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Comprehensive analysis of the secretome and transcriptome stability from WJ-Mesenchymal Stromal cells throughout GMP-Manufacturing

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NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE269586
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Mesenchymal stromal cells (MSCs) hold promise for cell-based therapies due to their ability to stimulate tissue repair and modulate immune responses. Umbilical cord-derived MSC from Wharton’s jelly (WJ), offer advantages such as low immunogenicity and potent immune modulatory effects. However, ensuring consistent quality and safety throughout their manufacturing process remains critical. RNA sequencing (RNA-seq) emerges as a crucial tool for assessing genetic stability and expression dynamics. This study examines the secretome and transcriptome signatures throughout WJ-MSCs Good Manufacturing Practice (GMP) production, focusing in the role of Total RNA or Massive Analysis of cDNA Ends (MACE-Seq)-RNA sequencing. Through comprehensive transcriptomic analysis, we demonstrated the stability of WJ-MSC transcriptome across manufacturing stages. Notably, MACE-Seq enhanced the identification of key expression patterns related to senescence and immunomodulation. These findings highlight the potential of MACE-Seq as a routine quality assessment tool for WJ-MSC-based therapies, ensuring their efficacy and safety in clinical applications. Importantly, MACE-Seq proves valuable for characterizing WJ-MSC products, providing insights unattainable through traditional assays. Using conventional RNA-Seq and MACE-seq, we compared the transcriptome of WJ-MSCs at different stages of GMP production. MSCs from a master cell bank (MCB) were seeded at two different cell densities (high and low). Cells at high density reached passage 3, while cells at low density reached passage 2. RNA was extracted from cells of the MCB and from pasasges 2 and 3. The same RNA sample was used for both RNA sequencing techniques. Comparative gene expression analysis was performed, focusing on the effect of the two seeding densities and the unique gene expression signatures identified by each sequencing approach
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2024-09-29
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