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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.
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“Semi-supervised pseudotime analysis with Monocle 2 (Qiu et al., 2017a, 2017b; Trapnell et al., 2014) aligned microglial cells along a two-branched trajectory (Figure 2A).”
From The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019) · cites this paper as Trapnell et al., 2014
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“Monocle is an unsupervised algorithm designed for pseudotime inference analysis, capitalizing on the high variability in gene expression levels.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [14]
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“Single cell transcriptome analysis enables the reconstruction of gene expression dynamics along differentiation trajectories (Setty et al., 2016; Shin et al., 2015; Trapnell et al., 2014).”
From Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord (2019) · cites this paper as Trapnell et al., 2014
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“Above the level of individual proteins and genomic loci, many recent studies use the technique of single-cell RNA sequencing to generate cell-cell networks, in which nodes represent the RNA expression state of an individual cell, and edges connect pairs of cells that are highly similar (Bendall et al., 2014; Trapnell et al., 2014).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Trapnell et al., 2014
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Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Self-citationHastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 12,145
Self-citationIndependent component analysis: algorithms and applications
Hyvärinen, A., & Erkki Oja, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,926
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Estimation of Relationships for Limited Dependent Variables
Tobin, J. (1958) · Econometrica · doi:10.2307/1907382 · Cited by 6,996
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492
Self-citationChromHMM: automating chromatin-state discovery and characterization
Ernst, J., & Kellis, M. (2012) · Nature Methods · doi:10.1038/nmeth.1906 · Cited by 2,457
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Items 801–900 of 1,070
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
Myc and Dnmt1 impede the pluripotent to totipotent state transition in embryonic stem cells
Fu, X., Wu, X., Djekidel, M. N., & Zhang, Y. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0343-0 · Cited by 108
Weinstock, A., Brown, E. J., Garabedian, M. L., Pena, S., Sharma, M., Lafaille, J. J., Moore, K. J., & Fisher, E. A. A. (2019) · Immunometabolism · doi:10.20900/immunometab20190008 · Cited by 110
Image-based high-content screening in drug discovery
Lin, S., Schorpp, K., Rothenaigner, I., & Hadian, K. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.06.001 · Cited by 118
SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks
Dibaeinia, P., & Sinha, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.003 · Cited by 120
Stadler, M., Pudelko, K., Biermeier, A., Walterskirchen, N., Gaigneaux, A., Weindorfer, C., Harrer, N. E., Klett, H., Hengstschläger, M., Schüler, J. B., et al. (2021) · Cancer Letters · doi:10.1016/j.canlet.2021.07.006 · Cited by 116
Cell surface fluctuations regulate early embryonic lineage sorting
Yanagida, A., Corujo-Simón, E., Revell, C. K., Sahu, P., Stirparo, G. G., Aspalter, I. M., Winkel, A. K., Peters, R., Belly, H. D., Cassani, D. A. D., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.022 · Cited by 106
Single-cell RNA-Seq reveals changes in immune landscape in post-traumatic osteoarthritis
Sebastian, A., Hum, N. R., McCool, J. L., Wilson, S. P., Murugesh, D. K., Martin, K. A., Rios‐Arce, N. D., Amiri, B., Christiansen, B., & Loots, G. G. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.938075 · Cited by 112
Liu, L. A., Kim, S., Buckley, M. T., Reyes, J. M., Kang, J., Tian, L., Wang, M., Lieu, A. C., Mao, M., Rodríguez-Mateo, C., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.03.016 · Cited by 115
Feng, Y., Ma, W., Zang, Y., Guo, Y., Li, Y., Zhang, Y., Dong, X., Liu, Y., Zhan, X., Pan, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54710-3 · Cited by 128
Handel, A. E., Chintawar, S., Lalic, T., Whiteley, E. S., Vowles, J., Giustacchini, A., Argoud, K., Sopp, P., Nakanishi, M., Bowden, R. J., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddv637 · Cited by 108
Single-Cell Transcriptional Analysis
Wu, A. R., Wang, J., Streets, A., & Huang, Y. (2017) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-061516-045228 · Cited by 107
Ramond, C., Beydag-Tasöz, B. S., Azad, A., van de Bunt, M., Petersen, M. B. K., Beer, N. L., Glaser, N., Berthault, C., Gloyn, A. L., Hansson, M. D., et al. (2018) · Development · doi:10.1242/dev.165480 · Cited by 105
Wang, P., Chen, Y., Yong, J., Cui, Y., Wang, R., Wen, L., Qiao, J., & Tang, F. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.056 · Cited by 108
CMF-Impute: an accurate imputation tool for single-cell RNA-seq data
Xu, J., Cai, L., Liao, B., Zhu, W. Q., & Yang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa109 · Cited by 107
Zhang, J. M., Ubas, A. A., de Borja, R., Svensson, V., Thomas, N., Thakar, N., Lai, I. K., Winters, A. F., Khan, U., Jones, M. G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.20.639398 · Cited by 146
Autoreactive CD8+ T cells are restrained by an exhaustion-like program that is maintained by LAG3
Grebinoski, S., Zhang, Q., Cillo, A. R., Manne, S., Xiao, H., Brunazzi, E. A., Tabib, T., Cardello, C., Lian, C. G., Murphy, G. F., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01210-5 · Cited by 107
The potential of single-cell profiling in plants
Efroni, I., & Birnbaum, K. D. (2016) · Genome biology · doi:10.1186/s13059-016-0931-2 · Cited by 107
Metabolic regulation of pluripotency and germ cell fate through α‐ketoglutarate
Tischler, J., Gruhn, W. H., Reid, J., Allgeyer, E. S., Buettner, F., Marr, C., Theis, F. J., Simons, B., Wernisch, L., & Surani, M. (2018) · EMBO Journal · doi:10.15252/embj.201899518 · Cited by 101
Specification of diverse cell types during early neurogenesis of the mouse cerebellum
Wizeman, J. W., Guo, Q., Wilion, E. M., & Li, J. Y. H. (2019) · eLife · doi:10.7554/elife.42388 · Cited by 108
Levitin, H. M., Yuan, J., Cheng, Y. L., Ruiz, F. J. R., Bush, E. C., Bruce, J. N., Canoll, P., Iavarone, A., Lasorella, A., Blei, D. M., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188557 · Cited by 98
Regional specialization and fate specification of bone stromal cells in skeletal development
Sivaraj, K. K., Jeong, H., Dharmalingam, B., Zeuschner, D., Adams, S., Potente, M., & Adams, R. H. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109352 · Cited by 106
Zhang, B., Moorlag, S. J. C. F. M., Dominguez-Andres, J., Bulut, O. P., Kilic, G., Liu, Z., van Crevel, R., Xu, C.-J., Joosten, L. A. B., Netea, M. G., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci147719 · Cited by 111
Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag
Meers, M. P., Llagas, G., Janssens, D. H., Codomo, C. A., & Henikoff, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01522-9 · Cited by 105
Li, K., Du, Y., Cai, Y., Liu, W., Lv, Y., Huang, B., Zhang, L., Wang, Z., Liu, P., Sun, Q., et al. (2022) · Leukemia · doi:10.1038/s41375-022-01789-6 · Cited by 108
Cynomolgus monkey embryo model captures gastrulation and early pregnancy
Li, J., Zhu, Q., Cao, J., Liu, Y., Lu, Y., Sun, Y., Li, Q., Huang, Y., Shang, S., Bian, X., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.03.009 · Cited by 108
Sagner, A., Gaber, Z. B., Delile, J., Kong, J. H., Rousso, D. L., Pearson, C. A., Weicksel, S. E., Melchionda, M., Mousavy Gharavy, S. N., Briscoe, J., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003127 · Cited by 96
Molecular mechanisms of lineage decisions in metabolite-specific T cells
Legoux, F. P., Gilet, J., Procopio, E., Echasserieau, K., Bernardeau, K., & Lantz, O. J. (2019) · Nature Immunology · doi:10.1038/s41590-019-0465-3 · Cited by 105
IL-17 production by tissue-resident MAIT cells is locally induced in children with pneumonia
Lu, B., Liu, M., Wang, J., Fan, H., Yang, D., Zhang, L., Gu, X., Nie, J., Chen, Z., Corbett, A. J., et al. (2020) · Mucosal Immunology · doi:10.1038/s41385-020-0273-y · Cited by 101
Ding, J., & Regev, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22851-4 · Cited by 110
Zhang, T., Li, Y., Zhai, E., Zhao, R., Qian, Y., Huang, Z., Liu, Y., Zhao, Z., Xu, X., Liu, J., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-2580 · Cited by 114
Gerlach, J. P., van Buggenum, J. A. G., Tanis, S. E. J., Hogeweg, M., Heuts, B. M. H., Muraro, M. J., Elze, L., Rivello, F., Rakszewska, A., van Oudenaarden, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37977-7 · Cited by 104
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
Little, D. R., Lynch, A. M., Yan, Y., Akiyama, H., Kimura, S., & Chen, J. (2021) · Nature Communications · doi:10.1038/s41467-021-22817-6 · Cited by 102
A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data
Li, G., Fu, S., Wang, S., Zhu, C., Duan, B., Tang, C., Chen, X., Chuai, G., Wang, P. Q., & Liu, Q. (2022) · Genome biology · doi:10.1186/s13059-021-02595-6 · Cited by 100
Genome-wide analysis of somatic noncoding mutation patterns in cancer
Dietlein, F., Wang, A. B., Fagre, C. R., Tang, A., Besselink, N. J. M., Cuppen, E. P. J. G., Li, C., Sunyaev, S. R., Neal, J. T., & Allen, E. M. V. (2022) · Science · doi:10.1126/science.abg5601 · Cited by 103
Single-cell transcriptomics reveals functionally specialized vascular endothelium in brain
Jeong, H., Diéguez‐Hurtado, R., Arf, H., Song, J., Park, H., Kruse, K., Sorokin, L. M., & Adams, R. H. (2022) · eLife · doi:10.7554/elife.57520 · Cited by 110
Lu, Y.-C., Sanada, C. D., Xavier-Ferrucio, J. M., Wang, L., Zhang, P., Grimes, H. L., Venkatasubramanian, M., Chetal, K., Aronow, B. J., Salomonis, N., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.084 · Cited by 106
Dysfunction of Hair Follicle Mesenchymal Progenitors Contributes to Age-Associated Hair Loss
Shin, W., Rosin, N. L., Sparks, H. D., Sinha, S., Rahmani, W. M., Sharma, N. C., Workentine, M., Abbasi, S., Labit, E., Stratton, J. A., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.03.019 · Cited by 105
Wang, S., Zhang, Q., Hui, H., Agrawal, K., Karris, M., & Rana, T. M. (2020) · Emerging Microbes & Infections · doi:10.1080/22221751.2020.1826361 · Cited by 103
Heinrich, S., Craig, A. J., Ma, L., Heinrich, B., Greten, T. F., & Wang, X. W. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.11.036 · Cited by 105
Koiwai, K., Koyama, T., Tsuda, S., Toyoda, A., Kikuchi, K., Suzuki, H., & Kawano, R. (2021) · eLife · doi:10.7554/elife.66954 · Cited by 104
The cellular architecture of the antimicrobial response network in human leprosy granulomas
Ma, F., Hughes, T. K., Teles, R. M. B., Andrade, P. R., Silva, B. J. D. A., Plazyo, O., Tsoi, L. C., Do, T. H., Wadsworth, M. H., Oulee, A., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00956-8 · Cited by 104
Lin, W., Wang, Y., Chen, Y., Wang, Q., Gu, Z., & Zhu, Y. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/8060477 · Cited by 107
Cherif, H., Mannarino, M., Pacis, A., Ragoussis, J., Rabau, O., Ouellet, J. A., & Haglund, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073993 · Cited by 105
Metabolomic profiling of single enlarged lysosomes
Zhu, H., Li, Q., Liao, T., Yin, X., Chen, Q., Wang, Z., Dai, M., Yi, L., Ge, S., Miao, C., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01182-8 · Cited by 103
Cribbs, A. P., Terlecki-Zaniewicz, S., Philpott, M., Baardman, J., Ahern, D., Lindow, M., Obad, S., Oerum, H., Sampey, B. P., Mander, P. K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919893117 · Cited by 102
Single-cell transcriptome profiling reveals vascular endothelial cell heterogeneity in human skin
Li, Q., Zhu, Z., Wang, L., Lin, Y., Fang, H., Lei, J., Cao, T., Wang, G., & Dang, E. (2021) · Theranostics · doi:10.7150/thno.54917 · Cited by 103
The continuum of Drosophila embryonic development at single-cell resolution
Calderon, D., Blecher‐Gonen, R., Huang, X., Secchia, S., Kentro, J. A., Daza, R. M., Martin, B., Dulja, A., Schaub, C., Trapnell, C., et al. (2022) · Science · doi:10.1126/science.abn5800 · Cited by 100
Self-citationXiong, A., Zhang, J., Chen, Y., Zhang, Y., & Yang, F. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104239 · Cited by 103
A single-cell transcriptomic inventory of murine smooth muscle cells
Muhl, L., Mocci, G., Pietilä, R. S., Liu, J., He, L., Genové, G., Leptidis, S., Gustafsson, S., Buyandelger, B., Raschperger, E., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.09.015 · Cited by 105
Reconstructing growth and dynamic trajectories from single-cell transcriptomics data
Sha, Y., Qiu, Y., Zhou, P., & Nie, Q. H. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00763-w · Cited by 119
From Omics to Multi-Omics: A Review of Advantages and Tradeoffs
Hayes, C. N., Nakahara, H., Ono, A., Tsuge, M., & Oka, S. (2024) · Genes · doi:10.3390/genes15121551 · Cited by 119
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
Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data
Barron, M., & Li, J. (2016) · Scientific Reports · doi:10.1038/srep33892 · Cited by 105
Selewa, A., Dohn, R., Eckart, H., Lozano, S., Xie, B., Gauchat, E., Elorbany, R., Rhodes, K., Burnett, J., Gilad, Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58327-6 · Cited by 102
Chamling, X., Kallman, A., Fang, W., Berlinicke, C. A., Mertz, J. L., Devkota, P., Pantoja, I. E. M., Smith, M. D., Ji, Z., Chang, C. K. W., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20892-3 · Cited by 103
Fu, Y., Yang, M., Yu, H., Wang, Y., Wu, X., Yong, J., Mao, Y., Cui, Y., Fan, X., Wen, L., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108788 · Cited by 101
Transcriptomic Profiling of Human Placenta in Gestational Diabetes Mellitus at the Single-Cell Level
Yang, Y., Guo, F., Peng, Y., Chen, R., Zhou, W., Wang, H. H., OuYang, Y., Yu, B., & Xu, Z. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.679582 · Cited by 113
Actuation enhances patterning in human neural tube organoids
Fattah, A. R. A., Daza, B., Rustandi, G., Berrocal-Rubio, M. Á., Gorissen, B., Poovathingal, S. K., Davie, K., Fano, J. B., Cóndor, M., Cao, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22952-0 · Cited by 103
Moreno‐Nieves, U. Y., Tay, J. K., Saumyaa, S., Horowitz, N. B., Shin, J. H., Mohammad, I., Luca, B. A., Mundy, D. C., Gulati, G. S., Bedi, N., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101169118 · Cited by 102
Wei, J., Chen, Z., Hu, M., He, Z., Jiang, D., Long, J., & Du, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.749210 · Cited by 107
Interneuron origin and molecular diversity in the human fetal brain
Yu, Y., Zeng, Z., Xie, D., Chen, R., Sha, Y., Huang, S., Cai, W., Chen, W., Li, W., Ke, R., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00940-3 · Cited by 108
Li, H., Liu, Z., Liu, L., Zhang, H., Han, C., Girard, L., Park, H., Zhang, A., Dong, C., Ye, J., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100554 · Cited by 102
Mapping the cardiac vascular niche in heart failure
Peisker, F., Halder, M., Nagai, J. S., Ziegler, S. M., Kaesler, N., Hoeft, K., Li, R., Bindels, E. M., Kuppe, C., Moellmann, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30682-0 · Cited by 103
Cao, Y., Ma, J., Han, S., Hou, M., Wei, X., Zhang, X., Zhang, Z. J., Sun, S., Ku, L., Tang, J., et al. (2023) · Plant Biotechnology Journal · doi:10.1111/pbi.14097 · Cited by 102
Xiao, J., Yu, X., Meng, F., Zhang, Y., Zhou, W., Ren, Y., Li, J., Sun, Y., Sun, H., Chen, G., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06598-6 · Cited by 119
Cellular plasticity balances the metabolic and proliferation dynamics of a regenerating liver
Chembazhi, U. V., Bangru, S., Hernaez, M., & Kalsotra, A. (2021) · Genome Research · doi:10.1101/gr.267013.120 · Cited by 102
Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsPreprint
Fu, H., Xu, H., Chong, K., Li, M., Ang, K. S., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.15.448542 · Cited by 98
Identification of dynamic undifferentiated cell states within the male germline
La, H. M., Mäkelä, J., Chan, A.-L., Rossello, F. J., Nefzger, C. M., Legrand, J. M. D., Seram, M. D., Polo, J. M., & Hobbs, R. M. (2018) · Nature Communications · doi:10.1038/s41467-018-04827-z · Cited by 102
Zhou, J. R., Xu, J., Zhang, L., Liu, S., Ma, Y., Wen, X., Hao, J., Li, Z., Ni, Y., Li, X., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2018.11.023 · Cited by 98
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
Johansen, N., & Quon, G. (2019) · Genome biology · doi:10.1186/s13059-019-1766-4 · Cited by 104
IL-5Rα marks nasal polyp IgG4- and IgE-expressing cells in aspirin-exacerbated respiratory disease
Buchheit, K. M., Dwyer, D. F., Ordovás-Montañés, J., Katz, H. R., Lewis, E., Vukovic, M., Lai, J., Bankova, L. G., Bhattacharyya, N., Shalek, A. K., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.02.035 · Cited by 101
Calcagno, D. M., Ng, R. P., Toomu, A., Zhang, C., Huang, K., Aguirre, A. D., Weissleder, R., Daniels, L. B., Fu, Z., & King, K. R. (2020) · Science Immunology · doi:10.1126/sciimmunol.aaz1974 · Cited by 107
Control of osteocyte dendrite formation by Sp7 and its target gene osteocrin
Wang, J. S., Kamath, T., Mazur, C. M., Mirzamohammadi, F., Rotter, D., Hojo, H., Castro, C. D., Tokavanich, N., Patel, R., Govea, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26571-7 · Cited by 105
Schwann cells contribute to keloid formation
Direder, M., Weiss, T., Copic, D., Vorstandlechner, V., Laggner, M., Pfisterer, K., Mildner, C. S., Klas, K., Bormann, D., Haslik, W., et al. (2022) · Matrix Biology · doi:10.1016/j.matbio.2022.03.001 · Cited by 104
Programme of self-reactive innate-like T cell-mediated cancer immunity
Chou, C., Zhang, X., Krishna, C., Nixon, B. G., Dadi, S., Capistrano, K. J., Kansler, E. R., Steele, M., Han, J., Shyu, A., et al. (2022) · Nature · doi:10.1038/s41586-022-04632-1 · Cited by 99
Self-citationHou, W., Ji, Z., Chen, Z., Wherry, E. J., Hicks, S. C., & Ji, H. (2023) · Nature Communications · doi:10.1038/s41467-023-42841-y · Cited by 112
Yu, L., Liebenberg, K., Shen, Y., Liu, F., Xu, Z., Hao, X., Wu, L., Zhang, W., Chan, H. L., Wei, B., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.03.002 · Cited by 111
GeneSwitches: ordering gene expression and functional events in single-cell experiments
Cao, E. Y., Ouyang, J. F., & Rackham, O. J. L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa099 · Cited by 100
Colombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01167-6 · Cited by 100
Williams, H. L., Costa, A. D., Zhang, J., Raghavan, S., Winter, P. S., Kapner, K. S., Ginebaugh, S. P., Väyrynen, S. A., Väyrynen, J. P., Yuan, C., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-3050 · Cited by 100
Gawel, D. R., Serra-Musach, J., Lilja, S., Aagesen, J., Arenas, À., Asking, B., Bengnér, M., Björkander, J., Biggs, S., Ernerudh, J., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0657-3 · Cited by 95
Transcriptional control of lung alveolar type 1 cell development and maintenance by NK homeobox 2-1
Little, D. R., Gerner-Mauro, K. N., Flodby, P., Crandall, E. D., Borok, Z., Akiyama, H., Kimura, S., Ostrin, E. J., & Chen, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906663116 · Cited by 94
CD200 promotes immunosuppression in the pancreatic tumor microenvironment
Choueiry, F., Torok, M. A., Shakya, R., Agrawal, K., Deems, A., Benner, B. N., Hinton, A. M., Shaffer, J. L., Blaser, B. W., Noonan, A. M., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2019-000189 · Cited by 100
The activation trajectory of plasmacytoid dendritic cells in vivo during a viral infection
Abbas, A., Manh, T. V., Valente, M., Collinet, N., Attaf, N., Dong, C., Naciri, K., Chelbi, R., Brelurut, G., Cervera-Marzal, I., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0731-4 · Cited by 98
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
Abe, Y., Sakata‐Yanagimoto, M., Fujisawa, M., Miyoshi, H., Suehara, Y., Hattori, K., Kusakabe, M., Sakamoto, T., Nishikii, H., Nguyen, T. B., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00866-3 · Cited by 103
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
Zhu, X., Ching, T., Pan, X., Weissman, S. M., & Garmire, L. (2017) · PeerJ · doi:10.7717/peerj.2888 · Cited by 86
Alignment of single-cell trajectories to compare cellular expression dynamics
Alpert, A., Moore, L. S., Dubovik, T., & Shen-Orr, S. S. (2018) · Nature Methods · doi:10.1038/nmeth.4628 · Cited by 96
Intrinsic Age-Dependent Changes and Cell-Cell Contacts Regulate Nephron Progenitor Lifespan
Chen, S., Brunskill, E. W., Potter, S. S., Dexheimer, P. J., Salomonis, N., Aronow, B. J., Hong, C. I., Zhang, T., & Kopan, R. (2015) · Developmental Cell · doi:10.1016/j.devcel.2015.09.009 · Cited by 97
Epigenetics and epitranscriptomics in temporal patterning of cortical neural progenitor competence
Yoon, K., Vissers, C. B., Ming, G., & Song, H. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201802117 · Cited by 96
Trajectory analysis quantifies transcriptional plasticity during macrophage polarization
Liu, S. X., Gustafson, H. H., Jackson, D. L., Pun, S. H., & Trapnell, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-68766-w · Cited by 103
Self-citationMadsen, J. G. S., Madsen, M. S., Rauch, A., Traynor, S., Hauwaert, E. L. V., Haakonsson, A. K., Javierre, B. M., Hyldahl, M., Fraser, P., & Mandrup, S. (2020) · Nature Genetics · doi:10.1038/s41588-020-0709-z · Cited by 97
Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis
Ben-Kiki, O., Bercovich, A., Lifshitz, A., & Tanay, A. (2022) · Genome Biology · doi:10.1186/s13059-022-02667-1 · Cited by 98
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 106
Disrupted prenatal RNA processing and myogenesis in congenital myotonic dystrophyPreprint
Thomas, J. D., Sznajder, Ł. J., Bardhi, O., Aslam, F. N., Anastasiadis, Z. P., Scotti, M. M., Nishino, I., Nakamori, M., Wang, E. T., & Swanson, M. S. (2017) · Genes & Development · doi:10.1101/gad.300590.117 · Cited by 103
Gu, W., Nowak, W. N., Xie, Y., Bras, A. L., Hu, Y., Deng, J., Bhaloo, S. I., Lu, Y., Yuan, H., Fidanis, E., et al. (2019) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.312732 · Cited by 95
Soria, C. L. D., Lee, J., Chong, T., Coghlan, A., Tracey, A., Young, M. D., Andrews, T. S., Hall, C. L., Ng, B. L., Rawlinson, K. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20092-5 · Cited by 96
Wang, F., Ding, P., Liang, X., Ding, X., Brandt, C. B., Sjöstedt, E., Zhu, J., Bolund, S., Zhang, L., de Rooij, L. P. M. H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31388-z · Cited by 96
Metadata
- Authors
- Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L.
- Affiliations
- Broad Institute (US)
- Standard BioTools (United States) (US)
- Corresponding author
- Rinn, John L
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2014-03-23
- Volume
- 32
- Issue
- 4
- Pages
- 381-386
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4122333
- OpenAlex
- W1984883254
- Cited by
- 7,314
- References
- 43
- Citations by year
- 2026: 990 · 2025: 1109 · 2024: 999 · 2023: 933 · 2022: 849 · 2021: 716 · 2020: 621 · 2019: 389 · 2018: 298 · 2017: 176 · 2016: 139 · 2015: 73 · 2014: 15
- FWCI
- 55.12
- 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)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP2OD006670, 1DP2OD00667)
- National Human Genome Research Institute (P50HG006193)
- National Institute of General Medical Sciences (P01GM099117)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- monocle, single-cell RNA sequencing, cell differentiation, transcriptional dynamics, myogenesis, cell fate determination, gene regulatory networks, gene expression kinetics, loss-of-function screen
- MeSH terms
- Algorithms, Cell Differentiation, Cells, Cultured, Gene Expression Regulation, Humans, Transcription Factors, Reproducibility of Results, Gene Expression Profiling, Genomics, Muscle Development, Myoblasts, Transcriptome
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