scholarly journals Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells

2011 ◽  
Vol 121 (3) ◽  
pp. 1217-1221 ◽  
Author(s):  
Kazim H. Narsinh ◽  
Ning Sun ◽  
Veronica Sanchez-Freire ◽  
Andrew S. Lee ◽  
Patricia Almeida ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
Transcriptional Profiling ◽  
Induced Pluripotent

scholarly journals Single-cell phenotyping of human induced pluripotent stem cells by high-throughput imaging

2015 ◽  
Author(s):  
Hans Christian Volz ◽  
Florian Heigwer ◽  
Tatjana Wuest ◽  
Marta Galach ◽  
Jochen Utikal ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
High Throughput ◽  
Pluripotent Stem Cells ◽  
Single Cells ◽  
Phenotypic Variability ◽  
Screening Experiments ◽  
Induced Pluripotent ◽  
High Throughput Imaging

Single-cell phenotyping promises to yield insights into biological responses in heterogeneous cell populations. We developed a method based on single-cell analysis to phenotype human induced pluripotent stem cells (hIPSC) by high-throughput imaging. Our method uses markers for morphology and pluripotency as well as social features to characterize perturbations using a meta-phenotype based on mapping single cells to distinct phenotypic classes. Analysis of perturbations on a single cell level enhances the applicability of human induced pluripotent stem cells (hIPSC) for screening experiments taking the inherently increased phenotypic variability of these cells into account. We adapted miniaturized culture conditions to allow for the utilization of hIPSC in RNA interference (RNAi) high-throughput screens and single cell phenotyping by image analysis. We identified key regulators of pluripotency in hIPSC masked in a population-averaged analysis and we confirmed several candidate genes (SMG1, TAF1) and assessed their effect on pluripotency.

Characterization of the single-cell derived bovine induced pluripotent stem cells

2017 ◽  
Vol 49 (5) ◽  
pp. 521-527 ◽  
Author(s):  
Lixia Zhao ◽  
Zixin Wang ◽  
Jindun Zhang ◽  
Jian Yang ◽  
Xuefei Gao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
Induced Pluripotent

Single-cell cloning and expansion of human induced pluripotent stem cells by a microfluidic culture device

2014 ◽  
Vol 453 (1) ◽  
pp. 131-137 ◽  
Author(s):  
Taku Matsumura ◽  
Kazuya Tatsumi ◽  
Yuichiro Noda ◽  
Naoyuki Nakanishi ◽  
Atsuhito Okonogi ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
Cell Cloning ◽  
Single Cell Cloning ◽  
Induced Pluripotent

scholarly journals Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations

2017 ◽  
Author(s):  
Quan H. Nguyen ◽  
Samuel W. Lukowski ◽  
Han Sheng Chiu ◽  
Anne Senabouth ◽  
Timothy J. C. Bruxner ◽  
...  
Keyword(s):  
Stem Cells ◽  
Induced Pluripotent Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Pluripotent Stem Cells ◽  
Single Cells ◽  
State Transitions ◽  
Transcriptional Level ◽  
Single Cell Rna Sequencing ◽  
Induced Pluripotent

AbstractHeterogeneity 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.

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