ArticleInternational journal of molecular sciences2018
Molecular Characterization of Gastric Epithelial Cells Using Flow Cytometry.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis.bioRxiv : the preprint server for biology · 2026Article
- The mechanical niche in gastric precancer: extracellular matrix remodeling, mechanotransduction, and stem-cell fate.Frontiers in cell and developmental biology · 2026Review
- Article
- Interleukin 27 Protects From Gastric Atrophy and Metaplasia During Chronic Autoimmune Gastritis.Cellular and molecular gastroenterology and hepatology · 2020Article
- The Major Histocompatibility Complex Class II-CD4 Immunologic Synapse in Alcoholic Hepatitis and Autoimmune Liver Pathology: The Role of Aberrant Major Histocompatibility Complex Class II in Hepatocytes.The American journal of pathology · 2020Review
- Molecular Features Distinguish Gastric Cancer Subtypes.International journal of molecular sciences · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The ability to analyze individual epithelial cells in the gastric mucosa would provide important insight into gastric disease, including chronic gastritis and progression to gastric cancer. However, the successful isolation of viable gastric epithelial cells (parietal cells, neck cells, chief cells, and foveolar cells) from gastric glands has been limited due to difficulties in tissue processing. Furthermore, analysis and interpretation of gastric epithelial cell flow cytometry data has been difficult due to the varying sizes and light scatter properties of the different epithelial cells, high levels of autofluorescence, and poor cell viability. These studies were designed to develop a reliable method for isolating viable single cells from the corpus of stomachs and to optimize analyses examining epithelial cells from healthy and diseased stomach tissue by flow cytometry. We performed a two stage enzymatic digestion in which collagenase released individual gastric glands from the stromal tissue of the corpus, followed by a Dispase II digestion that dispersed these glands into greater than 1 × 10⁶ viable single cells per gastric corpus. Single cell suspensions were comprised of all major cell lineages found in the normal gastric glands. A method describing light scatter, size exclusion, doublet discrimination, viability staining, and fluorescently-conjugated antibodies and lectins was used to analyze individual epithelial cells and immune cells. This technique was capable of identifying parietal cells and revealed that gastric epithelial cells in the chronically inflamed mucosa significantly upregulated major histocompatibility complexes (MHC) I and II but not CD80 or CD86, which are costimulatory molecules involved in T cell activation. These studies describe a method for isolating viable single cells and a detailed description of flow cytometric analysis of cells from healthy and diseased stomachs. These studies begin to identify effects of chronic inflammation on individual gastric epithelial cells, a critical consideration for the study of gastric cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.