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January 18, 2026Scientific Data0 citationsOpen Access

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

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AKAbdurrahman KeskinHSHani ShayyaAPAchchhe Patel

Key Points

  • To investigate the dynamics of gene expression and regulatory mechanisms during differentiation of hESCs into polyhormonal cells.
  • Used RNA-seq to analyze mRNA levels at various time points.
  • Employed ribosome profiling to examine translation efficiency.
  • Utilized mass spectrometry for protein abundance quantification.
  • Collected data at ten time points in biological duplicates.
  • Revealed significant changes in gene expression throughout differentiation.
  • Demonstrated high reproducibility between replicates.
  • Provided detailed insights into the regulatory mechanisms driving polyhormonal differentiation.

Abstract

Abstract 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.

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Cite This Study

Keskin et al. (2026) studied this question.

synapsesocial.com/papers/696c774feb60fb80d1395828https://doi.org/10.1038/s41597-026-06606-8
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