A qPCR ScoreCard quantifies the differentiation potential of human pluripotent stem cells

Abstract

Research on human pluripotent stem cells has been hampered by the lack of a standardized, quantitative, scalable assay of pluripotency. We have previously described an assay called ScoreCard that used gene expression signatures to quantify differentiation efficiency. Here we report an improved version of the assay based on qPCR that enables faster, more quantitative assessment of functional pluripotency. We provide an in-depth characterization of the revised signature panel through embryoid body and directed differentiation experiments as well as a detailed comparison to the teratoma assay. We also show that the improved ScoreCard enables applications such as screening of small molecules, genetic perturbations and assessment of culture conditions. Beyond stem cell applications, this approach can in principle be extended to other cell types and lineages.

High-Throughput and Cost-Effective Characterization of Induced Pluripotent Stem Cells

“…We found that the ESCs and iPSCs have very low germ layer scores, while the majority of the EBs have high scores for all three germ layers (Figure 4E). As expected (Tsankov et al., 2015), the relative strength of the three germ layer scores varied across the EBs, suggesting that they had different proportions of ectoderm, endoderm, and mesoderm cells. These data suggest that the in vitro differentiation potential of iPSC can be efficiently examined by assaying derived EBs for expression of these 12 genes.…”

Large-Scale Profiling Reveals the Influence of Genetic Variation on Gene Expression in Human Induced Pluripotent Stem Cells

“…Since gene expression is often used to estimate stem cell pluripotency, we compared our eGenes to nine stem cell marker genes from (Tsankov et al, 2015) and found that three ( CXCL5 , IDO1 , and POU5F1 ) had eQTLs (Figure 1B–C, Table S2). The lead variants for these four genes explained respectively 19%, 11%, and 18% of the variance in gene expression in a model using only batch, sex, and donor age as covariates.…”

Designing a blueprint for next‐generation stem cell bioprocessing development

“…Since numerous signaling pathways that sense the cellular state and phenotypic properties of single cells within their microenvironments exist, we expect an increase in future efforts toward the development of tools that detect and quantify heterogeneity. The existing evaluation methods employed for such analyses include polychromatic flow cytometry, mass cytometry, single‐cell RNA sequencing, single‐cell RT‐qPCR, microtools, and high‐resolution imaging (Avior, Biancotti, & Benvenisty, ; Morris & Daley, ; Patel et al, ; Sena, Galotto, Devitt, Connick, & Jacobi, ; Sirenko et al, ; Stern et al, ; Tsankov et al, ). Additionally, the establishment of standardized terminology, methods, and metrics will be essential for routinely extracting and communicating insights regarding biological heterogeneity.…”