Temporal multiomics gene expression data of human embryonic stem cell-derived cardiomyocyte differentiation.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Abdurrahman Keskin, Hani J Shayya, Dario Sirabella, Achchhe Patel, Barbara Corneo, Marko Jovanovic
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引用次数: 0

Abstract

Human embryonic stem cells (hESCs) serve as a valuable in vitro model for studying early human developmental processes due to their ability to differentiate into all three germ layers. Here, we present a comprehensive multi-omics dataset generated by differentiating hESCs into cardiomyocytes via the mesodermal lineage, collecting samples at 10 distinct time points. We measured mRNA levels by mRNA sequencing (mRNA-seq), translation levels by ribosome profiling (Ribo-seq), and protein levels by quantitative mass spectrometry-based proteomics. Technical validation confirmed high quality and reproducibility across all datasets, with strong correlations between replicates. This extensive dataset provides critical insights into the complex regulatory mechanisms of cardiomyocyte differentiation and serves as a valuable resource for the research community, aiding in the exploration of mammalian development and gene regulation.

Abstract Image

Abstract Image

Abstract Image

人胚胎干细胞来源的心肌细胞分化的时间多组学基因表达数据。
人类胚胎干细胞(hESCs)由于能够分化为所有三种胚层而成为研究早期人类发育过程的有价值的体外模型。在这里,我们提出了一个全面的多组学数据集,通过中胚层谱系将hESCs分化为心肌细胞,在10个不同的时间点收集样本。我们通过mRNA测序(mRNA-seq)测量mRNA水平,通过核糖体分析(Ribo-seq)测量翻译水平,通过基于定量质谱的蛋白质组学测量蛋白质水平。技术验证证实了所有数据集的高质量和可重复性,重复之间具有很强的相关性。这个广泛的数据集为心肌细胞分化的复杂调控机制提供了重要的见解,并为研究界提供了宝贵的资源,有助于探索哺乳动物的发育和基因调控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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