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
Flagged first, then by citations
Cited by
Sorted by citations
Items 601–700 of 1,070
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145
Sanchez-Castillo, M., Blanco, D., Tienda-Luna, I. M., Carrion, M. C., & Huang, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx605 · Cited by 151
Reactivation of the pluripotency program precedes formation of the cranial neural crest
Zalc, A., Sinha, R., Gulati, G. S., Wesche, D. J., Daszczuk, P., Swigut, T., Weissman, I. L., & Wysocka, J. (2021) · Science · doi:10.1126/science.abb4776 · Cited by 153
Neutralizing IL-8 potentiates immune checkpoint blockade efficacy for glioma
Liu, H., Zhao, Q., Tan, L., Wu, X., Huang, R., Zuo, Y., Chen, L., Yang, J.-G., Zhang, Z., Ruan, W., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.03.004 · Cited by 156
Kee, N., Volakakis, N., Kirkeby, A., Dahl, L., Storvall, H., Nolbrant, S., Lahti, L., Björklund, Å. K., Gillberg, L., Joodmardi, E., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2016.10.003 · Cited by 149
Discovery of rare cells from voluminous single cell expression data
Jindal, A., Gupta, P., Jayadeva, & Sengupta, D. (2018) · Nature Communications · doi:10.1038/s41467-018-07234-6 · Cited by 148
Temporal specificity and heterogeneity of Drosophila immune cells
Cattenoz, P. B., Sakr, R., Pavlidaki, A., Delaporte, C., Riba, A., Molina, N., Hariharan, N., Mukherjee, T., & Giangrande, A. (2020) · The EMBO Journal · doi:10.15252/embj.2020104486 · Cited by 146
A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma
Zhang, C., Shen, H., Yang, T., Li, T., Liu, X., Wang, J., Liao, Z., Wei, J., Lu, J., Liu, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34877-3 · Cited by 163
Sun, S., Shen, X., Li, Y., Li, Y., Wang, S., Li, R., Zhang, H., Shen, G., Guo, B., Wei, J., et al. (2022) · Nature Plants · doi:10.1038/s41477-022-01291-y · Cited by 148
Crinier, A., Dumas, P., Escalière, B., Pipéroglou, C., Gil, L., Villacreces, A., Vely, F., Ivanović, Z., Milpied, P. J., Narni-Mancinelli, É., et al. (2020) · Cellular and Molecular Immunology · doi:10.1038/s41423-020-00574-8 · Cited by 147
Single‐cell analyses reveal suppressive tumor microenvironment of human colorectal cancer
Mei, Y., Xiao, W., Hu, H., Lu, G., Chen, L., Sun, Z., Lü, M., Ma, W., Jiang, T., Gao, Y., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.422 · Cited by 151
Ide, S., Kobayashi, Y., Ide, K., Strausser, S. A., Abe, K., Herbek, S. L., O’Brien, L. L., Crowley, S. D., Barisoni, L. M. C., Tata, A., et al. (2021) · eLife · doi:10.7554/elife.68603 · Cited by 147
Pro-inflammatory and proliferative microglia drive progression of glioblastoma
Liu, H., Sun, Y., Zhang, Q., Jin, W., Gordon, R. E., Zhang, Y., Wang, J., Sun, C., Wang, Z., Qi, X., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109718 · Cited by 144
Zander, R., Kasmani, M. Y., Yao, C., Topchyan, P., Shen, J., Zheng, S., Burns, R., Ingram, J. T., Cui, C., Joshi, N. S., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.01.018 · Cited by 143
Heterogeneity of HSCs in a Mouse Model of NASH
Rosenthal, S. B., Liu, X., Ganguly, S., Dhar, D., Pasillas, M. P., Ricciardelli, E., Li, R. Z., Troutman, T. D., Kisseleva, T., Glass, C. K., et al. (2021) · Hepatology · doi:10.1002/hep.31743 · Cited by 145
Sexual Cell-Fate Reprogramming in the Ovary by DMRT1
Lindeman, R. E., Gearhart, M. D., Minkina, A., Krentz, A. D., Bardwell, V. J., & Zarkower, D. A. (2015) · Current Biology · doi:10.1016/j.cub.2015.01.034 · Cited by 146
Single-cell transcriptomics–based MacSpectrum reveals macrophage activation signatures in diseases
Li, C., Ménoret, A., Farragher, C., Ouyang, Z., Bonin, C. P., Holvoet, P., Vella, A. T., & Zhou, B. (2019) · JCI Insight · doi:10.1172/jci.insight.126453 · Cited by 146
HOTTIP-dependent R-loop formation regulates CTCF boundary activity and TAD integrity in leukemia
Luo, H., Zhu, G., Eshelman, M. A., Fung, T. K., Lai, Q., Wang, F., Zeisig, B. B., Lesperance, J., Ma, X., Shi, C., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.01.014 · Cited by 152
Upcoming progress of transcriptomics studies on plants: An overview
Tyagi, P., Singh, D., Mathur, S., Singh, A., & Ranjan, R. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1030890 · Cited by 156
MEK inhibition induces MYOG and remodels super-enhancers in RAS-driven rhabdomyosarcoma
Yohe, M. E., Gryder, B. E., Shern, J. F., Song, Y., Chou, H.-C., Sindiri, S., Mendoza, A., Patidar, R., Zhang, X., Guha, R., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan4470 · Cited by 147
Deciphering the functional heterogeneity of skin fibroblasts using single‐cell RNA sequencing
Vorstandlechner, V., Laggner, M., Kalinina, P., Haslik, W., Radtke, C., Shaw, L. E., Lichtenberger, B. M., Tschachler, E., Ankersmit, H. J., & Mildner, M. (2020) · The FASEB Journal · doi:10.1096/fj.201902001rr · Cited by 148
Pharmacological targeting of netrin-1 inhibits EMT in cancer
Lengrand, J., Pastushenko, I., Vanuytven, S., Song, Y., Venet, D., Sarate, R. M., Bellina, M., Moers, V., Boinet, A., Sifrim, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06372-2 · Cited by 163
Pseudotemporal Ordering of Single Cells Reveals Metabolic Control of Postnatal β Cell Proliferation
Zeng, C., Mulas, F., Sui, Y., Guan, T., Miller, N., Tan, Y., Liu, F., Jin, W. L., Carrano, A. C., Huising, M. O., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.04.014 · Cited by 141
Schatz, M. C., Philippakis, A., Afgan, E., Banks, E., Carey, V. J., Carroll, R. J., Culotti, A., Ellrott, K., Goecks, J., Grossman, R. L., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2021.100085 · Cited by 142
Xue, Q., Lu, Y., Eisele, M. R., Sulistijo, E. S., Khan, N., Fan, R., & Miller-Jensen, K. (2015) · Science Signaling · doi:10.1126/scisignal.aaa2155 · Cited by 143
Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma
Marín-Béjar, O., Rogiers, A., Dewaele, M., Femel, J., Karras, P., Poźniak, J., Bervoets, G., Raemdonck, N. V., Pedri, D., Swings, T., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.05.015 · Cited by 158
Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics
Tang, B., Li, F., Hulin, M. T., Ding, P., & Ma, W. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.08.019 · Cited by 150
Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer
Tong, X., Patel, A. S., Kim, E., Li, H., Chen, Y., Li, S., Liu, S., Dilly, J., Kapner, K. S., Zhang, N., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.01.012 · Cited by 142
Self-citationFunctionally heterogeneous human satellite cells identified by single cell RNA sequencing
Barruet, E., Garcia, S. M., Striedinger, K., Wu, J., Lee, S., Byrnes, L. E., Wong, A., Sun, X., Tamaki, S. J., Brack, A. S., et al. (2020) · eLife · doi:10.7554/elife.51576 · Cited by 142
Single Cell RNA Sequencing in Atherosclerosis Research
Williams, J. W., Winkels, H., Durant, C. P., Zaitsev, K., Ghosheh, Y., & Ley, K. F. (2020) · Circulation Research · doi:10.1161/circresaha.119.315940 · Cited by 142
Single-cell transcriptomic and spatial landscapes of the developing human pancreas
Olaniru, O. E., Kadolsky, U. D., Kannambath, S., Vaikkinen, H., Fung, K., Dhami, P., & Persaud, S. J. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.11.009 · Cited by 144
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Gayoso, A., Weiler, P., Lotfollahi, M., Klein, D., Hong, J., Streets, A., Theis, F. J., & Yosef, N. (2023) · Nature Methods · doi:10.1038/s41592-023-01994-w · Cited by 143
Pseudotime estimation: deconfounding single cell time series
(2016) · doi:10.1093/bioinformatics/btw372 · Cited by 128
Dee, C., Nagaraju, R. T., Athanasiadis, E. I., Gray, C., del Ama, L. F., Johnston, S. A., Secombes, C. J., Cvejic, A., & Hurlstone, A. F. L. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1600959 · Cited by 143
Microglial phenotypes in the human epileptic temporal lobe
Morin‐Brureau, M., Milior, G., Royer, J., Chali, F., Duigou, C. L., Savary, E., Blugeon, C., Jourdren, L., Akbar, D., Dupont, S., et al. (2018) · Brain · doi:10.1093/brain/awy276 · Cited by 150
Hurley, K., Ding, J., Villacorta-Martín, C., Herriges, M. J., Jacob, A., Vedaie, M., Alysandratos, K., Sun, Y. L., Lin, C. H., Werder, R. B., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2019.12.009 · Cited by 140
Zhang, D., Li, A. M., Hu, G., Huang, M., Yang, F., Zhang, L., Wellen, K. E., Xu, X., Conn, C. S., Zou, W., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.01.010 · Cited by 141
Kim, S., Lee, W., Jo, H., Sonn, S., Jeong, S., Seo, S., Suh, J., Jin, J., Kweon, H. Y., Kim, T. K., et al. (2022) · Redox Biology · doi:10.1016/j.redox.2022.102347 · Cited by 140
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
ATR Inhibition Induces CDK1–SPOP Signaling and Enhances Anti–PD-L1 Cytotoxicity in Prostate Cancer
Tang, Z., Pilie, P. G., Geng, C., Manyam, G. C., Yang, G., Park, S., Wang, D., Peng, S., Wu, C., Peng, G., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-1010 · Cited by 142
Yu, L., Cao, Y., Yang, J. Y. H., & Yang, P. (2022) · Genome biology · doi:10.1186/s13059-022-02622-0 · Cited by 136
TREM2 macrophages induced by human lipids drive inflammation in acne lesions
Do, T. H., Ma, F., Andrade, P. R., Teles, R., de Andrade Silva, B. J., Hu, C., Espinoza, A., Hsu, J.-E., Cho, C.-S., Kim, M., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abo2787 · Cited by 139
Zhao, Y. M., Zhang, P., Ge, W., Feng, Y., Li, L., Sun, Z., Zhang, H., & Shen, W. (2020) · Theranostics · doi:10.7150/thno.43189 · Cited by 138
Long, H., Jia, Q., Wang, L., Fang, W., Wang, Z., Jiang, T., Zhou, F., Zheng, J., Huang, J., Zhou, L., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.04.018 · Cited by 138
Nam, A. S., Dusaj, N., Izzo, F., Murali, R., Myers, R. M., Mouhieddine, T. H., Sotelo, J., Benbarche, S., Waarts, M., Gaiti, F., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01179-9 · Cited by 140
Hook, P. W., McClymont, S. A., Cannon, G. H., Law, W. D., Morton, A. J., Goff, L. A., & McCallion, A. S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.02.001 · Cited by 136
Single-Cell Virus Sequencing of Influenza Infections That Trigger Innate Immunity
Russell, A. B., Elshina, E., Kowalsky, J. R., Velthuis, A. J. W. T., & Bloom, J. D. (2019) · Journal of Virology · doi:10.1128/jvi.00500-19 · Cited by 128
Stem cells expand potency and alter tissue fitness by accumulating diverse epigenetic memories
Gonzales, K. A. U., Polak, L., Matos, I., Tierney, M. T., Gola, A., Wong, E., Infarinato, N. R., Nikolova, M., Luo, S., Liu, S., et al. (2021) · Science · doi:10.1126/science.abh2444 · Cited by 142
Li, L., Cui, L., Lin, P., Liu, Z., Bao, S., Ma, X., Nan, H., Zhu, W., Cen, J., Mao, Y., et al. (2023) · Cell Stem Cell · doi:10.1016/j.stem.2023.01.009 · 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
Hachey, S. J., Movsesyan, S., Nguyen, Q. H., Burton-Sojo, G., Tankazyan, A., Wu, J., Hoang, T., Zhao, D., Wang, S., Hatch, M. M., et al. (2021) · Lab on a Chip · doi:10.1039/d0lc01216e · Cited by 124
A Transcriptional Lineage of the Early C. elegans Embryo
Tintori, S., Nishimura, E. O., Golden, P., Lieb, J. D., & Goldstein, B. (2016) · Developmental Cell · doi:10.1016/j.devcel.2016.07.025 · Cited by 140
Chen, M., & Zhou, X. (2018) · Genome biology · doi:10.1186/s13059-018-1575-1 · Cited by 135
Wieghofer, P., Hagemeyer, N., Sankowski, R., Schlecht, A., Staszewski, O., Amann, L., Gruber, M., Koch, J., Hausmann, A., Zhang, P., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2020105123 · Cited by 130
Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics
Lu, L., Wang, J. R., Henderson, Y. C., Bai, S., Yang, J., Hu, M., Shiau, C.-K., Pan, T., Yan, Y., Tran, T. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci169653 · Cited by 144
Spatial oncology: Translating contextual biology to the clinic
Gong, D. H., Arbesfeld-Qiu, J. M., Perrault, E. N., Bae, J. W., & Hwang, W. L. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.09.001 · Cited by 140
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118
Human and Mouse Mononuclear Phagocyte Networks: A Tale of Two Species?
Reynolds, G., & Haniffa, M. A. (2015) · Frontiers in Immunology · doi:10.3389/fimmu.2015.00330 · Cited by 138
Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis
Adil, A., Kumar, V., Jan, A. T., & Asger, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.591122 · Cited by 138
Abbas, H. A., Hao, D., Tomczak, K. J., Barrodia, P., Im, J. S., Reville, P. K., Alaniz, Z., Wang, W., Wang, R., Wang, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26282-z · Cited by 130
Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
Zhang, L., & Zhang, S. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2848633 · Cited by 129
NF-κB–dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity
Ghislat, G., Cheema, A. S., Baudoin, E., Verthuy, C., Ballester, P. J., Crozat, K., Attaf, N., Dong, C., Milpied, P., MALISSEN, B., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abg3570 · Cited by 131
Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors
Tirosh, I., & Suvà, M. L. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.08.005 · Cited by 147
Dileep, V., Boix, C. A., Mathys, H., Marco, A., Welch, G. M., Meharena, H. S., Loon, A., Jeloka, R., Peng, Z., Bennett, D. A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.038 · Cited by 130
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130
Demystifying “drop-outs” in single-cell UMI data
Kim, T. H., Zhou, X., & Chen, M. (2020) · Genome biology · doi:10.1186/s13059-020-02096-y · Cited by 128
Adult Neural Stem Cells Are Alerted by Systemic Inflammation through TNF-α Receptor Signaling
Belenguer, G., Duart-Abadía, P., Jordán‐Pla, A., Domingo-Muelas, A., Blasco-Chamarro, L., Ferrón, S. R., Morante‐Redolat, J. M., & Fariñas, I. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.10.016 · Cited by 140
A single-cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana
Gala, H., Lanctot, A., Jean-Baptiste, K., Guiziou, S., Chu, J. C., Zemke, J. E., George, W., Queitsch, C., Cuperus, J. T., & Nemhauser, J. L. (2021) · The Plant Cell · doi:10.1093/plcell/koab101 · Cited by 136
Fang, H., Sun, Q., Zhou, J., Zhang, H., Song, Q., Zhang, H., Yu, G., Guo, Y., Huang, C., Mou, Y., et al. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01791-1 · Cited by 137
Fibroblasts – the cellular choreographers of wound healing
Knoedler, S. A., Broichhausen, S., Guo, R., Dai, R., Knoedler, L., Kauke‐Navarro, M., Diatta, F. H., Pomahač, B., Machens, H., Jiang, D., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1233800 · Cited by 141
A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development
Yao, Z., Mich, J. K., Ku, S., Menon, V., Krostag, A.-R., Martinez, R. A., Furchtgott, L., Mulholland, H., Bort, S. J., Fuqua, M. A., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.09.011 · Cited by 130
Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes
Fang, Z., Weng, C., Li, H., Tao, R., Mai, W., Liu, X., Lu, L., Lai, S., Duan, Q., Álvarez‐González, C. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.043 · Cited by 126
Walker, J. A., Clark, P. A., Crisp, A., Barlow, J. L., Szeto, A. C. H., Ferreira, A. C. F., Rana, B. M. J., Jolin, H. E., Rodríguez‐Rodríguez, N., Sivasubramaniam, M., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.05.002 · Cited by 129
SAFE-clustering: Single-cell Aggregated (from Ensemble) clustering for single-cell RNA-seq data
Yang, Y., Huh, R., Culpepper, H., Lin, Y., Love, M. I., & Li, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty793 · Cited by 126
Jorstad, N. L., Wilken, M. S., Todd, L. J., Finkbeiner, C., Nakamura, P. A., Radulovich, N. P., Hooper, M. J., Chitsazan, A., Wilkerson, B. A., Rieke, F. M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.075 · Cited by 129
Single-cell omics in ageing: a young and growing field
He, X., Memczak, S., Qu, J. T., Belmonte, J. C. I., & Liu, G. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0196-7 · Cited by 129
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
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
Moon, K. R., Stanley, J. S., Burkhardt, D. B., van Dijk, D., Wolf, G., & Krishnaswamy, S. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.008 · Cited by 133
Wnt Signaling Separates the Progenitor and Endocrine Compartments during Pancreas Development
Sharon, N., Vanderhooft, J., Straubhaar, J., Mueller, J. W., Chawla, R., Zhou, Q., Engquist, E. N., Trapnell, C., Gifford, D. K., & Melton, D. A. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.04.083 · Cited by 125
Self-citationHu, Y., Wang, X., Hu, B., Mao, Y., Chen, Y., Yan, L., Yong, J., Dong, J., Wei, Y., Wang, W., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000365 · Cited by 131
Wang, X., Yang, L. C., Wang, Y., Xu, Z., Ye, F., Zhang, J., Wang, Y., & Xu, C. (2020) · Cell Research · doi:10.1038/s41422-020-0378-6 · Cited by 132
Sun, Y., Wu, P., Zhang, Z., Wang, Z., Zhou, K., Song, M., Ji, Y., Zang, F., Lou, L., Rao, K., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.11.010 · Cited by 133
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
scSorter: assigning cells to known cell types according to marker genes
Guo, H., & Li, J. (2021) · Genome biology · doi:10.1186/s13059-021-02281-7 · Cited by 132
Shim, J., Oh, S. J., Yeo, E., Park, J., Bae, J. H., Kim, S., Lee, D., & Lee, J. H. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.01.017 · Cited by 128
Single-cell epigenomic variability reveals functional cancer heterogeneity
Litzenburger, U. M., Buenrostro, J. D., Wu, B., Shen, Y., Sheffield, N. C., Kathiria, A., Greenleaf, W. J., & Chang, H. Y. (2017) · Genome Biology · doi:10.1186/s13059-016-1133-7 · Cited by 115
Segal, J. M., Kent, D., Wesche, D. J., Ng, S. S., Serra, M. P., Oulès, B., Kar, G., Emerton, G., Blackford, S. J. I., Darmanis, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11266-x · Cited by 128
Molecular determinants of nephron vascular specialization in the kidney
Barry, D. M., McMillan, E. A., Kunar, B., Lis, R., Zhang, T., Lu, T. M., Daniel, E., Yokoyama, M., Gómez-Salinero, J. M., Sureshbabu, A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12872-5 · Cited by 130
Li, C., Guo, L., Li, S., & Hua, K. (2021) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2021.03.017 · Cited by 124
Self-citationWong, H. Y., Sheng, Q., Hesterberg, A. B., Croessmann, S., Rios, B. L., Giri, K. R., Jackson, J., Miranda, A. X., Watkins, E., Schaffer, K. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33780-1 · Cited by 131
Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling
Liang, Q., Dharmat, R., Owen, L. A., Shakoor, A., Li, Y., Kim, S., Vitale, A. T., Kim, I. K., Morgan, D. J., Liang, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12917-9 · Cited by 121
Human mononuclear phagocyte system reunited
Haniffa, M. A., Bigley, V., & Collin, M. P. (2015) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2015.05.004 · Cited by 133
Chimeric contribution of human extended pluripotent stem cells to monkey embryos ex vivo
Tan, T., Wu, J., Si, C., Dai, S., Zhang, Y., Sun, N., Zhang, E., Shao, H., Si, W., Yang, P., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.020 · Cited by 132
Mysore, V., Culleré, X., Mears, J., Rosetti, F., Okubo, K., Liew, P. X., Zhang, F., Madera-Salcedo, I. K., Rosenbauer, F., Stone, R. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24591-x · Cited by 129
Chari, T., Weissbourd, B., Gehring, J., Ferraioli, A., Leclère, L., Herl, M., Gao, F., Chevalier, S., Copley, R. R., Houliston, E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh1683 · Cited by 123
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
A NPAS4–NuA4 complex couples synaptic activity to DNA repair
Pollina, E. A., Gilliam, D. T., Landau, A. T., Lin, C., Pajarillo, N., Davis, C. P., Harmin, D. A., Yap, E.-L., Vogel, I. R., Griffith, E. C., et al. (2023) · Nature · doi:10.1038/s41586-023-05711-7 · Cited by 125
MafB-restricted local monocyte proliferation precedes lung interstitial macrophage differentiation
Vanneste, D., Bai, Q., Hasan, S., Peng, W., Pirottin, D., Schyns, J., Maréchal, P., Ruscitti, C., Meunier, M., Liu, Z., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01468-3 · Cited by 111
Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data
Campbell, K. R., & Yau, C. (2018) · Nature Communications · doi:10.1038/s41467-018-04696-6 · Cited by 127
Inferring population dynamics from single-cell RNA-sequencing time series data
Fischer, D. S., Fiedler, A., Kernfeld, E. M., Genga, R. M. J., Bastidas-Ponce, A., Bakhti, M., Lickert, H., Hasenauer, J., Maehr, R., & Theis, F. J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0088-0 · Cited by 119
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
