In vitro investigation on human development, disease modeling, and drug discovery has been empowered by human induced pluripotent stem cell (hiPSC) technologies that form the foundation of precision medicine. Race and sex genetic backgrounds have become a major focus of many diseases modeling and drug response evaluation in the pharmaceutical industry. Here, we gathered data from major stem cell repositories to analyze the diversity with respect to ethnicity, sex, and disease types; and we also analyzed public datasets to unravel transcriptomics differences between samples of different ethnicities and sexes. We found a lack of diversity despite the large sample size of human induced pluripotent stem cells. In the ethnic comparison, the White group made up the majority of the banked hiPSCs. Similarly, for the organ/disease type and sex comparisons, the neural and male hiPSCs accounted for the majority of currently available hiPSCs. Bulk RNA-seq and single-cell transcriptomic analysis coupled with Machine Learning and Network Analysis revealed panels of gene features differently expressed in healthy hiPSCs and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of different races and sexes. The data highlights the current ethnic and sex inequality in stem cell research and demonstrates the molecular biological diversity of hiPSCs and cardiomyocytes from different races and genders. We postulate that future efforts in stem cell biology, regenerative and precision medicine should be guided towards an inclusive, diverse repository reflecting the prevalence of diseases across racial and ethnic groups and the sexes, important for both common and rare disease modeling, drug screening, and cell therapeutics.
Keywords: diversity & inclusion; ethnicity; human induced pluripotent stem cells (hiPSC); machine learning; network analysis; sex; transcriptomics (RNA-Seq).
Copyright © 2023 Nguyen, Lac, Abdi, Banerjee, Deng and Zhang.