Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations
| Autor: | Quan Nguyen, Anne Senabouth, Han Sheng Chiu, Nathan J. Palpant, Timothy J. C. Bruxner, Joseph E. Powell, Angelika N. Christ, Samuel W. Lukowski |
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| Rok vydání: | 2018 |
| Předmět: |
Genetic Markers
0301 basic medicine Transcription Genetic Sequence analysis Induced Pluripotent Stem Cells Cell Population Gene Expression Method RNA-Seq Computational biology Biology 01 natural sciences Cell Line Genetic Heterogeneity 010104 statistics & probability 03 medical and health sciences Transcription (biology) Gene expression medicine Genetics Cluster Analysis Humans Clustered Regularly Interspaced Short Palindromic Repeats 0101 mathematics education Induced pluripotent stem cell Gene Genetics (clinical) 030304 developmental biology 0303 health sciences education.field_of_study Sequence Analysis RNA RNA Cell Differentiation medicine.anatomical_structure 030104 developmental biology Function (biology) |
| Zdroj: | Genome Research. 28:1053-1066 |
| ISSN: | 1549-5469 1088-9051 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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