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Abstract
Human lungs enable efficient gas exchange and form an interface with the environment,
which depends on mucosal immunity for protection against infectious agents. Tightly
controlled interactions between structural and immune cells are required to maintain
lung homeostasis. Here, we use single-cell transcriptomics to chart the cellular landscape
of upper and lower airways and lung parenchyma in healthy lungs, and lower airways
in asthmatic lungs. We report location-dependent airway epithelial cell states and
a novel subset of tissue-resident memory T cells. In the lower airways of patients
with asthma, mucous cell hyperplasia is shown to stem from a novel mucous ciliated
cell state, as well as goblet cell hyperplasia. We report the presence of pathogenic
effector type 2 helper T cells (TH2) in asthmatic lungs and find evidence for type
2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of
cell-cell interactions identifies a shift from airway structural cell communication
in healthy lungs to a TH2-dominated interactome in asthmatic lungs.
Barrier tissue dysfunction is a fundamental component of chronic human inflammatory diseases 1 . Specialized epithelial subsets—including secretory and ciliated cells—differentiate from basal stem cells to collectively protect the upper airway 2–4 . There, allergic inflammation can develop from persistent activation 5 of Type 2 immunity 6 (T2I), resulting in chronic rhinosinusitis (CRS): ranging from rhinitis to severe nasal polyps 7 . Basal cell hyperplasia is a hallmark of severe disease 7–9 , yet how these progenitors 2,10,11 contribute to clinical presentation and barrier tissue dysfunction in humans remains unexplored. Profiling primary human surgical CRS samples (18,036 cells, n=12) that span the disease spectrum with Seq-Well 12 for massively-parallel single-cell RNA-sequencing (scRNA-seq), we report transcriptomes for human respiratory epithelial, immune, and stromal cell types/subsets from a T2I inflammatory disease, and map key mediators. Through comparison with nasal scrapings (18,704 cells, n=9), we define core, healthy, inflamed, and polyp secretory cell signatures. We find striking differences between the epithelial compartments of the non-polyp and polyp cellular ecosystems, identifying and validating a global reduction of cellular diversity in polyps characterized by basal cell hyperplasia, concomitant decreases in glandular cells, and phenotypic shifts in secretory cell antimicrobial expression. We detect an aberrant basal progenitor differentiation trajectory in polyps, and propose cell-intrinsic 13 , epigenetic 14,15 , and extrinsic factors 11,16,17 that lock polyp basal cells into this uncommitted state. Finally, we functionally validate that basal cells ex vivo retain intrinsic memory of IL-4/IL-13 exposure, and test the potential for clinical administration of IL-4Rα blockade to modify basal and secretory cell states in vivo. Overall, we identify that a key element of T2I barrier tissue dysfunction is reduced epithelial diversity stemming from functional shifts in basal cells. Our data demonstrate that epithelial stem cells may contribute to the persistence of human disease by serving as repositories for allergic memories. The T2I module 6 regulates homeostatic processes 18 (metabolism), host defense 19 (parasites, venoms, allergens, toxins) and inflammatory tissue repair 11 . However, this module may become self-reinforcing in allergic inflammation leading to substantial alterations in gross tissue architecture 20 as observed in polyps 7 . To investigate how the overall tissue cellular ecosystem shifts in composition and state during chronic respiratory T2I disease in humans, we used Seq-Well 12 to profile the ethmoid sinus (EthSin) of patients spanning the CRS spectrum (Fig. 1a; Supplementary Table 1; Methods; Supplementary Information; n=12 samples: 6 non-polyp, 6 polyp). Deconstructing these tissues into their component cells provides a unique lens into the cellular ecosystem of human T2I, helping us to: 1. characterize each major cell type without biases typically introduced by marker pre-selection; 2. evaluate cell types/states with striking disease-associated transcriptional differences; and 3. reconstruct tissue-level dynamics. We derived a unified cells-by-genes expression matrix (18,036 cells) and performed dimensionality reduction and graph-based clustering (Fig. 1a; Extended Data Fig. 1a,b; Supplementary Table 2; Methods). We used complete lists of cluster-specific genes to identify epithelial 2 , stromal 7,20 , and immune cells 4,6 , recovering a reproducible distribution of cell types across patients (Fig. 1b,c; Extended Data Figs. 1c–e&2a–e; Supplementary Table 3; Methods; Supplementary Information). We highlight the major types recovered: basal (KRT5) and apical (KRT8)—which orient the pseudostratified epithelial division—further specialized ciliated (FOXJ1) and glandular 3 (LTF) cells, and supportive endothelial (DARC), fibroblast (COL1A2), plasma (CD79A), myeloid (HLA-DRA), T (TRBC2), and mast cells (TPSAB1) (Extended Data Fig. 1e). For each cell type, sub-clustering revealed further, potentially meaningful heterogeneity, providing a useful reference atlas for studying human inflammatory diseases of barrier tissues (Extended Data Fig. 3a–c; Supplementary Information). Next, we charted the cell-of-origin for chemokines and lipid mediators, which aid in the recruitment and positioning of lymphoid and myeloid cells in tissues during T2I 21 (Extended Data Fig. 4a; Supplementary Information). For example, we found mast cells specifically enriched for HPGDS, PTGS2, and ALOX5, suggesting they may be a dominant source of prostaglandin D2, implicated in activation of T-helper 2 (Th2) cells 4 . Alongside these mediators, the production of instructive first-order cytokines primes recruitment and activation of effector mechanisms. In particular, IL-25, IL-33, and TSLP are broadly regarded as epithelial-derived cytokines 4,5,16,20,22 , yet little is known about their cell-of-origin in human disease. TSLP was uniquely restricted to basal cells, which may link increased basal cell numbers to activation of effector cells (Fig. 1d; Extended Data Figs. 3a&4b,c; Supplementary Information). Second-order effector cytokines were identified in a subset of CD4+ T cells expressing IL4, IL5, IL13, and HPGDS, fitting the profile of allergen-specific Th2A cells 23 (Fig. 1d; Extended Data Fig. 4c–e; Methods; Supplementary Information). Additionally, substantial numbers of mast cells produced IL5 and IL13, which along with myeloid cells, were the main expressers of the tissue-reparative cytokine AREG 22 . Notably, patients with or without polyps showed consistent cells-of-origin for T2I-related chemokines, lipids, and cytokines, except select mediators (Extended Data Fig. 4a,b; Supplementary Information). Several genome-wide association studies (GWAS)-implicated genes for allergic diseases 24 were restricted to specific cell types. Thus, we mapped the expression of proposed risk genes including GATA2, IL1RL1 (IL-33R), CDHR3, KIF3A, TMEM232 and MYC (Extended Data Fig. 4f; Supplementary Information). Cellular maps of tissues frequently affected by inflammatory disease should aid in providing mechanistic insights into genotype-phenotype interactions. We further analyzed clusters within the broad epithelia (Fig. 2a; Extended Data Fig. 5a–c) providing single-cell human transcriptomes 25 for basal, secretory, glandular, and ciliated cell types from a T2I ecosystem (Fig. 2a,b; Extended Data Fig. 5; Supplementary Table 3). Epithelial marker gene analysis identified conserved programs present in basal (clusters=3), differentiating/secretory (clusters=3), glandular (clusters=2) and ciliated (clusters=1) types (Fig. 2a,b; Extended Data Fig. 5a–d; Supplementary Table 3, Supplementary Information) 2,3 . Observing striking polyposis-related differences across clusters within cell types (Fig. 2c; Extended Data Fig. 5e; Supplementary Information), we quantified the numerical over-representation of cells from non-polyp and polyp ecosystems within each cluster and type. The clusters comprising basal, differentiating/secretory, and glandular cells showed the most significant links to disease-state (p-values: Fisher’s with least-significant-difference; Fig. 2c; Supplementary Table 3). We compared transcriptomes of differentiating/secretory cells 3 (containing KRT8 secretory and apical goblet cells), noting that secretory cells from polyps appear to supplant antimicrobial function with tissue-repair (Fig. 2d; Supplementary Table 3, Supplementary Information). Intriguingly, we observe expression of MUC5B within glandular mucus cells (cluster 13), but MUC5AC in a distinct subset of secretory goblet cells co-expressing SCGB1A1 and FOXA3 (Fig. 2b; Extended Data Fig. 5f,g, Supplementary Information). This suggests the goblet cell program is layered atop a secretory cell base 2 . We also assessed glandular heterogeneity identifying five discrete subsets with variegated antimicrobial expression 3 (Fig. 2a; Extended Data Fig. 6a,b; Supplementary Table 3; Supplementary Information). This compartmentalization may represent a mechanism for regulated secretion, with imbalances affecting innate host defense. To contextualize disease-associated shifts, we turned to sino-nasal scrapings as a method of sampling healthy apical cells through Seq-Well (Extended Data Fig. 6c,d, Methods; Supplementary Tables 3&6; 18,704 additional cells: n=3 healthy inferior turbinate (InfTurb), n=4 polyp-patient InfTurb, n=2 polyp directly). We recovered immune cells, differentiating/secretory and ciliated epithelial cells from the InfTurb of patients with polyposis and healthy controls, but basal cells only in polyp scrapings (Extended Data Fig. 6d–f; Supplementary Table 3; Supplementary Information). By combining all epithelial cells from surgical resections with scrapings (Fig. 2a–c,e), we identified a conserved secretory core gene set present in all sites sampled, and healthy, CRS-InfTurb, CRS-EthSin-non-polyp and CRS-EthSin-polyp specific gene signatures. Overall, we note a shift from IFNα-/IFNγ-induced to IL-4/IL-13-induced genes with increasing disease severity (Fig. 2e–g; Supplementary Table 3; Supplementary Information). Secretory cells from involved CRS-EthSin tissue differ significantly from the InfTurb, and secretory cells in non-polyp and polyp EthSin reach distinct states whose altered functionality may be linked to disease severity. As specialized epithelial cell types arise from basal progenitors 2,10 , we formally examined their distribution in each sample (Fig. 3a; Extended Data Fig. 7a). Our data indicate a significant loss of epithelial ecological diversity in nasal polyps by Simpson’s Index (Methods), largely driven by glandular and ciliated cell depletion, and an enrichment in basal cells (Fig. 3a,b; Extended Data Fig. 7a–d; Supplementary Information). This altered diversity tracked closely with rank-ordered pathology of patient tissue samples, which correlated positively with basal cell frequency (r=0.6252) and negatively with epithelial diversity (r=−0.6824; Extended Data Fig 7e). We speculate immune cells in polyps may represent an overcorrection in attempting to balance the epithelial compartment (Extended Data Fig. 7f; Supplementary Information). To confirm our epithelial findings, we utilized complementary approaches. With flow cytometry 10 , we demonstrated that the frequency of basal cells significantly increased in polyps at the expense of differentiated epithelial cells in 13 additional patients (Fig. 3c; Extended Data Fig. 7g,h). Using histology (not subject to dissociation-induced artifacts like scRNA-seq or flow cytometry), we confirmed 8 a significant increase of p63+ cells per 1,000μm2 of epithelial area, and a striking loss of glands in polyps (Fig. 3d,e; Extended Data Fig. 7i,j; Methods). Finally, we utilized marker genes for specialized lineages to deconvolve bulk EthSin-tissue RNA-seq of another 27 individuals. We identified four patient clusters and confirmed glandular enrichment in non-polyps, and shifts in secretory cell states and the progressive acquisition of basal-associated transcripts in polyps (Fig. 2d,f; Fig. 3f,g; Supplementary Tables 1&3; Methods, Supplementary Information). We also validated these findings with publicly-available RNA-seq datasets containing normal human sinus tissue and polyps (Extended Data Fig. 7k,l; Supplementary Information). To identify what mechanisms might account for decreased epithelial diversity in polyps, we compared the transcriptomes of non-polyp and polyp basal progenitors 2,10 , identifying elevated polyp expression of transcripts involved in extracellular matrix remodeling and chemo-attraction of effector cells, and a decrease in protease-inhibitor expression and metabolic genes (Fig. 4a). As some of these upregulated genes are IL-4/IL-13 responsive 7 , we assessed cytokine-induced gene sets. A combined IL-4/IL-13 signature is strongly induced not only in differentiated polyp epithelium, but also in basal cells, with a large effect size between disease states (Fig. 4b,c; Extended Data Fig. 8a; Methods; Supplementary Table 4). IFNα- and IFNγ-signatures—indicative of a Type-1 immune module 6 —have small effect sizes (Extended Data Fig. 8b; Supplementary Table 4). Furthermore, from specific hallmark genes, we observed altered balance between Wnt (CD44) and Notch (HEY1) signaling in polyp epithelium favoring Wnt 26,27 (Fig. 4a,c; Supplementary Table 4). We further contextualized our basal cell findings by defining alterations in the fibroblast niche that correlate with basal hyperplasia, and identifying significant changes in myeloid and endothelial cell gene expression (Extended Data Figs. 7b&8c–f; Supplementary Information). Next, we used diffusion pseudotime mapping (Methods), aligning and reconstructing how basal cells differentiate to mature secretory cells to identify where basal cells become “stuck” in polyps. In the non-polyp ecosystem, we observed that basal cells traverse a wider swath of common pseudotime, with the majority of secretory cells distributed towards the trajectory’s terminus (Fig. 4d,e; Extended Data Fig. 9a; Methods). Conversely, in polyps, basal cells accumulate shy of the trajectory’s midpoint, losing the true progenitor position occupied by cluster 8, yet failing to contribute towards later differentiation states (Fig 4d,e; Extended Data Fig. 9a). Ordering cells along this common axis, we identified several genes dysregulated in polyps during epithelial cell differentiation (Extended Data Fig. 9b; Supplementary Table 3; Supplementary Information). We sorted basal cells (Extended Data Fig. 7h) from 3 non-polyp and 7 polyp tissues and performed Omni-assay for transposase accessible chromatin-(ATAC)-Seq to identify intrinsic epigenetic changes from the integration of extrinsic cellular signaling events 28 , and subsequent bulk RNA-seq to confirm/extend our findings (Methods). Polyp basal cells were enriched in peaks for bZIP transcription factor motifs, including various AP-1 family members 11 , such as JUN, FOXA1, ATF3, KLF5 and p63 itself associated with the maintenance of an undifferentiated state, chromatin opening, and oncogenesis (Fig. 4f; Extended Data Fig. 9b–f; Supplementary Table 5, Supplementary Information). Clustering of enriched motifs revealed changes in correlation by disease state (Extended Data Fig. 9c–f; Supplementary Information). We identified expressed candidate transcription factors that may bind to these accessible sites (Fig. 4f,g; Extended Data Fig. 9e,f; Supplementary Information). Collectively, our transcriptomic, pseudotemporal, and epigenetic studies led us to hypothesize that during chronic T2I, basal cell differentiation is intrinsically impaired through the influence of extrinsic cues (e.g., IL-4/IL-13 and Wnt pathway). To functionally test for intrinsically altered differentiation potential in vitro, we first seeded basal cells from non-polyp or polyp tissue into air-liquid interface (ALI) cultures (Fig. 5a; Extended Data Fig. 9g; Supplementary Table 3; Methods; Supplementary Information). Our data suggest that basal cells from polyps can be released from their “stuck” state and differentiate towards a mixed-tissue secretory cell phenotype if provided with strong and sustained extrinsic cues, even in the presence of IL-13 (Fig. 5b; Extended Data Figs. 7h&9f,h,i; Supplementary Information). Second, as ALI cultures enforced strong terminal differentiation, we directly tested how IL-4/IL-13 act to induce rapid expression of genes in basal cells cultured 5 weeks ex vivo hypothesizing that polyp basal cells would respond more vigorously to exogenous cytokines than non-polyp ones 14 . Surprisingly, we identified 482 genes induced in non-polyp basal cells, but only 42 in polyps (Fig. 5c; Supplementary Table 3). PCA highlighted that while unstimulated non-polyp basal cells grouped together, those from polyp basal cells were distributed along PC1, which captured cytokine stimulation (Fig. 5c). Identifying overlaps in genes significantly induced by cytokine treatment in non-polyp basal cells with genes upregulated at baseline in polyp vs. non-polyp basal cells resolved 132 genes (Fig. 5c; Supplementary Table 3). We focused on the central overlap of these three differential expression tests, which included CTNNB1 (β-catenin), the key effector of Wnt pathway activation 26,27 (Fig. 5c). We highlight the fundamental finding that CTNNB1 was robustly induced in non-polyp and polyp basal cells in a dose-sensitive fashion to IL-4 and IL-13. Moreover, baseline CTNNB1 expression in polyp basal cells was equivalent to the levels induced by cytokine treatment of non-polyp cells (Fig. 5d). Wnt-pathway target genes were significantly upregulated across doses tested, confirming activation of the pathway overall, and of specific factors (CTGF) (Fig. 5d; Extended Data 9j; Supplementary Table 4; Supplementary Information). Based on polyp epithelial gene signatures (Fig. 4c), and our functional testing for IL-4/IL-13 induced genes “remembered” by polyp basal cells (Fig. 5c,d), we propose that chronic IL-4/IL-13 exposure in vivo can lead to persistent expression of Wnt/CTNNB1 target genes in a cell-intrinsic fashion, even in the absence of exogenous cytokine. One polyp patient sampled through scraping commenced treatment with a monoclonal antibody (mAb) targeting the shared IL-4Rα subunit of the IL-4 and IL-13 receptors to treat atopic dermatitis, allowing us the chance to examine the in vivo relevance of our observational, mechanistic and functional data on how T2I cytokines influence basal cell states (Fig. 5e; Extended Data Fig. 10a,b; Methods). We compared cells recovered from pre- and 6-week-post-mAb scrapings, and through surgical intervention at 7 weeks post-mAb (Fig. 5e; Extended Data Fig. 10a,b; Supplementary Table 7; Supplementary Information). We identified basal cells and generated a heatmap containing their top marker genes, agnostic to treatment, followed by genes differentially expressed pre- and post-treatment, leveraging myeloid cells to identify basal-specific changes (Fig. 5f; Extended Data Fig. 10a–c; Supplementary Table 3; Supplementary Information). Contextualizing these findings within our previous data, we identify several key gene sets, including a conserved core set of basal cell genes (Extended Data Fig. 10d). Intriguingly, transcription factors upregulated in polyp basal cells identified through Omni-ATAC-seq/RNA-seq (ATF3, KLF5, FOSB) were significantly decreased by treatment (Fig. 5g). While Wnt pathway target gene expression was globally reduced, CTNNB1 expression was notably retained, as were genes upregulated both in vitro and in vivo in polyp basal cells, suggesting that some genes in this patient, and at this timepoint, persist (Fig. 5g; Extended Data Fig. 10d; Supplementary Information). Lastly, we sought to understand how changes in the basal epithelium propagated through to secretory cells. Within secretory cells recovered from scrapings of both InfTurb and accessible polyp tissue pre- and post-treatment, our data suggest that even though EthSin-CRS samples have unique secretory cell signatures (Fig. 2f), cytokine blockade leads to gene expression associated with healthy InfTurb secretory cells, even in polyp tissue (Extended Data Fig. 10e–h; Supplementary Table 8; Supplementary Information). One goal of understanding the cellular and molecular pathways activated in T2I is to provide mechanisms which explain persistent chronic allergic inflammatory disease 29 . Utilizing scRNA-seq applied to patients across the CRS spectrum, our study provides descriptive, mechanistic and functional insights into an enigmatic basal cell state and productive differentiation of a barrier tissue. We show striking differences in antimicrobial expression by secretory cells relative to healthy tissue, a loss of glandular cell heterogeneity, and that IL-4/IL-13 strongly induce a transcriptional program already at the level of basal progenitor cells 15 . Our data may help to explain why nasal polyposis is associated with infections by specific microorganisms 7 , and how a mAb targeting the shared IL-4/IL-13 receptor can reduce nasal polyp burden (Methods). Taken together with recent work in the murine intestinal tract and skin 11,13,16,17,30 , we provide human evidence for the emerging paradigm of stem cell dysfunction altering the set-point of barrier tissues, highlighting substantial overlap amongst putative driving transcription factors (ATF3, AP-1, TP63, and KLF5) 13 . This demonstrates that the principle of inflammatory memory 28 underlying barrier tissue adaptation is a generalizable phenomenon observed in distinct anatomical locations, inflammatory modules, and species. We build on these findings by culturing basal cells ex vivo and identifying the indelible mark of IL-4/IL-13 as a baseline induction of the Wnt pathway. We propose that basal cells form “memories” of chronic exposure to an inflammatory T2I environment, shifting the entire cellular ecosystem away from productive differentiation, and propagating disease. Future work will seek to determine the relative contributions of memory stored in distinct cellular compartments to develop the most effective mechanisms by which to erase them. METHODS Study Participants and Design for Single-Cell Study from Ethmoid Sinus Tissue Subjects between the ages of 18 and 75 years were recruited from the Brigham and Women’s Hospital (Boston, Massachusetts) Allergy and Immunology clinic and Otolaryngology clinic between May 2014 and March 2018 (Supplementary Table 1). The Institutional Review Board approved the study, and all subjects provided written informed consent. Ethmoid sinus (EthSin) tissue was collected at the time of elective endoscopic sinus surgery from patients with physician-diagnosed CRS with and without nasal polyps based on established guidelines 31 . Patients with polyps include both aspirin-tolerant chronic rhinosinusitis with nasal polyps (CRS polyp) and individuals with aspirin-exacerbated respiratory disease (AERD), both referred to as CRS-EthSin-polyp for the purposes of this study. Patients were suspected of having AERD if they had asthma, nasal polyposis, and a history of respiratory reaction on ingestion of a COX 1 inhibitor, with confirmation via a graded oral challenge to aspirin. Subjects with cystic fibrosis and unilateral polyps were excluded from the study. No distinctions were made between these two disease endotypes in our study as both present with polyposis, but we present the information of clinical diagnosis in Supplementary Table 1. A tissue segment (one per patient) for bulk tissue RNA-seq was immediately placed in RNAlater (Qiagen) for RNA extraction, For patient samples loaded on Seq-Well and for flow-sorting to Omni-ATAC-seq/RNA-seq, tissue was received in-hand, placed in RPMI (Corning) with 10% FBS (ThermoFisher 10082–147) and immediately put on ice for transport. Details of the subjects’ characteristics included in scRNA-seq cohort, tissue RNA-seq cohort, and basal cell flow cytometry/ATAC-seq/RNA-seq cohort (including age, gender, medication use, and disease severity) are included in Supplementary Table 1. NB: Originally, we enrolled a healthy control subject with no known history of CRS or nasal polyposis who was undergoing sinus surgery for concha bullosa. However, this subject upon pathology evaluation was noted to have mild eosinophilia. A chart review revealed a history of allergic rhinitis and asthma, and their diagnosis was updated to CRS non-polyp clinically by the surgeon upon follow-up visits so we updated their status accordingly in our study. Additionally, non-polyp patient 6 was sampled twice (denoted as 6A and 6B), representing distinct cells that were captured on two different Seq-Well arrays. As such, they should not be viewed as a technical replicate and are referred to as distinct samples. Collection of Inferior Turbinate and Nasal Polyp Samples through Nasal Scraping Nasal samples were collected from the inferior turbinate (InfTurb) of healthy control subjects and from the inferior turbinate and accessible polyp tissue in subjects with CRS-EthSin-polyps using the Rhino-Pro® Curette, a sterile, disposable, mucosal collection device, as described 32,33 . One sample was taken from the right and left mid-inferior portion of the inferior turbinate using a gentle scraping motion. In two subjects with CRS polyp, with accessible nasal polyp tissue, the polyp tissue was sampled using the Rhino-Pro® Curette under direct visualization. The nasal scrapings were placed directly in RPMI with 10% FBS and immediately put on ice for transport before loading on Seq-Well arrays. Details of the subjects’ characteristics (including age, gender, medication use, and disease severity) are included in Supplementary Table 1. Nasal scraping allows for access to the superficial epithelial cell layer of the inferior turbinate 34 ; in contrast, the surgical resections from ethmoid sinus we utilize as the central data set of this paper contain both epithelial cells and underlying tissue, including sub-mucosal glands 34 (Extended Data Fig. 6c). Since scraping samples a proximal but distinct anatomical location with a distinct technique, in addition to collecting inferior turbinate scrapings from healthy controls (n=3), we also collected inferior turbinate scrapings from individuals with polyps (n=4), and, from two of these individuals, from accessible polyps protruding beyond the middle meatus (n=2). One subject with CRS polyps and co-morbid severe atopic dermatitis was started on dupilumab 35 , a human monoclonal antibody that binds to the IL-4Rα subunit approved for severe atopic dermatitis 36 , and in a randomized, double-blind, placebo-controlled parallel-group study was shown to significantly reduce endoscopic nasal polyp burden after 16 weeks 37 . The inferior turbinate and nasal polyp tissue was sampled with the Rhino-Pro® Curette pre- and post-treatment with 3 doses of dupilumab, and through endoscopic sinus surgery as noted above. Tissue Digestion Single-cell suspensions from collected surgical specimens were obtained using a modified version of a previously published protocol 38 , described below in detail. Each specimen was received directly in hand and processed directly with an average time from patient to loading onto the Seq-Well platform of 3 total hours, and never exceeding 4 hours. Surgical specimens were collected into 30 mL of ice cold RPMI (Corning). Specimens were finely minced between two scalpel blades and incubated for 15 minutes at 37°C in a rotisserie rack with end-over-end rotation in 25 mL digestion buffer supplemented with 600 U/mL collagenase IV (Worthington) and 20 μg/mL DNAse 1 (Roche) in RPMI with 10% fetal bovine serum. After 15 minutes, samples were triturated five times using a syringe with a 16G needle and returned to the rotisserie rack for another 15 minutes. At the conclusion of the second digest period, samples were triturated an additional five times using a syringe with a 16G needle, at which point the digest process was stopped via the addition of EDTA to 20mM. Nasal scrapings were only dissociated with one 15 minute dissociation via collagenase and omission of the 16G needle trituration, instead replaced with P1000 pipette trituration, as typically cell yields were 1,000bp tailing off to beyond 5000bp, and a small/non-existent primer peak, indicating a successful preparation. Libraries were constructed using the Nextera XT DNA tagmentation method (Illumina FC-131-1096) on a total of 600 pg of pooled cDNA library from 12,000 recovered beads using index primers with format as in Gierahn et al 12 . Tagmented and amplified sequences were purified at a 0.6X SPRI ratio yielding library sizes with an average distribution of 650–750 base pairs in length as determined using the Agilent hsD1000 Screen Tape System (Agilent Genomics). Two arrays were sequenced per sequencing run with an Illumina 75 Cycle NextSeq500/550v2 kit (Illumina FC-404-2005) at a final concentration of 2.2–2.8pM. The read structure was paired end with Read 1 starting from a custom read 1 primer 12 containing 20 bases with a 12bp cell barcode and 8bp unique molecular identifier (UMI) and Read 2 containing 50 bases of transcript information. Single-cell RNA-seq Computational Pipelines and Analysis Read alignment was performed as in Macosko et al 39 . Briefly, for each NextSeq sequencing run, raw sequencing data was converted to demultiplexed FASTQ files using bcl2fastq2 based on Nextera N700 indices corresponding to individual samples/arrays. Reads were then aligned to Hg19 genome using the Galaxy portal maintained by the Broad Institute for Drop-Seq alignment using standard settings. Individual reads were tagged according to the 12-bp barcode sequenced and the 8-bp UMI contained in Read 1 of each fragment. Following alignment, reads were binned onto 12-bp cell barcodes and collapsed by their 8-bp UMI. Digital gene expression matrices (e.g. cells-by-genes tables) for each sample were obtained from quality filtered and mapped reads, with an automatically determined threshold for cell count. UMI-collapsed data was utilized as input into Seurat 40 (https://github.com/satijalab/seurat) for further analysis. Before incorporating a sample into our merged dataset, we individually inspected the cells-by-genes matrix of each as a Seurat object. For analysis of all sequenced surgical ethmoid sinus resection samples, we merged UMI matrices across all genes detected in any condition and generated a matrix retaining all cells with at least 500 UMI detected (19,196 cells and 31,032 genes). This table was then utilized to setup the Seurat object in which any cell with at least 300 unique genes was retained and any gene expressed in at least 5 cells was retained (Supplementary Information: an R Script is included from this point to set up Seurat object and walk reader through dimensionality reduction and basic data visualization). The object was initiated with log-normalization, from a UMI+1 count matrix, scaling, and centering set to True. The total number of cells passing these filters captured across all patients was 18,624 cells with 22,575 genes, averaging 1,503 cells per sample with a range between 789 cells and 3,109 cells (Extended Data Fig. 1a,b, Supplementary Table 2). Before performing dimensionality reduction, data was subset to include cells with less than 12,000 UMI, and a list of 1,627 most variable genes was generated by including genes with an average normalized and scaled expression value greater than 0.13 and with a dispersion (variance/mean) greater than 0.28. We then performed principal component analysis (PCA) over the list of variable genes. For both clustering and t-stochastic neighbor embedding (tSNE), we utilized the first 12 principal components, as upon visual inspection of genes contained within, each contributed to a non-redundant cell type and this reflected the inflection point of the elbow plot. We used FindClusters within Seurat (which utilizes a shared nearest neighbor (SNN) modularity optimization based clustering algorithm) with a resolution of 1.2 and tSNE set to Fast with the Barnes-hut implementation to identify 21 clusters across the 12 input samples. For analysis of all sequenced inferior turbinate scraping samples, the object was initiated with log-normalization, from a UMI+1 count matrix, scaling, and centering set to True. The total number of cells passing these filters captured across all patients was 18,704 cells with 24,842 genes, averaging 2,078 cells per sample with a range between 65 cells and 5,625 cells (NB: The 65 cell sample was a very mucus-laden polyp inferior turbinate sample, perhaps explaining the low cell yield, but clustered well within the three other samples each containing 253, 599, and 1,381 cells). Before performing dimensionality reduction, data was subset to include cells with less than 10,000 UMI, and a list of 1,499 most variable genes was generated by including genes with an average normalized and scaled expression value greater than 0.22 and with a dispersion (variance/mean) greater than 0.26. We then performed PCA over the list of variable genes. For both clustering and tSNE, we utilized the first 16 principal components, as upon visual inspection of genes contained within, each contributed to a non-redundant cell type and this reflected the inflection point of the elbow plot. We used FindClusters (which utilizes an SNN modularity optimization based clustering algorithm) with a resolution of 1 and tSNE set to Fast with the Barnes-hut implementation to identify 18 clusters across the 9 input samples. For analysis of all sequenced ALI cultures, the object was initiated with log-normalization, from a UMI+1 count matrix, scaling, and centering set to True. The total number of cells passing these filters captured across all patients was 16,173 cells with 27,396 genes, averaging 2,448 cells per sample with a range between 1,980 cells and 3,009 cells. Before performing dimensionality reduction, data was subset to include cells with less than 25,000 UMI, and a list of 1,670 most variable genes was generated by including genes with an average normalized and scaled expression value greater than 0.35 and with a dispersion (variance/mean) greater than 0.35. We then performed PCA over the list of variable genes. For both clustering and tSNE, we utilized the first 16 principal components, as upon visual inspection of genes contained within, each contributed to a non-redundant cell state and this reflected the inflection point of the elbow plot. We used FindClusters (which utilizes an SNN modularity optimization based clustering algorithm) with a resolution of 0.6 and tSNE set to Fast with the Barnes-hut implementation to identify 11 clusters across the 4 input samples. Cell Type Identification and within Cell Type Analysis To identify genes which defined each cluster, we performed a ROC test implemented in Seurat with a threshold set to an area under the curve of 0.65. Top marker genes with high specificity were used to classify cell subsets into cell types (Fig. 1a–c; Extended Data Fig. 1e) based on existing biological knowledge. Three clusters were considered doublets (588 cells) based on co-expression of markers indicative of distinct cell types at ~1/2 the expression level detected in the parent cell cluster (e.g. T cell and myeloid cell) and removed from further analyses yielding a matrix with 18,036 cells used in all subsequent steps. Closely related clusters were merged to cell types based on biological curation and analysis of hierarchical cluster trees yielding ten total cell types (Fig. 1a–c; Extended Data Fig. 1e). We identified a much smaller number of eosinophils than expected in our single-cell data. Specifically, if we do not place bulk tissue immediately into RNA-later within 10 minutes, we cannot reliably detect eosinophil associated transcripts. However, flow cytometrically we recover from 0.5% to 5% of total cells fitting eosinophil profiles from polyps, and focused single-cell studies on granulocytes at the expense of the full ecosystem are possible and the topic of future work (data not shown). With the gentler tissue dissociation required for scrapings (Methods), we did recover a greater frequency of eosinophils from polyps in line with flow data (0.31% to 4.6% of cells; Extended Data Fig. 6d). We also did not find a distinct cluster of ILCs as they are around 0.01 to 0.1% of CD45 cells across the CRS spectrum per existing literature 41 and extrapolating to the number of CD45 cells we captured, we would have detected between 0.8 and 8 ILCs. To investigate further granularity present within cell types, such as T cells, myeloid cells, fibroblasts, endothelial cells, and epithelial cells, we subset these cells from the Seurat object and re-ran dimensionality reduction and clustering (Extended Data Figs. 3, 4 and 6). The process used for clustering and subset identification was adapted for each cell type to optimize the parameters of variable genes, principal components, and resolution of clusters desired. Canonical correlation analysis 42 (CCA) was also performed to validate epithelial cell type classification across disease states (Extended Data Fig. 5; Supplementary Information). Differential Expression and Fractional Contribution of Gene Set to Transcriptome To identify differentially expressed genes within cell types across non-polyp and polyp disease states, we utilized the ‘bimod’ setting in FindMarkers implemented in Seurat based on a likelihood ratio test designed for single-cell differential expression incorporating both a discrete and continuous component 43 . To determine the expression contribution to a cell’s transcriptome of a particular gene list, we summed the total log-normalized expression values for genes within a “list of interest” and divided by the total amount of log-normalized transcripts detected in that cell, giving the proportion of a cell’s transcriptome dedicated to producing those genes. For comparison of Wnt and Notch signaling, we z-scored the expression contribution metric and subtracted the value of Notch from Wnt yielding a metric centered on zero if both scores are equivalent, or weighted in the positive direction if enriched in Wnt. For reference gene lists used, including basal cell 44 , IFNα-, IFNγ-, IL-4-, IL-13-, IL-4/IL-13-induced genes 45 , Wnt and Notch please see Supplementary Table 4. Simpson’s Index of Diversity, and Fibroblast Gene correlation with Basal Cell Frequency To measure the “richness” of the epithelial ecosystem 46 , we employed Simpson’s Index of Diversity (D), which we present as (1-D), and ranges between 0 and 1, with greater values indicating larger sample diversity 47 . We used Simpson’s Index to characterize the composition of epithelial cells across basal, differentiating/secretory, glandular, and ciliated groupings in the non-polyp and polyp ethmoid sinus tissue ecosystems, as this metric accounts for both the number of distinct cell types present (e.g. species), and the evenness of the cellular composition across those cell types (e.g. relative abundance of species to each other). This measure takes into account the total number of members of a cell type, the number of cell types, and the total number of cells present. We calculate (1-D) for each sample. To determine genes correlated in specific cell types (e.g. fibroblasts) with the frequency of basal cells present in a cellular ecosystem, we correlated the average log-normalized single-cell count data for each gene to the rank of samples determined by increasing frequency of basal cells in each ecosystem (8.2% to 19.1% for non-polyp and 27.9% to 70.1% for polyp samples, Extended Data Fig. 7b). Tissue and Sorted Basal Cell RNA-seq Population RNA-seq was performed using a derivative of the Smart-Seq2 protocol for single cells 48 . In brief, tissue was collected directly into RNAlater (Qiagen) in the surgical suite and stored at −80°C until RNA isolation. RNA was isolated from 30 patients using phenol/chloroform extraction and normalized to 5ng as the input amount for a 2.2X SPRI ratio cleanup using Agencourt RNAClean XP beads (Beckman Coulter, A63987). RNA-seq from sorted basal cells was done as a bulk population using Smart-Seq2 chemistry starting with a 2.2X SPRI ratio cleanup. After oligo-dT priming, Maxima H Minus Reverse Transcriptase (ThermoFisher EP0753) was utilized to synthesize cDNA with an elongation step at 52°C before PCR amplification (15 cycles for tissue, 18 cycles for sorted basal cells) using KAPA HiFi PCR Mastermix (Kapa Biosystems KK2602). Sequencing libraries were prepared using the Nextera XT DNA tagmentation kit (Illumina FC-131-1096) with 250pg input for each sample. Libraries were pooled post-Nextera and cleaned using Agencourt AMPure SPRI beads with successive 0.7X and 0.8X ratio SPRIs and sequenced with an Illumina 75 Cycle NextSeq500/550v2 kit (Illumina FC-404-2005) with loading density at 2.2pM, with paired end 35 cycle read structure. Tissue samples were sequenced at an average read depth of 7.98 million reads per sample and 3 samples not meeting quality thresholds were excluded from further analyses yielding 27 total useable samples. Sorted basal cell samples were sequenced at an average read depth of 21.15 million reads per sample and all samples met quality thresholds regarding genomic and transcriptomic alignment. Tissue and Sorted Basal Cell RNA-seq Data Analysis Tissue and sorted basal cell samples were aligned to the Hg19 genome and transcriptome using STAR 49 and RSEM 50 . 3 samples were excluded for low transcriptome alignment ( 25% of cells in indicated sample have non-zero measurement for gene, widest aspect represents centre of positive measures, minima and maxima are represented within the scale with minima at 0 and maxima encompassing all points for the count-based expression level (log(scaled UMI+1)) of each gene. Exact values for all genes displayed and tested available in Supplementary Table 3 organized by panel. All violin plots contain at minimum 100 individual cells in any one cluster (Supplementary Table 3 for precise numbers of cells per cluster and type, most are included in figure legends where space allows), and have points suppressed for ease of legibility. Some violin plots with less than 100 cells have individual data points displayed and corresponding statistical metrics are available in accompanying figure legend and Supplementary Table 3. As some scores followed non-normal distributions as tested for using a Lilliefors normality test, we utilized a Mann-Whitney U-test where indicated for determining statistical significance. For scores in single-cell data, we report effect sizes in addition to statistical significance as an additional metric for the magnitude of the effect observed. The calculation was performed as Cohen’s d where: effect size d = (Mean1-Mean2)/(S.D. pooled). Unpaired two-tailed t-tests for direct comparisons and t-test with Holm-Sidak correction, Bonferroni correction, or Benjamini-Hochberg for multiple comparisons, depending on software package used, where appropriate. Mann-Whitney U-test for quantification of histological data due to non-normally distributed data. Pearson correlation thresholds were determined as significant through determination of asymptotic p-values through use of rcorr function in Hmisc, but exact corrected p-values by Holm-Sidak method for multiple comparisons are calculated for those highlighted in text using RcmdrMisc package. Comparison of Pearson correlation coefficients in pseudotime analyses was done using Fisher’s 1925 z-statistic accounting for the number of cells. Data Availability Statement The cells-by-genes matrix generated from ethmoid sinus surgical resections and analyzed during the current study is available along with the manuscript as Supplementary Table 2 along with R code for standard implementation of Seurat. A cells-by-genes matrix from inferior turbinate and polyp scraping data is also available as Supplementary Table 6. Dupilumab treatment cells-by-genes matrices as Supplementary Tables 7 and 8. A metadata table encompassing all scRNA-seq samples is provided as Supplementary Table 9. The count and TPM matrices and associated metadata from bulk tissue RNA-seq are available as Supplementary Tables 10, 11, and 12. FASTQ file format data will be available through dbGaP under accession number XXXX. Marker gene lists for cell types identified in Fig. 1a,b, and from resultant analyses in Fig. 2b, for frequencies of cell clusters and types in Fig. 2c, for cell types identified in Fig. 2e, Fig. 2f, Fig. 3g, Fig. 5a, Fig. 5e, Extended Data Fig. 3a,b,c, Extended Data Fig. 4c, Extended Data Fig. 5e, Extended Data Fig. 6b,d, Extended Data Fig. 10a, selected comparisons of differential expression in Fig. 2d, Fig. 4a, Fig. 5c, Fig. 5f, Extended Data Fig. 2c, Extended Data Fig. 10h, and pseudotime correlation Extended Data Fig. 9b, are available as tabs in Supplementary Table 3. Differential peak calling from epigenetic profiling available in Supplementary Table 5. Additional R code for analyses available on http://shaleklab.com/resources/. Extended Data Extended Data Figure 1 | Consistency of cell capture and identification in surgical EthSin scRNA-seq patient cohort (a) Number of unique molecular identifiers (nUMI) and genes identified, and fraction of reads mapping to mitochondrial or ribosomal genes across recovered cell types; 3,222 basal cells, 4,362 apical cells, 2,192 glandular cells, 498 ciliated cells, 835 T cells, 2,976 plasma cells, 1,724 fibroblasts, 1,143 endothelial cells, 811 myeloid cells, 273 mast cells. (b) nUMI and genes identified, and fraction of reads mapping to mitochondrial or ribosomal genes across patient samples; 789 Polyp 1 cells, 1,309 Polyp 2 cells, 1,153 Polyp 3 cells, 913 Polyp 4 cells, 1,219 Polyp 5 cells, 1,141 Polyp 6A cells, 1,334 Polyp 6B cells, 1,314 Polyp 7 cells, 1,286 Polyp 8 cells, 1,481 Polyp 9 cells, 2,988 Polyp 11 cells, 3,109 Polyp 12 cells. (c) tSNE plot as in Fig. 1b colored by cell types across all patients and then separated by sample; 18,036 single cells (n=12 samples). (d) The percentage of each cell type recovered within each sample. (e) Select marker gene overlays displaying binned count-based UMI-collapsed expression level (log(scaled UMI+1)) on a tSNE plot from Fig. 1b for key cell types identified (see Supplementary Table 3 for full gene lists); area under the curve (AUC) 0.998 to 0.7 for all markers displayed. Extended Data Figure 2 | Top marker genes for cell types by scRNA-seq and bulk tissue RNA-seq from EthSin recovers expected T2I and eosinophilic modules (a) Row-normalized heatmap of the top-10 marker genes identified by ROC-test (AUC>0.73 for all) over all cell types (Fig. 1b,c) with select genes displayed on y-axis and cells on x-axis (see Supplementary Table 3 for full gene lists); maximum 500 cells/type. (b) An overlay of CLC displaying binned count-based expression level (log(scaled UMI+1)) amongst myeloid cells (a pathognomonic gene for eosinophils); 811 myeloid cells from n=12 samples. (c) A row-normalized and row-clustered heatmap over the top 100 positively and negatively differentially-expressed genes (50 in each direction) in bulk tissue RNA-seq of 27 samples from non-polyp (n=10) and polyp (n=17) tissue with select genes displayed; DESeq2 Wald Test, all p 0.6) within each cell type for each cell cluster with genes displayed on y-axis and cluster annotations on x-axis (see Supplementary Table 3 for full gene lists). (f) Select overlays on clusters 0 and 4 (differentiating/secretory) and 13 (glandular) displaying binned count-based expression level (log(scaled UMI+1)) in tSNE space for canonical goblet (MUC5B, MUC5AC, SPDEF, FOXA3) and secretory (SCGB1A1) genes; 3,526 cells. (g) A clustered correlation matrix of glandular, goblet, and secretory cell genes; Pearson’s abs(r)> 0.038 is p 0.7651, p 0.048 is p 0.8), and genes differentially expressed (bimodal test) by disease state, with disease state annotations on x-axis; bimodal test, all non-core genes p 0.75), and genes differentially expressed (bimodal test) by disease state, with disease state annotations on x-axis; bimodal test, all non-core genes p 2 Z, full results including Bonferroni corrected p-values in Supplementary Table 3). (c) Correlation matrices (row and column clustered) of the normalized read counts per sample in motif associated-peaks for non-polyp or polyp samples; Pearson correlation, n=3 non-polyp, n=7 polyp. (d) A column-normalized heatmap (row and column clustered) for the fraction of peaks with a motif corresponding to accessibility of the respective transcription factor displayed by patient; n=3 non-polyp, n=7 polyp. (e) IGV tracks for ATF3 and KLF5 based on peaks detected and averaged by non-polyp and polyp samples from ATAC-seq profiling. (f) IGV tracks for S100A9 and MUC4 based on peaks detected and averaged by non-polyp and polyp samples from ATAC-seq profiling. (g) Violin plots for the count-based expression level (log(scaled UMI+1)) for key marker genes using ROC test across cell types identified in (Fig. 5a; Supplementary Table 3); 1,345 basal; 6,420 secretory; 6,381 hybrid; and 2,027 ciliated cells; from n=2 non-polyp and 2 polyp patients; AUC 0.943 for KRT5, 0.667 for TP63, 0.644 for LYPD2, 0.662) with select genes displayed on y-axis including a core secretory signature (ROC-test secretory cells vs. rest of cells), and then within secretory cells a ROC-test used to identify marker genes within each disease/location category, and basal-cell derived annotations on x-axis (see Supplementary Table 3 for full gene lists, all AUC>0.65 for markers displayed in Fig. 2f). (i) Quantification of flow cytometry for the ratio of basal to Epcamhi cells (gating as in Extended Data Fig. 7h) from ALI cultures at 21 days stimulated with IL-13 over the indicated doses; points represent individual biological replicates; n=6 non-polyp, n=5 polyp samples for each dose; *2-way ANOVA, n.s. between disease groups at any dose tested; *2-way ANOVA, p=0.0224 for IL-13 dose; mean±s.e.m. (j) Expression levels for CTGF (Log2 expression value of log-normalized count data) in basal cells from non-polyp or polyp individuals across doses of cytokines displayed; n=4 samples each dose; 2-way ANOVA p 0.8) with select genes displayed on y-axis including a core myeloid signature (ROC-test myeloid cells vs. rest of cells), and then genes found to be differentially expressed from (Fig. 5f) in basal cells, and treatment annotations on x-axis; bimodal test, * denotes differential genes in both basal cells and myeloid cells pre- vs post-treatment p<0.003 or less with Bonferroni correction for multiple hypothesis testing based on number of genes tested. (d) Violin plots for basal cells (200 cells pre-dupilumab and 151 cells post-dupilumab, noted in (a)) for the count-based expression level (log(scaled UMI+1)), except where indicated for gene scores, fraction of transcriptome and z-score (see Methods, Supplementary Table 4 for gene set used) for key basal cell genes for selected biological processes, or from the baseline upregulated gene set from polyp basal cells in vitro (Fig. 5c); differential expression testing for decreased expression post-treatment using bimodal test n.s. unless denoted by * for p<0.00087 or less with Bonferroni correction for multiple hypothesis testing based on number of genes tested; see Supplementary Table 3 for full list; Basal in vitro score Pre vs Post: *t-test, two-tailed, p<3.897×10−15, effect size 0.822. (e) tSNE plot of 4,486 single cells (related to Fig. 2e, and Fig. 5e) from the inferior turbinate or nasal polyps of an anti-IL-4Rα (dupilumab) treated individual (n=4 samples) colored by timepoint and tissue of origin from inferior turbinate pre-dupilumab scraping (643 cells), from inferior turbinate post-dupilumab scraping (1,596 cells), polyp pre-dupilumab scraping (1,600 cells), and polyp post-dupilumab scraping (647 cells); and tSNE plot colored by cell types identified through marker discovery (ROC test) and biological curation of identified clusters (Supplementary Table 3; Methods)); black outline indicates cells considered in (g). (f) Select deconvolution score overlays for cell types indicated in original core data set (see Supplementary Table 3 for full gene list). (g) Violin plot for the gene set score over Wnt pathway (z-score) and expression contribution to a cell’s transcriptome over IFNα- and IL-4/IL-13-commonly induced gene signature in secretory cells grouped as in (e) and sub-sampled to a maximum of 150 cells from each disease/location category from inferior turbinate pre-dupilumab scraping (150 cells), from inferior turbinate post-dupilumab scraping (23 cells), polyp pre-dupilumab scraping (150 cells), and polyp post-dupilumab scraping (38 cells); see Methods, Supplementary Table 3, Supplementary Table 4 for gene lists used; *t-test, two-tailed, Wnt score Pre vs Post Polyp Tissue: effect size 1.02, p=1.091×10−14; Wnt score Pre vs Post Inferior Turbinate Tissue: effect size −0.17, p=0.3706; IL-4/IL-13 score Pre vs Post Polyp Tissue: effect size 1.17, p<2.2×10−16; IL-4/IL-13 score Pre vs Post Inferior Turbinate Tissue: effect size −0.17, p=0.163; IFNα score Pre vs Post Polyp Tissue: effect size −1.25, p=4.254×10−05; IFNα score Pre vs Post Inferior Turbinate Tissue: effect size −0.304, p=0.2766; differential expression testing for decreased expression post-treatment using bimodal test denoted by * and p<7.81×10−06 or less between pre- and post-treated polyp. (h) Violin plots of secretory cells grouped as in (e) and sub-sampled to a maximum of 150 cells from each disease/location category from inferior turbinate pre-dupilumab scraping (150 cells), inferior turbinate post-dupilumab scraping (23 cells), polyp pre-dupilumab scraping (150 cells), and polyp post-dupilumab scraping (38 cells) for the count-based expression level (log(scaled UMI+1)) and for secretory cell genes from the gene set used in Fig. 2f affected by treatment within anatomical regions indicated by heading; differential expression testing for decreased expression post-treatment using bimodal test n.s. unless denoted by *, all p<6.36×10−5 or less except KLF5 (p=0.0033) and FOSB (p=0.0053) with Bonferroni correction for multiple hypothesis testing based on number of genes tested, see Supplementary Table 3 for all genes tested. Supplementary Material Research Summary Sup Table 6 Sup Table 7 Sup Table 8 Sup Table 9 Supplemental Information Legends Supplementary Discussion Sup Table 1 Sup Table 10 Sup Table 11 Sup Table 12 Sup Table 2 Sup Table 3 Sup Table 4 Sup Table 5
Allergen-specific type 2 helper T (TH2) cells play a central role in initiating and orchestrating the allergic and asthmatic inflammatory response pathways. One major factor limiting the use of such atopic disease–causing T cells as both therapeutic targets and clinically useful biomarkers is the lack of an accepted methodology to identify and differentiate these cells from overall nonpathogenic TH2 cell types. We have described a subset of human memory TH2 cells confined to atopic individuals that includes all allergen-specific TH2 cells. These cells are terminally differentiated CD4+ T cells (CD27– and CD45RB–) characterized by coexpression of CRTH2, CD49d, and CD161 and exhibit numerous functional attributes distinct from conventional TH2 cells. Hence, we have denoted these cells with this stable allergic disease–related phenotype as the TH2A cell subset. Transcriptome analysis further revealed a distinct pathway in the initiation of pathogenic responses to allergen, and elimination of these cells is indicative of clinical responses induced by immunotherapy. Together, these findings identify a human TH2 cell signature in allergic diseases that could be used for response-monitoring and designing appropriate immunomodulatory strategies.
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