MassIVE MSV000098914

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Temporal multi-omics gene expression data across human embryonic stem cell-derived polyhormonal cell differentiation

Description

Human embryonic stem cells (hESCs) provide a powerful in vitro model to study lineage specification and the regulatory programs underlying early human development. Here, we present a high-resolution, temporal multi-omics dataset tracking mRNA, translation, and protein expression dynamics during hESC differentiation into definitive endoderm and subsequent polyhormonal (PH) cells, a key pancreatic lineage. RNA-seq, ribosome profiling, and quantitative mass spectrometry-based proteomics were performed on matched samples collected at ten time points in biological duplicates, allowing detailed characterization of transcriptional, translational, and protein abundance changes over the differentiation timeline. The dataset exhibits high technical quality, with strong reproducibility between replicates and rigorous quality control metrics across all omics platforms. This extensive dataset provides critical insights into the complex regulatory mechanisms driving polyhormonal cell differentiation and serves as a valuable resource for the research community, enabling deeper exploration of mammalian development, endodermal lineage specification, and gene regulation. [doi:10.25345/C5NP1WX5Z] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: hESC differentiation ; DIA ; Gene expression ; DatasetType:Proteomics

Contact

Principal Investigators:
(in alphabetical order)
Marko Jovanovic, Columbia University, USA
Submitting User: ak3952
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Experimental Design
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Identification Results
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Quantification Results
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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