{"title":"Temporal single-cell RNA sequencing dataset of gastroesophagus development from embryonic to post-natal stages.","authors":"Pon Ganish Prakash, Naveen Kumar, Rajendra Kumar Gurumurthy, Cindrilla Chumduri","doi":"10.1038/s41597-024-04081-7","DOIUrl":null,"url":null,"abstract":"<p><p>Gastroesophageal disorders and cancers impose a significant global burden. Particularly, the prevalence of esophageal adenocarcinoma (EAC) has increased dramatically in recent years. Barrett's esophagus, a precursor of EAC, features a unique tissue adaptation at the gastroesophageal squamo-columnar junction (GE-SCJ), where the esophagus meets the stomach. Investigating the evolution of GE-SCJ and understanding dysregulation in its homeostasis are crucial for elucidating cancer pathogenesis. Here, we present the technical quality of the comprehensive single-cell RNA sequencing (scRNA-seq) dataset from mice that captures the transcriptional dynamics during the development of the esophagus, stomach and the GE-SCJ at embryonic, neonatal and adult stages. Through integration with external scRNA-seq datasets and validations using organoid and animal models, we demonstrate the dataset's consistency in identified cell types and transcriptional profiles. This dataset will be a valuable resource for studying developmental patterns and associated signaling networks in the tissue microenvironment. By offering insights into cellular programs during homeostasis, it facilitates the identification of changes leading to conditions like metaplasia and cancer, crucial for developing effective intervention strategies.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1238"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11569200/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04081-7","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Gastroesophageal disorders and cancers impose a significant global burden. Particularly, the prevalence of esophageal adenocarcinoma (EAC) has increased dramatically in recent years. Barrett's esophagus, a precursor of EAC, features a unique tissue adaptation at the gastroesophageal squamo-columnar junction (GE-SCJ), where the esophagus meets the stomach. Investigating the evolution of GE-SCJ and understanding dysregulation in its homeostasis are crucial for elucidating cancer pathogenesis. Here, we present the technical quality of the comprehensive single-cell RNA sequencing (scRNA-seq) dataset from mice that captures the transcriptional dynamics during the development of the esophagus, stomach and the GE-SCJ at embryonic, neonatal and adult stages. Through integration with external scRNA-seq datasets and validations using organoid and animal models, we demonstrate the dataset's consistency in identified cell types and transcriptional profiles. This dataset will be a valuable resource for studying developmental patterns and associated signaling networks in the tissue microenvironment. By offering insights into cellular programs during homeostasis, it facilitates the identification of changes leading to conditions like metaplasia and cancer, crucial for developing effective intervention strategies.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.