ArticleScientific reports2024
Unsupervised analysis of whole transcriptome data from human pluripotent stem cells cardiac differentiation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 5 citations in OpenAlex.
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7 authors at 3 institutions in 2 countries.
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Abstract
The main objective of the present work was to highlight differences and similarities in gene expression patterns between different pluripotent stem cell cardiac differentiation protocols, using a workflow based on unsupervised machine learning algorithms to analyse the transcriptome of cells cultured as a 2D monolayer or as 3D aggregates. This unsupervised approach effectively allowed to portray the transcriptomic changes that occurred throughout the differentiation processes, with a visual representation of the entire transcriptome. The results allowed to corroborate previously reported data and also to unveil new gene expression patterns. In particular, it was possible to identify a correlation between low cardiomyocyte differentiation efficiencies and the early expression of a set of non-mesodermal genes, which can be further explored as predictive markers of differentiation efficiency. The workflow here developed can also be applied to analyse other stem cell differentiation transcriptomic datasets, envisaging future clinical implementation of cellular therapies.
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