U.S. Department of Health and Human ServicesHHS National Institutes of HealthNIH National Center for Advancing Translational SciencesNCATS

Systematic analysis of the genetic determinants of human neural lineage commitment by quantitative small molecule-based transcriptomic profiling

Posted on June 24th, 2020 by claire.malley@nih.gov

Members of SCTL presented a poster at ISSCR 2020 Virtual. The poster is entitled: Systematic analysis of the genetic determinants of human neural lineage commitment by quantitative small molecule-based transcriptomic profiling.

Authors (underlined, presenting): Pei-Hsuan Chu, Claire Malley, John Braisted, Carlos A. Tristan, Ruili Huang, Yuhong Wang, Pinar Ormanoglu, Anton Simeonov, Ilyas Singeç

Download the full-size poster here.

More information about ISSCR 2020 Virtual.


Small molecules offer great advantages for directed differentiation of human pluripotent stem cells (hPSCs) into defined lineages. Although small molecule combinations are used for neural lineage induction (e.g. dual SMAD inhibition, d-SMADi), there is no systematic understanding of how these small molecules, either applied alone or in combination, affect quantitative gene expression and lineage commitment. Here, we employed a high-throughput transcriptomics method (RASL-Seq) to profile dose-dependent transcriptional responses of induced pluripotent stem cells (iPSCs) along a 7-day neural induction experiment using small molecule inhibitors for bone morphogenetic protein (BMP) and/or transforming growth factor-beta (TGF-β) pathways (i.e. mono- and d-SMADi). A total of 358 lineage-related genes and transcription factors were tested in single or combined drug treatments across 7 dosages. Gene expression was analyzed using a custom- built algorithm to obtain dose-dependent curve classification, maximum efficacy and critical concentrations. This strategy identified genes regulated in opposite directions upon BMP inhibitor (LDN-193189) or TGF-β inhibitor (A83-01) treatment such as SMOC1, GADD45A, SESN3 and WLS. We also discovered genes that exclusively responded to LDN-193189 (e.g. GAD2, HESX1, PAX3 and FOXG1) but not A83-01 and vice versa (e.g. GAP43, MT1X and ZFHX4). Next, drug combination effects were addressed by comparing mono- and dSMADi revealing antagonistic effects on GBX2, TNNT2, HESX1 and PAX3. Notably, the correlation of differential drug response on gene expression profiles identified the distinct contribution of BMP inhibition and TGF-β inhibition to fate determination to either the CNS or PNS lineages. Lastly, to establish our approach as a general chemical biology resource, we mapped transcriptomic profiles regulated by 16 compounds that are widely used in the stem cell field but are not well-characterized. In summary, precisely defining the quantitative relationships between small molecules and gene expression changes represents a novel approach to better control and predict the differentiation trajectory of hPSCs.