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.

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.

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.

Guidelines for Genome-Scale Analysis of Biological Rhythms.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze.

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.