Transcriptomic entropy – a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation

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.

Researchers at Johns Hopkins School of Medicine have 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, the researchers analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled them to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. They further found that their entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. This study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.

scRNA-seq constructs a reference for CM maturation

A. Mouse model used to generate perinatal maturation reference scRNA-seq library. In the aMHC-cre x mTmG mouse, CMs are labeled by GFP. This image was obtained and modified from “Brown Mouse Lab” by SVG-Clipart.com under a CC BY 4.0 license. B. UMAP dimensionality reduction (via Monocle 3) for the maturation reference. C. mnnCorrect-based integration of Wang and Yao et al. dataset with reference dataset. D. Our model for changes in gene distribution over CM maturation. As CMs undergo the maturation process, they transition from a broad gene distribution (characterised by high entropy) to a more narrow distribution (characterised by low entropy). E. Shannon Entropy S computed for each timepoint in the maturation reference dataset. F. Smoothed density estimates for genes expressed at 0–5000 counts per million (CPM) for each timepoint in the maturation reference dataset.