Identification of RNA-based cell-type markers for stem-cell manufacturing systems with a statistical scoring function.

Autor: Shi Y; School of Biological Sciences, University of Nebraska, Lincoln, NE, USA., Yang W; School of Biological Sciences, University of Nebraska, Lincoln, NE, USA., Lin H; Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, NE, USA., Han L; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA., Cai AJ; Newark Academy, 91 W S Orange Ave, Livingston, NJ, USA., Saraf R; Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, NE, USA., Lei Y; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.; Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA., Zhang C; School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
Jazyk: angličtina
Zdroj: Gene reports [Gene Rep] 2024 Mar; Vol. 34. Date of Electronic Publication: 2023 Dec 12.
DOI: 10.1016/j.genrep.2023.101869
Abstrakt: Cell-type biomarkers are useful in stem-cell manufacturing to monitor cell purity, quantity, and quality. However, the study on cell-type markers, specifically for stem cell manufacture, is limited. Emerging questions include which RNA transcripts can serve as biomarkers during stem cell culture and how to discover these biomarkers efficiently and precisely. We developed a scoring function system to identify RNA biomarkers with RNA-seq data for systems that have a limited number of cell types. We applied the method to two data sets, one for extracellular RNAs (ex-RNAs) and the other for intracellular microRNAs (miRNAs). The first data set has RNA-seq data of ex-RNAs from cell culture media for six different types of cells, including human embryonic stem cells. To get the RNA-seq data from intracellular miRNAs, we cultured three types of cells: human embryonic stem cells (H9), neural stem cells (NSC), hESC-derived endothelial cells (EC) and conducted small RNA-seq to their intracellular miRNAs. Using these data, we identified a set of ex-RNAs/smRNAs as candidates of biomarkers for different types of cells for cell manufacture. The validity of these findings was confirmed by the utilization of additional data sets and experimental procedures. We also used deep-learning-based prediction methods and simulated data to validate these discovered biomarkers.
Competing Interests: Declaration of competing interest YL owns equity in CellGro Technologies, LLC. This financial interest has been reviewed by the University’s Individual Conflict of Interest Committee and is currently being managed by the University. Other authors do not have any conflict of interest.
Databáze: MEDLINE
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