从胚胎到出生后胃食管发育的单细胞 RNA 测序数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Pon Ganish Prakash, Naveen Kumar, Rajendra Kumar Gurumurthy, Cindrilla Chumduri
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引用次数: 0

摘要

胃食管疾病和癌症给全球带来了沉重的负担。特别是近年来,食管腺癌(EAC)的发病率急剧上升。巴雷特食管是 EAC 的前体,其特点是食管与胃交界处的胃食管鳞柱交界处(GE-SCJ)有独特的组织适应性。研究 GE-SCJ 的演变和了解其平衡失调对阐明癌症发病机制至关重要。在这里,我们展示了小鼠单细胞RNA测序(scRNA-seq)数据集的技术质量,该数据集捕捉了食管、胃和GE-SCJ在胚胎、新生儿和成年阶段的发育过程中的转录动态。通过与外部 scRNA-seq 数据集的整合以及使用类器官和动物模型的验证,我们证明了该数据集在识别细胞类型和转录特征方面的一致性。该数据集将成为研究组织微环境中发育模式和相关信号网络的宝贵资源。通过深入了解稳态过程中的细胞程序,它有助于识别导致变态反应和癌症等病症的变化,这对制定有效的干预策略至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporal single-cell RNA sequencing dataset of gastroesophagus development from embryonic to post-natal stages.

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
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.
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