Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level. PLoS Comput Biol 2021 Sep;17(9):e1009305
Date
09/18/2021Pubmed ID
34534204Pubmed Central ID
PMC8448341DOI
10.1371/journal.pcbi.1009305Scopus ID
2-s2.0-85115404050 (requires institutional sign-in at Scopus site) 23 CitationsAbstract
The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.
Author List
Kannan S, Farid M, Lin BL, Miyamoto M, Kwon CAuthor
Brian L. Lin PhD Assistant Professor in the Cell Biology, Neurobiology and Anatomy department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
BenchmarkingGene Expression
Hepatocytes
Humans
Insulin-Secreting Cells
Myocytes, Cardiac
Pluripotent Stem Cells
Sequence Analysis, RNA
Single-Cell Analysis
Tissue Engineering
Transcriptome
