Single-Cell Transcriptional Analyses Identify Lineage-Specific Epithelial Responses to Inflammation and Metaplastic Development in the Gastric Corpus.

BACKGROUND & AIMS: Chronic atrophic gastritis can lead to gastric metaplasia and increase risk of gastric adenocarcinoma. Metaplasia is a precancerous lesion associated with an increased risk for carcinogenesis, but the mechanism(s) by which inflammation induces metaplasia are poorly understood. We investigated transcriptional programs in mucous neck cells and chief cells as they progress to metaplasia mice with chronic gastritis. METHODS: We analyzed previously generated single-cell RNA-sequencing (scRNA-seq) data of gastric corpus epithelium to define transcriptomes of individual epithelial cells from healthy BALB/c mice (controls) and TxA23 mice, which have chronically inflamed stomachs with metaplasia. Chronic gastritis was induced in B6 mice by Helicobacter pylori infection. Gastric tissues from mice and human patients were analyzed by immunofluorescence to verify findings at the protein level. Pseudotime trajectory analysis of scRNA-seq data was used to predict differentiation of normal gastric epithelium to metaplastic epithelium in chronically inflamed stomachs. RESULTS: Analyses of gastric epithelial transcriptomes revealed that gastrokine 3 (Gkn3) mRNA is a specific marker of mouse gastric corpus metaplasia (spasmolytic polypeptide expressing metaplasia, SPEM). Gkn3 mRNA was undetectable in healthy gastric corpus; its expression in chronically inflamed stomachs (from TxA23 mice and mice with Helicobacter pylori infection) identified more metaplastic cells throughout the corpus than previously recognized. Staining of healthy and diseased human gastric tissue samples paralleled these results. Although mucous neck cells and chief cells from healthy stomachs each had distinct transcriptomes, in chronically inflamed stomachs, these cells had distinct transcription patterns that converged upon a pre-metaplastic pattern, which lacked the metaplasia-associated transcripts. Finally, pseudotime trajectory analysis confirmed the convergence of mucous neck cells and chief cells into a pre-metaplastic phenotype that ultimately progressed to metaplasia. CONCLUSIONS: In analyses of tissues from chronically inflamed stomachs of mice and humans, we expanded the definition of gastric metaplasia to include Gkn3 mRNA and GKN3-positive cells in the corpus, allowing a more accurate assessment of SPEM. Under conditions of chronic inflammation, chief cells and mucous neck cells are plastic and converge into a pre-metaplastic cell type that progresses to metaplasia.

Single-cell transcriptional analyses of spasmolytic polypeptide-expressing metaplasia arising from acute drug injury and chronic inflammation in the stomach.

OBJECTIVE: Spasmolytic polypeptide-expressing metaplasia (SPEM) is a regenerative lesion in the gastric mucosa and is a potential precursor to intestinal metaplasia/gastric adenocarcinoma in a chronic inflammatory setting. The goal of these studies was to define the transcriptional changes associated with SPEM at the individual cell level in response to acute drug injury and chronic inflammatory damage in the gastric mucosa. DESIGN: Epithelial cells were isolated from the gastric corpus of healthy stomachs and stomachs with drug-induced and inflammation-induced SPEM lesions. Single cell RNA sequencing (scRNA-seq) was performed on tissue samples from each of these settings. The transcriptomes of individual epithelial cells from healthy, acutely damaged and chronically inflamed stomachs were analysed and compared. RESULTS: scRNA-seq revealed a population Mucin 6 (Muc6)+gastric intrinsic factor (Gif)+ cells in healthy tissue, but these cells did not express transcripts associated with SPEM. Furthermore, analyses of SPEM cells from drug injured and chronically inflamed corpus yielded two major findings: (1) SPEM and neck cell hyperplasia/hypertrophy are nearly identical in the expression of SPEM-associated transcripts and (2) SPEM programmes induced by drug-mediated parietal cell ablation and chronic inflammation are nearly identical, although the induction of transcripts involved in immunomodulation was unique to SPEM cells in the chronic inflammatory setting. CONCLUSIONS: These data necessitate an expansion of the definition of SPEM to include Tff2+Muc6+ cells that do not express mature chief cell transcripts such as Gif. Our data demonstrate that SPEM arises by a highly conserved cellular programme independent of aetiology and develops immunoregulatory capabilities in a setting of chronic inflammation.

Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues.

Skeletal muscle comprises a family of diverse tissues with highly specialized functions. Many acquired diseases, including HIV and COPD, affect specific muscles while sparing others. Even monogenic muscular dystrophies selectively affect certain muscle groups. These observations suggest that factors intrinsic to muscle tissues influence their resistance to disease. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNAseq to profile mRNA expression in skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We identify candidate genes that may drive tissue specialization, including Smarca4, Vegfa, and Myostatin. By demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for studying the mechanisms of tissue specialization.

ExpressionDB: An open source platform for distributing genome-scale datasets.

RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been developed to decrease the amount of bioinformatics expertise necessary to analyze these data. Nevertheless, many barriers persist which discourage new labs from using functional genomics approaches. Since high-quality gene expression studies have enduring value as resources to the entire research community, it is of particular importance that small labs have the capacity to share their analyzed datasets with the research community. Here we introduce ExpressionDB, an open source platform for visualizing RNA-seq and microarray data accommodating virtually any number of different samples. ExpressionDB is based on Shiny, a customizable web application which allows data sharing locally and online with customizable code written in R. ExpressionDB allows intuitive searches based on gene symbols, descriptions, or gene ontology terms, and it includes tools for dynamically filtering results based on expression level, fold change, and false-discovery rates. Built-in visualization tools include heatmaps, volcano plots, and principal component analysis, ensuring streamlined and consistent visualization to all users. All of the scripts for building an ExpressionDB with user-supplied data are freely available on GitHub, and the Creative Commons license allows fully open customization by end-users. We estimate that a demo database can be created in under one hour with minimal programming experience, and that a new database with user-supplied expression data can be completed and online in less than one day.