CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Open accessAbstract
High-throughput sequencing has allowed for unprecedented detail in gene expression analyses, yet its efficient application to single cells is challenged by the small starting amounts of RNA. We have developed CEL-Seq, a method for overcoming this limitation by barcoding and pooling samples before linearly amplifying mRNA with the use of one round of in vitro transcription. We show that CEL-Seq gives more reproducible, linear, and sensitive results than a PCR-based amplification method. We demonstrate the power of this method by studying early C. elegans embryonic development at single-cell resolution. Differential distribution of transcripts between sister cells is seen as early as the two-cell stage embryo, and zygotic expression in the somatic cell lineages is enriched for transcription factors. The robust transcriptome quantifications enabled by CEL-Seq will be useful for transcriptomic analyses of complex tissues containing populations of diverse cell types.
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Rapid technological advances have enabled genome-wide profiling of RNA, DNA, protein, epigenetic modifications, chromatin accessibility, and other molecular events in single cells.
“rapid technological advances have enabled genome-wide profiling of RNA7–10, DNA11–17, protein18–21, epigenetic modifications22–26, chromatin accessibility27,28, and other molecular events29 in single cells.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [10]
Many scRNA-seq approaches introduced over the last ten years are now key for measuring cell-to-cell variation and its effects on function across populations.
“Several scRNA-seq methods developed in the recent decade have become essential for capturing intercellular variation and its impact on cell functions at the population scale15–19.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [15]
Other protocols have sacrificed full-length coverage to sequence part of the cDNA primer, enabling early cell-specific barcoding and increasing scRNA-seq library generation throughput by one to three orders of magnitude.
“Other protocols have sacrificed full-length coverage in order to sequence part of the primer used for cDNA generation. This enables early barcoding of libraries (i.e., the incorporation of cell-specific barcodes), allowing for multiplexing the cDNA amplification and thereby increasing the throughput of scRNAseq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaiti”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Hashimshony et al., 2012
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
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 · see citing works
Embryonic Stem Cell Lines Derived from Human Blastocysts
Thomson, J. A., Itskovitz‐Eldor, J., Shapiro, S. S., Waknitz, M. A., Swiergiel, J. J., Marshall, V. S., & Jones, J. M. (1998) · Science · doi:10.1126/science.282.5391.1145 · Cited by 15,947 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Goecks, J., Nekrutenko, A., Taylor, J., & The Galaxy Team (2010) · Genome biology · doi:10.1186/gb-2010-11-8-r86 · Cited by 3,437 · see citing works
Galaxy: a platform for interactive large-scale genome analysis.Preprint
Giardine, B., Riemer, C., Hardison, R., Burhans, R. C., Elnitski, L., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J., et al. (2005) · Genome Research · doi:10.1101/gr.4086505 · Cited by 2,217 · see citing works
Analysis of gene expression in single live neurons.
Eberwine, J. H., Yeh, H., Miyashiro, K., Cao, Y., Nair, S., Finnell, R. H., Zettel, M. F., & Coleman, P. D. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.7.3010 · Cited by 953 · see citing works
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
Single cell analysis: the new frontier in 'Omics'
Wang, D., & Bodovitz, S. (2010) · Trends in biotechnology · doi:10.1016/j.tibtech.2010.03.002 · Cited by 669 · see citing works
Quantitative analysis of mRNA amplification by in vitro transcription
Baugh, L. R. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.5.e29 · Cited by 500 · see citing works
The External RNA Controls Consortium: a progress report
The External RNA Controls Consortium (2005) · Nature Methods · doi:10.1038/nmeth1005-731 · Cited by 373 · see citing works
Development and applications of single-cell transcriptome analysis
Tang, F., Lao, K., & Surani, M. A. (2011) · Nature Methods · doi:10.1038/nmeth.1557 · Cited by 337 · see citing works
Repression of gene expression in the embryonic germ lineage of C. elegans
Seydoux, G., Mello, C. C., Pettitt, J., Wood, W. B., Priess, J. R., & Fire, A. Z. (1996) · Nature · doi:10.1038/382713a0 · Cited by 311 · see citing works
MEX-3 Is a KH Domain Protein That Regulates Blastomere Identity in Early C. elegans Embryos
Draper, B. W., Mello, C. C., Bowerman, B. A., et al. (1996) · Cell · doi:10.1016/s0092-8674(00)81339-2 · Cited by 294 · see citing works
The Landscape of C. elegans 3′UTRs
Mangone, M., Manoharan, A. P., Thierry‐Mieg, D., Thierry‐Mieg, J., Han, T., Mackowiak, S. D., Mis, E. K., Zegar, C., Gutwein, M. R., Khivansara, V., et al. (2010) · Science · doi:10.1126/science.1191244 · Cited by 247 · see citing works
Maduro, M., Meneghini, M. D., Bowerman, B. A., Broitman-Maduro, G., & Rothman, J. H. (2001) · Molecular Cell · doi:10.1016/s1097-2765(01)00195-2 · Cited by 185 · see citing works
Chapter 13 Blastomere Culture and Analysis
Edgar, L. G. (1995) · Methods in cell biology · doi:10.1016/s0091-679x(08)61393-x · Cited by 151 · see citing works
Matul, M., McCord, M. J., Phily, C., et al. (2010) · Cited by 108 · see citing works
DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells
Spike, C. A., Bader, J. R., Reinke, V., & Strome, S. (2008) · Development · doi:10.1242/dev.015552 · Cited by 80 · see citing works
TAp63 Is Important for Cardiac Differentiation of Embryonic Stem Cells and Heart Development
Rouleau, M., Medawar, A., Hamon, L., Shivtiel, S., Wolchinsky, Z., Zhou, H., Rosa, L. D., Candi, E., Divonne, S. B.-D. L. F., Mikkola, M. L., et al. (2011) · Stem Cells · doi:10.1002/stem.723 · Cited by 59 · see citing works
Yanai, I., & Hunter, C. P. (2009) · Genome Research · doi:10.1101/gr.093815.109 · Cited by 56 · see citing works
Self-citationHistone gene expression and histone mRNA 3' end structure in Caenorhabditis elegans
Keall, R., Whitelaw, S., Pettitt, J., et al. (2007) · BMC Molecular Biology · doi:10.1186/1471-2199-8-51 · Cited by 29 · see citing works
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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 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
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 · see citing works
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
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 · see citing works
Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients
van de Wetering, M., Francies, H. E., Francis, J. M., Bounova, G., Iorio, F., Pronk, A., van Houdt, W., van Gorp, J., Taylor-Weiner, A., Kester, L., et al. (2015) · Cell · doi:10.1016/j.cell.2015.03.053 · Cited by 2,408 · see citing works
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 · see citing works
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
Self-citationIntegrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq
Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125 · see citing works
Self-citationSingle-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177 · see citing works
Single-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979 · see citing works
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) · Nature · doi:10.1038/nature13437 · Cited by 1,000 · see citing works
Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations
van den Brink, S. C., Sage, F., Vértesy, Á., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., Robin, C., & van Oudenaarden, A. (2017) · Nature Methods · doi:10.1038/nmeth.4437 · Cited by 1,072 · see citing works
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
Xue, Z., Huang, K., Cai, C., Cai, L., Jiang, C.-Y., Feng, Y., Liu, Z., Zeng, Q., Cheng, L., Sun, Y. E., et al. (2013) · Nature · doi:10.1038/nature12364 · Cited by 963 · see citing works
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works
Single-cell RNA-seq: advances and future challenges
Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847 · see citing works
Kowalczyk, M. S., Tirosh, I., Heckl, D., Rao, T. N., Dixit, A., Haas, B. J., Schneider, R. K., Wagers, A. J., Ebert, B. L., & Regev, A. (2015) · Genome Research · doi:10.1101/gr.192237.115 · Cited by 778 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647 · see citing works
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696 · see citing works
Spatially resolved transcriptomes—next generation tools for tissue exploration
Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666 · see citing works
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
Identification of cell types from single-cell transcriptomes using a novel clustering method
Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559 · see citing works
Design and Analysis of Single-Cell Sequencing Experiments
Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536 · see citing works
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568 · see citing works
From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing
Marinov, G. K., Williams, B. A., McCue, K., Schroth, G. P., Gertz, J., Myers, R. M., & Wold, B. J. (2014) · Genome Research · doi:10.1101/gr.161034.113 · Cited by 468 · see citing works
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works
Spatially resolved transcriptomics and beyond
Crosetto, N., Bienko, M., & van Oudenaarden, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3832 · Cited by 509 · see citing works
Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421 · see citing works
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 · see citing works
Nuclear RNA-seq of single neurons reveals molecular signatures of activation
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S. L., Paquola, A. C. M., Venepally, P., Novotny, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11022 · Cited by 405 · see citing works
Combinatorial labeling of single cells for gene expression cytometry
Fan, H. C., Fu, G. K., & Fodor, S. P. A. (2015) · Science · doi:10.1126/science.1258367 · Cited by 455 · see citing works
Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r31 · Cited by 455 · see citing works
Joost, S., Zeisel, A., Jacob, T., Sun, X., La Manno, G., Lönnerberg, P., Linnarsson, S., & Kasper, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.010 · Cited by 429 · see citing works
Single Cell Genomics: Advances and Future Perspectives
Macaulay, I. C., & Voet, T. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004126 · Cited by 384 · see citing works
RNA-sequencing from single nuclei
Grindberg, R. V., Yee-Greenbaum, J. L., McConnell, M. J., Novotny, M., O’Shaughnessy, A. L., Lambert, G. M., Araúzo-Bravo, M. J., Lee, J., Fishman, M., Robbins, G. E., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319700110 · Cited by 422 · see citing works
Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR
Zemmour, D., Zilionis, R., Kiner, E., Klein, A. M., Mathis, D., & Benoist, C. (2018) · Nature Immunology · doi:10.1038/s41590-018-0051-0 · Cited by 384 · see citing works
BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks
Yan, W. X., Mirzazadeh, R., Garnerone, S., Scott, D., Schneider, M. W., Kallas, T., Custodio, J., Wernersson, E., Li, Y., Gao, L., et al. (2017) · Nature Communications · doi:10.1038/ncomms15058 · Cited by 369 · see citing works
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq
Chen, J., Suo, S., Tam, P. P., Han, J.-D. J., Peng, G., & Jing, N. (2017) · Nature Protocols · doi:10.1038/nprot.2017.003 · Cited by 339 · see citing works
Highly multiplexed simultaneous detection of RNAs and proteins in single cells
Frei, A. P., Bava, F.-A., Zunder, E. R., Hsieh, E. W. Y., Chen, S.-Y., Nolan, G. P., & Gherardini, P. F. (2016) · Nature Methods · doi:10.1038/nmeth.3742 · Cited by 330 · see citing works
Disentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293 · see citing works
Single-cell sequencing techniques from individual to multiomics analyses
Kashima, Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., & Suzuki, A. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00499-2 · Cited by 270 · see citing works
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · see citing works
Clark, S. J., Smallwood, S. A., Lee, H. J., Krueger, F., Reik, W., & Kelsey, G. (2017) · Nature Protocols · doi:10.1038/nprot.2016.187 · Cited by 270 · see citing works
A systematic evaluation of single cell RNA-seq analysis pipelines
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265 · see citing works
Rizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3854 · Cited by 248 · see citing works
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230 · see citing works
Cancer proteogenomics: current impact and future prospects
Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229 · see citing works
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Cell fixation and preservation for droplet-based single-cell transcriptomics
Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., et al. (2017) · BMC Biology · doi:10.1186/s12915-017-0383-5 · Cited by 218 · see citing works
“Rather than studying global gene expression of a tissue as a whole, it has been recognized that transcriptional profiling at a single-cell resolution (1-4) provides a much more complete and accurate description of its biological function (5,6).”
BackgroundSingle-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“These advancements have facilitated the development of full-length transcriptome profiling, high-throughput capabilities, and high-sensitivity scRNA-seq [2–4].”
BackgroundTroy+ brain stem cells cycle through quiescence and regulate their number by sensing niche occupancy
Basak, O., Krieger, T. G., Muraro, M. J., Wiebrands, K., Stange, D. E., Frias-Aldeguer, J., Rivron, N. C., van de Wetering, M., van Es, J. H., van Oudenaarden, A., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715911114 · Cited by 186 · see citing works
Tissue-resident memory CD8+ T cells shape local and systemic secondary T cell responses
Behr, F. M., Parga-Vidal, L., Kragten, N. A. M., van Dam, T. J. P., Wesselink, T. H., Sheridan, B. S., Arens, R., van Lier, R. A. W., Stark, R., & van Gisbergen, K. P. J. M. (2020) · Nature Immunology · doi:10.1038/s41590-020-0723-4 · Cited by 184 · see citing works
Cadwell, C. R., Scala, F., Li, S., Livrizzi, G., Shen, S., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · Nature Protocols · doi:10.1038/nprot.2017.120 · Cited by 147 · see citing works
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 · see citing works
powsimR: power analysis for bulk and single cell RNA-seq experiments
Vieth, B., Ziegenhain, C., Parekh, S., Enard, W., & Hellmann, I. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx435 · Cited by 155 · see citing works
SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells
Han, K. Y., Kim, K.-T., Joung, J.-G., Son, D.-S., Kim, Y. J., Jo, A., Jeon, H.-J., Moon, H.-S., Yoo, C. E., Chung, W., et al. (2018) · Genome Research · doi:10.1101/gr.223263.117 · Cited by 143 · see citing works
SNES: single nucleus exome sequencing
Leung, M. L., Wang, Y., Waters, J., & Navin, N. E. (2015) · Genome Biology · doi:10.1186/s13059-015-0616-2 · Cited by 112 · see citing works
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 · see citing works
Determining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112 · see citing works
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 · see citing works
Droplet-based single cell RNAseq tools: a practical guide
Salomon, R., Kaczorowski, D., Valdes-Mora, F., Nordon, R. E., Neild, A., Farbehi, N., Bartonicek, N., & Gallego-Ortega, D. (2019) · Lab on a Chip · doi:10.1039/c8lc01239c · Cited by 109 · see citing works
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104 · see citing works
Using single‐cell multiple omics approaches to resolve tumor heterogeneity
Ortega, M. A., Poirion, O., Zhu, X., Huang, S., Wolfgruber, T. K., Sebra, R., & Garmire, L. X. (2017) · Clinical and Translational Medicine · doi:10.1186/s40169-017-0177-y · Cited by 81 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Azazmeh, N., Assouline, B., Winter, E., Ruppo, S., Nevo, Y., Maly, A., Meir, K., Witkiewicz, A. K., Cohen, J., Rizou, S. V., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16475-3 · Cited by 57 · see citing works
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625 · see citing works
Miller, J. A., Menon, V., Goldy, J., Kaykas, A., Lee, C.-K., Smith, K. A., Shen, E. H., Phillips, J. W., Lein, E. S., & Hawrylycz, M. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-154 · Cited by 48 · see citing works
Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles
Li, W., Calder, R. B., Mar, J. C., & Vijg, J. (2015) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2015.01.002 · Cited by 34 · see citing works
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31 · see citing works
Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering
Vallejo, A. F., Davies, J., Grover, A., Tsai, C.-H., Jepras, R., Polak, M. E., & West, J. (2019) · doi:10.1101/800631 · Cited by 17 · see citing works
SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs
Tian, L., Schreuder, J., Zalcenstein, D., Tran, J., Kocovski, N., Su, S., Diakumis, P., Bahlo, M., Sargeant, T., Hodgkin, P. D., et al. (2018) · doi:10.1101/403113 · Cited by 10 · see citing works
- Authors
- Hashimshony, T., Wagner, F., Sher, N., & Yanai, I.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Corresponding author
- Yanai, Itai
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 2
- Issue
- 3
- Pages
- 666-673
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W2139838862
- Cited by
- 1,231
- References
- 24
- Citations by year
- 2026: 42 · 2025: 56 · 2024: 82 · 2023: 107 · 2022: 111 · 2021: 108 · 2020: 123 · 2019: 112 · 2018: 109 · 2017: 117 · 2016: 88 · 2015: 75 · 2014: 56 · 2013: 37 · 2012: 6
- FWCI
- 22.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Israel Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- single-cell RNA sequencing, linear amplification, in vitro transcription, sample pooling, transcriptome quantification, zygotic gene expression, transcription factors
- MeSH terms
- Animals, Gene Expression Regulation, RNA, Messenger, Caenorhabditis elegans, Sequence Analysis, DNA, RNA, Helminth, Reverse Transcriptase Polymerase Chain Reaction, Multiplex Polymerase Chain Reaction
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