Cited by 479Pluripotent Stem Cells ResearchDevelopmental Biology and Gene RegulationSingle-cell and spatial transcriptomics

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Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm

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Chu, Li-Fang, Leng, Ning, Zhang, Jue, Hou, Zhonggang, Mamott, Daniel, Vereide, David T., Choi, Jeea, Kendziorski, Christina, Stewart, Ron, Thomson, James A.·2016·Genome Biology

DOI 10.1186/s13059-016-1033-xPMC PMC4989499

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Abstract

BACKGROUND: Human pluripotent stem cells offer the best available model to study the underlying cellular and molecular mechanisms of human embryonic lineage specification. However, it is not fully understood how individual stem cells exit the pluripotent state and transition towards their respective progenitor states. RESULTS: Here, we analyze the transcriptomes of human embryonic stem cell-derived lineage-specific progenitors by single-cell RNA-sequencing (scRNA-seq). We identify a definitive endoderm (DE) transcriptomic signature that leads us to pinpoint a critical time window when DE differentiation is enhanced by hypoxia. The molecular mechanisms governing the emergence of DE are further examined by time course scRNA-seq experiments, employing two new statistical tools to identify stage-specific genes over time (SCPattern) and to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to DE (Wave-Crest). Importantly, presumptive DE cells can be detected during the transitory phase from Brachyury (T) (+) mesendoderm toward a CXCR4 (+) DE state. Novel regulators are identified within this time window and are functionally validated on a screening platform with a T-2A-EGFP knock-in reporter engineered by CRISPR/Cas9. Through loss-of-function and gain-of-function experiments, we demonstrate that KLF8 plays a pivotal role modulating mesendoderm to DE differentiation. CONCLUSIONS: We report the analysis of 1776 cells by scRNA-seq covering distinct human embryonic stem cell-derived progenitor states. By reconstructing a differentiation trajectory at single-cell resolution, novel regulators of the mesendoderm transition to DE are elucidated and validated. Our strategy of combining single-cell analysis and genetic approaches can be applied to uncover novel regulators governing cell fate decisions in a variety of systems.

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Fig. 1 Snapshot scRNA-seq analysis of human ES-derived progenitors. a Schematics of experimental strategy. Human ES-derived lineage-specific progenitors were profiled at the single-cell resolution. b Principal component analysis (PCA) of all the cell types profiled. Shown are PC4 vs. PC1 and PC2 vs. PC4. c Hierarchical clustering analysis of progenitors differentiated from H1 cells with selected lineage-specific markers shown on the right. d PCA of all the cell types profiled, shown are PC5 vs. PC6. The light green shade highlights all the single DE cells. e Enrichment analysis of PC5 shown in (d). Bar graph shows the significant z scores of selected GO terms. Summary on collections of GO terms are shown on the right. Dashed line indicates statistical significant threshold at z score = 1.62 (one tailed p value <0.05). NPC neuronal progenitor cell, DEC definitive endoderm cell, EC endothelial cell, TB trophoblast-like cell, HFF human foreskin fibroblastsCC BY · PubMed Central

Fig. 2 Acute hypoxic treatment enhanced DE formation. a FACS analysis of anti-CXCR4 staining of H1 cells differentiated for three days towards (blue population) under various hypoxia conditions. Undifferentiated H1 cells were gated as negative controls (gray populations). The x-axis indicates the APC channel. b Summary of the percentages of CXCR4+ cells (with various oxygen concentrations) from H1 or H9 differentiation for three days toward DE cells. c QPCR analysis of experiments performed in (b). All expression levels were first normalized to endogenous GAPDH. For pluripotency markers (upper panels), samples were normalized to undifferentiated H1 or H9 samples, which were arbitrarily set to 1. For other markers (mid and lower panels), samples were normalized to the 20 % O2 samples, which were arbitrarily set to 1. d Left panel, schematics of various lengths of hypoxic treatment. Right panel, qPCR analysis at day four of differentiation for H1 or H9 cells. The x-axis indicates the number of days treated with 1.5 % O2 as indicated in the left panel. Samples were normalized to those from normoxia, which were arbitrarily set to 1. e Confocal images of OCT4, FOXA2, and SOX17 immunofluorescence staining at day two of differentiation under 20 % or 1.5 % O2. Scale bars = 50 μm. All data are mean ± S.D. ***p <0.001; **p <0.01; *p <0.05, by one-tailed t-test. n.s. no significant differenceCC BY · PubMed Central

Fig. 3 Time course scRNA-seq data analysis and reconstructing DE differentiation trajectory. a Upper panel, schematics of experimental strategy illustrating time points of scRNA-seq sampling along the differentiation from pluripotent state though mesendoderm to DE cells. Lower panel, PCA of scRNA-seq data, shown is PC1 vs. PC2. b Violin plots of selected stage-specific markers identified by SCPattern analysis. The y-axis indicates normalized expression value, Log2(expected count + 1). The x-axis indicates time points of sampling. c Reconstructed single-cell order by Wave-Crest. Shown are four of the eight genes used for temporal reconstruction. The x-axis indicates cells following Wave-Crest recovered cell order. The y-axis indicates normalized expression value. Fitted lines of gene-specific expression are shown in black. d Top 25 upregulated or downregulated genes identified by Wave-Crest at 36 h of differentiation. The x-axis indicates gene symbol, the y-axis indicates the significance value, -Log (p value) of each geneCC BY · PubMed Central

Fig. 4 Generation of T-2A-EGFP knock-in reporter cell line. a Gene targeting strategy of knock-in T-2A-EGFP-PGK-Puro cassette to replace the endogenous T stop codon, facilitated by the CRISPR/Cas9. Gray boxes indicate the exons of the endogenous gene. The arrow indicates the position of the Cas9/sgRNA cut site. The position of the probe for southern blot is indicated. E EcoRI sites. The lengths for each EcoRI-digested genomic DNA fragments are indicated. b Southern blot shows the targeted allele shifted from 4.2 (wild type genomic fragment, WT) to 4.8 kb (knock-in genomic fragment, KI). c Confocal images of SOX2, T, and EGFP immunofluorescence staining over three days of differentiation. Hours of differentiation are indicated. Scale bars = 50 μm. d FACS analysis monitoring the dynamics of the percentage of CXCR4+ and T-2A-EGFP+ cells over three days of differentiation. The x-axis indicates GFP/FITC channel. The y-axis indicates APC channelCC BY · PubMed Central

Fig. 5 KLF8 mediates mesendoderm to DE differentiation. a Schematics of siRNA knockdown strategy. FACS analysis of CXCR4 and T-2A-EGFP expression at day two of differentiation with representative gene-specific siRNA transfection results. b Summary Differentiation Scores of siRNA knockdown experiments of the top 25 genes tested. The dashed line indicates a Differentiation Score of 1, set by the non-target siRNA control. Genes in blue font indicate control experiments. c Upper panel, schematics of KLF8 overexpression strategy during differentiation. Lower panel, FACS analysis shows CXCR4 and T-2A-EGFP expression at day two of differentiation. DOX Doxycycline. d QPCR analysis of three independent KLF8 overexpression clones tested at day two of differentiation. All data are shown as mean ± S.D. ***p <0.001; **p <0.01; *p <0.05, all by one-tailed t-test. In all FACS plots, the x-axis indicates GFP/FITC channel, the y-axis indicates APC channelCC BY · PubMed Central

Fig. 6 Snapshot and temporal scRNA-seq profiling on progenitor cell states. scRNA-seq from snapshots of lineage-specific progenitors revealed that hypoxia enhances DE differentiation in a time-sensitive manner, likely acting on the mesendodermal progenitors. The differentiation trajectory was reconstructed by Wave-Crest (blue arrow) using time course scRNA-seq along the differentiation towards DE cells. Results from loss-of-function and gain-of-function experiments demonstrated that KLF8 function as a positive regulator mediating cell states transitions from T + mesendoderm to CXCR4 + DE cells. We hypothesize that KLF8 can suppress T + state and in turn enhance the activation of CXCR4 + state. The molecular mechanisms of how KLF8 directly or indirectly suppress T + state require future investigationCC BY · PubMed Central

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1 retracted

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Additional file 1 of Hypoxia is fine-tuned by Hif-1α and regulates mesendoderm differentiation through the Wnt/β-Catenin pathway
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Additional file 7 of Hypoxia is fine-tuned by Hif-1α and regulates mesendoderm differentiation through the Wnt/β-Catenin pathway
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Additional file 9 of Hypoxia is fine-tuned by Hif-1α and regulates mesendoderm differentiation through the Wnt/β-Catenin pathway
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“%) during differentiation was associated with reduced proliferation and increased apoptosis [107].”
TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data
Wu, Y., Kang, Y., Gu, J., Zhu, H., & Li, Z. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0080
“…used scRNA-seq datasets from 2 human cell lines and 5 mouse cell lines, derived from BEELINE [ 4 ], including human embryonic stem cells (hESCs) [ 30 ], human mature liver cells (hHEP) [ 31 ], mouse embryonic stem cells (mESCs) [ 32 ], mouse dendritic cells (mDCs) [ 33 ], and cell lineages from…”
GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model
Wang, T., Dong, H., Zhao, H., Zhen, P., Wang, Y., Shang, X., Peng, J., Xiao, B., & Chen, J. Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag163
“…property distributions between real and simulated data across three benchmark datasets, including the human pluripotent stem cells dataset (hPSCs) [39], the human T lymphocyte line dataset (Jurkat) [40], and the mouse embryonic stem cells dataset (mESCs) [41] (See Dataset availability for…”
Influential
PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic gold standards
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“We benchmarked PSGRN and other algorithms across six single-cell RNA sequencing (scRNA-seq) datasets: Human Embryonic Stem Cells [hESC ( 18 )], Human Mature Hepatocytes [hHep ( 19 )], Mouse Embryonic Stem Cells [mESC ( 20 )], and three subsets of Mouse Hematopoietic Stem Cells [mHSC ( 21…”
Multiscale Grassmann manifolds for single-cell RNA-seq data analysis
Wang, X. X., Cottrell, S., & Wei, G.-W. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae6752
Benchmarking Static Gene Regulatory Network Reconstruction and Dynamic Transition Probing in Single-Cell Foundation ModelsPreprint
Ye, Z., Yang, N., Yang, X., Mao, X., & Tang, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.17.725083
“…from six datasets in the BEELINE framework [7]: mouse dendritic cells (mDC) [19], three mouse hematopoietic stem-cell lineages [20]: erythroid (mHSC-E), granulocyte–macrophage (mHSC-GM), and lymphoid (mHSC-L), human mature hepatocytes (hHep) [21], and human embryonic stem cells (hESC) [22].”
From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximationPreprint
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Functional Separation of mRNA Domains Coordinates Pluripotent Cell BehaviorPreprint
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A single-cell feature selection method based on subspace and minimum redundancy, applicable to multi-omics
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Characterizing common loss-of-function genes and their potential utility in assessing population variability and chemical susceptibility in vitro
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Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional networkPreprint
Tang, R., Liu, J., Zhang, P., & Liang, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.05.730394
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Stochastic hypergraph co-contrastive learning for single-cell RNA-seq data imputation
Zi, T., Liu, Y., Tang, M., Shen, J., Liu, L., Qiao, S., & Gao, W. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02360-x
Forward–backward gene expression binarization for boolean state inference over a known regulatory network
Belgacem, I., & Delaplace, F. (2026) · Biosystems · doi:10.1016/j.biosystems.2026.105863
Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data
Akhter, H., Piktel, D., Gibson, L. F., Hu, G., & Adjeroh, D. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag342
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A Transcriptomic Analysis of Human iPSC-Derived Parathyroid Lineage Cells Reveals Limited Maturation Beyond the Parathyroid–Thymic Primordium
Kise, C., Nakatsuka, R., Kawamoto, A., Sasaki, Y., Hitomi, H., Takahashi, K., & Nozaki, T. (2026) · Cells · doi:10.3390/cells15141252
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Zhong, J., Gu, X., Ding, D., Wei, Q., Niu, B., Tao, T., Chen, P., & Liu, R. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014475
Single‐cell marker gene clustering: A unified deep learning framework for marker gene‐based clustering of single‐cell RNA‐sequencing data
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MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse OmicsPreprint
Xiang, Y., Li, Y., Tian, C., Gu, R., He, F., Wen, H., Xie, L., & Zhou, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.05.742921
IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference
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Endogenous tagging of the TBXT (Brachyury) gene with 2A-EGFP in BJNhem19 human embryonic stem cells using CRISPR-Cas9 genome editing
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scASprofiler: profiling single-cell RNA splicing with a deep convolutional generative network
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Gene Regulatory Network Inference Based on Ensemble of Complex-Valued S-System Models
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Generation of scRNA-seq Data by Combining Generation Adversarial Network and Label
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“Deng [26] 124 22148 10 GSE60749 Kumar [27] 268 45687 3 GSE52529 Trapnell [28] 288 65219 3 GSE57872 Patel [29] 864 65219 7 GSE75748 Celltype [30] 350 19097 2”

Authors: Chu, L.-F., Leng, N., Zhang, J., Hou, Z., Mamott, D., Vereide, D. T., Choi, J., Kendziorski, C., Stewart, R., & Thomson, J. A.
Affiliations: Morgridge Institute for Research (US)
Genentech (US)
Harvard University (US)
University of Wisconsin–Madison (US)
Corresponding author: Chu, Li-Fang
Thomson, James A.
ORCID: Chu, Li-Fang
Leng, Ning
Zhang, Jue
Hou, Zhonggang
Mamott, Daniel
Kendziorski, Christina
Stewart, Ron
Thomson, James A.
Venue: Genome Biology
Publisher: Springer Science and Business Media LLC
Published: 2016-08-17
Volume: 17
Issue: 1
Pages: 173-173
ISSN: 1465-6906 (ISSN-L)
1474-760X
DOI: 10.1186/s13059-016-1033-x
PMC: PMC4989499
OpenAlex: W2511896561
Cited by: 477
References: 83
Citations by year: 2026: 41 · 2025: 52 · 2024: 71 · 2023: 48 · 2022: 61 · 2021: 66 · 2020: 49 · 2019: 39 · 2018: 21 · 2017: 22 · 2016: 5
FWCI: 15.07
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Genome biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Funders: National Institutes of Health (4UH3TR000506-03, 5U01HL099773-06, GM102756, U54 AI117924)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords: definitive endoderm differentiation, human embryonic stem cells, mesendoderm, lineage specification, hypoxia, KLF8, Brachyury, CXCR4, cell fate determination, pluripotency exit
MeSH terms: Human Embryonic Stem Cells, Cell Differentiation, Endoderm, Humans, RNA, Gene Expression Regulation, Developmental, Cell Culture Techniques, Hepatocytes, Pluripotent Stem Cells, Single-Cell Analysis, High-Throughput Nucleotide Sequencing

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Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm

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