Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis

Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis View ORCID Profile Neil J. McKenna , Scott A. Ochsner , Alan Waich , Juan Cala-Garcia , Maria E. Ruiz Echartrea , Sandra Grimm , Fernando Poli , Rafael Cardenas Castillo , Juan D. Zuluaga , View ORCID Profile Sergio Poli , Taylor S. Adams , Ricardo Pineda , Benjamin Moss , Stefan W Ryter , Rudolf T. Pillich , Julian A. Villalba , Kosuke Kato , Louise Hecker , Lindsay J. Celada , Maor Sauler , Melanie Koenigshoff , View ORCID Profile Naftali Kaminski , Benjamin Raby , Sandeep Agarwal , Konstantin Tsoyi , Cristian Coarfa , Ivan O. Rosas doi: https://doi.org/10.1101/2025.06.16.659944 Neil J. McKenna 1 Department of Molecular and Cellular Biology, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Neil J. McKenna Scott A. Ochsner 1 Department of Molecular and Cellular Biology, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alan Waich 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Cala-Garcia 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria E. Ruiz Echartrea 3 Center for Precision Environmental Health, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandra Grimm 3 Center for Precision Environmental Health, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fernando Poli 4 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rafael Cardenas Castillo 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan D. Zuluaga 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergio Poli 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergio Poli Taylor S. Adams 5 Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ricardo Pineda 6 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin Moss 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefan W Ryter 7 Division of Pulmonary and Critical Care Medicine, Department of Medicine , Weill Cornell Medicine, New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rudolf T. Pillich 8 Department of Medicine, University of California San Diego , La Jolla, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julian A. Villalba 9 Department of Pathology and Laboratory Medicine, Emory University School of Medicine , Atlanta, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kosuke Kato 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Louise Hecker 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lindsay J. Celada 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maor Sauler 5 Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melanie Koenigshoff 6 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Naftali Kaminski 5 Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Naftali Kaminski Benjamin Raby 10 Division of Pulmonary Medicine, Boston Children’s Hospital , Boston, MA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandeep Agarwal 11 Section of Immunology, Allergy & Rheumatology, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Konstantin Tsoyi 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cristian Coarfa 3 Center for Precision Environmental Health, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivan O. Rosas 2 Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ivan.rosas{at}bcm.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic pulmonary disease with unknown etiology. Since approved idiopathic pulmonary fibrosis (IPF) drugs only slow disease progression, novel therapeutics are required that improve clinical outcomes. Here, we report a single cell RNA-Seq and regulatory network analysis of the largest IPF cohort assembled to date. Segregating this cohort based on status of treatment with approved antifibrotics (untreated, nintedanib- and pirfenidone-treated), we describe for the first time the transcriptional landscape of untreated IPF across 40 lung cell types, and the elements of this program that are impacted by approved antifibrotics. On average, 60% of the untreated IPF-dysregulated transcriptome is refractory to treatment with these drugs, a transcriptional deficit we refer to as the IPF therapeutic gap. Regulatory network analysis indicated a dominant functional footprint for the transcription factor STAT3 in both untreated IPF and in the IPF therapeutic gap. Validating our analysis in a translational precision cut lung slice platform that recapitulates IPF explants, treatment with a STAT3 inhibitor reduced the IPF therapeutic gap in numerous lung cell types. Finally, we implicated STAT3 as a master transcription factor that regulates a network comprising numerous profibrotic transcription factors in IPF alveolar fibroblasts, a critical fibrotic cell lineage. Our study represents a comprehensive resource for translational lung fibrosis research and establishes a novel strategy for drug discovery in human disease more broadly. INTRODUCTION Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by progressive decline of lung function leading to respiratory failure. Disease progression in IPF is rapid, with a median survival from time of diagnosis of 3-5 years. The underlying mechanisms of IPF pathogenesis remain unclear but may be initiated by alveolar epithelial cell injury leading to immune, stromal and endothelial cell recruitment, fibroblast activation and dysregulated extracellular matrix (ECM) production that ultimately remodels lung architecture. Intrinsic and extrinsic signaling pathways across the IPF pulmonary niche comprise a complex network of signaling nodes, including membrane receptors, enzymes, transcription factors, and other ancillary factors. These include receptor tyrosine kinases that mediate signaling by a diverse set of growth factors (e.g., EGF, VEGF, PDGF, TGFB), enzyme second messengers (e.g., JAK, p38) and transcription factor modules involved in the direct regulation of gene expression such as SMADs, AP-1, STAT3 and others ( 1 , 2 ). Two small molecules have been approved for treatment of IPF, nintedanib and pirfenidone. Nintedanib is a broad spectrum receptor tyrosine kinase inhibitor originally developed as a non-small cell lung cancer therapeutic ( 3 ), whereas pirfenidone reduces the activity of p38 MAPK. Although their direct targets have been characterized, the transcriptional and post-translational events affected by these drugs in specific lung cell lineages in IPF remains largely undefined. Current insights into these events derived from cell culture and animal models have limited direct relevance to clinical data. The dearth of information on specific transcriptional programs impacted by nintedanib and pirfenidone in patient lungs has hampered the development of novel therapeutics in IPF. To address this, we and others have previously published single cell-resolution transcriptional atlases that have afforded insight into transcriptional programs underlying the etiology of IPF ( 4 – 7 ). In these studies, the IPF cohorts contained subjects who had undergone therapy using approved antifibrotics. As such, these atlases were not designed to discriminate between transcriptional events in untreated and treated IPF. The overall objective of the current study was to identify small molecule therapeutics that would complement existing therapies in IPF. Logically, such molecules would target pathways that are not impacted by approved antifibrotics. To this end, we segregated our IPF cohort into untreated subjects and those who were treated with either nintedanib or pirfenidone. We computed single cell RNA-Seq (scRNA-Seq) profiles against a comprehensive curated library of ChIP-Seq signatures representing high confidence transcriptional targets for hundreds of transcription factors. Combining this approach with conventional ontology analysis, we describe three previously unreported facets of IPF: (i) the transcriptional and transcriptional regulatory landscape of untreated IPF; (ii) the impact of approved therapies on this program; and (iii) transcription factors and their downstream pathways whose functions are refractory to treatment with approved therapies. Using this approach, we identified STAT3 as a primary transcriptional driver of untreated IPF as well as gene expression programs not impacted by nintedanib or pirfenidone. We validated this analysis using a small molecule inhibitor of STAT3 (TTI-101) in a translational precision cut lung slice (PCLS) platform. Finally, our analysis indicates that STAT3 occupies a central transcriptional regulatory position in alveolar fibroblasts, recently identified as a critical cell type in IPF. Our study represents a novel approach to drug discovery that prioritizes transcriptional targets not impacted by existing approved therapeutics for IPF, and which is broadly applicable to other human diseases. RESULTS A treatment status-based single cell transcriptional Atlas of IPF With the overall goal of developing novel complementary therapeutics in IPF, we established and analyzed the largest IPF treatment cohort to date, which was segregated according to treatment status prior to transplant surgery. This cohort consisted of lungs from 22 untreated, 24 pirfenidone-treated and 28 nintedanib-treated IPF patients, and 64 control lungs ( Fig. 1A ). This cohort enabled us to (i) define untreated IPF-dysregulated transcriptional programs; (ii) determine their regulation by approved antifibrotics; and (iii) identify IPF processes that were refractory to established antifibrotic treatments ( Fig. 1A ). Table S1 describes the subject characteristics for our study. Download figure Open in new tab Figure 1. A treatment status single cell transcriptional Atlas of IPF. (A) Overview of experimental design. (i) Collection of disease lung explants ( n = 74) and donor lungs ( n = 64). The IPF cohort was segregated into three subcohorts: untreated IPF ( n = 22), pirfenidone-treated IPF ( n = 24) and nintedanib-treated IPF ( n = 28). (ii) Dissociation into single-cell suspension. (iii) scRNA-seq library preparation and sequencing. (iv) Differential gene expression analysis. (v) Regulatory network and pathway analysis. (vi) Prioritization and validation of STAT3 as a novel clinical IPF target. (B-E) UMAP plots and accompanying bar charts showing effect of approved antifibrotics on cell counts in untreated IPF for selected cell types: (B) PTBSC and AT2 cells; (C) MACA and MACMOND; (D) FBALV and FBPERIB; (E) VEN and CAPG. C, control; UNT, untreated IPF; TRT, approved antifibrotic-treated IPF. To maximize statistical power for detecting differential gene expression, we combined our new cohort with our previous IPF scRNA-Seq analysis ( 4 ). We generated differentially-expressed genes (DEGs; log 2 FC>0.32 or <-0.32, FDR<0.1) across 40 Human Lung Cell Atlas (HLCA) ( 8 ) cell types for three treatment-segregated subcohorts: (i) untreated IPF vs. control; (ii) pirfenidone-treated IPF vs. untreated IPF; and (iii) nintedanib-treated IPF vs. untreated IPF. For brevity we assigned acronyms to the 40 cell types ( table S2 ). The scRNA-Seq analysis was organized into a 1,494,240 data point DEG Atlas documenting relative abundance values for 12,452 genes across the 40 cell types in the three sub-cohorts ( table S3) . To facilitate data mining, DEGs were annotated for functional categories, classes and families using our previously described Signaling Pathways Project (SPP) vocabulary ( 9 ). To promote sharing and re-use of these single cell datasets, we deposited the transcriptional networks in NDEx, a Cytoscape-based data sharing commons ( 10 – 14 ) ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Links to NDEx single cell RNA-Seq and regulatory networks for this study The transcriptional landscape of untreated IPF at single cell resolution We first wished to assess the macro effect of approved antifibrotics on cell type numbers in IPF. Figure 1B-E shows the effect of antifibrotic treatment on representative cell types for epithelial ( Fig. 1B ), myeloid ( Fig. 1C ), stromal ( Fig. 1D ) and endothelial ( Fig. 1E ) compartments. Comparing cell counts between control, untreated IPF and antifibrotic-treated IPF, we found that the only cell types whose changes in the untreated IPF group were partially rescued by antifibrotic treatment were AT2 cells ( Fig. 1B ) and peribronchial fibroblasts ( Fig. 1D ). For the majority of cell types, we observed negligible effect of approved therapeutics on reversing cell count changes between control and untreated IPF. Next, to identify cellular pathways dysregulated in untreated IPF, we performed Panther GO enrichment analysis on genes induced in untreated IPF vs. control (log 2 FC>0.32, FDR< 0.1) across the 40 cell types ( table S4 ). We then conducted k-means clustering of untreated IPF-induced GO terms that were enriched ( p adj < 0.05) in at least six cell types ( n = 508), then partitioned the analysis at k=25 to resolve a set of 25 GO term clades ( Fig. 2A ). Leveraging the full power of the Atlas, this analysis identified terms that were shared across most or all compartments, as well as those restricted to a subset of cell types. Clades 1-5 reflected strong induction of ribosomal subunit genes across multiple compartments. Ribosomal biogenesis contributes to signature IPF processes such as epithelial to mesenchymal transition ( 15 ), fibroblast to myofibroblast differentiation ( 16 ) and T cell activation ( 17 ), and is regulated by canonical IPF pathways like TGFB ( 18 ) and PI3K-MTOR ( 19 ). Notably, ribosomal genes ranked among the top classes in an RNAi screen for genes whose inactivation led to increased collagen uptake, a process that is deficient in lung fibrosis ( 20 ). Download figure Open in new tab Figure 2. The transcriptional landscape of untreated IPF at single cell resolution. (A) Hierarchical clustering of the top significantly enriched GO terms in untreated IPF-induced gene sets. GO enrichment analysis was performed on genes induced in untreated IPF (log 2 FC>0.32, FDR<0.1) relative to controls. Color palette represents -log10p enrichment scale from p adj < 0.05 (light orange) to p adj <1E-70 (deep red); blue indicates no significant enrichment. Terms that were significantly enriched ( p adj < 0.05) in at least six cell types were subjected to hierarchical clustering as described in the methods section. Panels B-H represent predicted TF regulatory network plots for unique gene sets from the indicated panel A GO term clades across all cell types; in these plots log 2 odds ratio is on the x axis and -log 10 p is on the y axis, such that TFs that have the strongest and most significant footprints within the gene set of interest are distributed towards the top right of the plot. (B) Regulatory network plot for clade 1. (C) Regulatory network plot for clade 7. (D) Regulatory network plot for clade 8. (E) Regulatory network plot for clade 10b. (F) Regulatory network plot for clade 12. (G) Regulatory network plot for clade 16. (H) Regulatory network plot for clade 25. Full GO data are in table S4 ; full clade regulatory network data are in table S5 . We observed strong induction of Golgi-related terms (clade 6a) in myeloid, B plasma and AT1 cells ( Fig. 2A ). Golgi processes are intimately involved in the transport of MHC class II molecules ( 21 ), polymorphisms in which confer IPF susceptibility ( 22 , 23 ). Reflecting the established connection between cellular metabolism and fibrosis ( 24 ), mitochondrial oxidative phosphorylation and respirasome terms (clade 7) were present in endothelial and epithelial cell types, and in the myeloid and stromal compartments. The strong T cell character of terms in clade 8 was consistent with their enrichment peaks in natural killer, CD4 and CD8 T cells ( Fig. 2A ). Clade 10a encompassed terms with peaks in the lymphoid and myeloid cell types whereas 10b comprised terms largely specific to myeloid cell types (i.e., NF-κB signaling, ERK and MAPK cascades, TNF signaling). Ras GTPase signaling has been implicated in endothelial cell migration ( 25 ) and dysfunction in pulmonary fibrosis ( 26 ). Consistently, we observed peaks of GTPase signaling (clade 11) and endothelial cell migration (clade 12) enrichment in cells of the endothelial compartment (AT1, CAPA, CAPG) ( Fig. 2A ). Clades 16-25 were largely restricted to the stromal compartment, and encompassed processes with strong connections to IPF, including growth factor ( 27 ) and Wnt ( 28 ) signaling (clade 16); oxidative stress ( 29 ) in clade 17; TGFB ( 30 ), PDGFR ( 31 ) and SMAD ( 32 ) signaling in clade 20; and collagen-containing ECM and wound healing ( 33 ) in clade 25 ( Fig. 2A ). A single cell regulatory network analysis of untreated IPF Regulatory network analysis uses high confidence transcriptional targets (HCTs), computed from public ChIP-Seq datasets, to predict functional footprints for transcription factors (TFs) within clinical gene sets of interest ( fig. S2 ) ( 34 – 36 ). To identify candidate TFs regulating the pathways identified in our GO analysis ( Fig. 2A ), we next performed regulatory network analysis on the unique set of genes in each of the 25 clades (see table S5 for the full data). The plots in Fig. 2B-H represent predicted regulatory networks for each of the indicated clades. TFs with the largest and most significant footprints within the gene set are distributed towards the top right of the plots. Key factors in ribosomal biogenesis, including MYC ( 37 ), MYCN ( 38 ), ZBTB33 ( 39 ) and ERG ( 40 ) were represented in the regulatory network for clade 1 ( Fig. 2B ). Notably, several of these TFs are associated with fibrotic disease ( 41 – 43 ). The strong OxPhos character of clade 7 was corroborated by strong footprints for TFs known to regulate OxPhos, NAD + metabolism and related pathways, including ARNT and HIF1A ( 44 ), HSF1 ( 45 ) and BACH1 ( 46 ) ( Fig. 2C ). Preferential enrichment of clade 10a terms in lymphoid cells was reflected in strong footprints for TFs with critical roles in lymphoid cell biology, including TBX21 ( 47 ), IRF4 ( 48 ) and BATF3 ( 49 ) ( Fig. 2D ). Conversely, the regulatory network for clade 10b was dominated by known drivers of myeloid transcriptional programs, such as STAT3 ( 50 ), the RELA subunit of NF-κB ( 51 ), PU.1 ( 52 ) and CEBPA ( 53 ) ( Fig. 2E ). TFs with known roles in regulating endothelial and/or epithelial cell biology, including members of the GATA family ( 54 ), TEAD1 ( 55 ), STAT3 ( 56 ) and NFATC1 ( 57 ), had prominent footprints in clade 12 ( Fig. 2F ). Finally, the classical IPF character of terms in clades 16 and 25, encompassing growth factor signaling, collagen-containing ECM and wound healing, was reflected in strong footprints for TFs with previously characterized roles in stromal facets of experimental pulmonary fibrosis ( Fig. 2G &H ). These included TWIST1 ( 58 ), SMAD family members ( 32 ), FOXA2 ( 59 ), members of the AP-1 family ( 60 ) and STAT3 ( 61 ). In summary, our regulatory network analysis affords insight into the diversity and cell type-specificity of transcriptional complexes whose gain- or loss-of-function contributes to IPF progression. Global impact of approved antifibrotics in IPF We next evaluated the effects of nintedanib and pirfenidone on the untreated IPF transcriptome. Given the clinical efficacy of the two approved antifibrotics, we anticipated enrichment of antifibrotic-repressed genes among untreated IPF-induced genes from the corresponding cell type. To investigate this, we performed enrichment analysis between untreated IPF vs. control-induced and (i) nintedanib-IPF vs. untreated IPF-repressed or (ii) pirfenidone-IPF vs. untreated IPF-repressed gene sets. We observed significant enrichment of genes repressed by nintedanib ( fig. S3A ) and pirfenidone ( fig. S3B ) among untreated IPF-induced genes across nearly all cell types represented. Overall, the patterns of enrichment for both drugs across all cell types were strikingly similar, particularly in the lymphoid compartment, potentially reflecting common mechanisms of action. To place this analysis in context with the broader IPF transcriptome, we next computed the percentage of untreated IPF-DEGs (induced and repressed) that were reversed by antifibrotics in each cell type (cutoffs: log2FC >0.32 or <-0.32, FDR < 0.1). The largest changes in transcriptional volume in untreated IPF were observed in the stromal compartment, most notably alveolar fibroblasts (856 DEGs), with additional peaks in cell types such as alveolar epithelial AT1 (680 DEGs) and AT2 (595 DEGs) cells and monocyte-derived macrophages (651 DEGs). Remarkably, we found that on average, nearly 60% of untreated IPF DEGs were not reversed by treatment with either antifibrotic ( Fig. 3A ; table S6 ). Moreover, of the genes that were reversed by one antifibrotic, many were also regulated by the other, indicating considerable redundancy in their mechanisms of action. On average, across all cell types, nintedanib exerted stronger reversal of untreated IPF DEGs than pirfenidone (average number of reversed genes: nintedanib = 55, pirfenidone = 21). Our results indicate that although approved IPF drugs had appreciable antifibrotic effects on a subset of untreated IPF DEGs ( fig. S3 ), a substantial proportion of the IPF transcriptome was refractory to treatment with these drugs ( Fig. 3A ). Because it represents a gap between desired and actual performance of approved therapeutics, we referred to this transcriptional deficit as the IPF therapeutic gap. Download figure Open in new tab Figure 3. Single cell transcriptional biology of approved antifibrotics in IPF. (A) Stacked bar chart representation of differentially expressed genes derived from IPF Atlas scRNA-Seq data. Bars represent total numbers of untreated IPF vs. control-dysregulated (log2 FC>0.32 or <-0.32, FDR<0.1) genes in each cell type at the indicated cutoffs. Subsets of these genes whose dysregulation is reversed by nintedanib only (green), pirfenidone only (blue), both antifibrotics (yellow) and neither (red) are indicated. Full data are in table S6. (B) Heatmap indicating the extent to which untreated IPF-induced GO terms are reversed by treatment with nintedanib or pirfenidone in each cell type. The 508 untreated IPF-induced enriched GO terms from Fig. 2A are arranged in the identical order on the vertical axis; the 40 Atlas cell types are arranged on the horizontal axis. Within the heatmap, the color palette represents -log10p enrichment scale from p adj < 0.05 (yellow) to p adj <1E-25 (red); blue indicates no significant enrichment. On the horizontal edges, for a given cell type-GO term intersection, UNT (red) indicates enrichment of that GO term in untreated IPF-induced genes; NTD (blue) indicates enrichment of that GO term in nintedanib-IPF-repressed genes; and PFD (blue) indicates enrichment of that GO term in pirfenidone-IPF-repressed genes. Full data are in table S4 . Panels C-G represent scatterplots of pirfenidone-IPF v untreated IPF (x axis) and nintedanib-IPF vs. untreated IPF (y axis) log 2 FC values for untreated IPF-induced genes mapped to the following GO terms: (C) cadherin binding in AT2 cells; (D) immune response-activating signal transduction in NK cells; (E) secretory granule lumen in MACIPs; (F) SMAD binding in FBADs; and (G) GTPase regulator activity in CAPA cells. Single cell transcriptional biology of approved antifibrotics in IPF To investigate the biology of approved antifibrotics in IPF, we evaluated the extent to which functional ontologies that were dysregulated in untreated IPF were reversed by antifibrotic treatment. As in Fig. 2A , we focused on antifibrotic reversal of untreated IPF-induced functional ontologies (see table S4 for full data). We performed Panther GO enrichment analysis on (i) nintedanib-treated IPF vs. untreated IPF-repressed gene sets; and (ii) pirfenidone-treated IPF vs. untreated IPF-repressed gene sets (log 2 FC <-0.32, FDR < 0.1) and compared the results with the GO analysis of untreated IPF-induced gene sets ( Fig. 2A ). Figure 3B shows a side-by-side representation across the 40 cell types of enrichment of the 508 GO terms from Figure 2A in (i) untreated IPF-induced, (ii) nintedanib-repressed and (iii) pirfenidone-repressed gene sets. Our analysis indicated robust effects of antifibrotics across all compartments on ribosomal biogenesis, cell-substrate junction and focal adhesion (clades 1-5) and OxPhos (clade 7). The latter is consistent with reported effects of approved antifibrotics on oxidative stress ( 62 ). Similarly, repression by approved antifibrotics of terms related to regulation of GTPase activity (clade 11) as well as epithelial and endothelial cell migration (clade 12) was evident. In general, and consistent with the DEG breakdown by therapy status ( Fig. 3A ), nintedanib exerted a stronger effect on untreated IPF-induced programs compared to pirfenidone. Figure 3C-G shows common effects of approved antifibrotics on specific GO term-mapped genes in cell types representing each of the five compartments. Strong effects of antifibrotics were observed on cellular processes previously implicated in IPF progression, including cadherin binding ( 63 ) in AT2 cells ( Fig. 3C ), immune signal transduction in natural killer cells ( 64 ) ( Fig. 3D ), secretory granule processing in interstitial macrophages ( 65 ) ( Fig. 3E ), SMAD binding in adventitial fibroblasts ( 32 ) ( Fig. 3F ) and Ras GTPase signaling in capillary aerocyte endothelial cells ( 26 ) ( Fig. 3G ). Surprisingly, adventitial fibroblast untreated IPF-induced collagen genes COL1A2 and COL3A1 , rather than being repressed by antifibrotics, were actually further induced ( Fig. 3F ). In several instances, antifibrotics impacted untreated IPF-dysregulated processes in a cell type-specific manner. For example, Golgi and MHC II-related terms (clade 6a) were at least partially repressed by both antifibrotics in MACIP cells but not in other myeloid cells ( Fig. 3B ) . We also noted cell type-specific repressive effects of approved antifibrotics on processes that were not induced in untreated IPF, such as OxPhos in ABAS cells (clade 7, Fig. 3B ). To identify candidate TFs targeted by approved antifibrotics to effect repression of the GO term-mapped genes in Fig. 3C-G , we performed regulatory network analysis on those mapped genes that were induced in untreated IPF and repressed by both antifibrotics in the indicated cell type. Analysis of GO cadherin binding genes in AT2 cells reflected previously characterized roles of Notch/HEY1 ( 66 ), TCF12 ( 67 ), SIX2 ( 68 ) and RELB/p65 ( 69 ) in regulation of cadherin biology ( fig. S3C ) . Similarly, previous studies have implicated HOXB13 ( 70 ), HOXC9 ( 71 ), MAFG ( 72 ) and STAT5B ( 73 ) in NK cell activation ( fig. S3D ) , and ETV2 ( 74 ), NFATC1 ( 75 ), BACH1 ( 76 ), MTA3 ( 77 ) and MAFK ( 78 ) in macrophage activation and differentiation ( fig. S3E ) . Moreover, regulatory analysis of GO SMAD binding-mapped genes in adventitial fibroblasts ( fig. S3F ) prioritized several TFs with established connections to SMAD signaling, including RUNX1T1 ( 79 ), MYB ( 80 ) and TBX5 ( 81 ), in addition to SMAD1 and SMAD4 themselves. Finally, elevated rankings assigned to BATF ( 82 ), MEF2C ( 83 ), GATA4 ( 84 ), BACH1 ( 85 ) and RELA ( 86 ) in the CAPA analysis align with existing studies implicating these TFs in endothelial cell morphology and function ( fig. S3G ) . STAT3 dominates transcriptional regulatory programs in untreated IPF The overarching strategy in this study was to prioritize TFs that were strongly implicated in the development of clinical IPF and that were largely refractory to treatment with approved antifibrotics i.e., were prominent therapeutic gap TFs. To this end, we first generated a pan-Atlas regulatory network to identify TFs that had the strongest functional footprints across all untreated IPF cell types. To achieve this, we performed regulatory network analysis on untreated IPF-induced gene sets across the 40 cell types. TFs were then ranked by number of cell types containing a p <0.05 footprint, then by mean footprint -log 10 p . The full data are shown in table S7 , annotated using the SPP vocabulary ( 9 ). Similar to the scRNA-Seq analysis, regulatory networks were deposited in NDEx ( Table 1 ). Fig. 4A shows the top 50 TFs prioritized by this analysis. Validating our analysis, a set of TFs with established roles in pulmonary fibrosis curated by the FIBROAD resource ( 87 ) was strongly enriched within the top ranked TFs in the pan-Atlas regulatory network ( fig. S4A ). The TF with the broadest predicted gain-of-function transcriptional footprint in untreated IPF was STAT3 ( Fig. 4A ), which we previously demonstrated was activated in experimental pulmonary fibrosis ( 61 ), and which is a predictor of worse transplant-free survival in IPF patients ( 88 ). Download figure Open in new tab Figure 4. STAT3 is a dominant transcription factor in untreated IPF and in the IPF therapeutic gap. (A) Heat map showing predicted top 50 TFs in untreated IPF-induced pan-Atlas regulatory network. Regulatory network analysis was performed on untreated IPF-induced genes in the 40 cell types. TFs are ranked based on (i) number of significant footprints (and (ii) average footprint size. Full data are in table S9 . Color palette represents -log10p enrichment scale from p adj < 0.05 (yellow) to p adj <1E-35 (red); blue indicates no significant enrichment. (B) Predicted STAT3 footprint in untreated IPF-induced genes across the 40 cell types. (C) Regulatory network plot showing predicted TFs driving severe IPF in the LTRC dataset GSE32537. Full data are in table S10. (D) Regulatory network plot for untreated IPF-induced genes in Atlas alveolar fibroblasts showing TFs that are transcriptionally induced in untreated IPF. (E) Volcano plot of untreated IPF vs. control Atlas alveolar fibroblast DEGs highlighting genes encoding TFs labelled yellow in panel D. (F) Master regulatory network plot showing TFs predicted to regulate genes encoding untreated IPF-induced network TFs in alveolar fibroblasts. Full data are in table S14. (G) Same plot as panel D highlighting TFs encoded by genes that were hits in an RNAi screen of lung fibroblast differentiation (Oh et al.). Scrutinizing the predicted STAT3 untreated IPF-induced functional footprints across the 40 cell types ( Fig. 4B ), we observed the strongest footprint occurred in alveolar fibroblasts, a cell type recently implicated as the primary source of fibroblast subtypes in response to lung injury ( 89 ). Other peaks of predicted STAT3 activity were observed in pericytes, and cell types in the epithelial (AT2, AT1, resting basal epithelial cells) and endothelial (artery, general capillary endothelial and venous) compartments ( Fig. 4B ). The predicted STAT3 footprint in alveolar fibroblast untreated IPF-induced genes ( fig. S4B ) included numerous genes implicated in fibrosis in the lung and other organs, including MT2A ( 90 ), NNMT ( 91 ), LMNA ( 92 ) and NAMPT ( 93 ). In addition to STAT3, our analysis assigned elevated rankings to other known profibrotic TFs in the alveolar fibroblast untreated IPF-induced regulatory network, including NFATC1 ( 94 ), NEUROD1 ( 95 ), TWIST1 ( 41 ) and members of the CEBP ( 96 ) and AP-1 ( 97 ) TF families (fig. S4C). To validate our findings in an independent dataset, we identified a transcriptomic whole lung analysis of IPF from the Lung Tissue Research Consortium (LTRC) initiative (GSE32537) ( 98 ). We first partitioned the cohort into severe and mild IPF based on provided subject forced vital capacity values prior to differential gene expression analysis. We then performed regulatory network analysis on the severe vs. mild IPF DEGs ( table S8 ). Validating our single cell-based analysis, the top 20 ranked TFs from our pan-Atlas regulatory network were strongly enriched (ENR = 11, p = 3E-08) among the top ranked TFs from the LTRC analysis ( Fig. 4C ). Reiterating its predicted status as a primary transcriptional driver of clinical IPF, STAT3 had the largest severe vs. mild IPF-induced footprint in the LTRC cohort. As shown in fig. S4D , we identified genes in the severe IPF-induced STAT3 footprint that have known roles in IPF pathogenesis, including NAMPT and NNMT (also induced in Atlas untreated IPF alveolar fibroblasts) ( fig. S4B ), and FOSL2 ( 99 ), as well as others with no established role in IPF. The latter group includes SBNO2 , a mediator of STAT3-driven hematological malignancy ( 100 ). Remarkably, the STAT3 footprint was absent from the severe vs. mild-repressed genes ( fig. S4D ), suggesting that STAT3 gain of activation function may represent the primary driver of IPF progression. A substantial body of literature implicates secretion of signaling molecules that promote epithelial injury as an early event in IPF ( 101 , 102 ). STAT3 is activated by numerous receptors in different cellular and experimental contexts ( 50 ). We next identified potential upstream regulators of STAT3 in untreated IPF by applying transcriptomic high confidence transcriptional target intersection analysis to untreated IPF-induced genes across the 40 Atlas cell types for a panel of receptors curated as part of our SPP initiative ( 9 ). Next, for each candidate receptor we evaluated the correlation of its untreated IPF-induced footprint with that of STAT3 across all 40 cell types. We identified strong correlations with the STAT3 footprint for numerous receptors with established roles in pulmonary fibrosis, including EGFR ( 103 ), TGFBR ( 104 ), TNFR ( 105 ) and Wnt ( 106 ) ( fig. S4E ). Of the receptors analyzed, the strongest correlation with the STAT3 footprint among untreated IPF-induced genes was for EGFR ( r = 0.86, p = 1E-12), an established upstream regulator of STAT3 function, particularly in lung cancer. Comparison of the enrichment profiles of EGFR and STAT3 in untreated IPF ( fig. S4F ) resolved predicted peaks of function in alveolar fibroblasts, with additional peaks in pericytes and other cell types in which peaks of STAT3 had been previously observed. Numerous studies have established the interdependence of STAT3 and EGFR in the regulation of gene expression ( 107 , 108 ), as well as implicating EGFR in the regulation of pro-fibrotic transcriptional programs in the lung ( 103 ). Indeed, STAT3 was originally cloned as a TF activated by EGF signaling ( 109 ). Collectively, our results suggest the importance of an EGFR-STAT3 pathway in clinical IPF, which was particularly strong in the epithelial, stromal and endothelial compartments. STAT3 is a master transcription factor in IPF alveolar fibroblasts Comparing the alveolar fibroblast untreated IPF-induced network ( table S7, column AG) with genes transcriptionally induced in alveolar fibroblasts in untreated IPF ( table S3, column AI), we observed that many TFs with predicted untreated IPF-induced functional footprints in alveolar fibroblasts were also transcriptionally induced in untreated IPF. Accordingly, we hypothesized that gain-of-function of profibrotic TFs in IPF alveolar fibroblasts was related to transcriptional induction of their encoding genes. To investigate this we defined a set of TFs with significant footprints in alveolar fibroblast untreated IPF-induced genes and designated these TFs as the alveolar fibroblast IPF network. We compared the list of alveolar fibroblast IPF network TFs with the list of TF genes that were transcriptionally induced in untreated IPF Atlas alveolar fibroblasts. Strikingly, a total of 23 TFs of the alveolar fibroblast IPF network were encoded by genes that were transcriptionally induced in untreated IPF alveolar fibroblasts ( Fig. 4D ; table S3 , column AV and table S7 , column AW). Consistent with our hypothesis, genes encoding IPF network TFs were robustly enriched among alveolar fibroblast untreated IPF-induced genes (ENR = 4, p = 3E-08; Fig. 4E ). Master TFs supervise regulatory hierarchies in specific cell types and states by modulating expression and function of other TFs ( 110 – 112 ). To characterize the master regulatory network controlling the expression of genes encoding IPF network TFs in alveolar fibroblasts, we performed regulatory network analysis on the set of 23 IPF network TF genes that were transcriptionally induced in Atlas alveolar fibroblasts ( table S9 ). STAT3 had the strongest regulatory footprint in this gene set ( Fig. 4F ). Surveying this network, we identified known STAT3-interacting TFs, including NANOG ( 113 ), AP-1 family members ( 114 ), DDIT3 ( 115 ), MEF2C ( 116 ) and TWIST1 ( 117 ). Moreover, among the top 15 ranked TFs, BATF3 and IRF4 are members of a STAT3 core regulatory network identified in anaplastic large cell lymphoma ( 118 ) (ENR = 31, p = 9E-04; hypergeometric test). In addition, another IPF master network TF, ZNF341, is essential for STAT3 expression in autosomal-dominant hyper-immunoglobulin E (IgE) syndrome ( 119 ). Validating our analysis, a set of TFs emerging from an RNAi screen of lung fibroblast to myofibroblast differentiation ( 120 ), which included STAT3 and the AP-1 members FOS and JUND, was enriched within the master network (ENR = 5, p = 2E-03; Fig. 4G ). In summary, we identified STAT3 as a dominant TF both in the IPF niche and in alveolar fibroblasts, a critical IPF lineage. Characterization of the IPF-induced therapeutic gap Since it represented a logical target for the development of novel antifibrotics, we next wished to characterize the biology of the IPF therapeutic gap. Due to space considerations, we focus here on the IPF-induced therapeutic gap. To characterize this gap, for each cell type we identified genes induced in untreated IPF but not repressed by either approved antifibrotic ( table S10 ). We then performed GO analysis on these genes and ranked terms based on (i) the number of significant enrichments and (ii) mean enrichment -log 10 p across the 40 cell types ( table S11 , columns AS-AU). We repeated this ranking step on the GO terms generated by our previous analysis of all untreated IPF-induced genes ( table S4 , columns AU-AW); a total of 4439 terms were common to both analyses. To prioritize terms with a higher ranking in the IPF-induced gap analysis than in the analysis of all untreated IPF-induced genes, we next calculated the ratio (untreated IPF-induced rank/ IPF-induced gap rank; table S11 , column AW) and ranked terms on this value ( Fig. 5A ). Validating our approach, the GO terms that we had identified as strongly regulated by approved antifibrotics, such as mitochondrial OxPhos and ribosomal biogenesis (refer to Fig. 3A ), were among the lowest overall rankings in table S11 . The top gap-prioritized terms emerging from this analysis were strongly represented in stromal, endothelial and epithelial cell types ( Fig. 5A ) . Consistent with the relatively strong effects of approved antifibrotics observed in the lymphoid and myeloid compartments ( Fig. 3B ), enrichment of GO terms in the IPF-induced gap in these cell types was relatively low. Download figure Open in new tab Figure 5. Characterization of the IPF-induced therapeutic gap. (A) Heatmap showing enrichment of prioritized GO terms in the IPF-induced gap (i.e., genes induced in untreated IPF but not repressed by either antifibrotic) in each cell type. GO terms were prioritized as described in the text. Color palette represents -log10p enrichment scale from p adj < 0.05 (yellow) to p adj <1E-25 (red); blue indicates no significant enrichment. Panels B-F indicate induction in untreated IPF vs. control of genes mapped to the following gap-prioritized GO terms: (B) cell-cell junction in AT2 cells; (C) T cell receptor signaling pathway in TCD8s; (D) cell leading edge in MACMONDs; (E) collagen-containing extracellular matrix in MFBs; (F) endothelial cell development in CAPAs. Full data are in table S8. (G) Scatterplot comparing TF rankings in the untreated IPF-induced (x axis) and IPF-induced gap (y axis) pan-Atlas regulatory networks. Full data are in table S11. (H) Regulatory network plot for the IPF-induced gap in Atlas IPF alveolar fibroblasts. Fig. 5B-F demonstrates selected strongly prioritized IPF-induced gap GO terms in cell types representing each of the five compartments. Untreated IPF-induced genes mapping to GO cell-cell junction in AT2 cells (ranked 2/4439 terms; Fig. 5B ) include EFNA5 , shown to function in a subset of CCR10+ epithelial cells that drive lung remodeling in IPF ( 121 ); KRT18 , a marker of an AT2 early differentiation state that persists in IPF ( 122 ), and VCL , which is induced in IPF epithelial cells ( 123 ). These also include KAZN , which contributes to the development of liver fibrosis ( 124 ) and PLPP3 , whose overexpression promotes cardiac fibrosis and remodeling ( 125 ). Evidence has linked T cell signaling (ranked 15/4439 GO terms; Fig. 5C ) to the progression of IPF ( 88 ). In this context, inhibition of the T-cell tyrosine kinase ITK reduced skin fibrosis in a mouse model of chronic graft versus host disease ( 126 ). The GO term “cell leading edge” (ranked 10/4439 terms) encompasses numerous genes that play critical roles in cell polarization, a process which in macrophages contributes to IPF ( 127 ). Genes induced in untreated IPF in monocyte-derived macrophages that map to this term include GSN ( Fig. 5D ), implicated in ventilator-induced lung injury ( 128 ) and vimentin ( VIM ), citrullination of which contributes to IPF development and progression ( 129 ). The prioritization in the IPF-induced gap of the term “collagen-containing extracellular matrix” (ranked 4/4439 GO terms) was surprising, indicating as it did that approved antifibrotics have only modest impact on the induction of these genes in IPF ( Fig. 5E ). This term contains numerous genes with well documented roles as critical effectors of IPF, including members of the collagen chain family ( 130 ). Finally, the extensive evidence connecting the pulmonary endothelium to the etiology of IPF ( 131 ) is reflected in several genes mapping to the GO term “endothelial cell development” (ranked 9/4439 terms) in IPF capillary aerocytes, including NOTCH4 ( 132 ) and ROBO4 , a prominent pathway node in liver fibrosis ( 133 ) ( Fig. 5F ). These and other IPF-induced gap-prioritized pathways represent attractive targets for the development of gap therapeutics to complement existing approved IPF interventions. Our therapeutic gap-based strategy was designed to prioritize candidate drug targets that were refractory to treatment with approved antifibrotics. Accordingly, we next sought to determine that the dominant position of STAT3 in the untreated IPF-induced pan-Atlas regulatory network ( Fig. 4A ) was recapitulated in the corresponding IPF-induced gap network. To do this, we performed regulatory network analysis on IPF-induced gap gene sets (i.e., genes induced in untreated IPF but not repressed by either antifibrotic) across the 40 cell types (see data in table S12 ). In our regulatory network analysis of all untreated IPF-induced genes, a total of 380 TFs had at least one significant footprint across the 40 cell types ( table S7 , column AV). In our analysis of the IPF-induced gap genes, the number of TFs was reduced to 70. We next ranked TFs in the IPF-induced pan-Atlas gap network using the same approach as for the untreated IPF-induced pan-Atlas network TFs (number of p <0.05 footprints and average footprint size). Fig. 5G plots untreated IPF-induced (x axis) and IPF-induced gap (y axis) rankings for TFs present in both networks. Consistent with its status as a prominent gap TF, STAT3, in addition to TFs with known regulatory relationships with STAT3, such as NFATC1 ( 134 , 135 ), TCF12 ( 136 ) and FOSL1 ( 137 ) had the strongest rankings in the pan-Atlas IPF-induced gap regulatory network. This was consistent with our previous regulatory analysis of antifibrotic-regulated GO terms: of the five GO term regulatory networks in fig. S2C-G , none contained a significant footprint for STAT3. Conversely, STAT3 was by a considerable margin the strongest TF footprint in the alveolar fibroblast IPF-induced gap regulatory network ( Fig. 5H ) . Notably, several TFs such as NFATC1, FOS and JUN had lower rankings in this gap network compared to the regular network, indicating that approved antifibrotics may at least partially impact the function of these TFs in alveolar fibroblasts. STAT3 inhibition reduces the IPF therapeutic gap in precision cut lung slices Based on our analysis to this point we hypothesized that STAT3 inhibition would reduce the therapeutic gap in IPF. The STAT3 inhibitor TTI-101 blocks phosphorylation of STAT3 tyrosine 705, a critical requirement for STAT3 activation ( 138 ), and has antifibrotic properties in experimental models of lung fibrosis ( 61 ). We next set out to validate our hypothesis in the PCLS platform, which has recently emerged as a translational model for IPF ( 139 – 141 ). We treated PCLSs with pirfenidone, nintedanib, TTI-101 or vehicle and conducted scRNA sequencing analysis; the full data are listed in table S13 . Validating the PCLS analysis, we observed robust ( p adj < 0.05) enrichment of PCLS IPF vs control-induced genes among the corresponding Atlas untreated IPF-induced genes for 22 of the 32 cell types in common between the two platforms ( Fig. 6A ). Relative to our explant analysis ( Fig. 3A ), on average across all PCLS cell types we observed a larger percentage of IPF-dysregulated genes that were not impacted by nintedanib and pirfenidone. The larger size of the therapeutic gap in PCLS may be attributable to the relatively short period of exposure to antifibrotics, as well as to the fact that most of the PCLS IPF samples were obtained from inviduals who had previously received antifibrotic therapy. Notably, the strongest identity between the two systems was observed for alveolar fibroblasts, the cell type in which we had previously observed the strongest STAT3 footprint. Superimposing Atlas alveolar fibroblast untreated IPF-induced genes on the IPF-induced network in PCLS alveolar fibroblasts ( Fig. 6B ), we observed induction in both systems of many genes with documented roles as both transcriptional drivers and effectors of IPF. Validating the STAT3-inhibitory effect of TTI-101, we observed strong enrichment of STAT3 HCTs among TTI-101-repressed genes across all cell types ( Fig. 6C ), again with a peak in alveolar fibroblasts. Download figure Open in new tab Figure 6. STAT3 inhibition reduces the IPF therapeutic gap in precision cut lung slices (A) Bar chart showing enrichment -log10p values for the intersection of PCLS IPF-induced and Atlas untreated IPF-induced gene sets across common cell types. (B) Volcano plot showing enrichment of Atlas untreated IPF-induced alveolar fibroblast genes among PCLS IPF-alveolar fibroblast induced genes (C) Functional footprint of STAT3 among TTI-101-repressed genes in PCLSs. Dotted line indicates p adj < 0.05. (D) Manhattan plot comparing the effect on PCLS alveolar fibroblast IPF-induced genes of treatment with nintedanib, pirfenidone and TTI-101. (E) Volcano plot showing enrichment of PCLS alveolar fibroblast IPF vs. CON-induced genes among TTI-101-repressed PCLS alveolar fibroblast genes. (F) Manhattan plot comparing the effect on PCLS AT2 IPF-induced genes of treatment with nintedanib, pirfenidone and TTI-101. (G) Manhattan plot comparing the effect on PCLS capillary aerocyte IPF-induced genes of treatment with nintedanib, pirfenidone and TTI-101. Panels H-L compare numbers of IPF-induced and nintedanib-, pirfenidone- and TTI-101-repressed genes mapping to the indicated IPF-induced gap-prioritized GO term and PCLS cell type; the right panels represent the TTI-101-IPF vs. IPF volcano plot for the corresponding cell type showing TTI-101 repression of the indicated GO term genes: (H) cell-cell junction in AT2 cells; (I) T cell receptor signaling pathway in TCD8s; (J) cell leading edge in MACMONDs; (K) collagen-containing extracellular matrix in MFBs; (L) endothelial cell development in CAPAs. See fig. S4D-H for corresponding TTI-101 volcano plots. We next compared the effect of TTI-101 and approved antifibrotics on the IPF-induced transcriptional program in PCLS alveolar fibroblasts. We first confirmed that nintedanib ( fig. S5A ) and pirfenidone ( fig. S5B )-repressed genes from Atlas alveolar fibroblasts were robustly enriched among respective nintedanib- and pirfenidone-repressed genes in PCLS alveolar fibroblasts, indicating that PCLS recapitulated the in vivo effects of approved antifibrotics in this critical IPF cell type. Consistent with our analysis in Fig. 3A , we observed strong repression by nintedanib and pirfenidone of ribosomal gene expression in alveolar fibroblasts ( fig. S5 A&B ). Next, comparing the relative impact of nintedanib, pirfenidone and TTI-101 on the IPF-induced transcriptional program in PCLS alveolar fibroblasts, we found that of 1182 IPF-induced genes, TTI-101 reversed 409, compared with 157 for nintedanib and 78 for pirfenidone ( Fig. 6D ). Moreover, TTI-101 exerted stronger repressive effects on the IPF-induced program (mean log 2 FC = −0.36) than either approved antifibrotic (nintedanib = −0.04, pirfenidone = −0.34). Confirming that TTI-101 reduced the IPF-induced gap in PCLS IPF alveolar fibroblasts, Atlas alveolar fibroblast IPF-induced gap genes were strongly enriched among TTI-101-repressed genes (ENR = 6, p = 1E-40), and included numerous established drivers and effectors of pulmonary fibrosis ( Fig. 6E ). The limited impact of approved antifibrotics on STAT3 function in alveolar fibroblasts, and the extent to which this deficit is corrected by treatment with TTI-101, is evident in Figure S5C , which shows strong STAT3 footprints in IPF-induced and TTI-101-repressed gene sets and relatively weak footprints in nintedanib- and pirfenidone-repressed gene sets. Although our primary focus was alveolar fibroblasts as the peak of predicted STAT3 activity across the niche, our explant analysis had predicted activation of STAT3 in numerous prominent cell types across the niche ( Fig. 4B ). Accordingly, we were interested to establish whether the robust performance of TTI-101 relative to approved antifibrotics in PCLS alveolar fibroblasts was recapitulated globally across these other cell types. With the exception of two basal epithelial cell types, TTI-101 exerted stronger effects than approved antifibrotics on IPF-induced gene sets across PCLS cell types common to all four experimental conditions (untreated, pirfenidone-, nintedanib- and TTI-101-treated ( fig. S5D ). In addition to alveolar fibroblasts, particularly strong relative effects of TTI-101 were observed in two other cell types with predicted peaks of STAT3 activity inferred from the Atlas analysis, namely AT2 ( Fig. 6F ) and CAPG ( Fig. 6G ) cells. We next evaluated the relative effects of nintedanib, pirfenidone and TTI-101 on PCLS IPF-induced genes mapped to the five gap-prioritized GO terms we had previously highlighted in Fig. 5 . TTI-101 exerted stronger repressive effects than nintedanib or pirfenidone across all five GO terms ( Fig. 6H-L ). AT2 GO cell-cell junction genes repressed in response to TTI-101 treatment ( fig. S5E ) included CADM1 , a mediator of mast cell-fibroblast crosstalk implicated as therapeutic target in IPF ( 142 ) and ANK3 , a member of a prognostic gene signature in IPF ( 143 ). Other TTI-101-repressed genes mapped to this term included CCND1 ( 144 ) and CLDN18 ( 145 ), both implicated in IPF, and PDLIM1 , a potential biomarker of hepatic fibrosis ( 146 ). The effect of STAT3 inhibition on IPF-induced T cell receptor signaling pathway-mapped genes in TCD8 cells was less striking ( Fig. 6I ), but still significant. Of note, one of the IPF-induced genes in this category that was robustly repressed by TTI-101 treatment encoded PDE4B, inhibition of which prevented decrease in lung function in a recent IPF clinical trial ( 147 ) ( fig. S5F ). In monocyte-derived macrophages, STAT3 inhibition resulted in repression of the GO cell leading edge genes GSN ( 128 ) and EVL , which appeared in a peripheral blood IPF phenotype signature ( 148 ) ( fig. S5G ). Reduction of the therapeutic gap by STAT3 inhibition was particularly striking with respect to GO collagen-containing ECM genes in myofibroblasts; treatment with TTI-101 downregulated 34 IPF-induced members of this term, compared to 18 for nintedanib and three for pirfenidone ( Fig. 6K ). These included COL1A1 , COL1A2 and COL1A8 members of the collagen chain gene family, in addition to CTHRC1 , a hallmark gene of profibrotic fibroblasts ( 89 ) ( fig. S5H ). Finally, genes mapped to GO endothelial cell development that were strongly repressed by TTI-101 in capillary aerocytes included SOX18 , associated with asthma exacerbation ( 149 ) and PECAM1, implicated in the pathogenesis of acute respiratory distress syndrome ( 150 ) ( fig. S5I ). STAT3 inhibition disrupts the IPF alveolar fibroblast regulatory network We speculated that the broad antifibrotic effect of the STAT3 inhibitor TTI-101 in Atlas IPF alveolar fibroblasts relative to approved antifibrotics ( Fig. 6D ) was related to its capacity to reverse STAT3-mediated induction of genes encoding members of the IPF network that we had characterized in this cell type ( Fig. 4D &E ). To test this hypothesis in PCLS, we conducted regulatory analysis on PCLS alveolar fibroblast IPF-induced genes ( table S14 , column F). Reflecting the strong identity between transcriptional regulatory mechanisms in Atlas and PCLS alveolar fibroblasts, the top ranked TFs in the Atlas untreated IPF-induced network in this cell type were strongly enriched among the top ranked TFs in its PCLS counterpart ( Fig. 7A ). Download figure Open in new tab Figure 7. STAT3 inhibition disrupts the IPF alveolar fibroblast regulatory network (A) Regulatory network plot for IPF-induced genes in PCLS alveolar fibroblasts showing enrichment of top-ranked Atlas alveolar fibroblast IPF network TFs. Full data are in table S15. (B) Volcano plot of IPF v control PCLS alveolar fibroblast DEGs showing enrichment of genes encoding the 23 untreated IPF-induced FBLAV network TFs. (C) Scatterplot comparing -log 10 p footprints of Atlas (x axis) and PCLS (y axis) alveolar fibroblast IPF master regulatory network TFs. Full data for atlas master network are in table S16. (D) Manhattan plot comparing effect of approved antifibrotics and TTI-101 on IPF-induced genes encoding the 23 untreated IPF-induced FBALV network TFs. (E) Volcano plot of TTI-101-IPF vs. IPF PCLS alveolar fibroblast DEGs highlighting TTI-101 repression of the 23 IPF-induced alveolar fibroblast IPF network TF genes. (F) Regulatory network plot for TTI-101-repressed alveolar fibroblast genes showing enrichment of top TFs in regulatory network for IPF-induced genes. Full data are in table S15. (G) Generalized schematic of STAT3 predicted mechanism of action in IPF alveolar fibroblasts supported by the Atlas and PCLS analysis. STAT3 has a dual role to (i) co-ordinate transcriptional induction of genes encoding TFs predicted to form IPF regulatory networks across numerous cell compartments; and (ii) as a functional member of many of those networks. Although we have focused here on STAT3, autoregulation has been reported for many other members of the FBALV master regulatory network, including AP-1 members and NFATC1, and this mechanism likely contributes to replenishment of the master network in IPF. Similarly, the 23 Atlas alveolar fibroblast IPF network TF genes that were transcriptionally induced in untreated IPF were strongly enriched among PCLS alveolar fibroblast IPF-induced genes ( Fig. 7B ). We next sought to establish that the PCLS alveolar fibroblast master IPF network recapitulated that observed in the Atlas analysis ( Fig. 4F ). We performed regulatory analysis on the set of 44 PCLS alveolar fibroblast IPF network TF genes that were transcriptionally induced in PCLS IPF alveolar fibroblasts ( table S15 ). As shown in a scatterplot of the footprints for TFs common to the Atlas and PCLS alveolar fibroblast master IPF networks ( Fig. 7C ), we observed a strong correlation ( r = 0.62, p = 9E-08) in footprint size between the two systems, with STAT3 occupying the top aggregate position across both networks. Referring to our Atlas analysis, we noted that of the 23 Atlas alveolar fibroblast untreated IPF-induced network TFs ( Fig. 4D ), only three were repressed by pirfenidone, and seven, with relatively small functional footprints, were repressed by nintedanib ( fig. S5 ). Next, we compared the effect of treatment with nintedanib, pirfenidone and TTI-101 on these 23 genes in PCLS. Of the 23 genes, 13 were IPF-induced in PCLS alveolar fibroblasts ( Fig. 7D, p = 3E-08), including STAT3 and the AP-1 members FOS , JUN and JUNB . Consistent with their modest effect in the corresponding Atlas analysis ( fig. S6 ), nintedanib reversed only one of the 23 genes, whereas pirfenidone reversed none ( Fig. 7D ). In contrast, treatment with TTI-101 reversed IPF induction of 12 of the 23 alveolar fibroblast IPF network TF genes ( p = 4E-10). Fig. 7E visualizes the effect of TTI-101 treatment on the 23 gene set in a TTI-101-IPF vs. IPF alveolar fibroblast volcano plot, showing transcriptional repression of genes encoding many IPF network TFs with familiar roles in IPF. Consistent with the known autoregulatory capacity of STAT3 ( 151 , 152 ), TTI-101 reversed induction of the STAT3 gene itself ( Fig. 7E ), whereas treatment with approved antifibrotics had no effect. Given the strong effect of TTI-101 in reversing IPF induction of genes encoding Atlas alveolar fibroblast network TFs in PCLS, we anticipated that TTI-101 would have a profound functional impact on the proteins encoded by these genes. To evaluate this, we carried out regulatory analysis on PCLS FBALV TTI-101-repressed gene sets ( table S14 , column G). Consistent with our thinking, we observed a very strong identity between the top ranked TFs in the Atlas alveolar fibroblast IPF-induced and TTI-101-repressed regulatory networks ( Fig. 7F ). Again, STAT3 and AP-1 family members were prominently represented in the TTI-101-repressed PCLS FBALV regulatory network. Fig. 7G represents a general model of action of STAT3 in IPF alveolar fibroblasts supported by our Atlas and PCLS analysis. A STAT3 autoregulatory loop sustained by profibrotic signaling establishes and amplifies a cellular pool of STAT3 that is largely refractory to treatment with approved antifibrotics (left). In response to profibrotic signaling, this pool of STAT3 drives transcriptional induction of genes encoding IPF network TFs (middle). STAT3 subsequently co-operates with these TFs in regulatory networks that drive induction of profibrotic pathways, such as fibroblast to myofibroblast differentiation ( 120 ). We propose a mechanism of action of TTI-101 comprising three elements: (i) disrupting the STAT3 autoregulatory loop depletes the cellular pool of STAT3 (left); (ii) inhibition of STAT3 transcriptional induction of network TFs diminishes reserves of network TFs (middle); and (iii) inhibition of STAT3 interactions with network TFs compromises induction of downstream profibrotic pathways that are transcriptionally dependent on these networks (right). In summary, our data position STAT3 as a primary transcriptional driver of clinical IPF pathways upon which approved antifibrotics have only a limited impact and, as such, is a strong candidate for the development of novel targeted therapeutics. DISCUSSION Despite their regulatory approval for nearly a decade, transcriptional programs impacted by nintedanib and pirfenidone in clinical IPF have been largely unstudied at scale. Existing IPF atlases have historically combined individuals that were treated or untreated with antifibrotic therapy into the same cohort. As a result, transcriptional changes in IPF have been understated and opportunities missed to develop novel therapeutic strategies to complement existing drugs. Here we set out to establish an IPF atlas that was descriptive not only of untreated IPF itself, but also leveraged the treatment status of IPF subjects as a strategy to discern transcriptional programs that were either responsive or refractory to treatment with approved antifibrotics. In doing so, we introduced the concept of the transcriptional therapeutic gap, which to our knowledge is novel not only in IPF but in human disease and drug discovery more broadly. Applying our regulatory analysis platform across the three subcohorts (untreated, nintedanib- and pirfenidone-treated) we identified STAT3 as a dominant TF in the pan-IPF niche whose function was only partially impacted by nintedanib, and to a negligible extent by pirfenidone. Using PCLS, which validated robustly against the explant analysis, we confirmed STAT3 inhibition as a powerful strategy for reducing the therapeutic gap in critical cell types across the IPF niche. Our study combines single cell expression profiling with clinical treatment status as a novel approach to identifying transcriptional networks with the strongest promise for pharmacological intervention in a given disease. While our data-driven analysis prioritized STAT3 in alveolar fibroblasts, STAT3 footprints were not prominent across all individual cell types in the IPF niche. For example, lymphoid compartment footprints were dominated by canonical T cell TFs with documented connections to fibrosis such as RELA/B, FOXP1, TBX21 and IRF4. Similarly, the myeloid compartment contained prominent footprints for profibrotic TFs with classical connections to dendritic cells and macrophages, including SPI1/PU.1, ZNF366/DC-SCRIPT and CEBP family members. Accordingly, we cannot exclude important profibrotic contributions of TFs other than STAT3 in individual cell types or compartments. In addition to reiterating established profibrotic roles for many TFs, our reduced-bias approach resolves evidence for numerous TFs with previously unappreciated pro- and antifibrotic roles in IPF. The transcriptional repressor TRPS1, for example, occupied an elevated position in our pan-Atlas regulatory network (7 th of 446 TFs; Fig. 4A ). Although loss of TRPS1 function contributes to renal fibrosis ( 153 ), this TF has not been studied in lung fibrosis. TRPS1 had a predicted peak in genes induced in untreated IPF AT2 cells ( Fig. 4A ). Given that its transcriptionally repressed targets include STAT3, RUNX2 and other profibrotic TFs, loss-of-function of TRPS1 may contribute to induction of profibrotic transcriptional programs in IPF. The availability of our analysis as a comprehensive data supplement and web-based resource in NDEx ( Table 1 ) will catalyze research into this and other signaling nodes whose roles in pulmonary fibrosis remain uncharacterized. Efficient drug discovery demands the prioritization of targets that have the broadest transcriptional footprint in a given disease state. Although a broad range of TFs and other signaling nodes have been implicated in experimental IPF, the disparate nature of the studies from which they emerged has complicated appreciation of their relative transcriptional contribution to the development of IPF. Here we overcame this knowledge gap by computing our single cell RNA-Seq atlas against an extensive library of high confidence transcriptional targets for ∼450 human TFs, from which activation of TFs in specific IPF cell types could be inferred and validated. Encouragingly, our regulatory network analysis prioritized numerous TFs that are linked by robust experimental evidence to the etiology of IPF ( Fig. 4A ). The strong PCLS validation of our prioritization of STAT3 reflects the potential of this approach to discover regulatory modules in other cell types of strong relevance to IPF. Building on our previous efforts to define regulatory networks in IPF ( 154 ), our study represents the most comprehensive analysis to date towards defining a transcriptional hierarchy in clinical IPF. Several previous studies from our laboratory and others described the profibrotic role of STAT3 in the lung and other organs ( 61 , 155 – 157 ). These studies provided little indication however of the position of STAT3 in the transcriptional hierarchy of IPF, the cell types in which it was most active in the disease, nor of the impact of approved antifibrotics on its function. By applying our regulatory network pipeline to single cell RNA-Seq analysis of the treatment-segregated cohort, our approach has resolved all three of these questions in a single study and positioned STAT3 as a pre-eminent therapeutic target in IPF. As such, our approach represents a compelling strategy to isolate transcriptional modules whose inhibition is most likely to result in reversal of transcriptional programs that drive other disease states. Master TFs integrate afferent inputs from upstream signaling entities and distribute these signals to regulatory networks whose function they regulate at the transcriptional and translational levels. Our characterization of STAT3 as a master TF in IPF alveolar fibroblasts is validated against several independent lines of evidence. First, the alveolar fibroblast IPF network contains numerous proteins, including several members of the immediate early response class such as AP-1, that interact with STAT3 and whose encoding genes are regulated by STAT3. Secondly, the master IPF regulatory network in these cells bears strong identity with a STAT3 core complex in leukemia. Thirdly, the master network encompassed several TF hits from a screen for genes with essential roles in lung fibroblast differentiation. Moreover, our data strongly align with previous mechanistic studies demonstrating that STAT3 establishes its own regulatory network at the transcriptional and translational levels, through both autoregulation and as a master regulator of other TFs ( 158 ). The status of STAT3 as a master TF may explain why inhibition of a single molecular event, (i.e., phosphorylation of STAT3 tyrosine 705) so comprehensively inverts the transcriptional landscape of IPF relative to approved antifibrotics. Although historically considered more cheminformatically challenging than conventional pharmaceutical targets, our results demonstrate that master TFs represent potentially rewarding targets for disease intervention. Our results align with a number of in vitro studies that have investigated transcriptional responses to treatment with approved antifibrotics. For example, regulation by both antibiotics of genes encoding ribosomal RNA genes reflects their reported inhibition of MTOR, a prominent regulator of ribosomal biogenesis ( 159 , 160 ). Moreover, consistent with previously described effects of nintedanib ( 161 ) and pirfenidone ( 162 ) on T cell activation, we found that several lymphoid cell types were among those with the smallest therapeutic gaps. The fact that these drugs delay but do not reverse the course of the disease, however, reflects a disconnect between their mechanism of action and cell types most relevant to IPF clinical endpoints. Consistent with this, we found that collagen-containing extracellular matrix was among the top gap-prioritized GO terms. Our results clearly demonstrate that approved antifibrotics have only a limited impact on STAT3 function and that this deficit significantly contributes to the extent of the therapeutic gap in IPF. Supporting this assertion, not only did TTI-101 reverse more genes than either nintedanib or pirfenidone across all but two IPF cell types, but the average magnitude of repression was robustly larger than either antifibrotic. A substantial body of evidence indicates that STAT3 is a convergence point for signals from a broad spectrum of cellular receptors. Although approved antifibrotics inhibit a subset of these receptors, the remaining inputs from receptors such as EGF, TNF and members of the cytokine superfamily (e.g., interleukins and chemokines) may sustain the profibrotic functions of STAT3 in the IPF niche. We speculate that incomplete resolution of IPF by approved antifibrotics may involve failure to transcriptionally silence genes encoding STAT3 and other TFs with functional roles as drivers of IPF. To validate our scRNA-seq findings in a translational model that more closely resembles the human lung architecture PCLSs treated with a STAT3 inhibitor. PCLSs retain the native tissue architecture and cellular interactions, providing a more physiologically relevant model compared to dissociated cells. Previous studies have demonstrated an alignment between PCLS exposed to a pro-fibrotic cocktail and single cell analyses of IPF ( 140 ). Here, we have recapitulated clinical IPF-dysregulated signatures ex vivo , to the extent that ∼70% of PCLS cell types exhibited significant transcriptional identity with their explant counterparts ( Fig. 6A ). Beyond the stromal compartment, these included cell types in the epithelial, immune and vascular compartments with known contributions to the development of IPF. We anticipate that free access to the antifibrotic-regulated signatures generated by this study will be valuable to investigators seeking to benchmark their small molecules of interest against approved antifibrotics, and to establish their ability to reduce the therapeutic gap in alveolar fibroblasts and other critical IPF cell lineages. Our study has a number of limitations. Technical shortcomings, such as incomplete coverage of the transcriptome, are common to all single cell high throughput sequencing studies. Moreover, our regulatory network analysis is necessarily restricted to TFs for which ChIP-Seq datasets are archived in public databases. That being the case, we cannot exclude the possibility that TFs not encompassed by our study make significant contributions to IPF pathogenesis. Treatment with TTI-101 reversed IPF induction of many but not all IPF-induced genes, reiterating that further investigation will be required to identify additional core transcriptional drivers of IPF. Furthermore, since our study did not include proteomic or metabolomic platforms, it does not address IPF-dysregulated events interrogated by these approaches nor the extent to which they are impacted by approved antifibrotics. Although our PCLS model recapitulated IPF-dysregulated transcriptional programs in the majority of IPF cell types, its failure to do so in all cell types indicates the need for additional technical refinement. Finally, given its retrospective nature, our study is not positioned to discover critical signaling events in the early stages of IPF development that may also represent promising targets for therapeutic intervention. Despite these limitations, the performance of TTI-101 against approved antifibrotics in PCLS was unequivocal and represents a strong endorsement of the study design. In conclusion, we have assembled a novel treatment-segregated IPF cohort, which we have leveraged to identify transcriptional networks important in IPF that are insufficiently targeted by approved antifibrotics, In doing so we have defined the therapeutic gap, a novel concept in transcriptomic analysis in lung fibrosis and, to our knowledge, in human disease more broadly. The principal outcome of our study was to position STAT3 as a master regulator of transcriptional networks in alveolar fibroblasts and potentially other cell types that drive the pathogenesis of IPF. We envision that the analytical methodology developed in our study, involving combined transcriptomics and regulatory network analyses, will be broadly applicable to similar therapeutic gap analysis and thereby support the development of novel therapeutics for other human diseases. MATERIALS AND METHODS Sample preparation for single cell RNA sequencing Explanted organs were sliced and washed with cold, sterile phosphate-buffered saline (PBS). Biopsies were cryopreserved in Dulbecco’s Modified Eagle Medium (D-MEM) with 10% DMSO, 10% Fetal Bovine Serum, and 1% Penicillin-Streptomycin-Glutamine, and stored at the Baylor College of Medicine Chronic Lung Disease Tissue Repository (H-46823). After thawing at 37°C, the tissue was minced into small pieces and incubated for one hour at 37°C with D-MEM containing Elastase (30 U/ml, Elastin Products Co. EC134), Recombinant DNAase I (500 U/mg, Roche Diagnostics 04536282001), Liberase (0.3 mg/ml, TM Research Grade 05401127001), and 1% Penicillin-Streptomycin-Glutamine. Digestion was halted with 10% Fetal Bovine Serum. The digested tissue was filtered through a 100-micron mesh and centrifuged at 300g for 10 minutes. Cell pellets were resuspended in red blood cell lysis buffer (VWR International, Radnor, PA) for 3 minutes at 37°C, centrifuged again, and resuspended in MACS medium. For cell concentration and viability assessments, cells were stained with Trypan Blue and counted using a DeNovix Celldrop (DeNovix, USA) or a Countess II Automated Cell Counter (Thermo Fisher Scientific, USA). Single cell barcoding, library preparation and sequencing Single cells were barcoded using the 10X Chromium X single-cell platform, and complementary DNA (cDNA) libraries were prepared following the manufacturer’s protocol (Single Cell 5′ Reagent Kits v2, 10X Genomics, USA). Cell suspensions, reverse transcription master mix, and partitioning oil were loaded onto a single-cell chip with a target of 20,000 cells per library, assuming 100% viability, and then processed on the Chromium X. Reverse transcription occurred within the droplets at 53°C for 45 minutes. cDNA was amplified for 12 cycles using a Bio-Rad C1000 Touch thermocycler. Size selection of cDNA was performed with SPRIselect beads (Beckman Coulter, USA) at a SPRIselect-to-sample volume ratio of 0.6. The resultant cDNA was analyzed using an Agilent Bioanalyzer High Sensitivity DNA chip for quality control. cDNA was fragmented with a proprietary enzyme blend, followed by end-repair and A-tailing at 65°C for 30 minutes. Double-sided size selection was conducted using SPRIselect beads. Sequencing adaptors were ligated to the cDNA at 20°C for 15 minutes. cDNA was then amplified with a sample-specific index oligo as a primer, followed by another round of double-sided size selection using SPRIselect beads. Final libraries were analyzed on an Agilent Bioanalyzer High Sensitivity DNA chip for quality control. Libraries were sequenced on the Illumina NovaSeq 6000 system. Cell Ranger (10X Genomics, USA) was used to generate FASTQ files from sequencing data. Adapter sequences were trimmed from the FASTQ data, and reads were demultiplexed, aligned, and counted. Cell type annotation and differential gene expression Single cell lung data from was mapped using 10x Genomics Cell Ranger v7.0.1 onto the human genome reference provided by 10x Genomics. Doublet detection was performed using scrubblet ( 163 ). Data were processed using the Python Scanpy library ( 164 ). We performed guided cell annotation using CellTypist ( 165 ) and references provided by HLCA ( 8 ) in addition to our previous aberrant basaloid cell type reference ( 4 ). UMAP plots were generated using Scanpy. Objects were converted to the Seurat format using the zellkonverter package in R. Cell type marker plots were generated using Seurat ( 166 ). Differential gene expression was generated using MAST ( 167 ) with significance achieved at FDR-adjusted p-value<0.1 and fold change exceeding ±log 2 0.32 (1.25x induced or repressed). Relative differences in abundance of cell types within specific compartments were derived using ChiSquare as implemented in the R statistical system. Precision cut lung slice (PCLS) model Explanted organs were collected under the Chronic Lung Disease Tissue Repository at Baylor College of Medicine (H-46823) with an combined warm and cold ischemia time of less than 12 hours. A complete lobe was dissected from each explant and inflated through the airway using a 60 ml syringe and a 10 Fr catheter with a solution of DMEM, 2% pre-heated low melting point agarose (UltraPure LMP Agarose, Invitrogen, USA), and 1% PSG. The agarose-filled lobe was cooled on wet ice (approximately 4°C) to allow solidification. The lobe was then sliced longitudinally and cored using a 10 mm diameter punch biopsy (P1025, Acu Punch, USA). Each core was subsequently glued to a coring tool, and the remaining space was filled with the previously described agarose solution. Cores were sliced into 500 µm PCLS using a VF-300-0Z Microtome (Precisionary Instruments, USA). Freshly sliced PCLS were immediately placed on a dish filled with DMEM/F12 + 1% FBS chilled on ice. Each PCLS was placed in a well of a 24-well plate and incubated overnight (37°C, 5% CO₂) in 1.5 ml of medium composed of DMEM/F-12 + GlutaMAX 1X (Gibco, Catalog # A41920-01), 1% Penicillin-Streptomycin (Corning, Catalog #30-002-Cl), and 1% FBS (Corning, Catalog # 35-011-CV). After overnight incubation, the medium was changed to 1.5 ml of treatment medium composed of DMEM/F-12 + GlutaMAX 1X (Gibco, Catalog # A41920-01), 1% Penicillin-Streptomycin (Corning, Catalog #30-002-Cl), and 0.1% FBS (Corning, Catalog # 35-011-CV). After 48 hours of incubation, the medium was changed, and six unique PCLS were treated with the previously described treatment medium and one of the following treatment conditions: 1 mM nintedanib (Sigma-Aldrich, SML2848-25MG), 500 mM pirfenidone (R&D Systems, 1093), or 10 mM TTI-101 (C188-9; TVARDI Therapeutics, USA), their respective vehicles, or without additional treatment. The PCLS were incubated for 72 hours with a medium change at 36 hours. After incubation, PCLS were collected and digested for downstream single-cell RNA sequencing analysis as described above. Regulatory network analysis Regulatory network analysis of gene sets has been previously described ( 35 , 168 ). Briefly, consensomes are gene lists ranked according to measures of the predicted strength of their regulatory relationship with upstream transcription factors derived across numerous independent publicly archived transcriptomic or ChIP-Seq datasets ( 9 ). Briefly, to generate human ChIP-Seq consensomes, we first retrieved processed gene lists from ChIP-Atlas ( 169 ), in which genes are ranked based on their mean MACS2 peak strength across available archived ChIP-Seq datasets in which a given TF is the IP antigen. We then mapped the IP antigen to its category, class and family, and organized the ranked lists into percentiles to generate human TF ChIP-Seq consensomes. Genes in the 95th percentile of a given TF consensome were designated high confidence transcriptional targets (HCTs) for that TF and used as the input for the HCT intersection analysis using the Bioconductor GeneOverlap analysis package implemented in R (v. 1.32.0). Given a whole set I of IDs and two sets A ∈ I and B ∈ I, and S = A ∩ B, GeneOverlap calculates the significance of obtaining S. The problem is formulated as a hypergeometric distribution or contingency table, which is solved by Fisher’s exact test. p values were adjusted for multiple testing using the method of Benjamini & Hochberg ( 170 ) to control the false discovery rate as implemented with the p.adjust function in R, to generate p adjusted values. The universe used in the HCTI intersection analysis was set at an estimate of the total number of transcribed (protein and non-protein-coding) genes in the human genome (43,000) ( 171 ). Evidence for a transcriptional regulatory relationship between a TF and the gene set of interest was represented by a larger intersection between the gene set and HCTs for a given node than would be expected by chance after FDR correction ( p adj 0.32 (induced) or <-0.32 (repressed) at FDR 0.32 (induced) or <-0.32 (repressed) at p < 0.05. Gene Ontology analysis and hierarchical clustering Gene Ontology (GO) enrichment analysis for all three GO subontologies was carried out for each of the 40 cell types from the untreated IPF vs control contrast using a universe derived from the Bioconductor org.Hs.eg.db (v. 3.15.0) human annotation package and the enrichGO function from the clusterProfiler (v. 4.4.4) BioC analysis package in R (v. 4.2.1). The 40 cell types from the untreated IPF vs control contrast were hierarchically clustered using GO terms which were significantly enriched (FDR < 0.05) in at least 6 or more of the 40 cell types. Heatmaps were generated using the Bioconductor pheatmap (v. 1.0.12) analysis package with default settings for Euclidean row and column clustering distance and complete clustering method. Analysis of Lung Tissue Research Consortium expression array To process expression array data, we utilized the log 2 summarized and normalized array feature expression intensities provided by the investigator and deposited in GEO. These data are available in the corresponding “Series Matrix File(s)”. The full set of summarized and normalized sample expression values for samples labeled with a final diagnosis of “control” or “IPF/UIP” were extracted and processed in the statistical program R (v. 4.2.1). The IPF/UIP samples were further subcategorized into three disease severity groups based on the reported fvc pre-bronchodilator % predicted values (Mild = FVC > 72%, Medium = FVC 50%, Severe = FVC < 50%). To calculate differential gene expression for experimental contrasts, we used the linear modeling functions from the Bioconductor limma (v. 3.52.3) analysis package ( 172 ). Initially, a linear model was fitted to a group-means parameterization design matrix defining each experimental variable (control, mild, medium, and severe). Subsequently, we fitted a contrast matrix that recapitulated the sample contrasts of interest, in this case IPF/UIP severe vs Control, IPF/UIP medium vs Control, and IPF/UIP mild vs Control, producing fold-change and Benjamini-Hochberg FDR corrected significance values for each array feature present on the Affymetrix Human Gene 1.0 ST Array. The current Bioconductor array annotation library was used for annotation of array identifiers. LIST OF SUPPLEMENTARY MATERIALS Supplementary tables Table S1. Subject characteristics Table S2. HLCA cell type classification and acronyms Table S3. IPF single cell RNA-Seq Atlas Table S4. Gene Ontology term enrichments Table S5. Transcription factor regulatory networks for 508 k=25 clades Table S6. Atlas DEG breakdown by therapy status Table S7. Cell type-specific TF regulatory networks for the IPF Atlas Table S8. LTRC severe vs. mild IPF regulatory network Table S9. Atlas alveolar fibroblast master regulatory network Table S10. IPF-induced therapeutic gap genes Table S11. Prioritization of IPF-induced gap GO terms Table S12. IPF therapeutic gap TF regulatory networks Table S13. IPF precision cut lung slice (PCLS) single cell RNA-Seq dataset Table S14. PCLS alveolar fibroblast regulatory networks: IPF-induced and TTI-101 repressed Table S15. PCLS alveolar fibroblast master regulatory network FUNDING This work was supported by the following: NCI 1U24CA269436-01A1 to RTP; NIAMS K01AR074558 and NHLBI R01HL176934 to KT; an Ann Theodore Foundation Breakthrough Sarcoidosis Initiative award to LJC; and a Tvardi Therapeutics sponsored research agreement and Three Lakes Foundation and Boehringer Ingelheim awards to IOR. COMPETING INTERESTS Julian A. Villalba is a federal employee of the U.S. government at the Division of High-Consequence Pathogens and Pathology at the Centers for Disease Control and Prevention. This work was not funded by CDC, and the data, results, and opinions included in this manuscript do not reflect the views or positions of this federal agency. Sandeep Agarwal holds a patent for the use of TTI-101 (formerly C188-9) in the treatment of fibrosis and has received licensing fees from Tvardi Therapeutics within the past year. DATA AND MATERIALS AVAILABILITY Processed RNA-Seq data and regulatory networks relevant to this study have been deposited in NDEx under the digital object identifiers (DOIs) indicated in table 1. SUPPLEMENTARY FIGURE LEGENDS Download figure Open in new tab Figure S1. Relative cell counts within lung compartments showing changes induced in untreated IPF in untreated patients relative to controls and by antifibrotic treatment (nintedanib or pirfenidone) in the (A) epithelial (B) lymphoid (C) myeloid (D) stromal and (E) endothelial compartments. Fisher’s exact test was used to assess significance of change in proportion; -log10(p-value) are indicated. Download figure Open in new tab Figure S2. Overview of regulatory network analysis platform. (A) Consensome analysis. We mapped over 10,000 public transcriptomic or ChIP-Seq experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships, we generated consensus’omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy in experiments mapped to a specific node family [1]. (B) HCT intersection analysis. The 95th percentile of each consensome was defined as high confidence transcriptional targets (HCT) for a specific node or node family. To predict transcriptional regulators for a given condition, a hypergeometric test is used to compute the overlap between HCTs and the clinical gene sets of interest. [2-4]. (C) Validation. Loss- and gain-of-function experiments are designed to validate the predictions generated by the regulatory network analysis. [1] Ochsner et al. (2019) Sci Data 6, 252. PMID: 31672983. [2] Ochsner et al. (2020) Sci Data. 7, 314. PMID: 32963239. [3] Celada et al. (2023) Sci Transl Med. 15, eade2581. [4] Rosas-Quintero et al. Am J Respir Crit Care Med. 209, 48-58. PMID: 37934672. Download figure Open in new tab Figure S3. (A) -log 10 p heat map showing enrichment of nintedanib-repressed Atlas gene sets (horizontal axis) among untreated IPF-induced Atlas gene sets (vertical axis) per hypergeometric test. (B) -log 10 p heat map showing enrichment of pirfenidone-repressed Atlas gene sets (horizontal axis) among untreated IPF-induced Atlas gene sets (vertical axis) per hypergeometric test. Panels C-G represent regulatory network plots for the genes in yellow in Fig. 3 C-G. (C) Regulatory network plot for Fig. 3C . (D) Regulatory network plot for Fig. 3D . (E) Regulatory network plot for Fig. 3E . (F) Regulatory network plot for Fig. 3F . (G) Regulatory network plot for Fig. 3G . Download figure Open in new tab Figure S4. (A) Validation of pan-Atlas regulatory network analysis using a panel of FIBROAD-curated TFs with established roles in pulmonary fibrosis. x axis: number of significant footprints; y axis: average footprint size. (B) Volcano plot showing distribution of STAT3 HCTs in untreated IPF vs. control DEGs in alveolar fibroblasts. (C) Regulatory network plot for untreated IPF-induced genes in alveolar fibroblasts. (D) Volcano plot showing distribution of STAT3 HCTs in severe vs. mild IPF DEGs in the LTRC dataset GSE32537. (E) Pearson correlation analysis of receptor families and STAT3 functional footprints across untreated IPF-induced gene sets in the 40 Atlas cell types. (F) Correlation plot of predicted EGFR and STAT3 footprints in untreated IPF-induced genes across the 40 cell types. Download figure Open in new tab Figure S5. (A) Volcano plot of NTD-IPF vs. IPF-regulated genes in PCLS alveolar fibroblasts showing enrichment of Atlas NTD-repressed genes among PCLS NTD-repressed genes. (B) Volcano plot of PFD-IPF vs. IPF-regulated genes in PCLS alveolar fibroblasts showing enrichment of Atlas NTD-repressed genes among PCLS NTD-repressed genes. (C) Bar chart comparing STAT3 footprints in PCLS alveolar fibroblast IPF-induced, nintedanib-repressed, pirfenidone-repressed and TTI-101-repressed gene sets. (D) Bar chart comparing nintedanib, pirfenidone and TTI-101 reversal of IPF-induced transcriptional programs across all PCLS cell types. (E-I) TTI-101-IPF vs. IPF volcano plots showing TTI-101 repression of the indicated IPF-induced gap-prioritized GO term genes in selected cell types: (E) cell-cell junction in AT2 cells; (F) T cell receptor signaling pathway in TCD8s; (G) cell leading edge in MACMONDs; (H) collagen-containing extracellular matrix in MFBs; (I) endothelial cell development in CAPA cells. Download figure Open in new tab Figure S6. Effect of nintedanib and pirfenidone on the 23 untreated IPF-induced Atlas alveolar fibroblast genes encoding IPF network TFs. 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