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A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
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Key Points: An expression map of HSPC differentiation from single-cell RNA sequencing of HSPCs provides insights into blood stem cell differentiation. A user-friendly Web resource provides access to single-cell gene expression profiles for the wider research community.
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“But recent state maps from scSeq (9), as well as clonal studies using barcodes (10) and single cell culture (11), suggest that the traditional intermediate cell types are internally heterogeneous in state and fate potential, with HSPCs lying along a continuum of states rather than a stepwise hierarchy.”
From Lineage tracing on transcriptional landscapes links state to fate during differentiation (2020) · cites this paper as [9]
In the citing paper
“When computationally reconstructed, single-cell RNA sequencing (scRNA-seq) profiling of the mammalian hematopoietic system typically produces a largely continuous distribution of cells from the earliest HSCs through the characteristic hematopoietic lineages (Nestorowa et al., 2016; Olsson et al., 2016; Paul et al., 2015; Pellin et al., 2019; Tusi et al., 2018; Weinreb et al., 2020).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Nestorowa et al., 2016
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“As part of the genomics revolution, increasingly accurate deep sequencing and novel methods of cell tracking have revealed hematopoiesis to be more of a continuous and less of a discrete and punctuated process than originally envisioned.”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Nestorowa et al., 2016
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Cites
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814
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
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Kiel, M. J., Yilmaz, Ö. H., Iwashita, T., Yilmaz, O. H., Terhorst, C., & Morrison, S. J. (2005) · Cell · doi:10.1016/j.cell.2005.05.026 · Cited by 3,168
A clonogenic common myeloid progenitor that gives rise to all myeloid lineages
Akashi, K., Traver, D., Miyamoto, T., & Weissman, I. L. (2000) · Nature · doi:10.1038/35004599 · Cited by 2,617
Wilson, A., Laurenti, E., Oser, G., van der Wath, R. C., Blanco-Bose, W., Jaworski, M., Offner, S., Dunant, C. F., Eshkind, L., Bockamp, E., et al. (2008) · Cell · doi:10.1016/j.cell.2008.10.048 · Cited by 1,908
Self-citationFast and SNP-tolerant detection of complex variants and splicing in short reads
Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
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Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
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Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
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-citationSingle-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
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-citationIntegrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
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Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113
Single-cell transcriptome profiling reveals neutrophil heterogeneity in homeostasis and infection
Xie, X., Shi, Q., Wu, P., Zhang, X., Kambara, H., Su, J., Yu, H., Park, S., Guo, R., Ren, Q., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0736-z · Cited by 894
Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela‐Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S. F. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0439-6 · Cited by 894
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 876
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Laurenti, E., & Göttgens, B. (2018) · Nature · doi:10.1038/nature25022 · Cited by 929
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
Lineage tracing on transcriptional landscapes links state to fate during differentiation
Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D., & Klein, A. M. (2020) · Science · doi:10.1126/science.aaw3381 · Cited by 759
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BackgroundMicroscopic examination of spatial transcriptome using Seq-Scope
Cho, C.-S., Xi, J., Si, Y., Park, S.-R., Hsu, J.-E., Kim, M., Jun, G., Kang, H. M., & Lee, J. H. (2021) · Cell · doi:10.1016/j.cell.2021.05.010 · Cited by 520
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451
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Ruth, K. S., Day, F. R., Hussain, J., Martínez-Marchal, A., Aiken, C., Azad, A., Thompson, D. J., Knoblochová, L., Abe, H., Tarry‐Adkins, J. L., et al. (2021) · Obstetrical & Gynecological Survey · doi:10.1038/s41586-021-03779-7 · Cited by 442
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Ambrosi, T. H., Marecic, O., McArdle, A., Sinha, R., Gulati, G. S., Tong, X., Wang, Y., Steininger, H. M., Hoover, M. Y., Koepke, L. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03795-7 · Cited by 392
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
Giladi, A., Paul, F., Herzog, Y., Lubling, Y., Weiner, A., Yofe, I., Jaitin, D., Cabezas-Wallscheid, N., Dress, R., Ginhoux, F., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0121-4 · Cited by 366
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Herman, J. S., Sagar, & Grün, D. (2018) · Nature Methods · doi:10.1038/nmeth.4662 · Cited by 363
Mapping the immune environment in clear cell renal carcinoma by single-cell genomics
Borcherding, N. C., Vishwakarma, A., Voigt, A. P., Bellizzi, A. M., Kaplan, J., Nepple, K. G., Salem, A. K., Jenkins, R. W., Zakharia, Y., & Zhang, W. (2021) · Communications Biology · doi:10.1038/s42003-020-01625-6 · Cited by 379
Schiroli, G., Conti, A., Ferrari, S., della Volpe, L., Jacob, A., Albano, L., Beretta, S., Calabria, A., Vavassori, V., Gasparini, P., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.02.019 · Cited by 342
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
Somatic mutations and cell identity linked by Genotyping of Transcriptomes
Nam, A. S., Kim, K.-T., Chaligne, R., Izzo, F., Ang, C., Taylor, J., Myers, R. M., Abu-Zeinah, G., Brand, R., Omans, N. D., et al. (2019) · Nature · doi:10.1038/s41586-019-1367-0 · Cited by 333
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Haas, S. F., Trumpp, A., & Milsom, M. D. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.04.003 · Cited by 362
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Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L., & Marioni, J. C. (2018) · Nature Communications · doi:10.1038/s41467-018-05083-x · Cited by 324
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308
Bogeska, R., Mikecin, A.-M., Kaschutnig, P., Fawaz, M., Büchler-Schäff, M., Le, D., Ganuza, M., Vollmer, A., Paffenholz, S. V., Asada, N., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.06.012 · Cited by 299
Yang, C., Siebert, J., Burns, R., Gerbec, Z. J., Bonacci, B. B., Rymaszewski, A. L., Rau, M. J., Riese, M. J., Rao, S., Carlson, K.-S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11947-7 · Cited by 306
Zhao, L., Yao, C., Xing, X., Jing, T., Li, P., Zhu, Z., Yang, C., Zhai, J., Tian, R., Chen, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19414-4 · Cited by 299
Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM
Chen, H., Albergante, L., Hsu, J. Y., Lareau, C. A., Bosco, G. L., Guan, J., Zhou, S., Gorban, A. N., Bauer, D. E., Aryee, M. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09670-4 · Cited by 296
New paradigms on hematopoietic stem cell differentiation
Cheng, H., Zheng, Z., & Cheng, T. (2019) · Protein & Cell · doi:10.1007/s13238-019-0633-0 · Cited by 350
The Human Cell Atlas bone marrow single-cell interactive web portal
Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L., & Salomonis, N. (2018) · Experimental Hematology · doi:10.1016/j.exphem.2018.09.004 · Cited by 256
Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19
Sinha, S., Rosin, N. L., Arora, R., Labit, E., Jaffer, A., Cao, L., Farias, R., Nguyen, A., de Almeida, L. G. N., Dufour, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01576-3 · Cited by 254
Revolutionizing immunology with single-cell RNA sequencing
Chen, H., Ye, F., & Guo, G. (2019) · Cellular and Molecular Immunology · doi:10.1038/s41423-019-0214-4 · Cited by 244
Single-cell analysis targeting the proteome
Labib, M., & Kelley, S. O. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0162-7 · Cited by 238
Genotoxic effects of base and prime editing in human hematopoietic stem cells
Fiumara, M., Ferrari, S., Omer-Javed, A., Beretta, S., Albano, L., Canarutto, D., Varesi, A., Gaddoni, C., Brombin, C., Cugnata, F., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01915-4 · Cited by 220
Modeling 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 223
Rifes, P., Isaksson, M., Rathore, G. S., Aldrin-Kirk, P., Møller, O. K., Barzaghi, G., Lee, J., Egerod, K. L., Rausch, D. M., Parmar, M., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0525-0 · Cited by 226
Future cancer research priorities in the USA: a Lancet Oncology Commission
Jaffee, E. M., Dang, C. V., Agus, D. B., Alexander, B. M., Anderson, K. C., Ashworth, A., Barker, A. D., Bastani, R., Bhatia, S. N., Bluestone, J. A., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30698-8 · Cited by 212
Tang, Q., Iyer, S., Lobbardi, R., Moore, J. C., Chen, H., Lareau, C. A., Hebert, C. M., Shaw, M. L., Neftel, C., Suvà, M. L., et al. (2017) · The Journal of Experimental Medicine · doi:10.1084/jem.20170976 · Cited by 206
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Tattikota, S. G., Hu, Y., Liu, Y., Cho, B., Barrera, V., Steinbaugh, M. J., Yoon, S.-H., Comjean, A., Li, F., Dervis, F., et al. (2020) · eLife · doi:10.7554/elife.54818 · Cited by 213
Triana, S., Vonficht, D., Jopp-Saile, L., Raffel, S., Lutz, R., Leonce, D., Antes, M., Hernández-Malmierca, P., Ordoñez-Rueda, D., Ramasz, B., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-01059-0 · Cited by 195
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Poller, W. C., Nahrendorf, M., & Świrski, F. K. (2020) · Circulation Research · doi:10.1161/circresaha.120.315895 · Cited by 191
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Rao, W. H., Wang, S., Dulęba, M., Niroula, S., Göller, K., Xie, J., Mahalingam, R., Neupane, R., Liew, A.-A., Vincent, M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.047 · Cited by 176
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
Giladi, A., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2017.11.011 · Cited by 168
Chemically defined cytokine-free expansion of human haematopoietic stem cells
Sakurai, M., Ishitsuka, K., Ito, R., Wilkinson, A. C., Kimura, T., Mizutani, E., Nishikii, H., Sudo, K., Becker, H., Takemoto, H., et al. (2023) · Nature · doi:10.1038/s41586-023-05739-9 · Cited by 166
Self-citationUnraveling Hematopoiesis through the Lens of Genomics
Liggett, L. A., & Sankaran, V. G. (2020) · Cell · doi:10.1016/j.cell.2020.08.030 · Cited by 163
Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis
Athanasiadis, E. I., Botthof, J. G., Andrés-Terré, H., Ferreira, L., Lió, P., & Cvejic, A. (2017) · Nature Communications · doi:10.1038/s41467-017-02305-6 · Cited by 164
Shao, X., Lu, X., Liao, J., Chen, H., & Fan, X. (2020) · Protein & Cell · doi:10.1007/s13238-020-00727-5 · Cited by 164
Warfvinge, R., Geironson, L., Sommarin, M. N. E., Lang, S., Karlsson, C., Roschupkina, T., Stenke, L., Stentoft, J., Olsson-Strömberg, U., Hjorth-Hansen, H., et al. (2017) · Blood · doi:10.1182/blood-2016-07-728873 · Cited by 154
Cell lineage and communication network inference via optimization for single-cell transcriptomics
Wang, S., Karikomi, M. K., MacLean, A. L., & Nie, Q. H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz204 · Cited by 157
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Wang, J., Chen, Y., & Zou, Q. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010942 · Cited by 154
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Frank, D. B., Penkala, I. J., Zepp, J. A., Sivakumar, A., Linares-Saldana, R., Zacharias, W. J., Stolz, K., Pankin, J., Lu, M., Wang, Q., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1813952116 · Cited by 151
Molecular transitions in early progenitors during human cord blood hematopoiesis
Zheng, S., Papalexi, E., Butler, A., Stephenson, W., & Satija, R. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178041 · Cited by 155
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Tsukasaki, M., Huynh, N. C., Okamoto, K., Muro, R., Terashima, A., Kurikawa, Y., Komatsu, N., Pluemsakunthai, W., Nitta, T., Abe, T., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00318-y · Cited by 142
Age-associated B cells are heterogeneous and dynamic drivers of autoimmunity in mice
Nickerson, K. M., Smita, S., Hoehn, K. B., Marinov, A. D., Thomas, K., Kos, J. T., Yang, Y., Bastacky, S., Watson, C. T., Kleinstein, S. H., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20221346 · Cited by 138
Skeletal muscle stem cells in comfort and stress
Evano, B., & Tajbakhsh, S. (2018) · npj Regenerative Medicine · doi:10.1038/s41536-018-0062-3 · Cited by 144
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Spira, A. E., Yurgelun, M. B. B., Alexandrov, L. B., Rao, A., Bejar, R., Polyák, K., Giannakis, M., Shilatifard, A., Finn, O. J., Dhodapkar, M., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2346 · Cited by 134
Law, N. C., Oatley, M. J., & Oatley, J. M. (2019) · Nature Communications · doi:10.1038/s41467-019-10596-0 · Cited by 130
Metabolism as a Target for Modulation in Autoimmune Diseases
Huang, N., & Perl, A. (2018) · Trends in Immunology · doi:10.1016/j.it.2018.04.006 · Cited by 130
Haematopoiesis in the era of advanced single-cell technologies
Jacobsen, S. E. W., & Nerlov, C. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0227-8 · Cited by 139
Lee, R. D., Munro, S. A., Knutson, T. P., LaRue, R. S., Heltemes-Harris, L. M., & Farrar, M. A. (2021) · Nature Communications · doi:10.1038/s41467-021-27232-5 · Cited by 131
Wang, Z., Li, X., Yang, J., Gong, Y., Zhang, H., Qiu, X., Liu, Y., Zhou, C., Chen, Y., Greenbaum, J., et al. (2021) · International Journal of Biological Sciences · doi:10.7150/ijbs.61950 · Cited by 122
Dong, F., Hao, S., Zhang, S., Zhu, C., Cheng, H., Yang, Z., Hamey, F. K., Wang, X., Gao, A., Wang, F., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0512-1 · Cited by 121
Self-citationIn situ mapping identifies distinct vascular niches for myelopoiesis
Zhang, J., Wu, Q., Johnson, C. B., Pham, G. H., Kinder, J. M., Olsson, A., Slaughter, A., May, M. L., Weinhaus, B., D’Alessandro, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03201-2 · Cited by 131
Pyramidal neuron subtype diversity governs microglia states in the neocortex
Stogsdill, J. A., Kim, K., Binan, L., Farhi, S. L., Levin, J. Z., & Arlotta, P. (2022) · Nature · doi:10.1038/s41586-022-05056-7 · Cited by 121
CD177 modulates the function and homeostasis of tumor-infiltrating regulatory T cells
Kim, M., Borcherding, N. C., Ahmed, K. K., Voigt, A. P., Vishwakarma, A., Kolb, R. H., Kluz, P., Pandey, G., De, U., Drashansky, T. T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26091-4 · Cited by 123
Gupta, K., Levinsohn, J. L., Linderman, G. C., Chen, D., Sun, T. Y., Dong, D., Taketo, M. M., Bosenberg, M., Kluger, Y., Choate, K. A., et al. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.11.032 · Cited by 117
Lieberman, Y., Rokach, L., & Shay, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205499 · Cited by 113
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Soto, J. A., Gálvez, N. M. S., Andrade, C. A., Pacheco, G. A., Böhmwald, K., Berríos-Rojas, R. V., Bueno, S. M., & Kalergis, A. M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01513 · Cited by 110
Computational strategies for single-cell multi-omics integration
Adossa, N. A., Khan, S., Rytkönen, K. T., & Elo, L. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.060 · Cited by 106
How Single-Cell Genomics Is Changing Evolutionary and Developmental Biology
Marioni, J. C., & Arendt, D. (2017) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100616-060818 · Cited by 109
scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells
Cao, Y., Zhu, J., Han, G., Jia, P., & Zhao, Z. (2017) · Genes · doi:10.3390/genes8120368 · Cited by 108
Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges
Sun, G., Li, Z., Rong, D., Zhang, H., Shi, X., Yang, W., Zheng, W., Sun, G., Wu, F., Cao, H., et al. (2021) · Molecular Therapy — Oncolytics · doi:10.1016/j.omto.2021.04.001 · Cited by 106
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Feng, W. H., Bais, A. S., He, H., Rios, C., Jiang, S., Xu, J., Chang, C. A., Kostka, D., & Li, G. (2022) · Nature Communications · doi:10.1038/s41467-022-35691-7 · Cited by 106
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Metadata
- Authors
- Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B.
- Affiliations
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- Department of Haematology and Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
- ORCID
- Venue
- Blood
- Publisher
- American Society of Hematology
- Published
- 2016-08-25
- Volume
- 128
- Issue
- 8
- Pages
- e20-e31
- ISSN
- 0006-4971 (ISSN-L)
- 1528-0020
- PMC
- PMC5305050
- OpenAlex
- W2465917013
- Cited by
- 768
- References
- 55
- Citations by year
- 2026: 46 · 2025: 87 · 2024: 81 · 2023: 89 · 2022: 99 · 2021: 102 · 2020: 104 · 2019: 64 · 2018: 60 · 2017: 31 · 2016: 3
- FWCI
- 27.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Blood (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (acceptedVersion, cc-by)
- Apollo
- Funders
- Wellcome Trust (107630/Z/15/Z, 097922/Z/11/Z)
- Cancer Research UK (12765)
- National Institute for Health and Care Research
- Directorate for Biological Sciences
- Medical Research Council (MR/M008975/1, MC_PC_12009)
- Biotechnology and Biological Sciences Research Council (BB/I00050X/1, 1800757)
- Cambridge Institute for Medical Research, University of Cambridge
- NIHR Cambridge Biomedical Research Centre
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- hematopoietic stem and progenitor cells, single-cell transcriptomics, differentiation trajectories, lymphoid differentiation, erythroid differentiation, cell cycle genes, oxidative phosphorylation, stem cell maintenance, mice
- MeSH terms
- Animals, Cell Cycle, Cell Differentiation, Female, Gene Expression Regulation, Hematopoietic Stem Cells, Mice, Inbred C57BL, Phenotype, RNA, Messenger, Transcription, Genetic, Biomarkers, Gene Expression Profiling, Single-Cell Analysis
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