RT Journal
A1 Nguyen, Quan H.
A1 Lukowski, Samuel W.
A1 Chiu, Han Sheng
A1 Senabouth, Anne
A1 Bruxner, Timothy J.C.
A1 Christ, Angelika N.
A1 Palpant, Nathan J.
A1 Powell, Joseph E.
T1 Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations
JF Genome Research 
JO Genome Research 
YR 2018 
FD July 01 
VO 28 
IS 7 
SP 1053 
OP 1066 
DO 10.1101/gr.223925.117 
UL http://genome.cshlp.org/content/28/7/1053.abstract 
AB Heterogeneity of cell states represented in pluripotent cultures has 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 composed of 165 unique genes that denote the specific pluripotency states; 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 threefold) 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.