Bruxner, T. | Search bioRxiv

bioRxiv2017

Cardiac directed differentiation using small molecule Wnt modulation at single-cell resolution

Differentiation into diverse cell lineages requires the orchestration of gene regulatory networks guiding diverse cell fate choices. Utilizing human pluripotent stem cells, we measured expression dynamics of 17,718 genes from 43,168 cells across five time points over a thirty day time-course of in vitro cardiac-directed differentiation. Unsupervised clustering and lineage prediction algorithms were used to map fate choices and transcriptional networks underlying cardiac differentiation. We leveraged this resource to identify strategies for controlling in vitro differentiation as it occurs in vivo. HOPX, a non-DNA binding homeodomain protein essential for heart development in vivo was identified as dys-regulated in in vitro derived cardiomyocytes. Utilizing genetic gain and loss of function approaches, we dissect the transcriptional complexity of the HOPX locus and identify the requirement of hypertrophic signaling for HOPX transcription in hPSC-derived cardiomyocytes. This work provides a single cell dissection of the transcriptional landscape of cardiac differentiation for broad applications of stem cells in cardiovascular biology.

developmental biology↗

bioRxiv2017

Single-Cell Transcriptome Sequencing Of 18,787 Human Induced Pluripotent Stem Cells Identifies Differentially Primed Subpopulations

Heterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how individual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 individual WTC CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method, and through this identified four subpopulations distinguishable on the basis of their pluripotent state including: a core pluripotent population (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). For each subpopulation we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four transcriptionally distinct predictor gene sets comprised of 165 unique genes that denote the specific pluripotency states; and using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to 3-fold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations, and support our conclusions with results from two orthogonal pseudotime trajectory methods.

genomics↗