Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm
Open accessAbstract
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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In leukemic bone marrow, t-SNE embedding of mass cytometry profiles showed some markers in distinct clusters and others in a continuous gradient.
“In leukemic bone marrow105, t-SNE108 embedding of mass cytometry profiles showed that while some markers were expressed in distinct clusters, others formed a continuous gradient.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [108]
t-SNE is a powerful nonlinear dimensionality-reduction alternative, especially when computed with an efficient approximation that scales to large genomic data sets.
“A powerful, nonlinear alternative5,105–107 is t-distributed stochastic neighbor embedding108 (t-SNE; introduced for single-cell analysis as viSNE105), especially when computed with an efficient approximation109 compatible with the scale of large genomic data sets110.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [108]
In single-cell data, unlike many other data types, the first principal components capture only a minute fraction of the variation in the data.
“For example, it is standard to visualize the first principal components of the data, yet in single-cell data, unlike many other data types, those capture only a minute fraction of the variation in the data (note that t-SNE108 too often considers only top principal components in high-dimensional data).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [108]
Ordering allowed the discovery of novel candidate regulators; KLF8 was identified and validated by testing for changes in the fraction of differentiated cells post-KLF8 knockdown.
“This ordering allowed the discovery of novel candidate regulators; for example, a driver of definitive endoderm differentiation (KLF8) was identified and validated by testing for changes in the fraction of differentiated cells post‐KLF8 knockdown.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Chu et al, 2016
Chu et al. ordered human definitive endoderm cells along the developmental pathway, successfully reconstructing the behaviour of known markers.
“In this study, cells were ordered along the developmental pathway, successfully reconstructing the behaviour of known markers.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Chu et al, 2016
In some datasets, such as in vitro differentiation of hESCs into gastrulation layers, the number of expressed genes per cell exhibited considerable intra-phenotypic variation.
“However, in some datasets, such as that for in vitro differentiation of hESCs into the gastrulation layers (27), the number of expressed genes per cell exhibited considerable intra-phenotypic variation (Fig. 2A, left).”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [27]
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“Its emergence is accompanied by the activation of the transcription factor Brachyury (T), which marks the onset of gastrulation [2, 11, 20].”
BackgroundMethodResultInfluentialSelf-citationRandom-set methods identify distinct aspects of the enrichment signal in gene-set analysis
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“A graphical user interface implementation of Wave-Crest is also available; details may be found in Additional file 1: Supplementary Methods and Figure S10.”
MethodInfluentialRodaway, A., Takeda, H., Koshida, S., Broadbent, J., Price, B. M. J., Smith, J. C., Patient, R. K., & Holder, N. (1999) · Development · doi:10.1242/dev.126.14.3067 · Cited by 186 · see citing works
Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments
Leng, N., Chu, L., Barry, C., Li, Y., Choi, J., Li, X., Jiang, P., Stewart, R. M., Thomson, J. A., & Kendziorski, C. M. (2015) · Nature Methods · doi:10.1038/nmeth.3549 · Cited by 171 · cited 2× · see citing works
“One possible factor is the cell cycle, which is unsynchronized across the population of cells [25] either at the initiation of differentiation and/or when single cells were collected for RNA-seq.”
BackgroundMethodSelf-citationNANOG and CDX2 Pattern Distinct Subtypes of Human Mesoderm during Exit from Pluripotency
Mendjan, S., Mascetti, V. L., Ortmann, D., Ortiz, M., Karjosukarso, D. W., Ng, Y., Moreau, T., & Pedersen, R. A. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.06.006 · Cited by 171 · cited 1× · see citing works
“In order to directly test the candidate genes identified from scRNA-seq analysis, we generated the first T-knockin reporter human ES cell line that faithfully reflects the endogenous T protein expression level, rather than at the transcriptional level [69, 70].”
MethodDefinitive endoderm of the mouse embryo: Formation, cell fates, and morphogenetic function
Lewis, S. L., & Tam, P. P. L. (2006) · Developmental Dynamics · doi:10.1002/dvdy.20846 · Cited by 164 · cited 1× · see citing works
“When applied WaveCrest to the scRNA-seq DE differentiation time course data, we selected the genes of interest by combining empirical results from SCPattern and prior knowledge [1, 2, 5, 7, 9, 20].”
MethodLengthened G1 Phase Indicates Differentiation Status in Human Embryonic Stem Cells
Calder, A. N., Roth-Albin, I., Bhatia, S., Pilquil, C., Lee, J., Bhatia, M., Levadoux‐Martin, M., McNicol, J. D., Russell, J., Collins, T., et al. (2012) · Stem Cells and Development · doi:10.1089/scd.2012.0168 · Cited by 160 · see citing works
Wang, P.-R., Rodriguez, R. T., Wang, J., Ghodasara, A., & Kim, S. K. (2011) · Cell Stem Cell · doi:10.1016/j.stem.2011.01.017 · Cited by 154 · cited 2× · see citing works
“These results also suggest that the presumptive DE transcriptional program begins between 24 and 36 h, which is surprisingly early considering most established human pluripotent stem cell protocols typically consider DE cells to emerge around days 4 or 5 of differentiation [7, 11, 15, 42, 52].”
BackgroundFaial, T., Bernardo, A. S., Mendjan, S., Diamanti, E., Ortmann, D., Gentsch, G. E., Mascetti, V. L., Trotter, M. W. B., Smith, J. C., & Pedersen, R. A. (2015) · Development · doi:10.1242/dev.117838 · Cited by 149 · cited 1× · see citing works
“Once the lineage decision is segregated, T expression appears to be continually associated with certain mesodermal derivatives but not DE derivatives [11, 21, 22].”
BackgroundTiming of developmental events in the early mouse embryo
Kojima, Y., Tam, O. H., & Tam, P. (2014) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2014.06.010 · Cited by 144 · see citing works
KLF8 transcription factor participates in oncogenic transformation
Wang, X., & Zhao, J. (2006) · Oncogene · doi:10.1038/sj.onc.1209796 · Cited by 106 · see citing works
Cell-Cycle Control of Bivalent Epigenetic Domains Regulates the Exit from Pluripotency
Singh, A. M., Sun, Y., Li, L., Zhang, W., Wu, T., Zhao, S., Qin, Z. S., & Dalton, S. (2015) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.07.005 · Cited by 93 · see citing works
Phenotypic annotation of the mouse X chromosomePreprint
Cox, B., Vollmer, M., Tamplin, O. J., Lu, M., Biechele, S., Gertsenstein, M., Campenhout, C. V., Floß, T., Kühn, R., Wurst, W., et al. (2010) · Genome Research · doi:10.1101/gr.105106.110 · Cited by 81 · see citing works
Wong, A. P., Chin, S., Xia, S., Garner, J., Bear, C. E., & Rossant, J. (2015) · Nature Protocols · doi:10.1038/nprot.2015.021 · Cited by 80 · cited 2× · see citing works
“5) mouse embryo [41]; and (2) human ESderived CXCR4 cells have been shown to display hallmarks of DE [7, 15, 42].”
BackgroundError estimates for the analysis of differential expression from RNA-seq count data
Burden, C. J., Qureshi, S. E., & Wilson, S. R. (2014) · PeerJ · doi:10.7717/peerj.576 · Cited by 62 · cited 1× · see citing works
“In general, for a given gene, the bulk RNA-seq methods usually assume expression values within a biological condition follow a unimodal distribution such as negative binomial distribution [44–48] or Poisson distribution [49].”
BackgroundThe contribution of cell cycle to heterogeneity in single-cell RNA-seq data
McDavid, A., Finak, G., & Gottardo, R. (2016) · Nature Biotechnology · doi:10.1038/nbt.3498 · Cited by 57 · see citing works
Yan, Q., Zhang, W., Yao, W., Wu, M., Zhang, M., Shi, X., Zhao, J., Nan, Q.-Z., Chen, Y., Wang, L., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.4517 · Cited by 53 · see citing works
Pimton, P., Lecht, S., Stabler, C. T., Johannes, G. J., Schulman, E. S., & Lelkes, P. I. (2014) · Stem Cells and Development · doi:10.1089/scd.2014.0343 · Cited by 51 · cited 2× · see citing works
“Interestingly, only one recent report thus far described a positive effect of hypoxia on differentiating mouse ES cells to DE [43].”
BackgroundResultBlimp1 Expression Predicts Embryonic Stem Cell Development In Vitro
Chu, L., Surani, M. A., Jaenisch, R., & Zwaka, T. P. (2011) · Current Biology · doi:10.1016/j.cub.2011.09.010 · Cited by 45 · cited 1× · see citing works
“All procedures were performed as described previously [76].”
MethodSelf-citationWang, P.-R., McKnight, K. D., Wong, D. J., Rodriguez, R. T., Sugiyama, T., Gu, X.-Y., Ghodasara, A., Qu, K., Chang, H. Y., & Kim, S. K. (2012) · Stem Cells and Development · doi:10.1089/scd.2011.0416 · Cited by 45 · cited 1× · see citing works
“Key DE markers continue to be expressed at high levels among single cells at the 96-h time point, including EOMES, CER1, GATA4, PRDM1, and POU2AF1, indicating that cells progressed toward the DE state [52, 53] (Fig.”
BackgroundAn Expandable, Inducible Hemangioblast State Regulated by Fibroblast Growth Factor
Vereide, D. T., Vickerman, V., Swanson, S. A., Chu, L., McIntosh, B. E., & Thomson, J. A. (2014) · Stem Cell Reports · doi:10.1016/j.stemcr.2014.10.003 · Cited by 20 · cited 2× · see citing works
“cloned into a PiggyBac Gateway Destination vector using Gateway LR clonase (Life Technologies), placing KLF8 downstream of a DOX-inducible promoter (pB-TetOKLF8) as previously reported [57].”
MethodSelf-citationOEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data
Leng, N., Choi, J., Chu, L., Thomson, J. A., Kendziorski, C. M., & Stewart, R. M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw004 · Cited by 15 · cited 3× · see citing works
“The OE genes were detected using OEFinder [79].”
MethodInfluentialSelf-citationLeng, N., Chu, L., Choi, J., Kendziorski, C. M., Thomson, J. A., & Stewart, R. M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/046110 · Cited by 3 · cited 20× · see citing works
“First, SCPattern [29] is used to identify stage-specific genes over time; and second, Wave-Crest is used to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to DE. Based on this highresolution temporal reconstruction, we detected presumptive DE cells characterized with CXCR4 an…”
MethodInfluentialSelf-citation
Flagged first, then by citations
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A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Single-cell transcriptional diversity is a hallmark of developmental potential
Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571 · see citing works
Single-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084 · see citing works
Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works
Self-citationBenchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
“The SCUBA algorithm 149 takes a slightly different departure point (taken also by Wave-Crest 108 ) and assumes that single-cell profiles are available along a time course, such that cells are a priori sampled from distinct time points, but still allows for an asynchronous process.”
BackgroundMethodAn accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works
Matsumoto, H., Kiryu, H., Furusawa, C., Ko, M. S. H., Ko, S. B. H., Gouda, N., Hayashi, T., & Nikaido, I. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx194 · Cited by 439 · see citing works
“Data3: human ES cells to definitive endoderm cells The third dataset was a scRNA-Seq time course (at 0, 12, 24, 36, 72, and 96 h) derived from definitive endoderm (DE) cells differentiated from human ES cells, containing 758 cells [33].”
MethodSingle-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome
Teschendorff, A. E., & Enver, T. (2017) · Nature Communications · doi:10.1038/ncomms15599 · Cited by 346 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works
“The success of the differentiation protocol was validated using canonical cell-surface marker expression: consistent with previous studies [9], an average of 72% cells were TRA-1-60(+) in the undifferentiated state (day0) and an average of 49% of cells were CXCR4(+) three days post differentiation…”
SCnorm: robust normalization of single-cell RNA-seq data
Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282 · see citing works
Self-citationUsing single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250 · see citing works
“One example is a study of the development of human definitive endoderm cells (Chu et al, 2016).”
BackgroundA statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · see citing works
Self-citationPapili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx575 · Cited by 235 · see citing works
“Such type of dataset is commonly generated in studies of cell differentiation process, where stem cells are induced to differentiate at the beginning of the experiment and afterwards cells are collected at multiple time points for single cell analysis [11,25,33].”
BackgroundModeling gene regulatory networks using neural network architectures
Shu, H., Zhou, J., Lian, Q., Li, H., Zhao, D. Y., Zeng, J., & Ma, J. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00099-8 · Cited by 218 · see citing works
“…62 , (2) mouse dendritic cells (mDC) 63 , (3) three lineages of mouse hematopoietic stem cells 64 , including erythroid lineage (mHSC-E), granulocyte-macrophage lineage (mHSC-GM) and lymphoid lineage (mHSC-L), (4) human mature hepatocytes (hHep) 65 and (5) human embryonic stem cells (hESC) 66 .”
BackgroundSingle-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression
Wu, S. J., Furlan, S. N., Mihalas, A. B., Kaya-Okur, H. S., Feroze, A. H., Emerson, S. N., Zheng, Y., Carson, K., Cimino, P. J., Keene, C. D., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00865-z · Cited by 201 · see citing works
Cell-specific network constructed by single-cell RNA sequencing data
Dai, H., Li, L., Zeng, T., & Chen, L. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz172 · Cited by 181 · see citing works
“Chu-type dataset (22) includes 1018 cells and seven cell types.”
BackgroundMethodInfluentialscIGANs: single-cell RNA-seq imputation using generative adversarial networks
Xu, Y., Zhang, Z., You, L., Liu, J., Fan, Z., & Zhou, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa506 · Cited by 182 · see citing works
“Using the scRNA-seq data (36) that have matched bulk RNA-seq data (Supplementary Table S1), we compare the performances of different imputation methods on improving the identification of differentially expressed genes (DEGs).”
MethodInfluentialGasch, A. P., Yu, F. B., Hose, J., Escalante, L. E., Place, M., Bacher, R. L., Kanbar, J. N., Ciobanu, D., Sandor, L., Grigoriev, I. V., et al. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2004050 · Cited by 159 · see citing works
“The cyclic orderings were used to identify additional genes following the same orders by fitting a third-degree polynomial to all genes using the WaveCrestIden function from the WaveCrest [66] R package version 0.0.1.”
MethodInfluentialSelf-citationSCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
Cao, Y., Wang, X., & Peng, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00490 · Cited by 169 · see citing works
“scRNA-seq methods have been used to characterize thousands to millions of cells in developing embryos (Chu et al., 2016), immune cells (Shalek et al.”
MethodMYC Controls Human Pluripotent Stem Cell Fate Decisions through Regulation of Metabolic Flux
Cliff, T. S., Wu, T., Boward, B., Yin, A., Yin, H., Glushka, J., Prestegaard, J. H., & Dalton, S. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.08.018 · Cited by 159 · see citing works
“DE, Meso, and NPC RNA sequencing (RNA-seq) data from cells used in this study cluster with corresponding cell types generated by other laboratories (Bernstein et al., 2010; Chu et al., 2016; van de Leemput et al., 2014) by multidimensional scaling analysis (Figure S1C).”
Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model
Wang, J., Chen, Y., & Zou, Q. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010942 · Cited by 153 · see citing works
A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data
Xiang, R., Wang, W., Yang, L., Wang, S., Xu, C., & Chen, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.646936 · Cited by 148 · see citing works
Single-cell RNAseq for the study of isoforms—how is that possible?
Arzalluz-Luque, Á., & Conesa, A. (2018) · Genome biology · doi:10.1186/s13059-018-1496-z · Cited by 135 · see citing works
Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby076 · Cited by 138 · see citing works
Chen, M., & Zhou, X. (2018) · Genome biology · doi:10.1186/s13059-018-1575-1 · Cited by 130 · see citing works
Topography of epithelial–mesenchymal plasticity
Font-Clos, F., Zapperi, S., & Porta, C. A. M. L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1722609115 · Cited by 125 · see citing works
“(C) Experimental data from single-cell embryonic-to-endoderm differentiation [GSE75748 (43)] move across the map as cells undergo EMT.”
BackgroundMethodImpulse model-based differential expression analysis of time course sequencing data
Fischer, D. S., Theis, F. J., & Yosef, N. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky675 · Cited by 126 · see citing works
A patterned human neural tube model using microfluidic gradients
Xue, X., Kim, Y. S., Ponce, I., O’Laughlin, R., Yan, R. Z., Kobayashi, N., Tshuva, R. Y., Tsai, Y.-H., Sun, S., Zheng, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07204-7 · Cited by 126 · see citing works
Gene expression distribution deconvolution in single-cell RNA sequencing
Wang, J., Huang, M. C., Torre, E. A., Dueck, H., Shaffer, S. M., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721085115 · Cited by 121 · see citing works
“DESCEND also allow a more detailed characterization of the activity of pluripotency factors during differentiation [4, 40].”
BackgroundUnderstanding development and stem cells using single cell-based analyses of gene expression
Kumar, P., Tan, Y., & Cahan, P. (2017) · Development · doi:10.1242/dev.133058 · Cited by 117 · see citing works
“…Analyzed the specification of tissueresident macrophages and proposed their subsequent differentiation to be an integral part of organogenesis Chu et al. (2016) Pluripotent stem cell differentiated endoderm derivatives No Human 1776 NA SmartSeq Elucidated novel regulators of mesendoderm…”
BackgroundInfluentialMinn, K. T., Fu, Y. C., He, S., Dietmann, S., George, S. C., Anastasio, M. A., Morris, S. A., & Solnica‐Krezel, L. (2020) · eLife · doi:10.7554/elife.59445 · Cited by 110 · see citing works
“, 2020), and hESC-differentiated cell types (Chen et al., 2019; Chu et al., 2016; Loh et al., 2016; Lu et al., 2018; Zheng et al., 2019).”
BackgroundHan, X., Chen, H., Huang, D., Chen, H., Fei, L., Cheng, C., Huang, H., Yuan, G., & Guo, G. (2018) · Genome biology · doi:10.1186/s13059-018-1426-0 · Cited by 119 · see citing works
“only capture 96 cells at most [40, 92], so cell sorting or other enrichment strategies are usually performed before scRNA-seq for the recovery of rare cell types.”
BackgroundAubin-Frankowski, P.-C., & Vert, J.-P. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa576 · Cited by 116 · see citing works
Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors
Krentz, N. A. J., Lee, M. Y. Y., Xu, E. E., Sproul, S. L. J., Maslova, A., Sasaki, S., & Lynn, F. C. (2018) · Stem Cell Reports · doi:10.1016/j.stemcr.2018.11.008 · Cited by 114 · see citing works
“Recently, several studies have investigated the single-cell transcriptome of healthy and type 2 diabetes human islets (Baron et al., 2016; Chu et al., 2016; Enge et al., 2017; Lawlor et al., 2017; Qiu et al., 2017; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016; Zeng et al., 2017).”
BackgroundSun, T., Song, D., Li, W. V., & Li, J. J. (2021) · Genome biology · doi:10.1186/s13059-021-02367-2 · Cited by 110 · see citing works
“Already, scRNA-seq technologies have led to breakthroughs in understanding biological processes such as stem cell differentiation and embryogenesis [10, 11], neurological disorders [12, 13], and tumorigenesis [14, 15].”
BackgroundA look into retinal organoids: methods, analytical techniques, and applications
Afanasyeva, T. A. V., Corral-Serrano, J. C., Garanto, A., Roepman, R., Cheetham, M. E., & Collin, R. W. J. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03917-4 · Cited by 101 · see citing works
SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
Peng, T., Zhu, Q., Yin, P.-H., & Tan, K. (2019) · Genome Biology · doi:10.1186/s13059-019-1681-8 · Cited by 92 · see citing works
AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
Aliee, H., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.006 · Cited by 103 · see citing works
Evaluating stably expressed genes in single cells
Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D. M., Speed, T., Yang, J. Y. H., & Yang, P. (2019) · GigaScience · doi:10.1093/gigascience/giz106 · Cited by 84 · see citing works
Ma, Y., Sun, S., Shang, X., Keller, E. T., Chen, M., & Zhou, X. (2020) · Nature Communications · doi:10.1038/s41467-020-15298-6 · Cited by 81 · see citing works
Zhao, M., He, W., Tang, J., Zou, Q., & Guo, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab568 · Cited by 97 · see citing works
Genga, R. M. J., Kernfeld, E. M., Parsi, K. M., Parsons, T. J., Ziller, M. J., & Maehr, R. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.076 · Cited by 91 · see citing works
“To see whether any cluster could be interpreted as a differentiation block, we characterized a bulk RNA-seq END-Diff time course of control ESCs (Figure 2C; transcript list curated from literature; Chu et al., 2016; Loh et al., 2014).”
BackgroundMethodHypoblast from human pluripotent stem cells regulates epiblast development
Okubo, T., Rivron, N. C., Kabata, M., Masaki, H., Kishimoto, K., Semi, K., Nakajima‐Koyama, M., Kunitomi, H., Kaswandy, B., Sato, H., et al. (2023) · Nature · doi:10.1038/s41586-023-06871-2 · Cited by 87 · see citing works
“2h), the raw fastq files of published SMARTer v2 scRNA-seq data 5 , published bulk RNAseq 21,23,35 and our bulk RNA-seq data were mapped and quantified into count data using STAR (v.2.7.8a; --soloType SmartSeq) with the same reference genome used in the Cell Ranger pipeline described above.”
InfluentialChen, J., Cheong, C., Lan, L., Zhou, X., Liu, J., Lü, A., Cheung, W. K., & Zhang, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab325 · Cited by 88 · see citing works
Morrison, J. A., McLennan, R., Wolfe, L. A., Gogol, M., Meier, S. R., McKinney, M. C., Teddy, J. M., Holmes, L., Semerad, C. L., Box, A., et al. (2017) · eLife · doi:10.7554/elife.28415 · Cited by 75 · see citing works
“…than high expression of any single gene (Dueck et al., 2016; Tasic et al., 2016), during single time-point embryonic and adult neurogenic events (Chu et al., 2016; DeLaughter et al., 2016; Nelson et al., 2016; Scholz et al., 2016; Tirosh et al., 2016a, 2016b; Dulken et al., 2017; Litzenburger…”
BackgroundFowler, J. L., Ang, L., & Loh, K. (2019) · Wiley Interdisciplinary Reviews: Developmental Biology · doi:10.1002/wdev.368 · Cited by 76 · see citing works
“Therefore, despite encouraging progress in generating nearly pure cultures of hPSC-derived MIXL1+ “primitive streak” (Chu et al., 2016; Takasato et al., 2014), it is critical to assess whether these protocols actually produce the specific primitive streak subtype (e.g., anterior, mid, or posterior)…”
BackgroundPredicting gene regulatory links from single-cell RNA-seq data using graph neural networks
Mao, G., Pang, Z., Zuo, K., Wang, Q., Pei, X., Chen, X., & Liu, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad414 · Cited by 80 · see citing works
“However,thepresenceofnoiseandsparsity resulting from dropout events presents significant challenges in inferring GRNs from scRNA-seq data in computational biology [5–7].”
BackgroundDeep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis
Flores, M. A., Liu, Z., Zhang, T., Hasib, M. M., Chiu, Y., Ye, Z., Paniagua, K., Jo, S., Zhang, J., Gao, S., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab531 · Cited by 72 · see citing works
Self-citationNeutrophils in innate immunity and systems biology‐level approaches
Rungelrath, V., Kobayashi, S. D., & DeLeo, F. R. (2020) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1458 · Cited by 72 · see citing works
“…(scRNAseq) and has provided the means to identify, characterize, and classify cell populations and associated biomarkers in multiple scientific disciplines (Chen, Ye, & Guo, 2019; Chu et al., 2016; Muller & Diaz, 2017; Ofengeim, Giagtzoglou, Huh, Zou, & Yuan, 2017; Stubbington et al., 2017).”
BackgroundDifferential landscape of non-CpG methylation in embryonic stem cells and neurons caused by DNMT3s
Lee, J. H., Park, S., & Nakai, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-11800-1 · Cited by 71 · see citing works
An In Vitro Human Segmentation Clock Model Derived from Embryonic Stem Cells
Chu, L., Mamott, D., Ni, Z., Bacher, R. L., Liu, C., Swanson, S. A., Kendziorski, C. M., Stewart, R. M., & Thomson, J. A. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.090 · Cited by 69 · see citing works
“CRISPR-Cas9 mediated gene targeting experiments were performed as previously described (Hou et al., 2015; Chu et al., 2016; Zhang et al., 2017).”
InfluentialSelf-citationSPARSim single cell: a count data simulator for scRNA-seq data
Baruzzo, G., Patuzzi, I., & Camillo, B. D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz752 · Cited by 61 · see citing works
Dai, C., Jiang, Y., Yin, C., Su, R., Zeng, X., Zou, Q., Nakai, K., & Wei, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac317 · Cited by 65 · see citing works
“This dataset consists of 758 cells, including 92 cells at 0 h, 102 cells at 12 h, 66 cells at 24 h, 172 cells at 36 h, 138 cells at 72 h and 188 cells at 96 h after the differentiation from H1 ESCs to DECs.”
InfluentialVerrier, L., Davidson, L., Gierliński, M., Dady, A., & Storey, K. G. (2018) · Development · doi:10.1242/dev.166215 · Cited by 65 · see citing works
“This was compared with RNAseq data for D8 NPs (not subjected to prior selection) and published RNA-seq data for H9 hESCs (Chu et al., 2016).”
BackgroundResultscRMD: imputation for single cell RNA-seq data via robust matrix decomposition
Chen, C., Wu, C., Wu, L., Wang, X.-C., Deng, M., & Xi, R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa139 · Cited by 63 · see citing works
Yu, Z., Su, Y., Lu, Y., Yang, Y., Wang, F., Zhang, S., Chang, Y., Wong, K., & Li, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36134-7 · Cited by 61 · see citing works
Cellular and developmental basis of orofacial clefts
Ji, Y., Garland, M. A., Sun, B. H., Zhang, S., Reynolds, K., McMahon, M., Rajakumar, R., Islam, M. S., Liu, Y., Chen, Y., et al. (2020) · Birth Defects Research · doi:10.1002/bdr2.1768 · Cited by 62 · see citing works
“…in vivo contexts of developmental mechanisms and has been utilized for investigating cell fate during embryogenesis (Blase, Cao, & Zhong, 2014; Chu et al., 2016; Farrell et al., 2018; Li et al., 2019; Tang et al., 2010; Wagner et al., 2018; Yan et al., 2013) and regeneration (Aztekin et al.,…”
MethodPhillips, M. J., Jiang, P., Howden, S. E., Barney, P., Min, J., York, N., Chu, L., Capowski, E. E., Cash, A., Jain, S., et al. (2017) · Stem Cells · doi:10.1002/stem.2755 · Cited by 59 · see citing works
Self-citationQuantifying Waddington’s epigenetic landscape: a comparison of single-cell potency measures
Shi, J., Teschendorff, A. E., Chen, W., Chen, L., & Li, T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby093 · Cited by 60 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia‐Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630996 · Cited by 57 · see citing works
Gao, Y., Li, L., Amos, C. I., & Li, W. (2021) · Genome Research · doi:10.1101/gr.271346.120 · Cited by 52 · see citing works
“2013) scRNA-seq data set containing 758 cells sequenced at 0, 12, 24, 36, 72, and 96 h of differentiation during human definitive endoderm (DE) emergence (Chu et al. 2016).”
BackgroundMethodInfluentialZhang, G., Song, C., Yin, M., Liu, L., Zhang, Y., Li, Y., Zhang, J., Guo, M., & Li, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58921-0 · Cited by 55 · see citing works
Self-citationGene trajectory inference for single-cell data by optimal transport metrics
Qu, R., Cheng, X., Sefik, E., Stanley, J. S., Landa, B., Strino, F., Platt, S., Garritano, J., Odell, I. D., Coifman, R. R., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02186-3 · Cited by 52 · see citing works
Hypergraph geometry reflects higher-order dynamics in protein interaction networks
Murgas, K. A., Saucan, E., & Sandhu, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24584-w · Cited by 50 · see citing works
“We first examine a scRNA-seq dataset (GSE75748) of stem cell differentiation, where gene expression was measured in cell types of varying degree of differentiation including pluripotent stem cells, multi-potent progenitors, and differentiated cells committed to certain lineages 56 .”
BackgroundMethodMadrigal, P., Deng, S., Feng, Y., Militi, S., Goh, K. J., Nibhani, R., Grandy, R. A., Osnato, A., Ortmann, D., Brown, S. E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36116-9 · Cited by 51 · see citing works
A descriptive marker gene approach to single-cell pseudotime inference
Campbell, K. R., & Yau, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty498 · Cited by 47 · see citing works
c-CSN: Single-Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network
Li, L., Dai, H., Fang, Z., & Chen, L. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.05.005 · Cited by 46 · see citing works
“Twelve scRNA-seq datasets and one bulk RNA-seq dataset [15, 35-41] were used to 257 validate our CCSN method.”
BackgroundMethodZhang, L., & Zhang, S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz488 · Cited by 46 · see citing works
“To our knowledge, the hypoxic treatment experiments suggest that the birth of nascent definitive endoderm cells is a well-timed event (18).”
BackgroundResultInfluentialTFvelo: gene regulation inspired RNA velocity estimation
Li, J., Pan, X., Yuan, Y., & Shen, H. (2024) · Nature Communications · doi:10.1038/s41467-024-45661-w · Cited by 46 · see citing works
“328 Next TFvelo was applied to another single-cell RNA-seq dataset comprising 1,529 cells 329 obtained from 88 human preimplantation embryos ranging from embryonic day 3 to 7 330 [40].”
Bacher, R. L., Leng, N., Chu, L., Ni, Z., Thomson, J. A., Kendziorski, C. M., & Stewart, R. M. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2405-x · Cited by 44 · see citing works
“…of such experiments is on the rise with an increase in time-course sequencing experiments to study dynamic biological processes and the proliferation of single-cell snapshot sequencing experiments in which cells can be computationally ordered and assigned a temporal (or spatial) order [25–27].”
BackgroundSelf-citationA non-invasive method to generate induced pluripotent stem cells from primate urine
Geuder, J., Wange, L. E., Janjic, A., Radmer, J., Janssen, P., Bagnoli, J., Müller, S., Kaul, A., Ohnuki, M., & Enard, W. (2021) · Scientific Reports · doi:10.1038/s41598-021-82883-0 · Cited by 48 · see citing works
scLink: Inferring Sparse Gene Co-Expression Networks from Single-Cell Expression Data
Li, W. V., & Li, Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.006 · Cited by 45 · see citing works
“We first compared scLink’s correlation between 51 lineage-specific marker genes [44] at different time points.”
InfluentialConstructing local cell-specific networks from single-cell data
Wang, X., Choi, D. S., & Roeder, K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113178118 · Cited by 43 · see citing works
“Previously published data were used for this work (15, 16, 18).”
The Human Cell Atlas: Technical approaches and challenges
Hon, C.-C., Shin, J. W., Carninci, P., & Stubbington, M. J. T. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx029 · Cited by 40 · see citing works
“Tools for definition of cell identity Goals Methods/features Tools Dimensionality reduction Linear, PCA PCA [93] Non-linear, t-SNE embedding t-SNE [94] Nonlinear, diffusion map destiny [97] Nonlinear, non-negative matrix factorization Nimfa [95], NMFEM [96] Linear, specifically designed to model, or to impute, dropout…”
BackgroundJohnson, T. S., Wang, T., Huang, Z., Yu, C. Y., Wu, Y., Han, Y., Zhang, Y., Huang, K., & Zhang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz295 · Cited by 42 · see citing works
Self-citationUmbaugh, D. S., Ramachandran, A., & Jaeschke, H. (2021) · Toxicological Sciences · doi:10.1093/toxsci/kfab052 · Cited by 41 · see citing works
Yilmaz, A., Braverman-Gross, C., Bialer-Tsypin, A., Peretz, M., & Benvenisty, N. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.06.023 · Cited by 39 · see citing works
Deep learning of gene relationships from single cell time-course expression data
Yuan, Y., & Bar‐Joseph, Z. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab142 · Cited by 39 · see citing works
GATA6-AS1 Regulates GATA6 Expression to Modulate Human Endoderm Differentiation
Yang, J., Lu, P., Li, M., Yan, C., Zhang, T., & Jiang, W. (2020) · Stem Cell Reports · doi:10.1016/j.stemcr.2020.07.014 · Cited by 37 · see citing works
“In addition, CXCR4 was a widely used surface marker to evaluating the differentiation efficiency of definitive endoderm (Chu et al., 2016; D’Amour et al., 2005; Drukker et al., 2012; Jiang et al., 2015; Jiang et al., 2013a; Jiang et al., 2013b).”
BackgroundZhong, J., Han, C., Zhang, X., Chen, P., & Liu, R. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.008 · Cited by 37 · see citing works
“For hESC-to-DEC data, there is a drastic change in the network structure at 36 h (Figure 3C), indicating the cell fate transitions of the differentiation induction into DE at 72 h [24].”
Wang, T., Zhao, H. F., Xu, Y., Wang, Y., Shang, X., Peng, J., & Xiao, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad384 · Cited by 37 · see citing works
Sialylation Is Dispensable for Early Murine Embryonic Development in Vitro
Abeln, M., Borst, K. M., Cajic, S., Thiesler, H., Kats, E., Albers, I., Kuhn, M., Kaever, V., Rapp, E., Münster-Kühnel, A. K., et al. (2017) · ChemBioChem · doi:10.1002/cbic.201700083 · Cited by 34 · see citing works
A comparative study of endoderm differentiation in humans and chimpanzees
Blake, L. E., Thomas, S. M., Blischak, J., Hsiao, C. J., Chavarria, C. I., Myrthil, M., Gilad, Y., & Pavlovic, B. J. (2018) · Genome biology · doi:10.1186/s13059-018-1490-5 · Cited by 36 · see citing works
“mesoderm, and ectoderm), revealed that cells differentiating down the endoderm lineage exhibit the most asynchrony, indicating that there is likely substantial heterogeneity of degree of differentiation at any given time point (34).”
BackgroundscGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network
Huang, Z., Wang, J., Lü, X., Zain, A. M., & Yu, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad040 · Cited by 35 · see citing works
Li, S., Liu, Y., Shen, L.-C., He, Y., Song, J., & Yu, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad529 · Cited by 36 · see citing works
Simulation, power evaluation and sample size recommendation for single-cell RNA-seq
Su, K., Wu, Z. J., & Wu, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa607 · Cited by 35 · see citing works
Guo, W.-F., Yu, X., Shi, Q., Liang, J., Zhang, S., & Zeng, T. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008962 · Cited by 34 · see citing works
“, cell type) of each cell was known from the previous study [33], the adjusted random index (ARI) [34] was used as the performance measurement in method comparisons.”
MethodSingle-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity
Zeng, T., & Dai, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00629 · Cited by 33 · see citing works
“…which is an important factor in the evolution of observed cell types. scRNA-seq provides a powerful tool to analyze particular regulatory mechanisms and their downstream influence in a corresponding subset of cells (Chu et al., 2016; Korthauer et al., 2016; Enge et al., 2017; Severo et al., 2018).”
Jin, K., Ou-Yang, L., Zhao, X., Yan, H., & Zhang, X.-F. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa108 · Cited by 33 · see citing works
Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis
Zhang, J., Sun, W. W., & Li, L. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1677242 · Cited by 32 · see citing works
Self-citationA data-driven clustering recommendation method for single-cell RNA-sequencing data
Tian, Y., Zheng, R., Liang, Z., Li, S., Wu, F., & Li, M. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2020.9010028 · Cited by 32 · see citing works
“Chu 1018 19 072 7 Gold TPM Homo sapiens[67]”
BackgroundSWEET: a single-sample network inference method for deciphering individual features in disease
Chen, H., Hsueh, C., Lee, C., Hao, T.-Y., Tu, T.-Y., Chang, L.-Y., Lee, J., & Lin, C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad032 · Cited by 30 · see citing works
“By applying the network degree matrix of SINs in identifying cell types on four single-cell RNA sequencing (scRNA-seq) datasets [85, 86], our SWEET method generally achieved the best (bold italic letters) or the second-best (bold letters) clustering performances (Supplementary Note 16 and Tables S12 and S13).”
MethodBeisang, D. J., Smith, K. A., Yang, L.-B., Benyumov, A., Gilbertsen, A., Herrera, J., Lock, E., Racila, E. V., Forster, C., Sandri, B. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66630-5 · Cited by 20 · see citing works
Panchy, N. L., Azeredo-Tseng, C., Luo, M., Randall, N., & Hong, T. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2019.01479 · Cited by 31 · see citing works
Single cell transcriptome dynamics from pluripotency to FLK1+ mesoderm
Zhao, H., & Choi, K. (2019) · Development · doi:10.1242/dev.182097 · Cited by 30 · see citing works
“Flk1+mesoderm can also contribute to smooth muscle, cardiac lineages and paraxial mesoderm tissues (Ema et al., 2006; Kataoka et al., 1997; Kattman et al., 2006; Motoike et al., 2003; Yamashita et al., 2000).”
BackgroundscGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data
Gu, H., Cheng, H., Ma, A., Li, Y., Wang, J., Xu, D. H., & Ma, Q. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac684 · Cited by 29 · see citing works
“With the above modifications, on eight benchmarking datasets (Chu, et al., 2016; Consortium*, et al., 2022; Goolam, et al., 2016; Klein, et al., 2015; Leng, et al., 2015; Semrau, et al., 2017; Trapnell, et al., 2014; Usoskin, et al., 2015), scGNN 2.0 outperforms Seurat (Hao, et al., 2021) on cell…”
InfluentialKaragiannis, K., Gannavaram, S., Verma, C., Pacheco‐Fernández, T., Bhattacharya, P., Nakhasi, H. L., & Satoskar, A. R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113097 · Cited by 28 · see citing works
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scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
Qian, K., Fu, S., Li, H., & Li, W. V. (2022) · Genome biology · doi:10.1186/s13059-022-02649-3 · Cited by 26 · see citing works
“Second, the expression parameters corresponding to the time point effect were learned from a time-course scRNA-seq dataset [62].”
Method
- 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
- Venue
- Genome Biology
- Published
- 2016-08-17
- Volume
- 17
- Issue
- 1
- Pages
- 173-173
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- 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
- 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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