The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Open accessAbstract
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.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
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
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
Cited by
Sorted by citations
Items 701–800 of 1,070
Wang, Y., Yao, F., Wang, L., Li, Z., Ren, Z., Li, D., Zhang, M., Han, L., Wang, S.-Q., Zhou, B., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16204-w · Cited by 123
Emergence of Neuronal Diversity during Vertebrate Brain Development
Raj, B., Farrell, J. A., Liu, J., Kholtei, J. E., Carte, A. N., Acedo, J. N., Du, L. Y., McKenna, A., Relić, Đ., Leslie, J. M., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.023 · Cited by 124
Immune cell profiling in atherosclerosis: role in research and precision medicine
Fernandez, D. M., & Giannarelli, C. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00589-2 · Cited by 126
Androgens increase excitatory neurogenic potential in human brain organoids
Kelava, I., Chiaradia, I., Pellegrini, L., Kalinka, A. T., & Lancaster, M. A. (2022) · Nature · doi:10.1038/s41586-021-04330-4 · Cited by 124
Zhang, Y., Donaher, J. L., Das, S., Li, X., Reinhardt, F., Krall, J. A., Lambert, A. W., Thiru, P., Keys, H. R., Khan, M., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00877-0 · Cited by 129
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
Direct pericyte-to-neuron reprogramming via unfolding of a neural stem cell-like program
Karow, M., Camp, J. G., Falk, S., Gerber, T., Pataskar, A., Santel, M., Kageyama, J., Brazovskaja, A., Garding, A., Fan, W., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0168-3 · Cited by 130
Single Cell RNA Sequencing of Rare Immune Cell Populations
Nguyen, A., Khoo, W. H., Moran, I., Croucher, P. I., & Phan, T. G. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01553 · Cited by 122
Amnion signals are essential for mesoderm formation in primates
Yang, R., Goedel, A., Kang, Y., Si, C., Chu, C., Zheng, Y., Chen, Z., Gruber, P. J., Xiao, Y., Zhou, C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25186-2 · Cited by 119
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
Jin, S., MacLean, A. L., Peng, T., & Nie, Q. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty058 · Cited by 113
Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing.
Choi, Y. H., & Kim, J. K. (2019) · Europe PMC (PubMed Central) · doi:10.14348/molcells.2019.2446 · Cited by 126
CCL5 mediates CD40-driven CD4+ T cell tumor infiltration and immunity
Huffman, A. P., Lin, J. H., Kim, S. I., Byrne, K. T., & Vonderheide, R. H. (2020) · JCI Insight · doi:10.1172/jci.insight.137263 · Cited by 122
Quah, H. S., Cao, E. Y., Suteja, L., Li, C. H., Leong, H. S., Chong, F. T., Gupta, S., Arcinas, C., Ouyang, J. F., Ang, V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37379-y · Cited by 126
Lv, J., Yuan, W., Yin, J., Chen, Y., Zhou, G., Chen, W., Liang, X., Zhang, Y., Zhang, C., He, S., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02369-6 · Cited by 132
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
Transcriptional trajectories of human kidney injury progression
Cippà, P. E., Sun, B., Liu, J., Chen, L., Naesens, M., & McMahon, A. P. (2018) · JCI Insight · doi:10.1172/jci.insight.123151 · Cited by 125
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing
Tsuyuzaki, K., Sato, H., Sato, K., & Nikaido, I. (2020) · Genome biology · doi:10.1186/s13059-019-1900-3 · Cited by 130
Progesterone Receptor Serves the Ovary as a Trigger of Ovulation and a Terminator of Inflammation
Park, C. J., Lin, P.-C. P., Zhou, S. Q., Barakat, R., Bashir, S. T., Choi, J. M., Cacioppo, J. A., Oakley, O. R., Duffy, D. M., Lydon, J. P., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.060 · Cited by 121
Efficient Generation of Transcriptomic Profiles by Random Composite Measurements
Cleary, B., Cong, L., Cheung, A., Lander, E. S., & Regev, A. (2017) · Cell · doi:10.1016/j.cell.2017.10.023 · Cited by 114
Single-cell transcriptomics reveal the dynamic of haematopoietic stem cell production in the aorta
Baron, C. S., Kester, L. A., Klaus, A., Boisset, J.-C., Thambyrajah, R., Yvernogeau, L., Kouskoff, V., Lacaud, G., van Oudenaarden, A., & Robin, C. (2018) · Nature Communications · doi:10.1038/s41467-018-04893-3 · Cited by 115
Sharon, N., Chawla, R., Mueller, J. W., Vanderhooft, J., Whitehorn, J., Rosenthal, B., Gürtler, M., Estanboulieh, R. R., Shvartsman, D., Gifford, D., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.003 · Cited by 116
Self-citationPost, Y., Puschhof, J., Beumer, J., Kerkkamp, H. M. I., Bakker, M., Slagboom, J., de Barbanson, B., Wevers, N. R., Spijkers, X. M., Olivier, T., et al. (2020) · Cell · doi:10.1016/j.cell.2019.11.038 · Cited by 125
Appyters: Turning Jupyter Notebooks into data-driven web apps
Clarke, D. J. B., Jeon, M., Stein, D. J., Moiseyev, N., Kropiwnicki, E., Dai, C. S., Xie, Z., Wojciechowicz, M. L., Litz, S., Hom, J., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100213 · Cited by 129
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
From bench to bedside: Single-cell analysis for cancer immunotherapy
Davis-Marcisak, E. F., Deshpande, A. M., Stein-O’Brien, G., Ho, W. J., Laheru, D. A., Jaffee, E. M., Fertig, E. J., & Kagohara, L. T. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.07.004 · Cited by 126
Single-cell transcriptomics identifies conserved regulators of neuroglandular lineages
Steger, J., Cole, A. G., Denner, A., Lebedeva, T. S., Genikhovich, G., Ries, A., Reischl, R., Taudes, E., Lassnig, M., & Technau, U. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111370 · Cited by 114
Linnorm: improved statistical analysis for single cell RNA-seq expression data
Yip, S. H., Wang, P., Kocher, J.-P. A., Sham, P. C., & Wang, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx828 · Cited by 121
An Introduction to the Analysis of Single-Cell RNA-Sequencing Data
Aljanahi, A. A., Danielsen, M., & Dunbar, C. E. (2018) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2018.07.003 · Cited by 122
Direct reprogramming of fibroblasts into antigen-presenting dendritic cells
Rosa, F. A., Pires, C. F., Kurochkin, I., Ferreira, A. G., Gomes, A. M., Palma, L. G., Shaiv, K., Solanas, L., Azenha, C. D. J., Papatsenko, D., et al. (2018) · Science Immunology · doi:10.1126/sciimmunol.aau4292 · Cited by 119
Lineage dynamics of the endosymbiotic cell type in the soft coral Xenia
Hu, M., Zheng, X., Fan, C., & Zheng, Y. (2020) · Nature · doi:10.1038/s41586-020-2385-7 · Cited by 119
Elucidating the fundamental fibrotic processes driving abdominal adhesion formation
Foster, D. S., Marshall, C. D., Gulati, G. S., Chinta, M. S., Nguyen, A. T., Salhotra, A., Jones, R. E., Burcham, A. R., Lerbs, T., Cui, L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17883-1 · Cited by 118
Li, D., Kim, S., Li, J., Gao, Q., Choi, J., Bian, F., Hu, J., Zhang, Y., Li, J., Lu, R., et al. (2020) · The Ocular Surface · doi:10.1016/j.jtos.2020.12.004 · Cited by 119
Single‐cell RNA sequencing reveals a high‐resolution cell atlas of xylem in Populus
Li, H., Dai, X., Huang, X., Xu, M., Wang, Q., Yan, X., Sederoff, R. R., & Li, Q. (2021) · Journal of Integrative Plant Biology · doi:10.1111/jipb.13159 · Cited by 119
Cell-by-cell dissection of phloem development links a maturation gradient to cell specialization
Roszak, P., Heo, J., Blob, B., Toyokura, K., Sugiyama, Y., Balaguer, M. A. D. L., Lau, W. W. Y., Hamey, F., Cirrone, J., Madej, E., et al. (2021) · Science · doi:10.1126/science.aba5531 · Cited by 132
The drug-induced phenotypic landscape of colorectal cancer organoids
Betge, J., Rindtorff, N., Sauer, J., Rauscher, B., Dingert, C., Gaitantzi, H., Herweck, F., Srour-Mhanna, K., Miersch, T., Valentini, E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30722-9 · Cited by 132
Nakano, M., Kikushige, Y., Miyawaki, K., Kunisaki, Y., Mizuno, S., Takenaka, K., Tamura, S., Okumura, Y., Ito, M., Ariyama, H., et al. (2018) · Oncogene · doi:10.1038/s41388-018-0480-0 · Cited by 117
Storer, M. A., Mahmud, N., Karamboulas, K., Borrett, M. J., Yuzwa, S. A., Gont, A., Androschuk, A., Sefton, M. V., Kaplan, D. R., & Miller, F. D. (2020) · Developmental Cell · doi:10.1016/j.devcel.2019.12.004 · Cited by 118
Coupled analysis of transcriptome and BCR mutations reveals role of OXPHOS in affinity maturation
Chen, D., Wang, Y., Vijay, G. K. M., Fu, S., Nash, C. W., Xu, D., He, D., Salomonis, N., Singh, H., & Xu, H. (2021) · Nature Immunology · doi:10.1038/s41590-021-00936-y · Cited by 121
Intratumoral follicular regulatory T cells curtail anti-PD-1 treatment efficacy
Eschweiler, S., Clarke, J. D., Ramírez-Suástegui, C., Panwar, B., Madrigal, A., Chee, S., Karydis, I., Woo, E. B. C., Alzetani, A., Elsheikh, S. E., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00958-6 · Cited by 119
Transcriptome dynamics of hippocampal neurogenesis in macaques across the lifespan and aged humans
Wang, W., Wang, M., Yang, M., Zeng, B., Qiu, W., Ma, Q., Jing, X., Zhang, Q., Wang, B., Yin, C., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00678-y · Cited by 125
Muto, Y., Dixon, E. E., Yoshimura, Y., Wu, H., Omachi, K., Ledru, N., Wilson, P. C., King, A. J., Olson, N. E., Gunawan, M. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34255-z · Cited by 118
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data
Tian, L., Su, S., Dong, X., Amann‐Zalcenstein, D., Biben, C., Seidi, A., Hilton, D. J., Naik, S. H., & Ritchie, M. E. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006361 · Cited by 121
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
Long-lived T follicular helper cells retain plasticity and help sustain humoral immunity
Künzli, M., Schreiner, D., Pereboom, T. C., Swarnalekha, N., Litzler, L. C., Lötscher, J., Ertuna, Y. I., Roux, J., Geier, F. M., Jakob, R. P., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.aay5552 · Cited by 115
Cellular heterogeneity landscape in laryngeal squamous cell carcinoma
Song, L., Zhang, S., Yu, S., Ma, F., Wang, B., Zhang, C., Sun, J., Mao, X., & Wei, L. (2020) · International Journal of Cancer · doi:10.1002/ijc.33192 · Cited by 116
Single-Cell Transcriptomics Reveals a Conserved Metaplasia Program in Pancreatic Injury
Ma, Z., Lytle, N. K., Chen, B., Jyotsana, N., Novak, S. W., Cho, C. J., Caplan, L. R., Ben‐Levy, O., Neininger, A. C., Burnette, D. T., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.10.027 · Cited by 120
CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information
Wang, S., Herriges, M. J., Hurley, K., Kotton, D. N., & Klein, A. M. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01209-1 · Cited by 112
Probe-based bacterial single-cell RNA sequencing predicts toxin regulation
McNulty, R., Sritharan, D., Pahng, S. H., Meisch, J. P., Liu, S., Brennan, M. A., Saxer, G., Hormoz, S., & Rosenthal, A. Z. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01348-4 · Cited by 115
Li, L., Mohanty, V., Dou, J., Huang, Y., Banerjee, P. P., Miao, Q., Lohr, J. G., Vijaykumar, T., Frede, J., Knoechel, B., et al. (2023) · Science Advances · doi:10.1126/sciadv.add6997 · Cited by 120
Temporal analyses of postnatal liver development and maturation by single-cell transcriptomics
Liang, Y., Kaneko, K., Xin, B., Lee, J., Sun, X., Zhang, K., & Feng, G.-S. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.01.004 · Cited by 116
Plant Systems Biology at the Single-Cell Level
Libault, M., Pingault, L., Zogli, P., & Schiefelbein, J. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.08.006 · Cited by 114
Han, 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 122
Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues
Birnbaum, K. D. (2018) · Annual Review of Genetics · doi:10.1146/annurev-genet-120417-031247 · Cited by 114
Commensal microbiota drive the functional diversification of colon macrophages
Kang, B., Alvarado, L. J., Kim, T., Lehmann, M. L., Cho, H., He, J., Li, P., Kim, B., Larochelle, A., & Kelsall, B. L. (2019) · Mucosal Immunology · doi:10.1038/s41385-019-0228-3 · Cited by 115
Single-nucleus transcriptomic landscape of primate hippocampal aging
Zhang, H., Li, J., Ren, J., Sun, S., Ma, S., Zhang, W., Yu, Y., Cai, Y., Yan, K., Li, W., et al. (2021) · Protein & Cell · doi:10.1007/s13238-021-00852-9 · Cited by 122
Su, C., Lv, Y., Lu, W., Yu, Z., Ye, Y., Guo, B., Liu, D., Yan, H., Li, T., Zhang, Q., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.719564 · Cited by 123
Single-cell analysis of the aging female mouse hypothalamus
Hajdarovic, K. H., Yu, D., Hassell, L.-A., Evans, S. A., Packer, S., Neretti, N., & Webb, A. E. (2022) · Nature Aging · doi:10.1038/s43587-022-00246-4 · Cited by 118
Cao, G., Xuan, X., Li, Y., Hu, J. H., Zhang, R., Jin, H., & Dong, H. (2023) · Cell Communication and Signaling · doi:10.1186/s12964-023-01120-5 · Cited by 116
Single-cell integration reveals metaplasia in inflammatory gut diseases
Oliver, A. J., Huang, N., Bartolomé-Casado, R., Li, R., Koplev, S., Nilsen, H. R., Moy, M. R., Çakır, B., Polański, K., Gudiño, V., et al. (2024) · Nature · doi:10.1038/s41586-024-07571-1 · Cited by 132
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101
A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene
Magusali, N., Graham, A. C., Piers, T. M., Panichnantakul, P., Yaman, U., Shoai, M., Reynolds, R. H., Botia, J. A., Brookes, K. J., Guetta-Baranes, T., et al. (2021) · Brain · doi:10.1093/brain/awab337 · Cited by 111
Chiang, D. S., Chen, X., Jones, S. M., Wood, R. A., Sicherer, S. H., Burks, A. W., Leung, D. Y. M., Agashe, C., Grishin, A. V., Dawson, P. M., et al. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.11.060 · Cited by 115
Single-Cell RNA-Seq of the Pancreatic Islets––a Promise Not yet Fulfilled?
Wang, Y. J., & Kaestner, K. H. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.11.016 · Cited by 112
Fiege, J. K., Thiede, J. M., Nanda, H. A., Matchett, W. E., Moore, P. J., Montanari, N. R., Thielen, B. K., Daniel, J. J., Stanley, E., Hunter, R. C., et al. (2021) · PLoS Pathogens · doi:10.1371/journal.ppat.1009292 · Cited by 108
Sebastian, A., McCool, J. L., Hum, N. R., Murugesh, D. K., Wilson, S. P., Christiansen, B., & Loots, G. G. (2021) · Cells · doi:10.3390/cells10061462 · Cited by 113
Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis
Weinstock, J. S., Gopakumar, J., Burugula, B. B., Uddin, M. M., Jahn, N., Belk, J. A., Bouzid, H., Dániel, B., Miao, Z., Ly, N., et al. (2023) · Nature · doi:10.1038/s41586-023-05806-1 · Cited by 120
A multi-omic atlas of human embryonic skeletal development
To, K., Fei, L., Pett, J. P., Roberts, K., Blain, R., Polański, K., Li, T., Yayon, N., He, P., Xu, C., et al. (2024) · Nature · doi:10.1038/s41586-024-08189-z · Cited by 123
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 111
Wang, H., Mei, Y., Luo, C., Huang, Q., Wang, Z., Lu, G.-M., Qin, L., Sun, Z., Huang, C.-W., Yang, Z.-W., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-4796 · Cited by 116
Itahashi, K., Irie, T., Yuda, J., Kumagai, S., Tanegashima, T., Lin, Y.-T., Watanabe, S., Goto, Y., Suzuki, J., Aokage, K., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abk0957 · Cited by 120
Du, J., Wang, Y., Chen, W., Xu, M., Zhou, R., Shou, H., & Chen, J. (2022) · Molecular Plant · doi:10.1016/j.molp.2023.03.005 · Cited by 113
Single-cell profiling of Arabidopsis leaves to Pseudomonas syringae infection
Zhu, J., Lolle, S., Tang, A., Guel, B., Kvitko, B. H., Cole, B., & Coaker, G. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112676 · Cited by 124
Xiao, Y., Kim, D., Dura, B., Zhang, K., Yan, R., Li, H., Han, E., Ip, J., Zou, P., Liu, J., et al. (2019) · Advanced Science · doi:10.1002/advs.201801531 · Cited by 115
Linking the T cell receptor to the single cell transcriptome in antigen‐specific human T cells
Eltahla, A. A., Rizzetto, S., Pirozyan, M. R., Betz‐Stablein, B., Venturi, V., Kedzierska, K., Lloyd, A. R., Bull, R. A., & Luciani, F. (2016) · Immunology and Cell Biology · doi:10.1038/icb.2016.16 · Cited by 114
Tritschler, S., Theis, F. J., Lickert, H., & Böttcher, A. (2017) · Molecular Metabolism · doi:10.1016/j.molmet.2017.06.021 · Cited by 112
Jevitt, A. M., Chatterjee, D., Xie, G., Wang, X., Otwell, T., Huang, Y., & Deng, W. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000538 · Cited by 101
Distinct regional ontogeny and activation of tumor associated macrophages in human glioblastoma
Landry, A., Balas, M., Alli, S. B., Spears, J., & Zador, Z. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-76657-3 · Cited by 114
Liu, H., Hu, D., Hu, D., Wang, L., Liang, X., Liang, X., Li, H., Lu, Q., Li, S., Chen, X., et al. (2021) · Plant Biotechnology Journal · doi:10.1111/pbi.13656 · Cited by 113
Self-citationCrnković, S., Valzano, F., Fließer, E., Gindlhuber, J., Puthenparampil, H. T., Basil, M. C., Morley, M. P., Katzen, J. B., Gschwandtner, E., Klepetko, W., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.153471 · Cited by 116
Zeng, A. G. X., Iacobucci, I., Shah, S., Mitchell, A., Wong, G., Bansal, S., Chen, D., Gao, Q., Kim, H., Kennedy, J. A., et al. (2025) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0342 · Cited by 126
A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples
Chen, W., Zhao, Y., Chen, X., Yang, Z., Xu, X., Bi, Y., Chen, V., Li, J., Choi, H., Ernest, B., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00748-9 · Cited by 114
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
Memory B cells are reactivated in subcapsular proliferative foci of lymph nodes
Moran, I., Nguyen, A., Khoo, W. H., Butt, D., Bourne, K., Young, C., Hermes, J. R., Biro, M., Gracie, G., Ma, C. S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05772-7 · Cited by 114
Dignum, T., Varnum‐Finney, B., Srivatsan, S. R., Dozono, S., Waltner, O. G., Heck, A. M., Ishida, T., Nourigat-Mckay, C., Jackson, D. L., Rafii, S., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109675 · Cited by 110
Self-citationLi, C., Wu, H., Guo, L., Liu, D., Yang, S., Li, S., & Hua, K. (2022) · Communications Biology · doi:10.1038/s42003-022-04142-w · Cited by 123
Self-citationYu, Z., Lv, Y., Su, C., Lu, W., Zhang, R., Li, J., Guo, B., Yan, H., Liu, D., Yang, Z., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-22-2224 · Cited by 116
Thibaudin, M., Fumet, J.-D., Chibaudel, B., Bennouna, J., Borg, C., Martin-Babau, J., Cohen, R., Fonck, M., Taïeb, J., Limagne, E., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02497-z · Cited by 117
Velasco, S., Ibrahim, M. M., Kakumanu, A., Garipler, G., Aydin, B., Al-Sayegh, M. A., Hirsekorn, A., Abdul-Rahman, F., Satija, R., Ohler, U., et al. (2017) · Cell Stem Cell · doi:10.1016/j.stem.2016.11.006 · Cited by 111
Ontogeny and Biology of Human Small Airway Epithelial Club Cells
Zuo, W.-L., Shenoy, S. A., Li, S., O’Beirne, S. L., Strulovici‐Barel, Y., Leopold, P. L., Wang, G., Staudt, M. R., Walters, M. S., Mason, C., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201710-2107oc · Cited by 111
Self-citationMcInturff, S., Burns, J. C., & Kelley, M. W. (2018) · Biology Open · doi:10.1242/bio.038083 · Cited by 115
Collin, J., Queen, R. A., Zerti, D., Dorgau, B., Hussain, R., Coxhead, J. M., Cockell, S., & Lako, M. (2018) · Stem Cells · doi:10.1002/stem.2963 · Cited by 111
An integrative approach for building personalized gene regulatory networks for precision medicine
van der Wijst, M. G. P., de Vries, D. H., Brugge, H., Westra, H.-J., & Franke, L. (2018) · Genome Medicine · doi:10.1186/s13073-018-0608-4 · Cited by 112
Single-cell analysis of RORα tracer mouse lung reveals ILC progenitors and effector ILC2 subsets
Ghaedi, M., Shen, S., Orangi, M., Martínez-González, I., Wei, L. L., Lu, X., Das, A., Heravi‐Moussavi, A., Marra, M. A., Bhandoola, A., et al. (2019) · The Journal of Experimental Medicine · doi:10.1084/jem.20182293 · Cited by 110
Sun, T., Song, D., Li, W. V., & Li, J. J. (2021) · Genome biology · doi:10.1186/s13059-021-02367-2 · Cited by 115
Chen, K., Wang, Y., Hou, Y., Wang, Q., Long, D., Liu, X., Tian, X., & Yang, Y. (2022) · Cancer Letters · doi:10.1016/j.canlet.2022.215834 · Cited by 115
Deng, G., Zhou, L., Wang, B., Sun, X., Zhang, Q., Chen, H., Wan, N., Ye, H., Wu, X., Sun, D., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004874 · Cited by 114
Qiu, J., Xu, B., Ye, D., Ren, D., Wang, S., Benci, J. L., Xu, Y., Ishwaran, H., Beltra, J., Wherry, E. J., et al. (2023) · Nature Cancer · doi:10.1038/s43018-022-00490-y · Cited by 117
Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis
Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods
Dal Molin, A., Baruzzo, G., & Di Camillo, B. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00062 · Cited by 107
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
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
