Integrated EVLP and single-cell profiling uncovers aberrant activation of tissue-resident lymphocytes and pro-fibrotic GZMK⁺ CD8 T cells in IPF

Integrated EVLP and single-cell profiling uncovers aberrant activation of tissue-resident lymphocytes and pro-fibrotic GZMK⁺ CD8 T cells in IPF | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated EVLP and single-cell profiling uncovers aberrant activation of tissue-resident lymphocytes and pro-fibrotic GZMK⁺ CD8 T cells in IPF Fei Gao, Zitao Wang, Tao Yan, Yanjing Su, Shuilian Chi, Dong Wei, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7500111/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Tissue-resident immune cells are crucial in chronic lung diseases, yet a comprehensive profile in human lungs is lacking. Here, we defined alterations of resident immune cells in idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease characterized by tissue inflammation and progressive scarring. Utilizing ex-vivo human lung perfusion coupled with single-cell RNA-sequencing, we successfully segregated the resident immune cells. Analyzing approximately 100,000 resident immune cells from 7 IPF and 5 control lungs, we identified 13 distinct cell types. Previously unrecognized aberrant lymphocyte phenotypes were uncovered. Specifically, among T lymphocytes, we observed an enrichment of GZMK + CD8 + T cells in IPF lungs, possessing a potential pro-fibrotic function. The fraction of pro-inflammatory HSP hi CD4 + T cells was increased in IPF lungs, while the quiescent subset decreased. Despite an increased presence of Tregs in IPF lungs, these cells showed reduced expression of genes associated with immune suppression. Moreover, significant B cell expansion and activation occurred, with continuous differentiation into IgG-producing plasma cells. Stromal niche interaction analysis showed that IPF fibroblasts, especially the CTHRC1 hi subset, exerted stronger effects on lymphocytes. These findings offer novel insights into dysregulated immune populations in IPF, advancing understanding of its immunopathology. Health sciences/Diseases Health sciences/Pathogenesis Biological sciences/Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Tissue-resident immune cells play intricate roles in maintaining tissue homeostasis, influencing local cellular dynamics, and shaping the microenvironment across various organs 1,2 . In barrier organs like the lung, these cells not only combat pathogens and conduct local immune surveillance but also significantly contribute to tissue integrity 3–5 . Chronic inflammation and dysregulation of tissue-resident immune cells have been consistently linked to the pathogenesis of diverse chronic lung diseases, such as pulmonary fibrosis 6,7 . Idiopathic pulmonary fibrosis (IPF) is a progressive lung disorder characterized by tissue inflammation and irreversible distal lung scarring, culminating in respiratory failure and mortality 8,9 . Immune cells contribute significantly to the progression of pulmonary fibrosis by promoting inflammation, tissue remodeling, and collagen deposition. Accumulating evidence has highlighted the substantial contribution of lymphocytes in pulmonary fibrosis 10–13 . Despite notable strides in unraveling immune cell functions in lung fibrosis, our understanding of the specific roles played by the resident immune cells in close interaction with lung structural cells remains limited. Experimental constraints hamper the isolation and study of human lung-resident immune cells. In this study, we employed ex-vivo human lung perfusion (EVLP) combined with single-cell RNA-sequencing (scRNA-seq) to unveil the intricacies of tissue-resident immune cells, uncovering aberrant activation of resident lymphocytes in IPF lungs. Results EVLP effectively distinguishes the resident from circulating immune cells We set out to establish whether a combination of EVLP and scRNA-seq can be used to define human lung-resident and circulating immune populations (Fig. 1A). We performed EVLP for the explanted human lung from a cadaveric donor without prior history of lung disease for one hour (Fig. 1B). During the EVLP, we conducted intraarterial (i.a.) anti-CD45 labeling by administering a CD45-conjugated antibody into the perfusate to differentiate circulating (i.a. anti-CD45 + /CD45 + ) from tissue-resident immune cells (i.a. anti-CD45 - /CD45 + ). Flow cytometry (FACS) analysis of the cell suspension after EVLP showed a distinct separation between the resident and circulating immune cells (Fig. 1C). To further define the two immune populations, we performed scRNA-seq on the FACS-isolated cells, respectively. Library preparation and sequencing were performed separately and then all the cells were merged for unsupervised clustering using uniform manifold approximation and projection (UMAP). The UMAP analysis showed 7 subsets in all the immune cells (Fig. 1D, S1A to S1C). Mast cells, tissue-resident myeloid cells, were exclusively found within the resident population (Fig. 1D and 1E). In mice, natural killer (NK) cells are predominantly found in the circulating population 14 . In agreement with this, the human circulating NK cells constituted most of the fraction in the entire NK cell group, with only 17.2 % of NK cells were from the resident population (Fig. 1D to 1F). Together, these data suggest that EVLP combined the labeling technique can successfully differentiate the resident from circulating immune cells. T cells accounted for more than half of the resident immune population (Fig. 1E). To further validate our EVLP segregation and characterize tissue-resident T cells, we compared the transcriptomic profiles of the resident with circulating T cells. T cells from the resident fraction demonstrated classic markers of tissue residency, including the higher expression of CD69 , IL7R , and CD44 , along with the lack of expression of circulating markers, such as S1PR1 , SELL , and CCR7 (Fig. 1G and S1D). Differentially expressed gene (DEG) analysis comparing T cells in the resident with circulating populations, coupled with ingenuity pathway analysis (IPA), showed pathways enriched in the resident T cells relevant to tissue retention, cell survival, and lower level of activation/proliferation (Fig. 1H). Consistent with these findings, the resident T cells demonstrated higher expression of inhibitory receptors CTLA4 and PDCD1 (Fig. 1I). These data suggest that tissue-resident lymphocytes are maintained in a quiescent state during homeostasis. Single-cell transcriptomic profiles of tissue-resident immune cells of IPF and control lungs Colocalization with the resident marker CD44 demonstrated a significant increase in tissue-resident immune cells (CD44 + /CD45 + ) in IPF samples compared with control lungs (Fig. 2A and 2B). To further characterize the alterations in tissue-resident immune cells in IPF lungs, we isolated these cells from IPF patients (n = 7) undergoing lung transplantation and from age-matched control cadaveric donor lungs without a history of lung disease (n = 5) (table S1) using FACS after EVLP. We then performed scRNA-seq on the isolated resident immune cells, removing low-quality cells, correcting batch effects, and clustering the 96,162 recovered cells using UMAP. Using canonical immune lineage-defining markers, we defined 13 cell types, with all expected cell types present in both diseased and control lungs (Fig. 2C, S2A to S2C). The abundance of these cell types, expressed as a percentage of resident immune cells, was altered in IPF samples (Fig. 2D and S2D). T lymphocytes, particularly CD4 + and CD8 + subsets, constituted the majority of the resident immune population in both control and IPF lungs (Fig. 2D and S2D). Tregs significantly increased in IPF lungs (5.0% in IPF vs. 2.2% in control lungs) (Fig. 2D and S2D). Although the proportions of CD4 + and CD8 + T cells among resident immune cells did not change significantly, the overall increase in resident immune cells, particularly T cells (Fig. 2E and 2F), in IPF lungs indicated a substantial rise in the absolute numbers of CD4 + and CD8 + T cells. This observation was further validated by immunostaining analyses comparing resident CD4 + and CD8 + T cells in IPF lungs with those in control lungs (Fig. 2G and 2H). In IPF lungs, the proportion of resident B cells significantly increased (15.3% in IPF vs. 4.9% in control lungs), along with a rising trend in the proportions of plasma cells (2.7% in IPF vs. 0.6% in control lungs) (Fig. 2D and S2D), which were further confirmed by immunostaining analyses (Fig. 2I, 2J, and S2E). Expansion of GZMK + CD8 + T cells with age-associated markers in IPF, displaying profibrotic potential To better characterize CD8 + T cells in IPF lungs, we segregated all CD8 + T cells from both IPF and control samples and re-clustered them. The UMAP analysis demonstrated two distinct subpopulations in CD8 + T cells, granzyme K positive (GZMK + ) and granzyme K negative (GZMK - ) subsets (Fig. 3A, 3B, and S3A). The fraction of GZMK + CD8 + T cells significantly increased by approximately 50% in IPF compared with control lungs (Fig. 3C). GZMK + CD8 + T subset was previously identified as a conserved feature of aging immune system in both mice and humans, named as age-associated (Taa) CD8 + T cells, with GZMK as one of the most specific markers 15 . CD8 + Taa cells exhibited unique features compared to conventional T effector memory cells, including high expression of the transcription factors TOX and EOMES , as well as checkpoint genes like TIGIT , LAG3 , CTLA4 , HAVCR2 and PDCD1 (PD-1), coupled with a low level of IL7R and TCF7 expression. In line with previous findings, our scRNA-seq data also demonstrated a significant enrichment of these markers in GZMK + CD8 + T cells (Fig. 3D and S3B). In addition, GZMK + CD8 + T cells in our dataset also exhibited a relatively lower expression of IL7R and TCF7 than the GZMK - subset (Fig. 3D and S3B). These findings suggest that GZMK + CD8 + T cells are Taa-like cells. It has been shown that the development of CD8 + Taa cells was driven by extrinsic factors of an aged environment. Of note, there was no significant difference in age between IPF and control lung donors (Fig. S3C). Together, these data suggest that IPF lungs, similar to aged organs, provide an environment that promotes the expansion of CD8 + Taa cells. To gain insight into the potential functions of CD8 + T subsets, we generated DEGs by comparing GZMK + vs. GZMK - subsets (table. S2). IPA analysis of the DEGs indicated T cell activation in the GZMK + population, marked by activation of T cell receptor signaling, interferon gamma signaling, and CD28 family costimulation, along with inhibition of PD-1/PDL1- and CTLA4-mediated T cell inhibitory pathways (Fig. 3E). Consistent with IPA results, GZMK + subset demonstrated higher expression of a set of human leukocyte antigen (HLA) class II molecules (Fig. 3F) and the activation-induced proteins CD137 ( TNFSR9 ) and CD137L ( TNFS9 ) (Fig. 3G), indicating T cell activation. Of note, GZMK + subset exhibited a lower expression of GZMB (Fig. S3D), a granzyme regulating T cell antiviral response. In contrast to GZMB's ability to cleave caspases for inducing apoptosis in viral infected cells, GZMK was characterized as a proinflammatory component within the granzyme family 16,17 . Additionally, the expression levels of proinflammatory cytokines ( CCL5 , CCL4L2 , TNF , etc.) were higher in GZMK + subset, while there was a lower expression of PRF1 (encoding perforin 1, a pore forming cytolytic protein) (Fig. 3H). To delve deeper into the changes observed in the GZMK + CD8 + T cells within IPF lungs, we conducted a DEG analysis of this subset in IPF compared with control lungs. Our analysis revealed a significant increase in the expression of GZMK and T cell activation markers, such as TNFSF9 and HLA class II molecules (Fig. 3I, S3E, and table S3), indicating that IPF GZMK + subset exhibited heightened activity and pro-inflammatory profile, compared with the control counterpart. To examine the effect of GZMK protein on IPF progression, we treated human lung fibroblasts with recombinant GZMK protein. qPCR analysis demonstrated that GZMK activated the expression of tissue fibrosis-associated proteins, such as COL1A1 , ACTA2 , and TGFB (Fig. 3J). Histologic and scRNA-seq analyses of IPF lungs have consistently revealed the presence of senescent fibroblasts, marked by p16 ( CDKN2A ) 18-20 . A recent study showed that p16 promoted collagen deposition in pulmonary fibrosis 21 . In line with these findings, we also observed a significant upregulation of CDKN2A expression in GZMK-treated fibroblasts (Fig. 3K). Fibroblasts treated with GZMK also exhibited senescence-associated β-galactosidase activity (Fig. 3L and 3M). These findings suggested a close association between the expansion of GZMK + CD8 + T cells and disease progression in IPF lungs. Increased pro-inflammatory HSP hi CD4 + T cells in fibrotic lungs We next focused on CD4 + T cells, which constituted the largest subset among resident immune cells in IPF (Fig. 2D). UMAP clustering analysis of all CD4 + T cells of IPF and control samples revealed 4 distinct clusters (Fig. 4A and S4A). DEG analysis produced a list of signature genes for each subset (table S4), cluster 1 (C1) was enriched for the expression of Th1-related genes, including IFNG (Fig. S4B). Cells in cluster 2 (C2) were marked by a number of circulating markers, such as SELL , CCR7 , and S1PR1 , suggesting that they were recirculating T cells between tissue and blood (Fig. S4C), as previously reported 22 . Cells of cluster 3 (C3) highly expressed a number of cell quiescent factors, including TOB1 , a negative regulator of T cell proliferation and cytokine production 23 , suggesting a quiescent state (Fig. 4B and 4C). Consistently, IPA pathway analysis of the signature genes predicted C3 as quiescent T cells with inhibited pathways regarding T cell activation and proliferation (Fig. 4D). Cluster 4 (C4) was enriched for the expression of heat shock proteins ( HSPs , inflammatory mediators 24,25 ) and TNF (Fig. 4E), indicating a pro-inflammatory phonotype. Consistently, IPA pathway analysis of the signature genes of C4 produced top activated pathways about tissue inflammation (cellular response to heat stress; TNFR2, iNOS and IL-1 signaling) and T cell activation (CD137 and CD27 signaling) (Fig. 4F). The fraction of pro-inflammatory CD4 + T cells (HSP hi ) increased, while the quiescent (TOB1 hi ) subset decreased in IPF, compared with control lungs (Fig. 4G). In line with the scRNA-seq data, immunostaining analysis revealed an accumulation of resident HSP hi CD4 + T cells within the alveoli of IPF lungs, particularly in the fibrotic regions (Fig. 4H). There were no significant changes in the fraction of Th1 or recirculating subsets between IPF and control lungs (Fig. S4D). To further characterize HSP hi subset in IPF lungs, we segregated the subset and performed DEG analysis by comparing IPF vs. control HSP hi CD4 + T cells (table. S5). Gene Ontology (GO) analysis of the DEGs revealed that IPF HSP hi CD4 + T cells were enriched in GO terms associated with tissue inflammation, T cell activation, proliferation, and survival. These included terms such as “Regulation of chronic inflammation,” “Positive regulation of T cell proliferation,” and “Negative regulation of apoptosis” (Fig. 4I). Consistent with the GO analysis, IPF HSP hi CD4 + T cells demonstrated increased expression of genes associated with pro-inflammation/pro-activation ( TNF , NFKB1 , REL , etc.) and anti-cell death ( BCL2 , PIM3 , B AG3 , etc.) (Fig. 4J; table S5). In contrast, the expression of anti-inflammation/anti-activation ( ZFP36 , SOCS1 , TOB1 , etc.) and pro-cell death ( BAX , TXNIP , CASP8 , etc.) genes were relatively lower in IPF HSP hi CD4 + T cells, compared with the control counterparts (Fig. 4J; table S5). These findings suggest that HSP hi CD4 + T cells of IPF lungs exhibit an enhanced pro-inflammatory phenotype with increased cell viability. Accumulation of Tregs with potentially impaired suppressive function in IPF The proportion of Tregs in the resident immune cells significantly increased in IPF lungs (Fig. 2D and S2D), in agreement with the previous findings 26 . UMAP clustering analysis of Tregs in IPF and control lungs revealed two distinct subsets characterized by high and low expression of FOXP3 , a master regulator of Tregs (Fig. 5A, 5B, and S5A). DEG analysis of the two subsets demonstrated that the FOXP3 hi population highly expressed markers of activated Tregs, such as IL2RA (encoding CD25), ICOS , CTLA4 , and HLA class II molecules, while the FOXP3 lo population with TGFB1 expression was resting Tregs (Fig. 5C and S5B; table S6), as previously described 27-29 . There was no significant difference in the ratio of the two subsets between control and IPF lungs (Fig. S5C). Tregs are essential for preventing autoimmunity and limiting chronic inflammatory diseases 30 . To assess the potential suppressive functions of Tregs in IPF, we examined the expression of genes associated with suppression in IPF Tregs compared with control counterparts. FOXP3 expression programs Treg development, and confers suppressive functions on conventional T cells 31 . Our scRNA-seq data showed a downregulated expression of FOXP3 in both resting and activating Treg subsets of the IPF lungs (Fig. 5D). CTLA-4 is constitutively expressed and required for the Treg’s immune suppressive activity by endowing dendritic cells with immune tolerance properties. Loss of CTLA-4 in Tregs impairs the immune suppressive function and leads to abnormal activation and expansion of conventional T cells 32 . IL2RA also plays a critical role in maintaining the suppressive function of Tregs 33 . In line with the FOXP3 expression result, CTLA-4 and IL2RA were downregulated in both resting and activating Tregs of the IPF samples, compared with Treg subsets in control lungs, respectively (Fig. 5D). Collectively, our results indicate a dysfunction in the suppressive capacity of Tregs in IPF. Indeed, studies have demonstrated that Treg-mediated suppression of conventional T cell activation is compromised in IPF lungs 34 . A transitional B cell subset emerged in IPF lungs, accompanied by an increase in plasma cells Several studies have implicated B cells in IPF progression 35-37 . Our scRNA-seq and immunostaining data showed that B cells were increased in the lungs of IPF patients, compared with the controls (Fig. 2D, 2I, and S2D). Cell clustering analysis of all B cells of the IPF and control lungs identified two distinct subsets, characterized by the presence or absence of CD79A expression (Fig. 6A and 6B). To further characterize the two subsets, we generated a list of DEGs by comparing the CD79A - with CD79A + subsets. We found that the CD79A - cells demonstrated a relatively higher expression of immunoglobulin genes, such as IGLC1 , IGLC2 , and IGHG1 , suggesting that they were transitional B cells in the process of becoming plasma cells (Fig. 6C; table S7). The CD79A + cells were enriched for B cell markers, including CD37 , CD79A , and CD19 , indicating that they were mature B cells (Fig. 6C; table S7). To validate this lineage transition, we combined plasma cells, the differentiated B cells, with the two B cell subsets and conducted RNA velocity and Monocle trajectory analyses, consistently demonstrating a clear differentiation pathway from mature B cells to plasma cells through transitional B cells (Fig. 6D to 6F, and S6A). Visualization of gene expression along the B cell differentiation trajectory showed that mature B cell markers, such as CD19 and MS4A1 (CD20), were gradually lost as mature B cells differentiated into transitional cells (Fig. 6G and 6H). The transitional B cells were marked by high expression of TNN , encoding the extracellular matrix protein Tenascin N (Fig. 6G and S6B). As differentiation progressed, the transitional B cells gave way to plasma cells, as all B cell markers were lost with the emergence of plasma cell markers, including IGKC and XBP1 (Fig. 6G and 6H). Of note, the expression of the anti-apoptotic gene MCL1 reached its highest level in plasma cells (Fig. 6H), a factor crucial for ensuring the survival of these cells 38 . The proportion of transitional B cells in IPF samples increased, both in terms of their percentage among the resident immune cells and their ratio to the mature B cells. (Fig. 6I and S6C). These data suggest that B cells in IPF lungs were activated to transition into plasma cells. To assess the alterations in the mature B cells of IPF lungs, we conducted the DEG analysis by comparing IPF mature B cells with those from control lungs (table S8). IPA pathway analysis of the DEGs predicted hyperactivation of the pathways that promote B cell activation, differentiation, and antibody production (such as RHO GTPase cycle, Phospholipase C, TEC, PI3K, and BCR signaling) in IPF mature B cells (Fig. 6J). Aligned with IPA findings, our scRNA-seq and immunostaining data revealed a substantial rise in plasma cell abundance within IPF lungs (Fig. 2D, 2J, S2D, and S2E). Additionally, DEG analysis comparing IPF plasma cells with those in control lungs revealed significantly elevated expression of immunoglobulin genes in IPF plasma cells (Fig. 6K and table S9), suggesting heightened activity in antibody production. Immunostaining analysis of IgG further confirmed elevation of IgG expression in the fibrotic tissue (Fig.6L). Altered interactions between the stromal niche and the resident lymphocytes in IPF We have previously shown that lung fibroblasts create a niche that maintains the homeostasis of resident lymphocytes. Dysregulation of this niche can lead to the expansion of lymphocytes, contributing to chronic lung diseases 5 . Here, we sought to examine the interactions between a fibrotic stromal niche and the resident lymphocytes in IPF lungs. To this end, we re-analyzed the scRNA-seq data of IPF stromal cells 39 , and identified alveolar fibroblasts, along with two pathological subsets, the HAS hi and CTHRC1 hi fibroblasts, as previously reported 18,39 . We then merged these three stromal subsets with our resident lymphocytes to perform CellChat analysis 40 , a tool for quantitatively analyzing ligand-receptor pairs, to evaluate the stromal niche's capacity to interact with lymphocytes in IPF compared with control lungs (Fig. 7A, S7A, and S7B). CellChat analysis showed that IPF alveolar fibroblasts exhibited a significant increase in both the number and intensity of interactions with lymphocytes, compared with the alveolar fibroblasts in control lungs (Fig. 7B and 7C). Furthermore, both of the two IPF specific pathological stromal subsets showed an increased interaction with lymphocytes in both number and strength, compared with control alveolar fibroblasts (Fig. 7B and 7C). Of note, in IPF, the CTHRC1 hi fibroblast subset exhibited the strongest effects on lymphocytes, as reflected by the increased interaction number and intensity (Fig. 7B and 7C). Consistent with the CellChat analysis, immunostaining analysis demonstrated that CTHRC1 hi fibroblasts, marked by COLLAGEN 39 , were closely associated with B and T lymphocytes in the fibroblastic foci (Fig. 7D). These data suggest an altered stromal niche in IPF that contributes to the abnormal activation of lymphocytes. To further dissect the underlying molecular pathways of stromal-immune interactions in IPF, we categorized the interaction pathways into 3 types, including Secreted Signaling, Extracellular Matrix (ECM) Receptor, and Cell-Cell Contact (Fig. 7E). Among the Secreted Signaling mode, IGFBP, pleiotrophin (PTN), GALECTIN, midkine (MDK), and GDF signaling pathways were the most prominent interactions observed between CTHRC1 hi fibroblasts and lymphocytes. These pathways have been shown to contribute to chronic tissue inflammation and/or support lymphocyte survival, activation, and proliferation 41-44 . The most notable interactions observed between HAS hi fibroblasts and lymphocytes involved CXCL (chemoattractant cytokines) and insulin-like growth factor (IGF) signaling (Fig. 7E). This suggests that this stromal subset not only attracts lymphocytes to the lungs but also supports their expansion. In line with these findings, CTHRC1 hi fibroblasts exhibited significantly elevated expression levels of IGFBP3 , PTN , LGALS9 ( GALECTIN9 ), MDK , and GDF15 . Additionally, CXCL12 and IGF1 were found to be more enriched in HAS hi fibroblasts. (Fig. 7F and S7C). As previously reported, CTHRC1 hi fibroblasts produced significantly high amount of ECM protein (Fig. 7G and S7C). Consistently, COLLAGEN, fibronectin (FN1), LAMININ, and thrombospondin (THBS) signaling were the highest ranked interactions between CTHRC1 hi fibroblasts and lymphocytes in the ECM-Receptor mode (Fig. 7E). These ECM proteins not only facilitate lymphocyte adhesion but also trigger signaling pathways in lymphocytes, promoting their activation, proliferation, and cytokine production 45-47 . CTHRC1 hi fibroblasts also directly interacted with lymphocytes, prominently activating the CD99 and ADGRE (CD97) signaling pathways in the Cell-Cell Contact mode (Fig. 7E), known for their roles in lymphocyte activation 48,49 . This is in line with the high expression of CD99 and genes encoding CD97-interacting partners, including LPAR1 , ITGAV , and THY1 in CTHRC1 hi fibroblasts (Fig. 7H and S7C). Collectively, these data suggest that the stromal niche in IPF regulates lymphocytes through secreted factors, ECM components, and direct cell-cell interactions. Discussion Although some mechanisms regarding the involvement of tissue-resident immune cells in modulating tissue homeostasis and remodeling have been identified through animal model studies, much remains unknown about how human lung-resident immune cells regulate the onset and progression of pulmonary fibrosis. In this study, we provide a single-cell landscape of the human lung-resident immune cells, with a focus on lymphocyte populations. We identify several previously unrecognized aberrations in IPF lung-resident lymphocytes, having potential functions in driving chronic inflammation and tissue fibrosis. Additionally, the fibrotic stromal niche supports resident lymphocytes, creating a feedback loop that exacerbates fibrosis. Here, our data reveal substantial disparities between the human lung-resident immune population and the circulating immune population, both in terms of cellular composition and expression profiles. Therefore, the failure to differentiate between resident and circulating immune cells during analysis could result in misinterpretation or the concealment of vital information. In healthy lungs, tissue-resident T lymphocytes tend to be more quiescent, exhibiting suppressed pathways related to T cell activation. They also express higher levels of inhibitory receptors, such as CTLA4 and PD-1. The finding of elevated PD-1 expression in human lung-resident T cells is in line with previous report 50 . Additionally, pro-survival pathways are activated while cell death pathways are inhibited in the resident T lymphocytes of healthy human lung tissue. From a technical perspective, maintaining a stable population of lung-resident T cells makes sense as it enables the organism to effectively combat potential lung infections while minimizing the risk of tissue inflammation. Indeed, previous studies in animal models have shown that blocking the PD-L1/PD-1 pathway can lead to the expansion of lung-resident T lymphocytes, contributing to tissue inflammation 51 . It’s noteworthy that the combination of EVLP with single-cell technologies provides an innovative approach for studying resident immune populations in human lungs. The role of CD8 + T cells in pulmonary fibrosis is multifaceted. With cytotoxic activity, CD8 + T cells can directly kill and clean infected or damaged cells, contributing to tissue repair. One the other hand, they can promote inflammation by secreting cytokines that exacerbate pulmonary fibrosis 12,52,53 . In this study, we demonstrate that GZMK-expressing CD8 + T cells with higher expression of pro-inflammatory cytokines and pro-fibrotic potential preferentially accumulated in IPF lungs, sharing similar expression profile to the age-associated GZMK + CD8 + T cells (Taa) that were previously identified in aged mice and humans 15 . The old environment of the organs promotes the development of CD8 + Taa cells. Considering that the age of control and IPF donors were matched in our study, IPF lungs might provide an age-related niche for driving the expansion of GZMK + CD8 + T cells. Indeed, the Cellchat analysis confirmed strong interaction of the pathological stromal niche with CD8 + T cells in IPF lungs. In turn, GZMK from CD8 + T cells can promote the formation of the pathological stromal niche by enhancing cell senescence and collagen production. Therefore, the GZMK + CD8 + T cells and pathological fibroblasts form a positive feedback loop to exacerbate tissue fibrotic remodeling. Our findings offer an explanation for the clinical observation that the accumulation of CD8 + T cells in the lungs directly correlates with the severity of pulmonary fibrosis in patients 54 . Furthermore, IPF predominantly affects older adults, potentially due to the excessive accumulation of GZMK + CD8 + T cells in their lungs. Similar to CD8 + T cells, CD4 + T cells also accumulate in the lung tissue of patients with IPF. CD4 + T cells promote pulmonary fibrosis in animal models by inducing collagen deposition in part via IL-17A 10,55 . Furthermore, the imbalanced ratio of CD4 + T cell subsets in IPF has been reported to be associated with lung function, highlighting the potential distinct roles of the subtypes in disease progression 56 . Here, we report an increased proportion of pro-inflammatory subtype and a decreased proportion of quiescent subtype in IPF lungs. Further studies will be required to determine the impacts of different CD4 + T cell subtypes on fibrotic tissue remodeling. The expansion of B cells and plasma cells has been consistently linked to pulmonary fibrosis 37,57 . Here, our results provide new insights into the intricate and dynamic changes within the lung-resident B cell lineage in pulmonary fibrosis. In IPF lungs, B cells remain persistently activated, leading to their differentiation into plasma cells that produce a significant amount of immunoglobulins. Supporting the pathogenic role of B cells, CD19, a positive regulator of B cell activation, exacerbates lung fibrosis in the murine bleomycin model 58 . Furthermore, depleting plasma cells can reduce lung fibrosis levels in the animal model 35 . A recent study has revealed that the accumulation of immunoglobulins during aging impairs adipose tissue function and contributes to tissue fibrosis 59 . These findings suggest that targeting the B cell lineage could be a promising therapeutic strategy for IPF. Tregs play a crucial role in immune regulation by suppressing lymphocytes and preventing overactive immune responses. The abnormal buildup and activation of lymphocytes in IPF lungs may be partly attributed to the impaired Treg function. Moreover, the pathological stromal niche in IPF creates a conducive environment for lymphocyte expansion within the lung. Together, our data offer a comprehensive portrait of the resident lymphocytes in both human healthy and IPF lungs. We have identified specific markers, pathways, and programs that define the aberrant lymphocytes in IPF. Several limitations in our study are worth noting. First, IPF samples were collected from the lungs at the time of organ transplantation, representing the end stage of disease progression. It remains unclear whether similar changes in cell type composition and gene expression profiles occur at the early stages. Second, these tissues were exclusively collected from the distal lung fragments, so our current data may not accurately reflect the resident immune cells in the proximal airways. Third, although we utilized bioinformatics techniques to characterize resident lymphocytes in IPF, extensive experimental validation is still needed. Future work will be required to understand why abnormal resident immune cells appear in IPF and how various types of resident immune cells influence disease progression. Methods Study design This study was designed to characterize human lung-resident immune cells and to determine whether there are any alterations in IPF samples. An additional goal was to define the interactions between the resident immune cells and the stromal niche. Deidentified human lung samples were obtained from brain-dead donors and IPF patients undergoing lung transplantation. The samples we used for EVLP, flow cytometry, and sequencing were unblinded. Samples for scRNA-seq analysis were unblinded. Tissue sections for imaging were blinded before processing and quantification by multiple independent researchers and were unblinded for statistical analysis and graphing. Our descriptions of independent technical and biological replicates are included in the figure legends. When technical or processing errors were recorded, we excluded data from those samples from our final quantification and statistical analyses. Human lung samples The studies involving human tissue were approved by the affiliated Wuxi People’s Hospital of Nanjing Medical University Institutional Review Board, under the approval number 2020(374). The control lungs were obtained from 5 brain-dead donors that were rejected for clinical use. IPF lungs were taken from the 7 patients undergoing lung transplantation for pulmonary fibrosis. The age and sex of tissue donors used in scRNA-seq were listed in table S1. EVLP and circulating immune cells labeling The EVLP technique used in this study was modified from the previous Toronto EVLP technique (Cypel M, et al. Normothermic ex vivo lung perfusion in clinical lung transplantation. N Engl J Med 2011). Briefly, the donor lung was prepared and connected with EVLP device with the pulmonary vein (PV) left open. The EVLP circuit was driven by a centrifugal pump, perfusate from the PV gathered in the perfusate pool, and was driven through a leukocyte filter to a Euroset® Trilly membranous oxygenator (Italy, EUROSET) which connected to balanced gas (6% oxygen, 8% carbon dioxide). After deoxygenation and heated, perfusate was driven to the pulmonary artery (PA). Two liters of Steen ® solution were used to prime the circuit. The lungs were ventilated using a protective ventilation mode with an effective tidal volume of 4.8 ml/kg body weight for the right lobes or 3.2 ml/kg for the left lobes at 10 times/min. The time of starting ventilation was set as 0 h. One-hour EVLP time was adopted for each lung. Before injecting antibodies, the leukocyte filter was bypassed. Then, 1.5 liters of 37°C perfusate were replaced in the circuit. To labeling circulating immune cells, 12.5 µg of anti-CD45-AF647 antibody (BioLegend, 304018) was diluted into 10 ml PBS and injected through PA. The labeling process was 30 min. After that, the lungs were disconnected from the EVLP and re-flushed using cold sterile low-potassium dextran fluid. Tissue dissociation and flow cytometry After EVLP, the distal regions of the lung tissues were collected for tissue dissociation and flow cytometry. After washed in PBS (2 X times) and HBSS for 15 min in total and compressed to remove liquid, the pieces were further diced with razor blades. The HBSS containing Dispase II (15 U/ml; Thermo Fisher, 17105041), 225 U/ml collagenase type I (Thermo Fisher, 17100017), 100 U/ml Dnase I (Sigma, DN25), and 1% Pen/Strep/ Fungizone were used to digest the pieces for 2 h at 37°C undergoing gentle rocking. The digested suspension was continuously filtered through gauze and 100 µm, 70 µm and 40 µm strainers. Red blood cells were removed using red blood cell lysis buffer (Sigma). For distinguishing resident and circulating immune cells, single cell suspensions were incubated with anti-CD45-AF488 (BioLegend, 304017) for 30 min at 4°C. Doublets and dead cells were excluded based on forward and side scatters and 7-AAD (BioLegend, 420404) or DAPI fluorescence. Circulating immune cells were sorted as live/CD45-AF488 + / CD45-AF647 + cells. Resident immune cells were sorted as live/CD45-AF488 + / CD45-AF647 - cells. Single-cell RNA sequencing Single cell sequencing was performed on a 10X Chromium instrument (10X Genomics). Briefly, live human lung cells were sorted and resuspended in 50 µl PBS with 0.04% BSA at 1,000 cells/µl and loaded onto a single lane into the ChromiumTM Controller to produce gel bead-in emulsions (GEMs). GEMs underwent reverse transcription for RNA barcoding and cDNA amplification. The library was prepped with the Chromium Single Cell 5’ Reagent Version 3 kit. The samples were sequenced using the HiSeq2500 (Illumina) in Rapid Run Mode. Single-cell RNA sequencing analysis FASTQ files were run through CellRanger v6.1.0 software with default settings for de-multiplexing, aligning reads with STAR software to GRCh38, and counting unique molecular identifiers (UMIs). Seurat package v5.0.2 in R v4.3.1 was used for downstream analysis. Low-quality cells were filtered (expressing 15% mitochondrial reads, or >7,000 unique gene counts). Principal component analysis was performed on log-normalized and scaled data using 2,000 variable genes. The top 25 principal component analyses were used for clustering and visualized using the UMAP algorithm in the Seurat package. The lists of DEGs were identified with a Model-based Analysis of Single-cell Transcriptomics (MAST) test. Pathway analysis of gene lists containing significantly differentially expressed genes were done with Ingenuity Pathway Analysis (Qiagen). Monocle trajectory analysis was performed using Monocle 3 by importing the counts from the Seurat object. RNA velocity was calculated using the scVelo v0.3.1 package in Python v3.11 and velocity calculations were overlaid on UMAP projections calculated in Seurat. For interrogating fibroblast and lymphocyte scRNAseq-data, we downloaded the public dataset GSE132771 and analyzed the interactions between fibroblast and lymphocyte by CellChat v2.1.2 package. Histology and immunofluorescence staining Human lung pieces were fixed in 4% PFA overnight at 4°C, washed with PBS four times for 30 min each at 4°C, and embedded in OCT after 30% sucrose incubation, then 8 µm sections were cut on a cryostat. Antigen retrieval (BRR2004CLX, Biocare Medical) was performed for 30 min at 95 °C. Slides were washed with 0.1% Tween-20 in PBS (PBST), blocked (3% donkey serum in PBST) for 1 h, and then incubated with primary antibodies overnight at 4°C. The following primary antibodies were used: Anti-CD44 (1:200, Biolegend, 103001), Anti-CD45 (1:200, Biolegend, 304058), Anti-CD3 (1:200, Abcam, ab16669), Anti-CD8 (1:200, SinoBiological, 10980-MM07), Anti-CD4 (1:200, Biolynx, 110301A), Anti-CD20 (1:200, Abcam, ab64088), Anti-CD138 (1:200, Sino Biological, 11429-R017-P), Anti-IgG (1:500, Sino Biological, SSA016), Anti-COL1A1 (1:2500, Proteintech, 67288-1-Ig). Slides were washed with PBST and then incubated for 1 h at room temperature in secondary antibodies diluted in PBST. The following secondary antibodies were used at 1:500: donkey anti-rabbit IgG Alexa Fluor 555 (Thermo Fisher, A-31572), donkey anti-mouse IgG Alexa Fluor 555 (Thermo Fisher, A-31570), donkey anti-rat Alexa Fluor 647 (Thermo Fisher, A21208), donkey anti-mouse Alexa Fluor 647 (Thermo Fisher, A31570), and donkey anti-goat Alexa Fluor 647 (Thermo Fisher, A21447). DAPI (0.2 µg/ml, Thermo Fisher, 1738176) was added for 5 min, then the slides were mounted. Co-staining images of CD44 or HSPA6 with immune cell marker X (such as CD45, CD3, CD4, CD8, CD20, and CD138) were processed in Cellprofiler (Broad Institute). Primary objects were generated using the Identify Primary Objects module for each florescent channel. Size and mean intensity filters were applied to remove debris. Double-positive cells were identified by overlapping primary objects with the Relate Objects module. Double-positive cells were used to generate a mask that was applied to both channels of the original image file. A mask was applied only to the CD44 (or HSPA6) channel to remove X - /CD44 + (or X - /HSPA6 + ) cells. Cell culture and GZMK treatment Freshly isolated lung fibroblasts from human lungs were cultured in DMEM/F-12 (Thermo Fisher, 11330032) with 10% FBS and 1% Pen/Strep. The medium was changed every 2-3 days and lung fibroblasts were maintained for no more than 5 passages. Human lung fibroblasts were treated with vehicle or 50 ng/mL recombinant granzyme K (Sino Biological, 19732-H08H) in DMEM without FBS for 48 h. SA-βgal staining Senescence assay was performed with the Senescence β-Galactosidase Staining Kit (Cell Signaling, 9860S) on human lung fibroblasts treated with GZMK as indicated above. Cells were rinsed with PBS and fixed with PFA for 15 min. Fresh β-galactosidase staining solution provided by senescence β-galactosidase staining kit was prepared according to manufacturer's instructions. After being washed with PBS for 2 times, cells were incubated with fresh staining solution at 37°C at least overnight in a dry incubator. Pictures were captured with EVOS M5000 (Thermo Fisher). Quantitative RT-PCR (qPCR) Total RNA was obtained using FastPure® RNA Isolation Kit (VazymE, RC112-01), following the manufacturers’ protocols. cDNA was synthesized from total RNA using the HiScript ® III RT SuperMix (VazymE, R323-01). Quantitative RT-PCR (qRT-PCR) was performed using the SYBR Green system (VazymE, Q711-02). Relative gene expression levels after qRT-PCR were defined using the ∆∆Ct method and normalizing to GAPDH. Primers were listed in table S10. Statistical analysis Statistical analysis was carried out using GraphPad Prism software. To compare the means between two groups, student’s t tests were used to generate P values. One-way analysis of variance was used to determine whether there were statistical differences among three groups followed by Fisher’s least significant difference (LSD) test for pairwise comparisons if the overall test was statistically significant. A P value less than 0.05 was considered significant. Declarations Written informed consent for research and publication was obtained from all subjects. Acknowledgments We thank patients and their families for donating lung specimens, which significantly contributed to this work. This study was supported by the National Natural Science Foundation of China, Grant No. 82070059 (JC); Major Program of Wuxi Medical Center of Nanjing Medical University, Grant No. WMCJ202301 (JC); National Natural Science Foundation of China, Grant No. 82370070 (CW); Major Project of Guangzhou National Laboratory, Grant No. GZNL2023A01003 (CW); High-level Innovative Research Institute, Grant No. 2021B0909050003 (CW), and Top Talent Support Program for young and middle-aged people of Wuxi Health Committee, Grant No. BJ2023021 (FG). Author contributions Conceptualization: C.W., J.C., F.G., M.H.; Methodology: F.G., Z.W., T.Y., C.W., Y.S., D.W., S.C., Y.H., J.S., Z.D.; Investigation: Z.W., C.W., T.Y., Y.S., D.W., S.C., Y.H., J.S., Z.D.; Visualization: C.W., Y.S., Z.W., T.Y.; Funding acquisition: J.C., C.W., F.G.; Project administration: F.G., J.C., C.W.; Supervision: C.W., F.G., J.C., M.H.; Writing – original draft: C.W., F.G., J.C.; Writing – review & editing: C.W., F.G., J.C., M.H., Z.W., T.Y. All authors approved and contributed to the final version of the manuscript. Competing interests Authors declare that they have no competing interests. References Gray, J. I. & Farber, D. L. Tissue-Resident Immune Cells in Humans. Annu Rev Immunol 40 , 195-220, doi:10.1146/annurev-immunol-093019-112809 (2022). Schenkel, J. M. & Masopust, D. Tissue-resident memory T cells. Immunity 41 , 886-897, doi:10.1016/j.immuni.2014.12.007 (2014). Narasimhan, H., Wu, Y., Goplen, N. P. & Sun, J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7500111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508347824,"identity":"e710be7a-5398-474d-b95d-ca409ac37693","order_by":0,"name":"Fei Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJCCDwwGNnL8DAwJIA5jAxE6GGcwGKQZSzaQpoXhcKLBASiPoBZ598MHGz4UpCUY3254upmHwUZ2wwHmZw/waTE8k5bYOMPAJs/szoG02zwMacYbDrCZG+DV0pBj/pjHIK3Y7EYCSMvhxA0HeNgk8Grpf2PY/MfgcOLmGWAt/wlrkZfIMWxmAGrZIAHWcoCwFgOJZ4mNPcBAlgA67OYcg2TjmYfZzPDb0p98sOHHH2BUzshJu/Gmwk6273jzM/y2HIAzeRKAXCDNjE89yJYGOJP9AE5Vo2AUjIJRMLIBACz3T9icmcEBAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Wuxi People's Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Fei","middleName":"","lastName":"Gao","suffix":""},{"id":508347825,"identity":"d1186d30-7943-46c8-9cc0-655c0cf9ea93","order_by":1,"name":"Zitao Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zitao","middleName":"","lastName":"Wang","suffix":""},{"id":508347826,"identity":"648d3936-3b87-40c2-83f6-a093d6e78053","order_by":2,"name":"Tao Yan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yan","suffix":""},{"id":508347827,"identity":"e49e053b-17ce-4527-a11c-989f3ac83286","order_by":3,"name":"Yanjing Su","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanjing","middleName":"","lastName":"Su","suffix":""},{"id":508347828,"identity":"ee637868-7697-43dd-b0d0-6f685e5bbb4e","order_by":4,"name":"Shuilian Chi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuilian","middleName":"","lastName":"Chi","suffix":""},{"id":508347829,"identity":"438aa692-09bc-4097-ab9d-175de5c9e2c5","order_by":5,"name":"Dong Wei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Wei","suffix":""},{"id":508347830,"identity":"d110dad7-a6a2-44fc-9252-54ea3a225533","order_by":6,"name":"Yanjun Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Huang","suffix":""},{"id":508347831,"identity":"d71de527-2b31-4998-bd75-d40d998df9a1","order_by":7,"name":"Jingbo Shao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jingbo","middleName":"","lastName":"Shao","suffix":""},{"id":508347832,"identity":"c205ee24-2253-4282-8a2d-41c2d41ea109","order_by":8,"name":"Zhenhang Dai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhenhang","middleName":"","lastName":"Dai","suffix":""},{"id":508347833,"identity":"4f4ffbad-ba51-4e8e-9b9b-d6a5f7c6b86f","order_by":9,"name":"Man Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Huang","suffix":""},{"id":508347834,"identity":"9b7c25d6-9760-448a-a6eb-1e0ed319cda2","order_by":10,"name":"Chaoqun Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chaoqun","middleName":"","lastName":"Wang","suffix":""},{"id":508347835,"identity":"51aedc73-c3da-42d8-8372-c09e5759014f","order_by":11,"name":"Jingyu Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-31 11:00:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7500111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7500111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90794280,"identity":"d7c473bc-0119-4ede-97ec-31ddf0659d1b","added_by":"auto","created_at":"2025-09-08 08:42:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparation of the resident vs. circulating immune cells isolated by EVLP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic approach for a combination of EVLP and scRNA-seq analysis of the resident (Res) and circulating (Cir) immune cells. (\u003cstrong\u003eB\u003c/strong\u003e) Human lung after EVLP. (\u003cstrong\u003eC\u003c/strong\u003e) FACS separation of the Res and Cir immune cells. (\u003cstrong\u003eD\u003c/strong\u003e) UMAP plots showing cell clusters in the Res (cell number: 9,095) and Cir (cell number: 7,312) immune populations. (\u003cstrong\u003eE\u003c/strong\u003e) Percentage of each cell type in Res and Cir immune cells. (\u003cstrong\u003eF\u003c/strong\u003e) Percentage of Res and Cir NK cells in the total NK population. (\u003cstrong\u003eG\u003c/strong\u003e) Marker expression in Res vs. Cir T cells. (\u003cstrong\u003eH\u003c/strong\u003e) Top activated and suppressed pathways in Res T cells, relative to Cir T cells, analyzed with IPA. (\u003cstrong\u003eI\u003c/strong\u003e) Violin plots showing enriched expression of \u003cem\u003eCTLA4\u003c/em\u003eand \u003cem\u003ePDCD1\u003c/em\u003e in Res T cells.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/076dfd199086fd7468e63b25.png"},{"id":90795991,"identity":"3513f8c0-54d6-4699-924b-c2f92c588f5d","added_by":"auto","created_at":"2025-09-08 08:58:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":568983,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident immune cells (CD44\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eB\u003c/strong\u003e) Image quantification of resident immune cell numbers per unit area in the alveoli of IPF vs. control lungs. (\u003cstrong\u003eC\u003c/strong\u003e) UMAP plots showing the cell clusters in the resident immune cells of IPF (cell number: 57,637) and control (cell number: 38,525) lungs. (\u003cstrong\u003eD\u003c/strong\u003e) Percentage of each cell type in the resident immune cells of IPF and control lungs. (\u003cstrong\u003eE\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident T cells (CD44\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eF\u003c/strong\u003e) Image quantification of resident T cell numbers per unit area in the alveoli of IPF vs. control lungs. (\u003cstrong\u003eG\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident CD4\u003csup\u003e+\u003c/sup\u003e T cells (CD44\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eH\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD44\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eI\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident B cells (CD44\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eJ\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTissue-resident plasma cells (CD44\u003csup\u003e+\u003c/sup\u003eCD138\u003csup\u003e+\u003c/sup\u003e) in the alveoli of IPF and control lungs. arrow: CD44\u003csup\u003e+\u003c/sup\u003eCD138\u003csup\u003e+\u003c/sup\u003e cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell landscape of the resident immune cells in IPF and control lungs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/7b238b5ca8bc53d9481056d6.png"},{"id":90794281,"identity":"455ee7f4-700e-4e3c-adf3-e4230bbaffbc","added_by":"auto","created_at":"2025-09-08 08:42:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":405272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of resident CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells in IPF lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e UMAP plots showing clusters in CD8\u003csup\u003e+\u003c/sup\u003e T cells of IPF (cell number: 10,774) and control (cell number: 7,979) lungs. (\u003cstrong\u003eB\u003c/strong\u003e) Feature plots showing the expression of \u003cem\u003eCD8A\u003c/em\u003e and \u003cem\u003eGZMK\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Percentage of GZMK+ T cells in total CD8\u003csup\u003e+\u003c/sup\u003e T cells. (\u003cstrong\u003eD\u003c/strong\u003e) Feature plots showing the expression of \u003cem\u003eTOX\u003c/em\u003e, \u003cem\u003eEOMES\u003c/em\u003e, \u003cem\u003eTIGIT\u003c/em\u003e, and \u003cem\u003eIL7R\u003c/em\u003e. (\u003cstrong\u003eE\u003c/strong\u003e) Top activated and suppressed pathways in GZMK\u003csup\u003e+\u003c/sup\u003e, relative to GZMK\u003csup\u003e-\u003c/sup\u003e T cells, analyzed with IPA. (\u003cstrong\u003eF \u003c/strong\u003eand\u003cstrong\u003e G\u003c/strong\u003e) Violin plots showing the expression of genes that indicating T cell activation. (\u003cstrong\u003eH\u003c/strong\u003e) Dot plots showing the expression of proinflammatory cytokines in GZMK\u003csup\u003e+\u003c/sup\u003e vs. GZMK\u003csup\u003e-\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells. (\u003cstrong\u003eI\u003c/strong\u003e) \u003cem\u003eGZMK\u003c/em\u003e expression in GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells, IPF vs. control. (\u003cstrong\u003eJ and K\u003c/strong\u003e) qPCR analysis of the expression of tissue fibrosis-associated genes and cell senescence marker \u003cem\u003eCDKN2A\u003c/em\u003e in GZMK-treated human lung fibroblasts. (\u003cstrong\u003eL \u003c/strong\u003eand\u003cstrong\u003e M\u003c/strong\u003e) Senescence-associated β galactosidase (SA-βgal) staining and quantification of GZMK-treated human lung fibroblasts.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/a0ba82efc54f50c06099f860.png"},{"id":90795042,"identity":"0047082c-6440-4fed-9aba-2b5ba7f67f42","added_by":"auto","created_at":"2025-09-08 08:50:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of resident CD4\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells in IPF lungs\\(A\u003c/strong\u003e) UMAP plots showing clusters in CD4\u003csup\u003e+\u003c/sup\u003e T cells of IPF (cell number: 13,288) and control (cell number: 6,976) lungs. (\u003cstrong\u003eB\u003c/strong\u003e) Feature plots showing the expression of \u003cem\u003eTOB1\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Dot plots showing the expression of quiescent factors in CD4\u003csup\u003e+\u003c/sup\u003e T cell subpopulations. (\u003cstrong\u003eD\u003c/strong\u003e) Top inhibited pathways in TOB1\u003csup\u003ehi\u003c/sup\u003e cluster, analyzed with IPA. (\u003cstrong\u003eE\u003c/strong\u003e) Feature plots showing the expression of markers in\u003cem\u003e \u003c/em\u003eHSP\u003csup\u003ehi\u003c/sup\u003e cluster. (\u003cstrong\u003eF\u003c/strong\u003e) Top activated pathways in HSP\u003csup\u003ehi\u003c/sup\u003e cluster, analyzed with IPA. (\u003cstrong\u003eG\u003c/strong\u003e) Percentage of HSP\u003csup\u003ehi\u003c/sup\u003e and TOB1\u003csup\u003ehi\u003c/sup\u003e cells in total CD4\u003csup\u003e+\u003c/sup\u003e T cells, respectively. (\u003cstrong\u003eH\u003c/strong\u003e) Accumulation of resident HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells in IPF. arrow: HSPA6\u003csup\u003e+\u003c/sup\u003eCD44\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e cells. (\u003cstrong\u003eI\u003c/strong\u003e) GO analysis of terms enriched in HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells of IPF lungs. (\u003cstrong\u003eJ\u003c/strong\u003e) DEG analysis of HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells in IPF vs. control.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/c75f58b79f630a72cde29306.png"},{"id":90795040,"identity":"9d2c2fda-be28-4d9c-a8de-618845c158dc","added_by":"auto","created_at":"2025-09-08 08:50:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":204212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of resident Treg cells in IPF lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e) UMAP plots showing clusters in Tregs of IPF (cell number: 1,770) and control (cell number: 714) lungs. (\u003cstrong\u003eB\u003c/strong\u003e) Feature plots showing the expression of \u003cem\u003eCD3E\u003c/em\u003e and \u003cem\u003eFOXP3\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Violin plots showing the expression of activation markers in Treg subsets. (\u003cstrong\u003eD\u003c/strong\u003e) Violin plots showing the expression of genes that are associated with suppressive functions of Tregs in Treg subsets of control and IPF lungs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/2073e8c8fa5a73897d1a1e71.png"},{"id":90794288,"identity":"93c679bf-d4c3-482f-b761-1bf65152af21","added_by":"auto","created_at":"2025-09-08 08:42:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":372137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of resident B and plasma cells in IPF lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e) UMAP plots showing clusters in resident B cells of IPF (cell number: 7,964) and control (cell number: 1,547) lungs. (\u003cstrong\u003eB\u003c/strong\u003e) Feature plots showing the expression of \u003cem\u003eMS4A1\u003c/em\u003e and \u003cem\u003eCD79A\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Dot plots showing the expression of B cell markers and immunoglobulin genes. (\u003cstrong\u003eD\u003c/strong\u003e) Clustering of mature B, transitional B, and plasma cells. (\u003cstrong\u003eE \u003c/strong\u003eto\u003cstrong\u003e G\u003c/strong\u003e) RNA velocity and Pseudotime trajectory analyses of B cell lineage transition. (\u003cstrong\u003eH\u003c/strong\u003e) Heatmap comparing the expression of signature genes in mature B, transitional B, and plasma cells. (\u003cstrong\u003eI\u003c/strong\u003e) Percentage of transitional B cells in total resident immune cells of IPF vs. control lungs. (\u003cstrong\u003eJ\u003c/strong\u003e) Top activated pathways in mature B cells of IPF vs. control lungs, analyzed with IPA. (\u003cstrong\u003eK\u003c/strong\u003e) DEG analysis of plasma cells in IPF vs. control lungs. (\u003cstrong\u003eL\u003c/strong\u003e) Immunofluorescence analysis of IgG expression in IPF lung.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/f407d7060c73bd001b43d39d.png"},{"id":90795046,"identity":"866b864c-af97-499f-952c-173006e71458","added_by":"auto","created_at":"2025-09-08 08:50:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":303166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteractions between the resident lymphocytes and fibroblasts in IPF lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e) UMAP plots showing lymphocyte and fibroblast clusters of IPF and control lungs. (\u003cstrong\u003eB\u003c/strong\u003e and\u003cstrong\u003e C\u003c/strong\u003e) Cellchat analysis of fibroblast subsets’ interactions with lymphocytes. (\u003cstrong\u003eD\u003c/strong\u003e) Immunofluorescence analysis of COLLAGEN, CD3, and CD20 in IPF lungs. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap showing fibroblasts’ interactions with lymphocytes, analyzed by Cellchat. (\u003cstrong\u003eF\u003c/strong\u003e to \u003cstrong\u003eH\u003c/strong\u003e) Violin plots showing the expression of the ligands involved in the Secreted Signaling, ECM-Receptor, and Cell-Cell Contact pathways in fibroblast subsets.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/ad32bdf89099f780985f8bbd.png"},{"id":90796158,"identity":"98995a26-0918-4365-a536-1ba98bdd7b2f","added_by":"auto","created_at":"2025-09-08 09:06:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3298599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/5a94bb6e-ecfe-468c-8dff-8e8cdb671e09.pdf"},{"id":90794285,"identity":"979f6030-c16c-4977-b4e2-6224fe3cfb96","added_by":"auto","created_at":"2025-09-08 08:42:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":617401,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/8c8bbe669c034970aab0702e.xlsx"},{"id":90794291,"identity":"b7835357-8827-4a7d-9602-84b1c3c0df79","added_by":"auto","created_at":"2025-09-08 08:42:50","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3389162,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7500111/v1/89cf725dcdb85575e498c1d7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Integrated EVLP and single-cell profiling uncovers aberrant activation of tissue-resident lymphocytes and pro-fibrotic GZMK⁺ CD8 T cells in IPF","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTissue-resident immune cells play intricate roles in maintaining tissue homeostasis, influencing local cellular dynamics, and shaping the microenvironment across various organs \u003csup\u003e1,2\u003c/sup\u003e. In barrier organs like the lung, these cells not only combat pathogens and conduct local immune surveillance but also significantly contribute to tissue integrity \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. Chronic inflammation and dysregulation of tissue-resident immune cells have been consistently linked to the pathogenesis of diverse chronic lung diseases, such as pulmonary fibrosis \u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a progressive lung disorder characterized by tissue inflammation and irreversible distal lung scarring, culminating in respiratory failure and mortality \u003csup\u003e8,9\u003c/sup\u003e. Immune cells contribute significantly to the progression of pulmonary fibrosis by promoting inflammation, tissue remodeling, and collagen deposition. Accumulating evidence has highlighted the substantial contribution of lymphocytes in pulmonary fibrosis \u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. Despite notable strides in unraveling immune cell functions in lung fibrosis, our understanding of the specific roles played by the resident immune cells in close interaction with lung structural cells remains limited. Experimental constraints hamper the isolation and study of human lung-resident immune cells. In this study, we employed ex-vivo human lung perfusion (EVLP) combined with single-cell RNA-sequencing (scRNA-seq) to unveil the intricacies of tissue-resident immune cells, uncovering aberrant activation of resident lymphocytes in IPF lungs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEVLP effectively distinguishes the resident from circulating immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe set out to establish whether a combination of EVLP and scRNA-seq can be used to define human lung-resident and circulating immune populations (Fig. 1A). We performed EVLP for the explanted human lung from a cadaveric donor without prior history of lung disease for one hour (Fig. 1B). During the EVLP, we conducted intraarterial (i.a.) anti-CD45 labeling by administering a CD45-conjugated antibody into the perfusate to differentiate circulating (i.a. anti-CD45\u003csup\u003e+\u003c/sup\u003e/CD45\u003csup\u003e+\u003c/sup\u003e) from tissue-resident immune cells (i.a. anti-CD45\u003csup\u003e-\u003c/sup\u003e/CD45\u003csup\u003e+\u003c/sup\u003e). Flow cytometry (FACS) analysis of the cell suspension after EVLP showed a distinct separation between the resident and circulating immune cells (Fig. 1C). To further define the two immune populations, we performed scRNA-seq on the FACS-isolated cells, respectively. Library preparation and sequencing were performed separately and then all the cells were merged for unsupervised clustering using uniform manifold approximation and projection (UMAP). The UMAP analysis showed 7 subsets in all the immune cells (Fig. 1D, S1A to S1C). Mast cells, tissue-resident myeloid cells, were exclusively found within the resident population (Fig. 1D and 1E). In mice, natural killer (NK) cells are predominantly found in the circulating population \u003csup\u003e14\u003c/sup\u003e. In agreement with this, the human circulating NK cells constituted most of the fraction in the entire NK cell group, with only 17.2 % of NK cells were from the resident population (Fig. 1D to 1F). Together, these data suggest that EVLP combined the labeling technique can successfully differentiate the resident from circulating immune cells.\u003c/p\u003e\n\u003cp\u003eT cells accounted for more than half of the resident immune population (Fig. 1E). To further validate our EVLP segregation and characterize tissue-resident T cells, we compared the transcriptomic profiles of the resident with circulating T cells. T cells from the resident fraction demonstrated classic markers of tissue residency, including the higher expression of \u003cem\u003eCD69\u003c/em\u003e,\u003cem\u003e\u0026nbsp;IL7R\u003c/em\u003e, and \u003cem\u003eCD44\u003c/em\u003e, along with the lack of expression of circulating markers, such as \u003cem\u003eS1PR1\u003c/em\u003e, \u003cem\u003eSELL\u003c/em\u003e, and \u003cem\u003eCCR7\u003c/em\u003e (Fig. 1G and S1D). Differentially expressed gene (DEG) analysis comparing T cells in the resident with circulating populations, coupled with ingenuity pathway analysis (IPA), showed pathways enriched in the resident T cells relevant to tissue retention, cell survival, and lower level of activation/proliferation (Fig. 1H). Consistent with these findings, the resident T cells demonstrated higher expression of inhibitory receptors \u003cem\u003eCTLA4\u003c/em\u003e and \u003cem\u003ePDCD1\u003c/em\u003e (Fig. 1I). These data suggest that tissue-resident lymphocytes are maintained in a quiescent state during homeostasis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic profiles of tissue-resident immune cells of IPF and control lungs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColocalization with the resident marker CD44 demonstrated a significant increase in tissue-resident immune cells (CD44\u003csup\u003e+\u003c/sup\u003e/CD45\u003csup\u003e+\u003c/sup\u003e) in IPF samples compared with control lungs (Fig. 2A and 2B). To further characterize the alterations in tissue-resident immune cells in IPF lungs, we isolated these cells from IPF patients (n = 7) undergoing lung transplantation and from age-matched control cadaveric donor lungs without a history of lung disease (n = 5) (table S1) using FACS after EVLP. We then performed scRNA-seq on the isolated resident immune cells, removing low-quality cells, correcting batch effects, and clustering the 96,162 recovered cells using UMAP. Using canonical immune lineage-defining markers, we defined 13 cell types, with all expected cell types present in both diseased and control lungs (Fig. 2C, S2A to S2C). The abundance of these cell types, expressed as a percentage of resident immune cells, was altered in IPF samples (Fig. 2D and S2D). T lymphocytes, particularly CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e subsets, constituted the majority of the resident immune population in both control and IPF lungs (Fig. 2D and S2D). Tregs significantly increased in IPF lungs (5.0% in IPF vs. 2.2% in control lungs) (Fig. 2D and S2D). Although the proportions of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells among resident immune cells did not change significantly, the overall increase in resident immune cells, particularly T cells (Fig. 2E and 2F), in IPF lungs indicated a substantial rise in the absolute numbers of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells. This observation was further validated by immunostaining analyses comparing resident CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in IPF lungs with those in control lungs (Fig. 2G and 2H). In IPF lungs, the proportion of resident B cells significantly increased (15.3% in IPF vs. 4.9% in control lungs), along with a rising trend in the proportions of plasma cells (2.7% in IPF vs. 0.6% in control lungs) (Fig. 2D and S2D), which were further confirmed by immunostaining analyses (Fig. 2I, 2J, and S2E).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpansion of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells with age-associated markers in IPF, displaying profibrotic potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better characterize CD8\u003csup\u003e+\u003c/sup\u003e T cells in IPF lungs, we segregated all CD8\u003csup\u003e+\u003c/sup\u003e T cells from both IPF and control samples and re-clustered them. The UMAP analysis demonstrated two distinct subpopulations in CD8\u003csup\u003e+\u003c/sup\u003e T cells, granzyme K\u003csup\u003e\u0026nbsp;\u003c/sup\u003epositive (GZMK\u003csup\u003e+\u003c/sup\u003e) and granzyme K\u003csup\u003e\u0026nbsp;\u003c/sup\u003enegative (GZMK\u003csup\u003e-\u003c/sup\u003e) subsets (Fig. 3A, 3B, and S3A). The fraction of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells significantly increased by approximately 50% in IPF compared with control lungs (Fig. 3C). GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T subset was previously identified as a conserved feature of aging immune system in both mice and humans, named as age-associated (Taa) CD8\u003csup\u003e+\u003c/sup\u003e T cells, with GZMK as one of the most specific markers \u003csup\u003e15\u003c/sup\u003e. CD8\u003csup\u003e+\u003c/sup\u003e Taa cells exhibited unique features compared to conventional T effector memory cells, including high expression of the transcription factors \u003cem\u003eTOX\u003c/em\u003e and \u003cem\u003eEOMES\u003c/em\u003e, as well as checkpoint genes like \u003cem\u003eTIGIT\u003c/em\u003e, \u003cem\u003eLAG3\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, \u003cem\u003eHAVCR2\u003c/em\u003e and \u003cem\u003ePDCD1\u003c/em\u003e (PD-1), coupled with a low level of \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eTCF7\u003c/em\u003e expression. In line with previous findings, our scRNA-seq data also demonstrated a significant enrichment of these markers in GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. 3D and S3B). In addition, GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in our dataset also exhibited a relatively lower expression of \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eTCF7\u003c/em\u003e than the GZMK\u003csup\u003e-\u003c/sup\u003e subset (Fig. 3D and S3B). These findings suggest that GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells are Taa-like cells. It has been shown that the development of CD8\u003csup\u003e+\u003c/sup\u003e Taa cells was driven by extrinsic factors of an aged environment. Of note, there was no significant difference in age between IPF and control lung donors (Fig. S3C). Together, these data suggest that IPF lungs, similar to aged organs, provide an environment that promotes the expansion of CD8\u003csup\u003e+\u003c/sup\u003e Taa cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo gain insight into the potential functions of CD8\u003csup\u003e+\u003c/sup\u003e T subsets, we generated DEGs by comparing GZMK\u003csup\u003e+\u003c/sup\u003e vs. GZMK\u003csup\u003e-\u003c/sup\u003e subsets (table. S2). IPA analysis of the DEGs indicated T cell activation in the GZMK\u003csup\u003e+\u003c/sup\u003e population, marked by activation of T cell receptor signaling, interferon gamma signaling, and CD28 family costimulation, along with inhibition of PD-1/PDL1- and CTLA4-mediated T cell inhibitory pathways (Fig. 3E). Consistent with IPA results, GZMK\u003csup\u003e+\u003c/sup\u003e subset demonstrated higher expression of a set of human leukocyte antigen (HLA) class II molecules (Fig. 3F) and the activation-induced proteins CD137 (\u003cem\u003eTNFSR9\u003c/em\u003e) and CD137L (\u003cem\u003eTNFS9\u003c/em\u003e) (Fig. 3G), indicating T cell activation. Of note, GZMK\u003csup\u003e+\u003c/sup\u003e subset exhibited a lower expression of GZMB (Fig. S3D), a granzyme regulating T cell antiviral response. In contrast to GZMB\u0026apos;s ability to cleave caspases for inducing apoptosis in viral infected cells, GZMK was characterized as a proinflammatory component within the granzyme family \u003csup\u003e16,17\u003c/sup\u003e. Additionally, the expression levels of proinflammatory cytokines (\u003cem\u003eCCL5\u003c/em\u003e, \u003cem\u003eCCL4L2\u003c/em\u003e, \u003cem\u003eTNF\u003c/em\u003e, etc.) were higher in GZMK\u003csup\u003e+\u003c/sup\u003e subset, while there was a lower expression of PRF1 (encoding perforin 1, a pore forming cytolytic protein) (Fig. 3H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo delve deeper into the changes observed in the GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells within IPF lungs, we conducted a DEG analysis of this subset in IPF compared with control lungs. Our analysis revealed a significant increase in the expression of \u003cem\u003eGZMK\u003c/em\u003e and T cell activation markers, such as \u003cem\u003eTNFSF9\u003c/em\u003e and HLA class II molecules (Fig. 3I, S3E, and table S3), indicating that IPF GZMK\u003csup\u003e+\u003c/sup\u003e subset exhibited heightened activity and pro-inflammatory profile, compared with the control counterpart. To examine the effect of GZMK protein on IPF progression, we treated human lung fibroblasts with recombinant GZMK protein. qPCR analysis demonstrated that GZMK activated the expression of tissue fibrosis-associated proteins, such as \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eACTA2\u003c/em\u003e, and \u003cem\u003eTGFB\u003c/em\u003e (Fig. 3J). Histologic and scRNA-seq analyses of IPF lungs have consistently revealed the presence of senescent fibroblasts, marked by p16 (\u003cem\u003eCDKN2A\u003c/em\u003e) \u003csup\u003e18-20\u003c/sup\u003e. A recent study showed that p16 promoted collagen deposition in pulmonary fibrosis \u003csup\u003e21\u003c/sup\u003e. In line with these findings, we also observed a significant upregulation of \u003cem\u003eCDKN2A\u003c/em\u003e expression in GZMK-treated fibroblasts (Fig. 3K). Fibroblasts treated with GZMK also exhibited senescence-associated \u0026beta;-galactosidase activity (Fig. 3L and 3M). These findings suggested a close association between the expansion of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells and disease progression in IPF lungs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncreased pro-inflammatory HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells in fibrotic lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next focused on CD4\u003csup\u003e+\u003c/sup\u003e T cells, which constituted the largest subset among resident immune cells in IPF (Fig. 2D). UMAP clustering analysis of all CD4\u003csup\u003e+\u003c/sup\u003e T cells of IPF and control samples revealed 4 distinct clusters (Fig. 4A and S4A). DEG analysis produced a list of signature genes for each subset (table S4), cluster 1 (C1) was enriched for the expression of Th1-related genes, including \u003cem\u003eIFNG\u003c/em\u003e (Fig. S4B). Cells in cluster 2 (C2) were marked by a number of circulating markers, such as \u003cem\u003eSELL\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;S1PR1\u003c/em\u003e, suggesting that they were recirculating T cells between tissue and blood (Fig. S4C), as previously reported \u003csup\u003e22\u003c/sup\u003e. Cells of cluster 3 (C3) highly expressed a number of cell quiescent factors, including \u003cem\u003eTOB1\u003c/em\u003e, a negative regulator of T cell proliferation and cytokine production\u003csup\u003e23\u003c/sup\u003e, suggesting a quiescent state (Fig. 4B and 4C). Consistently, IPA pathway analysis of the signature genes predicted C3 as quiescent T cells with inhibited pathways regarding T cell activation and proliferation (Fig. 4D). Cluster 4 (C4) was enriched for the expression of heat shock proteins (\u003cem\u003eHSPs\u003c/em\u003e, inflammatory mediators \u003csup\u003e24,25\u003c/sup\u003e) and \u003cem\u003eTNF\u003c/em\u003e (Fig. 4E), indicating a pro-inflammatory phonotype. Consistently, IPA pathway analysis of the signature genes of C4 produced top activated pathways about tissue inflammation (cellular response to heat stress; TNFR2, iNOS and IL-1 signaling) and T cell activation (CD137 and CD27 signaling) (Fig. 4F). The fraction of pro-inflammatory CD4\u003csup\u003e+\u003c/sup\u003e T cells (HSP\u003csup\u003ehi\u003c/sup\u003e) increased, while the quiescent (TOB1\u003csup\u003ehi\u003c/sup\u003e) subset decreased in IPF, compared with control lungs (Fig. 4G). In line with the scRNA-seq data, immunostaining analysis revealed an accumulation of resident\u0026nbsp;HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells within the alveoli of IPF lungs, particularly in the fibrotic regions (Fig. 4H). There were no significant changes in the fraction of Th1 or recirculating subsets between IPF and control lungs (Fig. S4D).\u003c/p\u003e\n\u003cp\u003eTo further characterize HSP\u003csup\u003ehi\u003c/sup\u003e subset in IPF lungs, we segregated the subset and performed DEG analysis by comparing IPF vs. control HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells (table. S5). Gene Ontology (GO) analysis of the DEGs revealed that IPF HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells were enriched in GO terms associated with tissue inflammation, T cell activation, proliferation, and survival. These included terms such as \u0026ldquo;Regulation of chronic inflammation,\u0026rdquo; \u0026ldquo;Positive regulation of T cell proliferation,\u0026rdquo; and \u0026ldquo;Negative regulation of apoptosis\u0026rdquo; (Fig. 4I). Consistent with the GO analysis, IPF HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells demonstrated increased expression of genes associated with pro-inflammation/pro-activation (\u003cem\u003eTNF\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e, \u003cem\u003eREL\u003c/em\u003e, etc.) and anti-cell death (\u003cem\u003eBCL2\u003c/em\u003e, \u003cem\u003ePIM3\u003c/em\u003e, B\u003cem\u003eAG3\u003c/em\u003e, etc.) (Fig. 4J; table S5). In contrast, the expression of anti-inflammation/anti-activation (\u003cem\u003eZFP36\u003c/em\u003e, \u003cem\u003eSOCS1\u003c/em\u003e, \u003cem\u003eTOB1\u003c/em\u003e, etc.) and pro-cell death (\u003cem\u003eBAX\u003c/em\u003e, \u003cem\u003eTXNIP\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, etc.) genes were relatively lower in IPF HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells, compared with the control counterparts (Fig. 4J; table S5). These findings suggest that HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells of IPF lungs exhibit an enhanced pro-inflammatory phenotype with increased cell viability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccumulation of Tregs with potentially impaired suppressive function in IPF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of Tregs in the resident immune cells significantly increased in IPF lungs (Fig. 2D and S2D), in agreement with the previous findings \u003csup\u003e26\u003c/sup\u003e. UMAP clustering analysis of Tregs in IPF and control lungs revealed two distinct subsets characterized by high and low expression of \u003cem\u003eFOXP3\u003c/em\u003e, a master regulator of Tregs (Fig. 5A, 5B, and S5A). DEG analysis of the two subsets demonstrated that the FOXP3\u003csup\u003ehi\u003c/sup\u003e population highly expressed markers of activated Tregs, such as \u003cem\u003eIL2RA\u003c/em\u003e (encoding CD25), \u003cem\u003eICOS\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, and HLA class II molecules, while the FOXP3\u003csup\u003elo\u003c/sup\u003e population with \u003cem\u003eTGFB1\u003c/em\u003e expression was resting Tregs (Fig. 5C and S5B; table S6), as previously described \u003csup\u003e27-29\u003c/sup\u003e. There was no significant difference in the ratio of the two subsets between control and IPF lungs (Fig. S5C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTregs are essential for preventing autoimmunity and limiting chronic inflammatory diseases \u003csup\u003e30\u003c/sup\u003e. To assess the potential suppressive functions of Tregs in IPF, we examined the expression of genes associated with suppression in IPF Tregs compared with control counterparts. FOXP3 expression programs Treg development, and confers suppressive functions on conventional T cells \u003csup\u003e31\u003c/sup\u003e. Our scRNA-seq data showed a downregulated expression of \u003cem\u003eFOXP3\u003c/em\u003e in both resting and activating Treg subsets of the IPF lungs (Fig. 5D). CTLA-4 is constitutively expressed and required for the Treg\u0026rsquo;s immune suppressive activity by endowing dendritic cells with immune tolerance properties. Loss of CTLA-4 in Tregs impairs the immune suppressive function and leads to abnormal activation and expansion of conventional T cells \u003csup\u003e32\u003c/sup\u003e. IL2RA also plays a critical role in maintaining the suppressive function of Tregs \u003csup\u003e33\u003c/sup\u003e. In line with the \u003cem\u003eFOXP3\u003c/em\u003e expression result, \u003cem\u003eCTLA-4\u003c/em\u003e and \u003cem\u003eIL2RA\u003c/em\u003e were downregulated in both resting and activating Tregs of the IPF samples, compared with Treg subsets in control lungs, respectively (Fig. 5D). Collectively, our results indicate a dysfunction in the suppressive capacity of Tregs in IPF. Indeed, studies have demonstrated that Treg-mediated suppression of conventional T cell activation is compromised in IPF lungs \u003csup\u003e34\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA transitional B cell subset emerged in IPF lungs, accompanied by an increase in plasma cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral studies have implicated B cells in IPF progression \u003csup\u003e35-37\u003c/sup\u003e. Our scRNA-seq and immunostaining data showed that B cells were increased in the lungs of IPF patients, compared with the controls (Fig. 2D, 2I, and S2D). Cell clustering analysis of all B cells of the IPF and control lungs identified two distinct subsets, characterized by the presence or absence of \u003cem\u003eCD79A\u003c/em\u003e expression (Fig. 6A and 6B). To further characterize the two subsets, we generated a list of DEGs by comparing the CD79A\u003csup\u003e-\u003c/sup\u003e with CD79A\u003csup\u003e+\u003c/sup\u003e subsets. We found that the CD79A\u003csup\u003e-\u003c/sup\u003e cells demonstrated a relatively higher expression of immunoglobulin genes, such as \u003cem\u003eIGLC1\u003c/em\u003e, \u003cem\u003eIGLC2\u003c/em\u003e, and \u003cem\u003eIGHG1\u003c/em\u003e, suggesting that they were transitional B cells in the process of becoming plasma cells (Fig. 6C; table S7). The CD79A\u003csup\u003e+\u0026nbsp;\u003c/sup\u003ecells were enriched for B cell markers, including \u003cem\u003eCD37\u003c/em\u003e, \u003cem\u003eCD79A\u003c/em\u003e, and \u003cem\u003eCD19\u003c/em\u003e, indicating that they were mature B cells (Fig. 6C; table S7). To validate this lineage transition, we combined plasma cells, the differentiated B cells, with the two B cell subsets and conducted RNA velocity and Monocle trajectory analyses, consistently demonstrating a clear differentiation pathway from mature B cells to plasma cells through transitional B cells (Fig. 6D to 6F, and S6A). Visualization of gene expression along the B cell differentiation trajectory showed that mature B cell markers, such as \u003cem\u003eCD19\u003c/em\u003e and \u003cem\u003eMS4A1\u003c/em\u003e (CD20), were gradually lost as mature B cells differentiated into transitional cells (Fig. 6G and 6H). The transitional B cells were marked by high expression of \u003cem\u003eTNN\u003c/em\u003e, encoding the extracellular matrix protein Tenascin N (Fig. 6G and S6B). As differentiation progressed, the transitional B cells gave way to plasma cells, as all B cell markers were lost with the emergence of plasma cell markers, including \u003cem\u003eIGKC\u003c/em\u003e and \u003cem\u003eXBP1\u0026nbsp;\u003c/em\u003e(Fig. 6G and 6H). Of note, the expression of the anti-apoptotic gene \u003cem\u003eMCL1\u003c/em\u003e reached its highest level in plasma cells\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 6H), a factor crucial for ensuring the survival of these cells \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe proportion of transitional B cells in IPF samples increased, both in terms of their percentage among the resident immune cells and their ratio to the mature B cells. (Fig. 6I and S6C). These data suggest that B cells in IPF lungs were activated to transition into plasma cells. To assess the alterations in the mature B cells of IPF lungs, we conducted the DEG analysis by comparing IPF mature B cells with those from control lungs (table S8). IPA pathway analysis of the DEGs predicted hyperactivation of the pathways that promote B cell activation, differentiation, and antibody production (such as RHO GTPase cycle, Phospholipase C, TEC, PI3K, and BCR signaling) in IPF mature B cells (Fig. 6J). Aligned with IPA findings, our scRNA-seq and immunostaining data revealed a substantial rise in plasma cell abundance within IPF lungs (Fig. 2D, 2J, S2D, and S2E). Additionally, DEG analysis comparing IPF plasma cells with those in control lungs revealed significantly elevated expression of immunoglobulin genes in IPF plasma cells (Fig. 6K and table S9), suggesting heightened activity in antibody production. Immunostaining analysis of IgG further confirmed elevation of IgG expression in the fibrotic tissue (Fig.6L).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAltered interactions between the stromal niche and the resident lymphocytes in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIPF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have previously shown that lung fibroblasts create a niche that maintains the homeostasis of resident lymphocytes. Dysregulation of this niche can lead to the expansion of lymphocytes, contributing to chronic lung diseases \u003csup\u003e5\u003c/sup\u003e. Here, we sought to examine the interactions between a fibrotic stromal niche and the resident lymphocytes in IPF lungs. To this end, we re-analyzed the scRNA-seq data of IPF stromal cells \u003csup\u003e39\u003c/sup\u003e, and identified alveolar fibroblasts, along with two pathological subsets, the HAS\u003csup\u003ehi\u003c/sup\u003e and CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts, as previously reported \u003csup\u003e18,39\u003c/sup\u003e. We then merged these three stromal subsets with our resident lymphocytes to perform CellChat analysis \u003csup\u003e40\u003c/sup\u003e, a tool for quantitatively analyzing ligand-receptor pairs, to evaluate the stromal niche\u0026apos;s capacity to interact with lymphocytes in IPF compared with control lungs (Fig. 7A, S7A, and S7B). CellChat analysis showed that IPF alveolar fibroblasts exhibited a significant increase in both the number and intensity of interactions with lymphocytes, compared with the alveolar fibroblasts in control lungs (Fig. 7B and 7C). Furthermore, both of the two IPF specific pathological stromal subsets showed an increased interaction with lymphocytes in both number and strength, compared with control alveolar fibroblasts (Fig. 7B and 7C). Of note, in IPF, the CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblast subset exhibited the strongest effects on lymphocytes, as reflected by the increased interaction number and intensity (Fig. 7B and 7C). Consistent with the CellChat analysis, immunostaining analysis demonstrated that CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts, marked by COLLAGEN \u003csup\u003e39\u003c/sup\u003e, were closely associated with B and T lymphocytes in the fibroblastic foci (Fig. 7D). These data suggest an altered stromal niche in IPF that contributes to the abnormal activation of lymphocytes.\u003c/p\u003e\n\u003cp\u003eTo further dissect the underlying molecular pathways of stromal-immune interactions in IPF, we categorized the interaction pathways into 3 types, including Secreted Signaling, Extracellular Matrix (ECM) Receptor, and Cell-Cell Contact (Fig. 7E). Among the Secreted Signaling mode, IGFBP, pleiotrophin (PTN), GALECTIN, midkine (MDK), and GDF signaling pathways were the most prominent interactions observed between CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts and lymphocytes. These pathways have been shown to contribute to chronic tissue inflammation and/or support lymphocyte survival, activation, and proliferation \u003csup\u003e41-44\u003c/sup\u003e. The most notable interactions observed between HAS\u003csup\u003ehi\u003c/sup\u003e fibroblasts and lymphocytes involved CXCL (chemoattractant cytokines) and insulin-like growth factor (IGF) signaling (Fig. 7E). This suggests that this stromal subset not only attracts lymphocytes to the lungs but also supports their expansion. In line with these findings, CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts exhibited significantly elevated expression levels of \u003cem\u003eIGFBP3\u003c/em\u003e, \u003cem\u003ePTN\u003c/em\u003e, \u003cem\u003eLGALS9\u003c/em\u003e (\u003cem\u003eGALECTIN9\u003c/em\u003e), \u003cem\u003eMDK\u003c/em\u003e, and \u003cem\u003eGDF15\u003c/em\u003e. Additionally, \u003cem\u003eCXCL12\u003c/em\u003e and \u003cem\u003eIGF1\u003c/em\u003e were found to be more enriched in HAS\u003csup\u003ehi\u003c/sup\u003e fibroblasts. (Fig. 7F and S7C). As previously reported, CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts produced significantly high amount of ECM protein (Fig. 7G and S7C). Consistently, COLLAGEN, fibronectin (FN1), LAMININ, and thrombospondin (THBS) signaling were the highest ranked interactions between CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts and lymphocytes in the ECM-Receptor mode (Fig. 7E). These ECM proteins not only facilitate lymphocyte adhesion but also trigger signaling pathways in lymphocytes, promoting their activation, proliferation, and cytokine production \u003csup\u003e45-47\u003c/sup\u003e. CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts also directly interacted with lymphocytes, prominently activating the CD99 and ADGRE (CD97) signaling pathways in the Cell-Cell Contact mode (Fig. 7E), known for their roles in lymphocyte activation \u003csup\u003e48,49\u003c/sup\u003e. This is in line with the high expression of \u003cem\u003eCD99\u003c/em\u003e and genes encoding CD97-interacting partners, including \u003cem\u003eLPAR1\u003c/em\u003e, \u003cem\u003eITGAV\u003c/em\u003e, and \u003cem\u003eTHY1\u003c/em\u003e in CTHRC1\u003csup\u003ehi\u003c/sup\u003e fibroblasts (Fig. 7H and S7C). Collectively, these data suggest that the stromal niche in IPF regulates lymphocytes through secreted factors, ECM components, and direct cell-cell interactions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough some mechanisms regarding the involvement of tissue-resident immune cells in modulating tissue homeostasis and remodeling have been identified through animal model studies, much remains unknown about how human lung-resident immune cells regulate the onset and progression of pulmonary fibrosis. In this study, we provide a single-cell landscape of the human lung-resident immune cells, with a focus on lymphocyte populations. We identify several previously unrecognized aberrations in IPF lung-resident lymphocytes, having potential functions in driving chronic inflammation and tissue fibrosis. Additionally, the fibrotic stromal niche supports resident lymphocytes, creating a feedback loop that exacerbates fibrosis.\u003c/p\u003e\n\u003cp\u003eHere, our data reveal substantial disparities between the human lung-resident immune population and the circulating immune population, both in terms of cellular composition and expression profiles. Therefore, the failure to differentiate between resident and circulating immune cells during analysis could result in misinterpretation or the concealment of vital information. In healthy lungs, tissue-resident T lymphocytes tend to be more quiescent, exhibiting suppressed pathways related to T cell activation. They also express higher levels of inhibitory receptors, such as CTLA4 and PD-1. The finding of elevated PD-1 expression in human lung-resident T cells is in line with previous report \u003csup\u003e50\u003c/sup\u003e. Additionally, pro-survival pathways are activated while cell death pathways are inhibited in the resident T lymphocytes of healthy human lung tissue. From a technical perspective, maintaining a stable population of lung-resident T cells makes sense as it enables the organism to effectively combat potential lung infections while minimizing the risk of tissue inflammation. Indeed, previous studies in animal models have shown that blocking the PD-L1/PD-1 pathway can lead to the expansion of lung-resident T lymphocytes, contributing to tissue inflammation \u003csup\u003e51\u003c/sup\u003e. It\u0026rsquo;s noteworthy that the combination of EVLP with single-cell technologies provides an innovative approach for studying resident immune populations in human lungs.\u003c/p\u003e\n\u003cp\u003eThe role of CD8\u003csup\u003e+\u003c/sup\u003e T cells in pulmonary fibrosis is multifaceted. With cytotoxic activity, CD8\u003csup\u003e+\u003c/sup\u003e T cells can directly kill and clean infected or damaged cells, contributing to tissue repair. One the other hand, they can promote inflammation by secreting cytokines that exacerbate pulmonary fibrosis \u003csup\u003e12,52,53\u003c/sup\u003e. In this study, we demonstrate that GZMK-expressing CD8\u003csup\u003e+\u003c/sup\u003e T cells with higher expression of pro-inflammatory cytokines and pro-fibrotic potential preferentially accumulated in IPF lungs, sharing similar expression profile to the age-associated GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (Taa) that were previously identified in aged mice and humans \u003csup\u003e15\u003c/sup\u003e. The old environment of the organs promotes the development of CD8\u003csup\u003e+\u003c/sup\u003e Taa cells. Considering that the age of control and IPF donors were matched in our study, IPF lungs might provide an age-related niche for driving the expansion of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells. Indeed, the Cellchat analysis confirmed strong interaction of the pathological stromal niche with CD8\u003csup\u003e+\u003c/sup\u003e T cells in IPF lungs. In turn, GZMK from CD8\u003csup\u003e+\u003c/sup\u003e T cells can promote the formation of the pathological stromal niche by enhancing cell senescence and collagen production. Therefore, the GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells and pathological fibroblasts form a positive feedback loop to exacerbate tissue fibrotic remodeling. Our findings offer an explanation for the clinical observation that the accumulation of CD8\u003csup\u003e+\u003c/sup\u003e T cells in the lungs directly correlates with the severity of pulmonary fibrosis in patients \u003csup\u003e54\u003c/sup\u003e. Furthermore, IPF predominantly affects older adults, potentially due to the excessive accumulation of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in their lungs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar to CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells also accumulate in the lung tissue of patients with IPF. CD4\u003csup\u003e+\u003c/sup\u003e T cells promote pulmonary fibrosis in animal models by inducing collagen deposition in part via IL-17A \u003csup\u003e10,55\u003c/sup\u003e. Furthermore, the imbalanced ratio of CD4\u003csup\u003e+\u003c/sup\u003e T cell subsets in IPF has been reported to be associated with lung function, highlighting the potential distinct roles of the subtypes in disease progression \u003csup\u003e56\u003c/sup\u003e. Here, we report an increased proportion of pro-inflammatory subtype and a decreased proportion of quiescent subtype in IPF lungs. Further studies will be required to determine the impacts of different CD4\u003csup\u003e+\u003c/sup\u003e T cell subtypes on fibrotic tissue remodeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe expansion of B cells and plasma cells has been consistently linked to pulmonary fibrosis \u003csup\u003e37,57\u003c/sup\u003e. Here, our results provide new insights into the intricate and dynamic changes within the lung-resident B cell lineage in pulmonary fibrosis. In IPF lungs, B cells remain persistently activated, leading to their differentiation into plasma cells that produce a significant amount of immunoglobulins.\u0026nbsp;Supporting the pathogenic role of B cells, CD19, a positive regulator of B cell activation, exacerbates lung fibrosis in the murine bleomycin model\u0026nbsp;\u003csup\u003e58\u003c/sup\u003e. Furthermore, depleting plasma cells can reduce lung fibrosis levels in the animal model\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e. A recent study has revealed that the accumulation of immunoglobulins during aging impairs adipose tissue function and contributes to tissue fibrosis\u0026nbsp;\u003csup\u003e59\u003c/sup\u003e. These findings suggest that targeting the B cell lineage could be a promising therapeutic strategy for IPF.\u003c/p\u003e\n\u003cp\u003eTregs play a crucial role in immune regulation by suppressing lymphocytes and preventing overactive immune responses. The abnormal buildup and activation of lymphocytes in IPF lungs may be partly attributed to the impaired Treg function. Moreover, the pathological stromal niche in IPF creates a conducive environment for lymphocyte expansion within the lung. Together, our data offer a comprehensive portrait of the resident lymphocytes in both human healthy and IPF lungs. We have identified specific markers, pathways, and programs that define the aberrant lymphocytes in IPF.\u003c/p\u003e\n\u003cp\u003eSeveral limitations in our study are worth noting. First, IPF samples were collected from the lungs at the time of organ transplantation, representing the end stage of disease progression. It remains unclear whether similar changes in cell type composition and gene expression profiles occur at the early stages. Second, these tissues were exclusively collected from the distal lung fragments, so our current data may not accurately reflect the resident immune cells in the proximal airways. Third, although we utilized bioinformatics techniques to characterize resident lymphocytes in IPF, extensive experimental validation is still needed. Future work will be required to understand why abnormal resident immune cells appear in IPF and how various types of resident immune cells influence disease progression.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was designed to characterize human lung-resident immune cells and to determine whether there are any alterations in IPF samples. An additional goal was to define the interactions between the resident immune cells and the stromal niche. Deidentified human lung samples were obtained from brain-dead donors and IPF patients undergoing lung transplantation. The samples we used for EVLP, flow cytometry, and sequencing were unblinded. Samples for scRNA-seq analysis were unblinded. Tissue sections for imaging were blinded before processing and quantification by multiple independent researchers and were unblinded for statistical analysis and graphing. Our descriptions of independent technical and biological replicates are included in the figure legends. When technical or processing errors were recorded, we excluded data from those samples from our final quantification and statistical analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman lung samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human tissue were approved by the affiliated Wuxi People\u0026rsquo;s Hospital of Nanjing Medical University Institutional Review Board, under the approval number 2020(374). The control lungs were obtained from 5 brain-dead donors that were rejected for clinical use. IPF lungs were taken from the 7 patients undergoing lung transplantation for pulmonary fibrosis. The age and sex of tissue donors used in scRNA-seq were listed in table S1. \u0026nbsp; \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEVLP and circulating immune cells labeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EVLP technique used in this study was modified from the previous Toronto EVLP technique (Cypel M, et al. Normothermic ex vivo lung perfusion in clinical lung transplantation. N Engl J Med 2011). Briefly, the donor lung was prepared and connected with EVLP device with the pulmonary vein (PV) left open. The EVLP circuit was driven by a centrifugal pump, perfusate from the PV gathered in the perfusate pool, and was driven through a leukocyte filter to a Euroset\u0026reg; Trilly membranous oxygenator (Italy, EUROSET) which connected to balanced gas (6% oxygen, 8% carbon dioxide). After deoxygenation and heated, perfusate was driven to the pulmonary artery (PA). Two liters of Steen\u003csup\u003e\u0026reg;\u003c/sup\u003e solution were used to prime the circuit. The lungs were ventilated using a protective ventilation mode with an effective tidal volume of 4.8 ml/kg body weight for the right lobes or 3.2 ml/kg for the left lobes at 10 times/min. The time of starting ventilation was set as 0 h. One-hour EVLP time was adopted for each lung. Before injecting antibodies, the leukocyte filter was bypassed. Then, 1.5 liters of 37\u0026deg;C perfusate were replaced in the circuit. To labeling circulating immune cells, 12.5 \u0026micro;g of anti-CD45-AF647 antibody (BioLegend, 304018) was diluted into 10 ml PBS and injected through PA. The labeling process was 30 min. After that, the lungs were disconnected from the EVLP and re-flushed using cold sterile low-potassium dextran fluid. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue dissociation and flow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter EVLP, the distal regions of the lung tissues were collected for tissue dissociation and flow cytometry. After washed in PBS (2\u0026nbsp;X times) and HBSS for 15 min in total and compressed to remove liquid, the pieces were further diced with razor blades. The HBSS containing Dispase II (15 U/ml; Thermo Fisher, 17105041), 225 U/ml collagenase type I (Thermo Fisher, 17100017), 100 U/ml Dnase I (Sigma, DN25), and 1% Pen/Strep/ Fungizone were used to digest the pieces for 2 h at 37\u0026deg;C undergoing gentle rocking. The digested suspension was continuously filtered through gauze and 100 \u0026micro;m, 70 \u0026micro;m and 40 \u0026micro;m strainers. Red blood cells were removed using red blood cell lysis buffer (Sigma). For distinguishing resident and circulating immune cells, single cell suspensions were incubated with anti-CD45-AF488 (BioLegend, 304017) for 30 min at 4\u0026deg;C. Doublets and dead cells were excluded based on forward and side scatters and 7-AAD (BioLegend, 420404) or DAPI fluorescence. Circulating immune cells were sorted as live/CD45-AF488\u003csup\u003e+\u003c/sup\u003e/ CD45-AF647\u003csup\u003e+\u003c/sup\u003e cells. Resident immune cells were sorted as live/CD45-AF488\u003csup\u003e+\u003c/sup\u003e/ CD45-AF647\u003csup\u003e-\u003c/sup\u003e cells.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle cell sequencing was performed on a 10X Chromium instrument (10X Genomics). Briefly, live human lung cells were sorted and resuspended in 50 \u0026micro;l PBS with 0.04% BSA at 1,000 cells/\u0026micro;l and loaded onto a single lane into the ChromiumTM Controller to produce gel bead-in emulsions (GEMs). GEMs underwent reverse transcription for RNA barcoding and cDNA amplification. The library was prepped with the Chromium Single Cell 5\u0026rsquo; Reagent Version 3 kit. The samples were sequenced using the HiSeq2500 (Illumina) in Rapid Run Mode.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFASTQ files were run through CellRanger v6.1.0 software with default settings for de-multiplexing, aligning reads with STAR software to GRCh38, and counting unique molecular identifiers (UMIs). Seurat package v5.0.2 in R v4.3.1 was used for downstream analysis. Low-quality cells were filtered (expressing \u0026lt; 200 genes, \u0026gt;15% mitochondrial reads, or \u0026gt;7,000 unique gene counts). Principal component analysis was performed on log-normalized and scaled data using 2,000 variable genes. The top 25 principal component analyses were used for clustering and visualized using the UMAP algorithm in the Seurat package. The lists of DEGs were identified with a Model-based Analysis of Single-cell Transcriptomics (MAST) test. Pathway analysis of gene lists containing significantly differentially expressed genes were done with Ingenuity Pathway Analysis (Qiagen). Monocle trajectory analysis was performed using Monocle 3 by importing the counts from the Seurat object. RNA velocity was calculated using the scVelo v0.3.1 package in Python v3.11 and velocity calculations were overlaid on UMAP projections calculated in Seurat.\u0026nbsp;For interrogating fibroblast and lymphocyte scRNAseq-data,\u0026nbsp;we downloaded the public dataset GSE132771 and analyzed the interactions between\u0026nbsp;fibroblast and lymphocyte by CellChat v2.1.2\u0026nbsp;package.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistology and immunofluorescence staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman lung pieces were fixed in 4% PFA overnight at 4\u0026deg;C, washed with PBS four times for 30 min each at 4\u0026deg;C, and embedded in OCT after 30% sucrose incubation, then 8 \u0026micro;m sections were cut on a cryostat. Antigen retrieval (BRR2004CLX, Biocare Medical) was performed for 30 min at 95 \u0026deg;C. Slides were washed with 0.1% Tween-20 in PBS (PBST), blocked (3% donkey serum in PBST) for 1 h, and then incubated with primary antibodies overnight at 4\u0026deg;C. The following primary antibodies were used: Anti-CD44 (1:200, Biolegend, 103001), Anti-CD45 (1:200, Biolegend, 304058), Anti-CD3 (1:200, Abcam, ab16669), Anti-CD8 (1:200, SinoBiological, 10980-MM07), Anti-CD4 (1:200, Biolynx, 110301A), Anti-CD20 (1:200, Abcam, ab64088), Anti-CD138 (1:200, Sino Biological, 11429-R017-P), Anti-IgG (1:500, Sino Biological, SSA016), Anti-COL1A1 (1:2500, Proteintech, 67288-1-Ig). Slides were washed with PBST and then incubated for 1 h at room temperature in secondary antibodies diluted in PBST. The following secondary antibodies were used at 1:500: donkey anti-rabbit IgG Alexa Fluor 555 (Thermo Fisher, A-31572), donkey anti-mouse IgG Alexa Fluor 555 (Thermo Fisher, A-31570), donkey anti-rat Alexa Fluor 647 (Thermo Fisher, A21208), donkey anti-mouse Alexa Fluor 647 (Thermo Fisher, A31570), and donkey anti-goat Alexa Fluor 647 (Thermo Fisher, A21447). DAPI (0.2 \u0026micro;g/ml, Thermo Fisher, 1738176) was added for 5 min, then the slides were mounted. Co-staining images of CD44 or HSPA6 with immune cell marker X (such as CD45, CD3, CD4, CD8, CD20, and CD138) were processed in Cellprofiler (Broad Institute). Primary objects were generated using the Identify Primary Objects module for each florescent channel. Size and mean intensity filters were applied to remove debris. Double-positive cells were identified by overlapping primary objects with the Relate Objects module. Double-positive cells were used to generate a mask that was applied to both channels of the original image file. A mask was applied only to the CD44 (or HSPA6) channel to remove X\u003csup\u003e-\u003c/sup\u003e/CD44\u003csup\u003e+\u003c/sup\u003e (or X\u003csup\u003e-\u003c/sup\u003e/HSPA6\u003csup\u003e+\u003c/sup\u003e) cells.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture and GZMK treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFreshly isolated lung fibroblasts from human lungs were cultured in DMEM/F-12 (Thermo Fisher, 11330032) with 10% FBS and 1% Pen/Strep. The medium was changed every 2-3 days and lung fibroblasts were maintained for no more than 5 passages. Human lung fibroblasts were treated with vehicle or 50 ng/mL recombinant granzyme K (Sino Biological, 19732-H08H) in DMEM without FBS for 48 h.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSA-\u0026beta;gal staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSenescence assay was performed with the Senescence \u0026beta;-Galactosidase Staining Kit (Cell Signaling, 9860S) on human lung fibroblasts treated with GZMK as indicated above. Cells were rinsed with PBS and fixed with PFA for 15 min. Fresh \u0026beta;-galactosidase staining solution provided by senescence \u0026beta;-galactosidase staining kit was prepared according to manufacturer\u0026apos;s instructions. After being washed with PBS for 2 times, cells were incubated with fresh staining solution at 37\u0026deg;C at least overnight in a dry incubator. Pictures were captured with EVOS M5000 (Thermo Fisher).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative RT-PCR (qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was obtained using FastPure\u0026reg; RNA Isolation Kit (VazymE, RC112-01), following the manufacturers\u0026rsquo; protocols. cDNA was synthesized from total RNA using the HiScript \u0026reg; III RT SuperMix (VazymE, R323-01). Quantitative RT-PCR (qRT-PCR) was performed using the SYBR Green system (VazymE, Q711-02). Relative gene expression levels after qRT-PCR were defined using the ∆∆Ct method and normalizing to GAPDH. Primers were listed in table S10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was carried out using GraphPad Prism software. To compare the means between two groups, student\u0026rsquo;s t tests were used to generate P values. One-way analysis of variance was used to determine whether there were statistical differences among three groups followed by Fisher\u0026rsquo;s least significant difference (LSD) test for pairwise comparisons if the overall test was statistically significant. A P value less than 0.05 was considered significant.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eWritten informed consent for research and publication was obtained from all subjects.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank patients and their families for donating lung specimens, which significantly contributed to this work. This study was supported by the National Natural Science Foundation of China, Grant No. 82070059 (JC); Major Program of Wuxi Medical Center of Nanjing Medical University, Grant No. WMCJ202301 (JC); National Natural Science Foundation of China, Grant No. 82370070 (CW); Major Project of Guangzhou National Laboratory, Grant No. GZNL2023A01003 (CW); High-level Innovative Research Institute, Grant No. 2021B0909050003 (CW), and Top Talent Support Program for young and middle-aged people of Wuxi Health Committee, Grant No. BJ2023021 (FG).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: C.W., J.C., F.G., M.H.; Methodology: F.G., Z.W., T.Y., C.W., Y.S., D.W., S.C., Y.H., J.S., Z.D.; Investigation: Z.W., C.W., T.Y., Y.S., D.W., S.C., Y.H., J.S., Z.D.; Visualization: C.W., Y.S., Z.W., T.Y.; Funding acquisition: J.C., C.W., F.G.; Project administration: F.G., J.C., C.W.; Supervision: C.W., F.G., J.C., M.H.; Writing \u0026ndash; original draft: C.W., F.G., J.C.; Writing \u0026ndash; review \u0026amp; editing: C.W., F.G., J.C., M.H., Z.W., T.Y. All authors approved and contributed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGray, J. I. \u0026amp; Farber, D. L. Tissue-Resident Immune Cells in Humans. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 195-220, doi:10.1146/annurev-immunol-093019-112809 (2022).\u003c/li\u003e\n\u003cli\u003eSchenkel, J. M. \u0026amp; Masopust, D. Tissue-resident memory T cells. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 886-897, doi:10.1016/j.immuni.2014.12.007 (2014).\u003c/li\u003e\n\u003cli\u003eNarasimhan, H., Wu, Y., Goplen, N. P. \u0026amp; Sun, J. 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Here, we defined alterations of resident immune cells in idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease characterized by tissue inflammation and progressive scarring. Utilizing ex-vivo human lung perfusion coupled with single-cell RNA-sequencing, we successfully segregated the resident immune cells. Analyzing approximately 100,000 resident immune cells from 7 IPF and 5 control lungs, we identified 13 distinct cell types. Previously unrecognized aberrant lymphocyte phenotypes were uncovered. Specifically, among T lymphocytes, we observed an enrichment of GZMK\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in IPF lungs, possessing a potential pro-fibrotic function. The fraction of pro-inflammatory HSP\u003csup\u003ehi\u003c/sup\u003e CD4\u0026thinsp;+\u0026thinsp;T cells was increased in IPF lungs, while the quiescent subset decreased. Despite an increased presence of Tregs in IPF lungs, these cells showed reduced expression of genes associated with immune suppression. Moreover, significant B cell expansion and activation occurred, with continuous differentiation into IgG-producing plasma cells. Stromal niche interaction analysis showed that IPF fibroblasts, especially the CTHRC1\u003csup\u003ehi\u003c/sup\u003e subset, exerted stronger effects on lymphocytes. These findings offer novel insights into dysregulated immune populations in IPF, advancing understanding of its immunopathology.\u003c/p\u003e","manuscriptTitle":"Integrated EVLP and single-cell profiling uncovers aberrant activation of tissue-resident lymphocytes and pro-fibrotic GZMK⁺ CD8 T cells in IPF","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:42:44","doi":"10.21203/rs.3.rs-7500111/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ecfa2c51-c078-4b33-8f52-4aa28eedac7c","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53984249,"name":"Health sciences/Diseases"},{"id":53984250,"name":"Health sciences/Pathogenesis"},{"id":53984251,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-05-08T05:51:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 08:42:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7500111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7500111","identity":"rs-7500111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}