\nsaline-induced lung fibrosis model treated with Yoda1 or GsMTx4 via intraperitoneal injection\n(n = 6). (C) Representative images of H&E, Masson and Sirius red staining in fibrotic lungs of\nmice in Yoda1 or GsMTx4-induced via intraperitoneal. Quantification is shown at right (n = 6).\n(D) Immunofluorescent staining of αSMA (red) in fibrotic lungs of mice in Yoda1 or\nGsMTx4-induced via intraperitoneal. Representative co-staining image of indicated mouse lungs is\nshown, and quantification (percentage of αSMA + integrated density relative to DAPI +) is\nshown at right (n = 6). Error bars represent mean ± SEM. Statistical significance was tested by\none-way one-way ANOVA followed by Tukey’s multiple comparisons test. *P < 0.05, **P <\n0.01, ***P < 0.001 and ****P < 0.0001.\n7)Revised Figure 1G: this still appears to be plotting differentially expressed genes\nacross the entire endothelial cell group, which will reflect compositional differences.\nSubject-level, cell-type specific pseudobulk differential expression would be the\nrigorous and robust means of testing for differentially expressed genes among IPF\nsubjects.\nAnswer: Thank you for your constructive advice. Pseudobulk analysis is indeed a better\nchoice for DEGs under the circumstances. We have reanalyzed the data based on your\ncomments, including DEG analysis of EC (Figure 1G) and EC subtypes.\nFigure 9 to Reviewer 1: Pseudobulk DEG analysis of FLP ECs (IPF vs Normal) and FLP EC\nsubtypes (IPF vs Normal) and MS scores of EC and EC subtypes of IPF and Normal groups.\n8)In line with comments from Reviewer 3, the Yoda-studies lack specificity; to have\nhigh confidence that these studies are mediating their effects through endothelial\ndeficient animals did or did not augment fibrosis.\nAnswer: We are grateful for your suggestions. Following your important advice, we\nintraperitoneally administered BLM-induced pulmonary fibrosis in Piezo1 endothelial\nknockout mice treated with Yoda1 and analyzed the fibrotic response.\nFigure10 to Reviewer 1: Yoda1 does not exacerbate BLM-induced pulmonary fibrosis in\nPiezo1ΔEC mice. Data are shown in revised Supplement Figure 6A-D.\n(A) The schematic illustrates intraperitoneal injection of Yoda1 for 3 weeks in BLM-induced\npulmonary fibrosis models in Piezo1WT and Piezo1ΔEC mice. (B) Representative images of H&E,\nMasson and Sirius red staining in BLM-induced pulmonary fibrosis models in Piezo1WT and\nPiezo1ΔEC mice with Yoda1 and Saline. Quantification is shown at right (n = 6). (C)\nImmunofluorescent staining of αSMA (red) in BLM-induced pulmonary fibrosis models in\nPiezo1WT and Piezo1ΔEC mice with Yoda1 and Saline. Representative co-staining image of\n9\nindicated mouse lungs is shown, and quantification (percentage of αSMA + integrated density\nrelative to DAPI +) is shown at right (n = 6). (D) The plot shows the hydroxyproline content in\nlung tissues from mice in the bleomycin- and saline-induced lung fibrosis model treated with\nYoda1 or GsMTx4 via intraperitoneal injection (n = 6). Error bars represent mean ± SEM.\nStatistical significance was tested by Two-way ANOVA followed by Tukey’s multiple\ncomparisons test. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001.\n9)The revised calpain studies add some strength to the overall body of evidence.\nAnswer: Thank you for recognizing the revised calpain studies.\n10) In several panels where there are many groups in bar-plots(for example, figures 4\nand 6), the statistical methods need to be included in the figure legends - it is difficult\nto assess these lacking details as to multiple testing adjustments, etc.\nAnswer: We appreciate your suggestion and will detail the statistical methods used in the\nexperiments in the figure legends.\nMinor comments:\n1)For many figure legends, there is a space missing between Figure and the number.\nAnswer: Thank you for your suggestion. We have carefully proofread the figure legends in\nthe manuscript.\n2)In numerous places, the revised figure legends are interpretative of the results rather\nthan descriptive of the study – the legends should describe the experiment, and any\ninterpretation belongs in the main text (1D, 1H-I, 1O, 2I-J, 2L, 5G, 5I, others).\nAnswer: We truly appreciate your suggestion and the non-standard legends have been\ncorrected.\nThe authors have addressed all my comments fully.\nThe authors have satisfactorily addressed most comments in the revision.\nThe regulation of IL33 locus by STAT-3 is not yet convincing. The authors\nacknowledge that YODA1 might not be the ideal system and have performed\nCUT&Tag-qPCR with 0h and 6h stretch. Why not replace the YODA1 data (Fig S7)\nwith genome wide data using 0h and 6h stretch? In addition, a genome browser track\nof the location of the regions selected for CUT&Tag-qPCR would be very helpful.\nFinally, I am wondering how qPCR could show regulation if in the sequencing data\nthere is no signal e.g. at the promoter.\nAnswer: We sincerely appreciate the reviewer's valuable suggestions. Considering that\nCUT&RUN is more advantageous than CUT&Tag for detecting transcription factor\nbinding across the genome, we performed CUT&RUN experiments with both 0h and\n6h stretching conditions, including IgG negative controls and H3K4me3 positive\ncontrols. Our results demonstrate that STAT3 binds to multiple sites in the upstream\nregulatory region of IL-33, covering the enhancer, promoter, as well as Peak1 and Peak2\nregions that we previously identified.\nWhile we acknowledge that the overall quality of our CUT&RUN data could be further\nimproved, it still represents a significant advancement over our initial CUT&Tag results.\nImportantly, the binding pattern of STAT3 shows reasonable consistency with the\nH3K4me3 signal in these regions.\nRegarding the reviewer's question about the discrepancy between CUT&Tag\nsequencing and qPCR results, we believe this is likely due to the relatively low\nabundance of STAT3-bound DNA. While such low-abundance binding might lead to\nsignificant bias during high-cycle amplification in library preparation for sequencing,\nqPCR with specific primers can still reliably detect these interactions due to its higher\nsensitivity for targeted amplification.\nFurthermore, given the rapid activation kinetics of mechanotransduction signaling, we\nemployed CUT&Tag-qPCR to assess the direct binding of STAT3 at the upstream\nregulatory region of the IL-33 gene following 3 hours of mechanical stretch. As showed\nin Figure , STAT3 binding showed significant enrichment at Peak1, Peak2, Enhancer1, and\nEnhancer2, with a similar increasing trend observed in the promoter region following activation\nof mechanosensitive pathways by stretch stimuli (20% for 3 hr).\nconcerns about STAT3 regulation of the IL-33 locus.\nFigure1 to Reviewer 3:\n(A) STAT3 and H3K4me3 peaks are broadly distributed across the genome of HUVEC after 6h\nstretch, including promoter regions, introns, and distal intergenic regions analyzed by\nCUT&RUN. (B) CUT&RUN was performed using a STAT3 antibody to identify binding sites\nof STAT3. The figure shows the sequencing results, highlighting the enrichment of STAT3 at\nspecific genomic regions of IL-33. (C) Fold enrichment of P-STAT3 binding to the promoter\nregion in HUVECs, as determined by Cut&Tag-qPCR. HUVECs were subjected to 20% stretch\nfor 3 hours. Data are presented as fold enrichment relative to unstretched controls, normalized\nto IgG (n = 3).\nContents\nPoint-by-point response to reviewers’ comments:\nReviewer #1: .................................................................................1\nReviewer #3: .................................................................................2\nReviewer #1 (Remarks to the Author):\nThe authors have made commendable efforts to address the issues raised in my prior\ncomments and from the other reviewers. These changes have addressed my concerns\nsatisfactorily.\nI appreciate the authors efforts to strengthen the data linked to direct regulation of IL33\nby STAT-3.\nI agree with the authors that CUT&RUN is better suited than CUT&Tag to profile\ntranscription factor binding due to potential tagmentation of open chromatin by pA-\nTn5.\nUnfortunately, as also stated by the authors, the presented quality of CUT&RUN data\nin the revision is not high, e.g. the promoter mark H3K4me3 does not show the expected\nenrichment at promoter regions and distal peaks (e.g. peak 1) would be characterized\nby absence of H3K4me3 (Reviewer Figure 1A, B). The STAT3 signal does not appear\ndifferent between peak and non-peak regions. Higher signal at peaks regions in 7S could\nalso be explained by overall stronger signal across the whole locus as shown in\nReviewer Figure 1B. Based on this it is very difficult to draw any robust conclusions of\nSTAT3 binding from the analysis of the presented data.\nRelated to the CUT&Tag data: The explanation that the lack of signal at the IL33\npromoter is due to a bias in library amplification is not convincing. For me it rather\nraises the question about what is amplified in qPCR? The signal in S7c overall is very\nsparse and comparing the peak calls with the signal tracks does not match well. I\nacknowledge that these experiments are difficult for TF and even more challenging for\np-TFs.\nBecause of this, the presented data do not convincingly show direct regulation of IL33\nby STAT. Thus, I think all what could be concluded is that STAT might directly control\nIL33.\nAnswer: Thanks for your careful review and valuable comments. We acknowledge and\nappreciate your concerns regarding the quality of the CUT&RUN and CUT&Tag data. Given\nthe current limitations of the data, we have revised the manuscript (including the Abstract,\nResults, and Discussion sections) to ensure our claims are rigorous. Instead of asserting “direct\nregulation”, we now state that STAT3 “may directly regulate” or “is involved in the regulation\nof” IL33. We are grateful for your guidance and suggestions.\n2"}]

Decision letter