ArticleMass spectrometry (Tokyo, Japan)2026
Proteomic Signatures in Extracellular Vesicles as Predictive Markers of Neural and Cardiac Differentiation from Human iPSCs.
Article in Mass spectrometry (Tokyo, Japan), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Human induced pluripotent stem cell (iPSC)-derived neural cells (NCs) and cardiomyocytes (CMs) show significant promise for clinical applications in regenerative medicine. Because the manufacturing of iPSC-derived products faces challenges in quality reproducibility, cost, and manufacturing time, closed automated culture systems have been developed. Recently, an efficient and noninvasive cell monitoring system based on extracellular markers secreted by cells represents a new strategy for closed systems. Extracellular vesicles (EVs), secreted by cells, contain proteins involved in stem cell differentiation; however, EV proteins in the cell culture medium are difficult to analyze because of their low abundance. Thus, their changes during cell differentiation, and the relationship between EV and cell proteins, remain unclear. In this study, we investigated the changes in EV proteins during iPSC differentiation into NCs and CMs using data-independent acquisition LC/MS/MS (DIA-MS), a highly comprehensive and quantitative approach. We observed significant positive correlations between the EV protein abundance and cellular protein expression levels during differentiation on days 0-7, and identified signature proteins characteristic of NCs and CMs. Among these, trophoblast glycoprotein (TPBG) for NC differentiation and keratin, type I cytoskeletal 19 (KRT19) for CM differentiation were particularly increased in both cells and EVs. Moreover, TPBG and KRT19 levels at day 7 showed similar trends to the differences in differentiation observed among the cell lines, as determined by flow cytometry. These findings suggest that these EV proteins may serve as predictive markers for monitoring differentiation into NC and CM.
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