The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Decoding the regulatory network of early blood development from single-cell gene expression measurements
Open accessAbstract
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“From the ordering of cells, it is possible to identify the transcriptional changes that accompany developmental processes, which can also permit the reconstruction of gene regulatory networks (Moignard et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“They have successfully been used to infer regulatory associations in multiple studies, and even to study regulatory mechanisms [9].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [9]
In the citing paper
“Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [146]
In the citing paper
“The cell cycle often has an enormous impact on cellular gene expression146,149,150 (although some87 have challenged this view), but this impact can extend to other processes, and in turn be affected by them.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [146]
In the citing paper
“This facilitated a mechanistic interpretation of the data, where predicted gene regulators were supported by motif searches and, for Erg1, validated in reporter systems.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“Mesodermal cells from 7‐ to 8‐day‐old mouse embryos were analysed using single‐cell qPCR to understand the early development of blood lineages (Moignard et al, 2015). Here, diffusion maps were used to identify developmental pseudotime trajectories (Haghverdi et al, 2015), recovering correctly the ordering of known markers.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“While this variable is related to transcriptional distances from a root cell, it is often interpreted as a proxy for developmental time (Moignard et al, 2015; Haghverdi et al, 2016; Fischer et al, 2018; Griffiths et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Moignard et al, 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
(2008) · Journal of Machine Learning Research · Cited by 33,051
Epithelial-Mesenchymal Transitions in Development and Disease
Thiery, J. P., Acloque, H., Huang, R. Y. J., & Nieto, M. A. (2009) · Cell · doi:10.1016/j.cell.2009.11.007 · Cited by 9,931
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Failure of blood-island formation and vasculogenesis in Flk-1-deficient mice
Shalaby, F., Rossant, J., Yamaguchi, T. P., Gertsenstein, M., Wu, X.-F., Breitman, M. L., & Schuh, A. C. (1995) · Nature · doi:10.1038/376062a0 · Cited by 3,931
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
Cited by
Sorted by citations
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationCurrent best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Self-citationSingle-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,904
Single-cell RNA sequencing to explore immune cell heterogeneity
Papalexi, E., & Satija, R. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.76 · Cited by 1,806
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,866
Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385
Self-citationSingle-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
Self-citationRethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894
Self-citationDefining cell types and states with single-cell genomics
Trapnell, C. (2015) · Genome Research · doi:10.1101/gr.190595.115 · Cited by 813
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792
A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768
Self-citationdestiny : diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M., Theis, F. J., Marr, C., & Buettner, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv715 · Cited by 640
Self-citationRevealing 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
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.014 · Cited by 578
Wishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524
Chu, L.-F., Leng, N., Zhang, J., Hou, Z., Mamott, D., Vereide, D. T., Choi, J., Kendziorski, C., Stewart, R., & Thomson, J. A. (2016) · Genome Biology · doi:10.1186/s13059-016-1033-x · Cited by 477
Transition states and cell fate decisions in epigenetic landscapes
Moris, N., Pina, C., & Arias, A. M. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.98 · Cited by 451
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 460
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
Blood Development: Hematopoietic Stem Cell Dependence and Independence
Dzierzak, E. A., & Bigas, A. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.04.015 · Cited by 390
Population snapshots predict early haematopoietic and erythroid hierarchies
Tusi, B. K., Wolock, S. L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J. R., Klein, A. M., & Socolovsky, M. (2018) · Nature · doi:10.1038/nature25741 · Cited by 384
Grieshaber‐Bouyer, R., Radtke, F. A., Cunin, P., Stifano, G., Levescot, A., Vijaykumar, B., Nelson-Maney, N., Blaustein, R. B., Monach, P. A., Nigrovic, P. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22973-9 · Cited by 371
Fundamental limits on dynamic inference from single-cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714723115 · Cited by 341
Resolving early mesoderm diversification through single-cell expression profiling
Scialdone, A., Tanaka, Y., Jawaid, W., Moignard, V., Wilson, N. K., Macaulay, I. C., Marioni, J. C., & Göttgens, B. (2016) · Nature · doi:10.1038/nature18633 · Cited by 293
Self-citationCHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 283
Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells
Karamitros, D., Stoilova, B., Aboukhalil, Z., Hamey, F., Reinisch, A., Samitsch, M., Quek, L. S., Otto, G., Repapi, E., Doondeea, J. B., et al. (2017) · Nature Immunology · doi:10.1038/s41590-017-0001-2 · Cited by 277
Self-citationFriedman, C. E., Nguyen, Q., Lukowski, S. W., Helfer, A., Chiu, H. S., Miklas, J., Levy, S., Suo, S., Han, J.-D. J., Osteil, P., et al. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.09.009 · Cited by 258
Using 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
Mapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235
Chen, S., & Mar, J. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2217-z · Cited by 235
Papili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx575 · Cited by 235
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230
Single-Cell Analysis in Cancer Genomics
Saadatpour, A., Lai, S., Guo, G., & Yuan, G. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.07.003 · Cited by 230
Single cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 218
Self-citationThe single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 218
Self-citationComputational methods for trajectory inference from single‐cell transcriptomics
Cannoodt, R., Saelens, W., & Saeys, Y. (2016) · European Journal of Immunology · doi:10.1002/eji.201646347 · Cited by 210
Computational methods for Gene Regulatory Networks reconstruction and analysis: A review
Delgado-Chaves, F. M., & Gómez-Vela, F. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.10.006 · Cited by 213
The Myeloid Cell Compartment—Cell by Cell
Bassler, K. E., Schulte-Schrepping, J., Warnat‐Herresthal, S., Aschenbrenner, A. C., & Schultze, J. L. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041728 · Cited by 200
Han, L., Chaturvedi, P., Kishimoto, K., Koike, H., Nasr, T., Iwasawa, K., Giesbrecht, K., Witcher, P. C., Eicher, A. K., Haines, L. E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17968-x · Cited by 185
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data
Nguyen, H., Tran, D., Tran, B., Pehlivan, B. A., & Nguyen, T. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa190 · Cited by 190
The many faces of Pluripotency: in vitro adaptations of a continuum of in vivo states
Morgani, S. M., Nichols, J., & Hadjantonakis, A. (2017) · BMC Developmental Biology · doi:10.1186/s12861-017-0150-4 · Cited by 167
SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell developmentPreprint
Cannoodt, R., Saelens, W., Sichien, D., Tavernier, S. J., Janssens, S., Guilliams, M., Lambrecht, B. N., Preter, K. D., & Saeys, Y. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/079509 · Cited by 160
Gene regulatory network inference resources: A practical overview
Mercatelli, D., Scalambra, L., Triboli, L., Ray, F., & Giorgi, F. M. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194430 · Cited by 169
SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
Welch, J. D., Hartemink, A. J., & Prins, J. F. (2016) · Genome biology · doi:10.1186/s13059-016-0975-3 · Cited by 153
Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data
Ocone, A., Haghverdi, L., Mueller, N. S., & Theis, F. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv257 · Cited by 155
Self-citationHu, P., Liu, J., Zhao, J., Wilkins, B. J., Lupino, K., Wu, H., & Pei, L. (2018) · Genes & Development · doi:10.1101/gad.316802.118 · Cited by 145
Inferring Interaction Networks From Multi-Omics Data
Hawe, J. S., Theis, F. J., & Heinig, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00535 · Cited by 148
Self-citationSingle-cell network biology for resolving cellular heterogeneity in human diseases
Cha, J., & Lee, I. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00528-0 · Cited by 150
Cheng, S., Pei, Y., He, L., Peng, G., Reinius, B., Tam, P., Jing, N., & Deng, Q. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.031 · Cited by 141
Robust classification of single-cell transcriptome data by nonnegative matrix factorization
Shao, C., & Höfer, T. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw607 · Cited by 139
Single-Cell Transcriptomics Bioinformatics and Computational Challenges
Poirion, O., Zhu, X., Ching, T. H., & Garmire, L. X. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00163 · Cited by 130
Lee, J., Zhou, Y. J., Ma, W., Zhang, W., Aljoufi, A., Luh, T., Lucero, K., Liang, D., Thomsen, M., Bhagat, G., et al. (2017) · Nature Immunology · doi:10.1038/ni.3789 · Cited by 139
Understanding 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
Statistical mechanics meets single-cell biology
Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 127
A single-cell transcriptional roadmap for cardiopharyngeal fate diversification
Wang, W., Niu, X., Stuart, T., Jullian, E., Mauck, W. M., Kelly, R. G., Satija, R., & Christiaen, L. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0336-z · Cited by 117
Aubin-Frankowski, P.-C., & Vert, J.-P. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa576 · Cited by 116
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts
Ntranos, V., Kamath, G. M., Zhang, J. M., Pachter, L. S., & Tse, D. N. C. (2016) · Genome biology · doi:10.1186/s13059-016-0970-8 · Cited by 110
Computational approaches for interpreting sc RNA‐seq data
Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017) · FEBS Letters · doi:10.1002/1873-3468.12684 · Cited by 107
Reconstructing blood stem cell regulatory network models from single-cell molecular profiles
Hamey, F., Nestorowa, S., Kinston, S., Kent, D. G., Wilson, N. K., & Göttgens, B. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610609114 · Cited by 102
Self-citationStudying hematopoiesis using single-cell technologies
Ye, F., Huang, W., & Guo, G. (2017) · Journal of Hematology & Oncology · doi:10.1186/s13045-017-0401-7 · Cited by 111
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 104
Stem cell bioengineering: building from stem cell biology
Tewary, M., Shakiba, N., & Zandstra, P. W. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0040-z · Cited by 97
Woodhouse, S., Piterman, N., Wintersteiger, C. M., Göttgens, B., & Fisher, J. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0581-y · Cited by 97
Self-citationPoincaré maps for analyzing complex hierarchies in single-cell data
Klimovskaia, A., López-Paz, D., Bottou, L., & Nickel, M. (2020) · Nature Communications · doi:10.1038/s41467-020-16822-4 · Cited by 102
Principles and challenges of modeling temporal and spatial omics data
Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 96
The many faces of hematopoietic stem cell heterogeneity
Crisan, M., & Dzierzak, E. A. (2016) · Development · doi:10.1242/dev.114231 · Cited by 97
Schuh, L., Saint-Antoine, M., Sanford, E. M., Emert, B. L., Singh, A., Marr, C., Raj, A., & Goyal, Y. (2020) · Cell Systems · doi:10.1016/j.cels.2020.03.004 · Cited by 79
Inferring gene regulatory networks from single-cell data: a mechanistic approach
Herbach, U., Bonnaffoux, A., Espinasse, T., & Gandrillon, O. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0487-0 · Cited by 83
BTR: training asynchronous Boolean models using single-cell expression data
Lim, C. Y., Wang, H., Woodhouse, S., Piterman, N., Wernisch, L., Fisher, J., & Göttgens, B. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1235-y · Cited by 81
Self-citationSpatial Genome Re-organization between Fetal and Adult Hematopoietic Stem Cells
Chen, C., Yu, W., Tober, J. M., Gao, P., He, B., Lee, K., Trieu, T., Blobel, G. A., Speck, N. A., & Tan, K. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.065 · Cited by 79
Böttcher, A., Büttner, M., Tritschler, S., Sterr, M., Aliluev, A., Oppenländer, L., Burtscher, I., Sass, S., Irmler, M., Beckers, J., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-020-00617-2 · Cited by 80
Self-citationSingle‐cell technologies are revolutionizing the approach to rare cells
Proserpio, V., & Lönnberg, T. (2015) · Immunology and Cell Biology · doi:10.1038/icb.2015.106 · Cited by 77
Variation is function: Are single cell differences functionally important?
Dueck, H., Eberwine, J. H., & Kim, J. (2015) · BioEssays · doi:10.1002/bies.201500124 · Cited by 84
Executable cancer models: successes and challenges
Clarke, M. A., & Fisher, J. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0258-x · Cited by 81
Self-citationThe Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 86
Self-citationMammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity
Wilkinson, A. C., Nakauchi, H., & Göttgens, B. (2017) · Cell Systems · doi:10.1016/j.cels.2017.07.004 · Cited by 76
Self-citationMeyer, P., Maity, P., Burkovski, A., Schwab, J. D., Müssel, C., Singh, K., Ferreira, F. F., Krug, L., Maier, H. J., Wlaschek, M., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005741 · Cited by 76
Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F., & Poidinger, M. (2016) · Nature Communications · doi:10.1038/ncomms11988 · Cited by 72
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2893170 · Cited by 78
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73
Single-cell network analysis identifies DDIT3 as a nodal lineage regulator in hematopoiesis
Pina, C., Teles, J., Fugazza, C., May, G., Wang, D., Guo, Y., Soneji, S., Brown, J., Edén, P., Ohlsson, M., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.05.016 · Cited by 72
Matsumoto, H., & Kiryu, H. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1109-3 · Cited by 76
O’Shaughnessy-Kirwan, A., Signolet, J., Costello, I., Gharbi, S., & Hendrich, B. (2015) · Development · doi:10.1242/dev.125450 · Cited by 70
Campbell, K. R., & Yau, C. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005212 · Cited by 66
Durán, R. C., González-Orozco, J. C., Velasco, I., & Wu, J. Q. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.884748 · Cited by 64
Single-Cell Analysis Identifies Distinct Stages of Human Endothelial-to-Hematopoietic Transition
Guibentif, C., Rönn, R. E., Böiers, C., Lang, S., Saxena, S. A., Soneji, S., Enver, T., Karlsson, G., & Woods, N. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.03.023 · Cited by 67
Vink, C. S., Calero‐Nieto, F. J., Wang, X., Maglitto, A., Mariani, S. A., Jawaid, W., Göttgens, B., & Dzierzak, E. A. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107627 · Cited by 60
Self-citationPark, J., Zhu, H., O’Sullivan, S. J., Ogunnaike, B. A., Weaver, D. R., Schwaber, J. S., & Vadigepalli, R. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00481 · Cited by 65
Tracing tumorigenesis in a solid tumor model at single-cell resolution
Praktiknjo, S. D., Obermayer, B., Zhu, Q., Fang, L., Liu, H., Quinn, H. M., Stoeckius, M., Kocks, C., Birchmeier, W., & Rajewsky, N. (2020) · Nature Communications · doi:10.1038/s41467-020-14777-0 · Cited by 61
scRMD: 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 62
Proserpio, V., Piccolo, A., Haim-Vilmovsky, L., Kar, G., Lönnberg, T., Svensson, V., Pramanik, J., Natarajan, K. N., Zhai, W., Zhang, X., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0957-5 · Cited by 60
Self-citationQuantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 60
Single‐cell technologies to study the immune system
Proserpio, V., & Mahata, B. (2015) · Immunology · doi:10.1111/imm.12553 · Cited by 65
Learning causal networks with latent variables from multivariate information in genomic data
Verny, L., Sella, N., Affeldt, S., Singh, P. P., & Isambert, H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005662 · Cited by 75
Metadata
- Authors
- Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., Nishikawa, S.-I., Piterman, N., Kouskoff, V., Theis, F. J., Fisher, J., & Göttgens, B.
- Affiliations
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- Helmholtz Munich (DE)
- Cancer Research UK (GB)
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- RIKEN (JP)
- University of Leicester (GB)
- Corresponding author
- Theis, Fabian J
- Fisher, Jasmin
- Göttgens, Berthold
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2015-02-09
- Volume
- 33
- Issue
- 3
- Pages
- 269-276
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4374163
- OpenAlex
- W2136453810
- Cited by
- 380
- References
- 58
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 24 · 2023: 26 · 2022: 29 · 2021: 30 · 2020: 45 · 2019: 38 · 2018: 42 · 2017: 50 · 2016: 51 · 2015: 25
- FWCI
- 24.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (acceptedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Leukemia and Lymphoma Society
- Microsoft Research
- Wellcome Trust (100140, 97922, 097922)
- Cancer Research UK (12486, 12765)
- Leukaemia and Lymphoma Research
- Medical Research Council (MR/M008975/1, MC_PC_12009)
- Biotechnology and Biological Sciences Research Council (BB/I00050X/1)
- Cambridge Institute for Medical Research, University of Cambridge
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- hematopoietic development, transcriptional regulatory networks, single-cell gene expression, diffusion maps, state diagrams, network synthesis, organogenesis, mouse embryo
- MeSH terms
- Animals, Base Sequence, Blood Cells, Computer Simulation, Diffusion, Female, Gene Expression Regulation, Male, Mice, Inbred ICR, Models, Genetic, Molecular Sequence Data, Transcription, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Gastrulation, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
