Single Cell RNA Sequencing of Human Eosinophils in Allergic Inflammation in the Esophagus.

BACKGROUND: Eosinophils are elusive cells involved in allergic inflammation. Single-cell RNA Sequencing (scRNA-seq) is an emerging approach to deeply characterize cellular properties, heterogeneity, and functionality. OBJECTIVE: To comprehensively characterize the transcriptome and biological functions of human eosinophils at a site of severe allergic inflammation in the esophagus (i.e., eosinophilic esophagitis (EoE)). METHODS: We employed a gravity-based scRNA-seq methodology to sequence blood eosinophils from patients with EoE and control individuals compared to a reanalyzed public scRNA-seq dataset of human esophageal eosinophils of EoE patients. We used flow cytometry, immunostaining, and a stimulation assay to verify mRNA findings. RESULTS: In total, scRNA-seq was obtained from 586 eosinophils (188 from blood [n=6 individuals] and 398 from esophagus [n=6 individuals]). The esophageal eosinophils were composed of a population of activated eosinophils (enriched in 659 genes compared with peripheral blood-associated eosinophils) and a small population of eosinophils resembling peripheral blood eosinophils (enriched in 62 genes compared with esophageal eosinophils). Esophageal eosinophils expressed genes involved in sensing and responding to diverse stimuli, most notably interferon-, interleukin 10, histamine and leukotrienes, and succinate. Esophageal eosinophils were most distinguished from other esophageal populations by gene expression of the receptors CCR3, HRH4, SUCNR1, and VSTM1; transcription factors CEBPE, OLIG1, and OLIG2; protease PRSS33; and hallmark eosinophil gene CLC. A web of bidirectional eosinophil interactions with other esophageal populations was derived. Comparing esophageal eosinophils and mast cells revealed that esophageal eosinophils expressed genes involved in DAP12 interactions, IgG receptor-triggered events, immunoregulation, and IL-10 signaling. CONCLUSIONS: In EoE, esophageal eosinophils exist as two populations, a minority population resembling blood eosinophils and the other population characterized by high de novo transcription of diverse sensing receptors and inflammatory mediators readying them to potentially intersect with diverse cell types.

Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. OBJECTIVES: Using machine learning, we localized and characterized esophageal mast cells (MCs) to decipher their potential role in disease pathology. METHODS: Esophageal biopsy samples (EoE, control) were stained for MCs by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize MCs, designated Mast Cell-Artificial Intelligence (MC-AI). RESULTS: MC-AI enumerated cell counts with high accuracy. During active EoE, epithelial MCs increased and lamina propria (LP) MCs decreased. In controls and EoE remission patients, papillae had the highest MC density and negatively correlated with epithelial MC density. MC density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater MC degranulation in the epithelium, papillae, and LP in patients with EoE compared with control individuals. MCs were localized further from the basement membrane in active EoE than EoE remission and control individuals but were closer than eosinophils to the basement membrane in active EoE. CONCLUSIONS: Using MC-AI, we identified a distinct population of homeostatic esophageal papillae MCs; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial MC levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of MCs in EoE and other disorders.

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis.

Background: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. Objective: Using machine learning, we localized and characterized esophageal mast cells to decipher their potential role in disease pathology. Methods: Esophageal biopsy samples (EoE, control) were stained for mast cells by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize mast cells, designated Mast Cell-Artificial Intelligence (MC-AI). Results: MC-AI enumerated cell counts with high accuracy. During active EoE, epithelial mast cells increased and lamina propria (LP) mast cells decreased. In controls and EoE remission patients, papillae had the highest mast cell density and negatively correlated with epithelial mast cell density. Mast cell density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater mast cell degranulation in the epithelium, papillae, and LP in EoE patients compared with control individuals. Mast cells were localized further from the basement membrane in active EoE than EoE remission and controls individuals but were closer than eosinophils to the basement membrane in active EoE. Conclusion: Using MC-AI, we identified a distinct population of homeostatic esophageal papillae mast cells; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial mast cell levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of mast cells in EoE and other disorders. Clinical Implication: We have developed a methodology for identifying, enumerating, and characterizing mast cells using artificial intelligence; this has been applied to decipher eosinophilic esophagitis and provides a platform approach for other diseases. Capsule Summary: A machine learning protocol for identifying mast cells, designated Mast Cell-Artificial Intelligence, readily identified spatially distinct and dynamic populations of mast cells in EoE, providing a platform to better understand this cell type in EoE and other diseases.

The minichromosome maintenance complex drives esophageal basal zone hyperplasia.

Eosinophilic esophagitis (EoE) is a chronic gastrointestinal disorder characterized by food antigen-driven eosinophilic inflammation and hyperproliferation of esophageal mucosa. By utilizing a large-scale, proteomic screen of esophageal biopsies, we aimed to uncover molecular drivers of the disease. Proteomic analysis by liquid chromatography-tandem mass spectrometry identified 402 differentially expressed proteins (DEPs) that correlated with the EoE transcriptome. Immune cell-related proteins were among the most highly upregulated DEPs in EoE compared with controls, whereas proteins linked to epithelial differentiation were primarily downregulated. Notably, in the inflamed esophageal tissue, all 6 subunits of the minichromosome maintenance (MCM) complex, a DNA helicase essential for genomic DNA replication, were significantly upregulated at the gene and protein levels. Furthermore, treating esophageal epithelial cells with a known inhibitor of the MCM complex (ciprofloxacin) blocked esophageal epithelial proliferation. In a murine model of EoE driven by overexpression of IL-13, ciprofloxacin treatment decreased basal zone thickness and reduced dilated intercellular spaces by blocking the transition of epithelial cells through the S-phase of the cell cycle. Collectively, a broad-spectrum proteomic screen has identified the involvement of the MCM complex in EoE and has highlighted MCM inhibitors as potential therapeutic agents for the disease.

Single cell transcriptomic analysis of HPV16-infected epithelium identifies a keratinocyte subpopulation implicated in cancer.

Persistent HPV16 infection is a major cause of the global cancer burden. The viral life cycle is dependent on the differentiation program of stratified squamous epithelium, but the landscape of keratinocyte subpopulations which support distinct phases of the viral life cycle has yet to be elucidated. Here, single cell RNA sequencing of HPV16 infected compared to uninfected organoids identifies twelve distinct keratinocyte populations, with a subset mapped to reconstruct their respective 3D geography in stratified squamous epithelium. Instead of conventional terminally differentiated cells, an HPV-reprogrammed keratinocyte subpopulation (HIDDEN cells) forms the surface compartment and requires overexpression of the ELF3/ESE-1 transcription factor. HIDDEN cells are detected throughout stages of human carcinogenesis including primary human cervical intraepithelial neoplasias and HPV positive head and neck cancers, and a possible role in promoting viral carcinogenesis is supported by TCGA analyses. Single cell transcriptome information on HPV-infected versus uninfected epithelium will enable broader studies of the role of individual keratinocyte subpopulations in tumor virus infection and cancer evolution.

Local type 2 immunity in eosinophilic gastritis.

BACKGROUND: Eosinophilic gastritis (EoG) associates with type 2 immunity. However, the type 2 cytokine cellular source, gastric T-cell composition, and gastric T-cell relationship (or relationships) with disease pathology remain understudied. OBJECTIVE: We defined gastric T-cell populations and their association with histologic and endoscopic EoG pathology. METHODS: Gastric biopsy samples (n = 6 EoG, n = 7 control) were subjected to histologic, endoscopic, and flow cytometry analyses. In a complementary cohort (n = 83 EoG), IL4, IL5, and IL13 mRNA levels were correlated with EoG pathologic parameters. RESULTS: Gastric biopsy samples contained CD3+ T cells that were mainly CD8+; the CD8/CD4 ratio was comparable in EoG and control biopsy samples (5.7 ± 3.0 and 4.3 ± 0.6, respectively; P = .28). Gastric regulatory T (CD3+CD4+FOXP3+) and TH2 (CD3+CD4+GATA3+) cell levels were increased in EoG versus controls (2-fold, P < .05 and 10-fold, P < .001, respectively) and correlated with gastric eosinophil levels (r = 0.63, P < .05 and r = 0.85, P < .001, respectively), endoscopic pathology (r = 0.56, P < .01; r = 0.84, P < .001, respectively), and histopathology (r = 0.72, P < .01; r = 0.82, P < .01, respectively). Cytokine-positive, most notably IL-4+, TH2 cell levels strongly correlated with histologic and endoscopic scores (r = 0.82, P < .0001 and r = 0.78, P < .0001, respectively). In an independent EoG cohort (n = 83), bulk gastric IL4, IL5, and IL13 mRNA levels correlated with histologic score (r = 0.22, P < .005; r = 0.54, P < .0001; and r = 0.36, P < .0001, respectively) and endoscopic score (r = 0.27, P < .001; r = 0.40, P < .0001; and r = 0.35, P < .0001, respectively). CONCLUSIONS: EoG is a TH2 cell-associated disease featuring increased gastric type 2 cytokine-producing CD3+CD4+GATA3+TH2 cells that strongly correlate with disease pathologies.

Molecular analysis of duodenal eosinophilia.

BACKGROUND: Eosinophilic duodenitis (EoD), characterized by nonspecific gastrointestinal symptoms and increased numbers of duodenal eosinophils, may be in the eosinophilic gastrointestinal disease spectrum. However, diagnostic thresholds and pathogenic processes of duodenal tissue eosinophilia are inadequately characterized. OBJECTIVE: We aimed to define an EoD transcriptome and pathologic pathways. METHODS: RNA sequencing and histologic features of human duodenal biopsy samples were analyzed as a function of duodenal eosinophils levels. For analyses, we defined EoD as more than 52 peak eosinophils/hpf (n = 8), duodenal eosinophilia as 30 to 52 eosinophils/hpf (n = 11), and normal controls as fewer than 30 eosinophils/hpf (n = 8). Associations between gene expression and histologic features were analyzed with Spearman correlation. RESULTS: We identified 382 differentially expressed genes (EoD transcriptome) between EoD and normal controls (>2-fold change [adjusted P < .05]). The EoD transcriptome distinguished EoD from controls (duodenal eosinophilia and normal controls). The duodenal eosinophil count was correlated with a distinct EoD transcriptome when 50 to 60 peak eosinophils/hpf were present. The EoD transcriptome was enriched in genes involved in IL-4/IL-13 signaling, mast cells, and myeloid progenitor cells. Among duodenal histologic features, lamina propria eosinophil sheets was the most associated with transcriptomic changes (r = 0.66; P < .01). EoD gene signatures were shared with eosinophilic esophagitis and eosinophilic gastritis but not with eosinophilic colitis or celiac disease. CONCLUSION: We have identified an EoD transcriptomic signature that emerges at 50 to 60 peak eosinophils/hpf and established EoD as part of a spectrum of upper eosinophilic gastrointestinal disorder associated with type 2 immunity and distinct from eosinophilic colitis and celiac disease. These findings provide a basis for improving diagnosis and treatment.

Epithelial cell-expressed type II IL-4 receptor mediates eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is a chronic, food-driven allergic disease, characterized by eosinophil-rich inflammation in the esophagus. The histopathological and clinical features of EoE have been attributed to overproduction of the type 2 cytokines IL-4 and IL-13, which mediate profound alterations in the esophageal epithelium and neutralizing of their shared receptor component (IL-4Rα) with a human antibody drug (dupilumab) demonstrates clinical efficacy. Yet, the relative contribution of IL-4 and IL-13 and whether the type II IL-4 receptor (comprised of the IL-4Rα chain in association with IL-13Rα1) mediates this effect has not been determined. METHODS: Experimental EoE was induced in WT, Il13ra1-/- , and Krt14Cre /Il13ra1fl/fl mice by skin-sensitized using 4-ethoxymethylene-2-phenyl-2-oxazolin (OXA) followed by intraesophageal challenges. Esophageal histopathology was determined histologically. RNA was extracted and sequenced for transcriptome analysis and compared with human EoE RNAseq data. RESULTS: Induction of experimental EoE in mice lacking Il13ra1 and in vivo IL-13 antibody-based neutralization experiments blocked antigen-induced esophageal epithelial and lamina propria thickening, basal cell proliferation, eosinophilia, and tissue remodeling. In vivo targeted deletion of Il13ra1 in esophageal epithelial cells rendered mice protected from experimental EoE. Single-cell RNA sequencing analysis of human EoE biopsies revealed predominant expression of IL-13Rα1 in epithelial cells and that EoE signature genes correlated with IL-13 expression compared with IL-4. CONCLUSIONS: We demonstrate a definitive role for IL-13 signaling via IL-13Rα1 in EoE. These data provide mechanistic insights into the mode of action of current therapies in EoE and highlight the type II IL-4R as a future therapeutic target.

Vitamin D receptor and STAT6 interactome governs oesophageal epithelial barrier responses to IL-13 signalling.

OBJECTIVE: The contribution of vitamin D (VD) deficiency to the pathogenesis of allergic diseases remains elusive. We aimed to define the impact of VD on oesophageal allergic inflammation. DESIGN: We assessed the genomic distribution and function of VD receptor (VDR) and STAT6 using histology, molecular imaging, motif discovery and metagenomic analysis. We examined the role of VD supplementation in oesophageal epithelial cells, in a preclinical model of IL-13-induced oesophageal allergic inflammation and in human subjects with eosinophilic oesophagitis (EoE). RESULTS: VDR response elements were enriched in oesophageal epithelium, suggesting enhanced VDR binding to functional gene enhancer and promoter regions. Metagenomic analysis showed that VD supplementation reversed dysregulation of up to 70% of the transcriptome and epigenetic modifications (H3K27Ac) induced by IL-13 in VD-deficient cells, including genes encoding the transcription factors HIF1A and SMAD3, endopeptidases (SERPINB3) and epithelial-mesenchymal transition mediators (TGFBR1, TIAM1, SRC, ROBO1, CDH1). Molecular imaging and chromatin immunoprecipitation showed VDR and STAT6 colocalisation within the regulatory regions of the affected genes, suggesting that VDR and STAT6 interactome governs epithelial tissue responses to IL-13 signalling. Indeed, VD supplementation reversed IL-13-induced epithelial hyperproliferation, reduced dilated intercellular spaces and barrier permeability, and improved differentiation marker expression (filaggrin, involucrin). In a preclinical model of IL-13-mediated oesophageal allergic inflammation and in human EoE, VD levels inversely associated with severity of oesophageal eosinophilia and epithelial histopathology. CONCLUSIONS: Collectively, these findings identify VD as a natural IL-13 antagonist with capacity to regulate the oesophageal epithelial barrier functions, providing a novel therapeutic entry point for type 2 immunity-related diseases.

Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis.

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE is a clinicopathologic disorder and the histological portion of the diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies, and evaluating additional features such as basal zone hyperplasia is helpful. However, this task requires time-consuming, somewhat subjective manual analysis, thus reducing the ability to process the complex tissue structure and infer its relationship with the patient's clinical status. Previous artificial intelligence (AI) approaches that aimed to improve histology-based diagnosis focused on recapitulating identification and quantification of the area of maximal eosinophil density, the gold standard manual metric for determining EoE disease activity. However, this metric does not account for the distribution of eosinophils or other histological features, over the whole slide image. Here, we developed an artificial intelligence platform that infers local and spatial biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions. Besides the maximal density of eosinophils [referred to as Peak Eosinophil Count (PEC)] and a maximal basal zone fraction, we identify the value of two additional metrics that reflect the distribution of eosinophils and basal zone fractions. This approach enables a decision support system that predicts EoE activity and potentially classifies the histological severity of EoE patients. We utilized a cohort that includes 1,066 biopsy slides from 400 subjects to validate the system's performance and achieved a histological severity classification accuracy of 86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approach highlights the importance of systematically analyzing the distribution of biopsy features over the entire slide and paves the way toward a personalized decision support system that will assist not only in counting cells but can also potentially improve diagnosis and provide treatment prediction.

A Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics.

Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring require determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Here, we developed a machine learning pipeline to identify, quantitate and diagnose EoE patients' at the whole slide image level. We propose a platform that combines multi-label segmentation deep network decision support system with dynamics convolution that is able to process whole biopsy slide. Our network is able to segment both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation enables the local quantification of intact eosinophils with a mean absolute error of 0.611 eosinophils. We examined a cohort of 1066 whole slide images from 400 patients derived from multiple institutions. Using this set, our model achieved a global accuracy of 94.75%, sensitivity of 94.13%, and specificity of 95.25% in reporting EoE disease activity. Our work provides state-of-the-art performances on the largest EoE cohort to date, and successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously, and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges.

Single-cell RNA-Seq of human esophageal epithelium in homeostasis and allergic inflammation.

Inflammation of the esophageal epithelium is a hallmark of eosinophilic esophagitis (EoE), an emerging chronic allergic disease. Herein, we probed human esophageal epithelial cells at single-cell resolution during homeostasis and EoE. During allergic inflammation, the epithelial differentiation program was blocked, leading to loss of KRT6hi differentiated populations and expansion of TOP2hi proliferating, DSPhi transitioning, and SERPINB3hi transitioning populations; however, there was stability of the stem cell-enriched PDPNhi basal epithelial compartment. This differentiation program blockade was associated with dysregulation of transcription factors, including nuclear receptor signalers, in the most differentiated epithelial cells and altered NOTCH-related cell-to-cell communication. Each epithelial population expressed genes with allergic disease risk variants, supporting their functional interplay. The esophageal epithelium differed notably between EoE in histologic remission and controls, indicating that remission is a transitory state poised to relapse. Collectively, our data uncover the dynamic nature of the inflamed human esophageal epithelium and provide a framework to better understand esophageal health and disease.

Evaluating Eosinophilic Colitis as a Unique Disease Using Colonic Molecular Profiles: A Multi-Site Study.

BACKGROUND & AIMS: Colonic eosinophilia, an enigmatic finding often referred to as eosinophilic colitis (EoC), is a poorly understood condition. Whether EoC is a distinct disease or a colonic manifestation of eosinophilic gastrointestinal diseases (EGIDs) or inflammatory bowel disease (IBD) is undetermined. METHODS: Subjects with EoC (n = 27) and controls (normal [NL, n = 20], Crohn's disease [CD, n = 14]) were enrolled across sites associated with the Consortium of Eosinophilic Gastrointestinal Disease Researchers. EoC was diagnosed as colonic eosinophilia (ascending ≥100, descending ≥85, sigmoid ≥65 eosinophils/high-power field) with related symptoms. Colon biopsies were subjected to RNA sequencing. Associations between gene expression and histologic features were analyzed with Spearman correlation; operational pathways and cellular constituents were computationally derived. RESULTS: We identified 987 differentially expressed genes (EoC transcriptome) between EoC and NL (>1.5-fold change, P < .05). Colonic eosinophil count correlated with 31% of EoC transcriptome, most notably with CCL11 and CLC (r = 0.78 and 0.77, P < .0001). Among EoC and other EGIDs, there was minimal transcriptomic overlap and minimal evidence of a strong allergic type 2 immune response in EoC compared with other EGIDs. Decreased cell cycle and increased apoptosis in EoC compared with NL were identified by functional enrichment analysis and immunostaining using Ki-67 and cleaved caspase-3. Pericryptal circumferential eosinophil collars were associated with the EoC transcriptome (P < .001). EoC transcriptome-based scores were reversible with disease remission and differentiated EoC from IBD, even after controlling for colonic eosinophil levels (P < .0001). CONCLUSIONS: We established EoC transcriptomic profiles, identified mechanistic pathways, and integrated findings with parallel IBD and EGID data. These findings establish EoC as a distinct disease compared with other EGIDs and IBD, thereby providing a basis for improving diagnosis and treatment.

Host-Microbiota Interactions in the Esophagus During Homeostasis and Allergic Inflammation.

BACKGROUND & AIMS: Microbiota composition and mechanisms of host-microbiota interactions in the esophagus are unclear. We aimed to uncover fundamental information about the esophageal microbiome and its potential significance to eosinophilic esophagitis (EoE). METHODS: Microbiota composition, transplantation potential, and antibiotic responsiveness in the esophagus were established via 16S ribosomal RNA sequencing. Functional outcomes of microbiota colonization were assessed by RNA sequencing analysis of mouse esophageal epithelium and compared with the human EoE transcriptome. The impact of dysbiosis was assessed using a preclinical model of EoE. RESULTS: We found that the murine esophagus is colonized with diverse microbial communities within the first month of life. The esophageal microbiota is distinct, dominated by Lactobacillales, and demonstrates spatial heterogeneity as the proximal and distal esophagus are enriched in Bifidobacteriales and Lactobacillales, respectively. Fecal matter transplantation restores the esophageal microbiota, demonstrating that the local environment drives diversity. Microbiota colonization modifies esophageal tissue morphology and gene expression that is enriched in pathways associated with epithelial barrier function and overlapping with genes involved in EoE, including POSTN, KLK5, and HIF1A. Finally, neonatal antibiotic treatment reduces the abundance of Lactobacillales and exaggerates type 2 inflammation in the esophagus. Clinical data substantiated loss of esophageal Lactobacillales in EoE compared with controls. CONCLUSIONS: The esophagus has a unique microbiome with notable differences between its proximal and distal regions. Fecal matter transplantation restores the esophageal microbiome. Antibiotic-induced dysbiosis exacerbates disease in a murine model of EoE. Collectively, these data establish the composition, transplantation potential, antibiotic responsiveness, and host-microbiota interaction in the esophagus and have implications for gastrointestinal health and disease.

Loss of Endothelial TSPAN12 Promotes Fibrostenotic Eosinophilic Esophagitis via Endothelial Cell-Fibroblast Crosstalk.

BACKGROUND & AIMS: Eosinophilic esophagitis (EoE) can progress to fibrostenosis by unclear mechanisms. Herein, we investigated gene dysregulation in fibrostenotic EoE, its association with clinical parameters and specific pathways, and the functional consequences. METHODS: Esophageal biopsies from subjects with EoE were collected across 11 Consortium of Eosinophilic Gastrointestinal Disease Researchers sites (n = 311) and 2 independent replication cohorts (n = 83). Inclusion criteria for fibrostenotic EoE were endoscopic rings, stricture, and/or a history of dilation. Endoscopic, histologic, and molecular features were assessed by the EoE Endoscopic Reference Score, EoE Histology Scoring System, EoE Diagnostic Panel, and RNA sequencing. Esophageal endothelial TSPAN12 expression and functional effects on barrier integrity and gene expression were analyzed in vitro. RESULTS: TSPAN12 was the gene most correlated with fibrostenosis (r = -0.40, P < .001). TSPAN12 was lower in fibrostenotic EoE and correlated with EoE Endoscopic Reference Score, EoE Diagnostic Panel, and EoE Histology Scoring System (r = 0.34-0.47, P < .001). Lower TSPAN12 associated with smaller esophageal diameter (r = 0.44, P = .03), increased lamina propria fibrosis (r = -0.41, P < .001), and genes enriched in cell cycle-related pathways. Interleukin (IL)-13 reduced TSPAN12 expression in endothelial cells. Conversely, anti-IL-13 therapy increased TSPAN12 expression. TSPAN12 gene silencing increased endothelial cell permeability and dysregulated genes associated with extracellular matrix pathways. Endothelial cell-fibroblast crosstalk induced extracellular matrix changes relevant to esophageal remodeling. CONCLUSIONS: Patients with fibrostenotic EoE express decreased levels of endothelial TSPAN12. We propose that IL-13 decreases TSPAN12, likely contributing to the chronicity of EoE by promoting tissue remodeling through fibroblast-endothelial cell crosstalk.

Remote immune processes revealed by immune-derived circulating cell-free DNA.

Blood cell counts often fail to report on immune processes occurring in remote tissues. Here, we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N = 242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N = 92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with eosinophilic esophagitis (N = 21) and B-cell lymphoma (N = 27) showed selective elevation of eosinophil and B-cell cfDNA, respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.

Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features.

GOAL: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies-a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. RESULTS: In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. CONCLUSIONS: We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.

Environmental allergens trigger type 2 inflammation through ripoptosome activation.

Environmental allergens, including fungi, insects and mites, trigger type 2 immunity; however, the innate sensing mechanisms and initial signaling events remain unclear. Herein, we demonstrate that allergens trigger RIPK1-caspase 8 ripoptosome activation in epithelial cells. The active caspase 8 subsequently engages caspases 3 and 7, which directly mediate intracellular maturation and release of IL-33, a pro-atopy, innate immunity, alarmin cytokine. Mature IL-33 maintained functional interaction with the cognate ST2 receptor and elicited potent pro-atopy inflammatory activity in vitro and in vivo. Inhibiting caspase 8 pharmacologically and deleting murine Il33 and Casp8 each attenuated allergic inflammation in vivo. Clinical data substantiated ripoptosome activation and IL-33 maturation as likely contributors to human allergic inflammation. Our findings reveal an epithelial barrier, allergen-sensing mechanism that converges on the ripoptosome as an intracellular molecular signaling platform, triggering type 2 innate immune responses. These findings have significant implications for understanding and treating human allergic diseases.

Zooming in on T cell clones: Are we heading to personalized treatment of allergy?

Single-cell RNA and TCR sequencing of peripheral blood and esophageal cells of human eosinophilic esophagitis uncovers antigen-restricted TH2 cells.

Bidirectional crosstalk between eosinophils and esophageal epithelial cells regulates inflammatory and remodeling processes.

Eosinophils accumulate adjacent to epithelial cells in the mucosa of patients with eosinophilic esophagitis (EoE), yet the bidirectional communication between these cells is not well understood. Herein, we investigated the crosstalk between human eosinophils and esophageal epithelial cells. We report that blood-derived eosinophils have prolonged survival when cocultured with epithelial cells; 96 ± 1% and 30 ± 6% viability was observed after 7 and 14 days of coculture, respectively, compared with 1 ± 0% and 0 ± 0% of monoculture. In the presence of IL-13 and epithelial cells, eosinophils had greater survival (68 ± 1%) at 14 days compared with cocultures lacking IL-13. Prolonged eosinophil viability did not require cellular contact and was observed when eosinophils were cultured in conditioned media from esophageal epithelial cells; neutralizing GM-CSF attenuated eosinophil survival. The majority of eosinophil transcripts (58%) were dysregulated in cocultured eosinophils compared with freshly isolated cells. Analysis of epithelial cell transcripts indicated that exposure to eosinophils induced differential expression of a subset of genes that were part of the EoE esophageal transcriptome. Collectively, these results uncover a network of crosstalk between eosinophils and esophageal epithelial cells involving epithelial mediated eosinophil survival and reciprocal changes in cellular transcripts, events likely to occur in EoE.