Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing
Abstract
Single-cell analysis of gene expression is increasingly important for the analysis of complex tissues, including cancer, developing organs and adult stem cell niches. Here we present a detailed protocol for quantitative gene expression analysis in single cells, by the sequencing of mRNA 5' ends. In all, 96 cells are lysed, and their mRNA is converted to cDNA. By using a template-switching mechanism, a bar code and an upstream primer-binding sequence are introduced simultaneously with reverse transcription. All cDNA is pooled and then prepared for 5' end sequencing, including fragmentation, adapter ligation and PCR amplification. The chief advantage of this approach is the great reduction in cost and time, afforded by the early bar-coding strategy. Compared with previous methods, it is more suitable for large-scale quantitative analysis, as well as for the characterization of transcription start sites, but it is unsuitable for the detection of alternatively spliced transcripts. Sample preparation takes 3 d, and two sets of 96 cells can be prepared in parallel. Finally, the sequencing and data analysis can take an additional 4 d altogether.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
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 [16]
Cites
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · 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
The UCSC Genome Browser database: update 2011
Fujita, P. A., Rhead, B. L., Zweig, A. S., Hinrichs, A. S., Karolchik, D., Cline, M., Goldman, M. J., Barber, G. P., Clawson, H., Coelho, A., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq963 · Cited by 1,836 · see citing works
Stochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737 · see citing works
Counting absolute numbers of molecules using unique molecular identifiers
Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. (2012) · Nature Methods · doi:10.1038/nmeth.1778 · Cited by 1,010 · see citing works
Self-citationCharacterization 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
Self-citationGuo, G., Huss, M., Tong, G. Q., Wang, C., Sun, L., Clarke, N. D., & Robson, P. (2010) · Developmental Cell · doi:10.1016/j.devcel.2010.02.012 · Cited by 806 · see citing works
Zhu, Y., Machleder, E. M., Chenchik, A. A., Li, R., & Siebert, P. D. (2001) · BioTechniques · doi:10.2144/01304pf02 · Cited by 773 · see citing works
Comprehensive comparative analysis of strand-specific RNA sequencing methods
Levin, J. Z., Yassour, M., Adiconis, X., Nusbaum, C., Thompson, D. A., Friedman, N., Gnirke, A., & Regev, A. (2010) · Nature Methods · doi:10.1038/nmeth.1491 · Cited by 753 · see citing works
Transcriptional Pulsing of a Developmental Gene
Chubb, J. R., Trcek, T., Shenoy, S. M., & Singer, R. H. (2006) · Current Biology · doi:10.1016/j.cub.2006.03.092 · Cited by 714 · see citing works
Single-cell dissection of transcriptional heterogeneity in human colon tumors
Dalerba, P. D., Tomer Kalisky, C., Sahoo, D., Rajendran, P. S., Rothenberg, M. E., Leyrat, A. A., Sim, S., Okamoto, J., Johnston, D. M., Qian, D., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.2038 · Cited by 693 · see citing works
Amplification and analysis of DNA sequences in single human sperm and diploid cells
Li, H., Gyllensten, U. B., Cui, X., et al. (1988) · Nature · doi:10.1038/335414a0 · Cited by 673 · see citing works
Synthetic spike-in standards for RNA-seq experiments
Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653 · see citing works
Tang, F., Bárbácioru, C., Bao, S., Lee, C. G. L., Nordman, E., Wang, X., Lao, K., & Surani, M. A. (2010) · Cell stem cell · doi:10.1016/j.stem.2010.03.015 · Cited by 499 · see citing works
Non-genetic heterogeneity of cells in development: more than just noise
Huang, S. (2009) · Development · doi:10.1242/dev.035139 · Cited by 482 · see citing works
Kurimoto, K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl050 · Cited by 403 · see citing works
Living with Noisy Genes: How Cells Function Reliably with Inherent Variability in Gene Expression
Maheshri, N., & O’Shea, E. K. (2007) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.36.040306.132705 · Cited by 315 · see citing works
Kurimoto, K., Yabuta, Y., Ohinata, Y., & Saitou, M. (2007) · Nature Protocols · doi:10.1038/nprot.2007.79 · Cited by 186 · see citing works
Deterministic and Stochastic Allele Specific Gene Expression in Single Mouse Blastomeres
Tang, F., Bárbácioru, C., Nordman, E., Bao, S., Lee, C. G. L., Wang, X., Tuch, B. B., Heard, É., Lao, K., & Surani, M. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021208 · Cited by 150 · see citing works
Quantitative analysis of gene expression in a single cell by qPCR
Taniguchi, K., Kajiyama, T., & Kambara, H. (2009) · Nature Methods · doi:10.1038/nmeth.1338 · Cited by 136 · see citing works
Increased Throughput by Parallelization of Library Preparation for Massive Sequencing
Lundin, S., Stranneheim, H., Pettersson, E., Klevebring, D., & Lundeberg, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010029 · Cited by 128 · see citing works
Transcriptome Analysis of Single Cells
Morris, J. F., Singh, J. M., & Eberwine, J. H. (2011) · Journal of Visualized Experiments · doi:10.3791/2634 · Cited by 68 · see citing works
NanoCAGE: A High-Resolution Technique to Discover and Interrogate Cell TranscriptomesPreprint
Salimullah, M., Salimullah, M., Sakai, M., & Plessy, C. (2011) · Cold Spring Harbor Protocols · doi:10.1101/pdb.prot5559 · Cited by 63 · see citing works
Esumi, S., Wu, S., Yanagawa, Y., Obata, K., Sugimoto, Y., & Tamamaki, N. (2008) · Neuroscience Research · doi:10.1016/j.neures.2007.12.011 · Cited by 54 · see citing works
NanoCAGE: A High-Resolution Technique to Discover and Interrogate Cell TranscriptomesPreprint
Salimullah, M., Sakai, M., Plessy, C., et al. (2011) · Cold Spring Harbor Protocols · doi:10.1101/pdb.erratum2011_01 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
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
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
Self-citationSingle-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
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
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
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
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
Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3443 · Cited by 386 · see citing works
Self-citationSingle 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
Giustacchini, A., Thongjuea, S., Barkas, N., Woll, P. S., Povinelli, B. J., Booth, C. A. G., Sopp, P., Norfo, R., Rodriguez-Meira, A., Ashley, N., et al. (2017) · Nature Medicine · doi:10.1038/nm.4336 · Cited by 403 · see citing works
Batch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356 · 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 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
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
Human pluripotent reprogramming with CRISPR activators
Weltner, J., Balboa, D., Katayama, S., Bespalov, M., Krjutškov, K., Jouhilahti, E.-M., Trokovic, R., Kere, J., & Otonkoski, T. (2018) · Nature Communications · doi:10.1038/s41467-018-05067-x · Cited by 181 · 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
Profiling chromatin states using single-cell itChIP-seq
Ai, S., Xiong, H., Li, C. C., Luo, Y., Shi, Q., Liu, Y., Yu, X., Li, C., & He, A. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0383-5 · Cited by 140 · see citing works
Behavior-related gene regulatory networks: A new level of organization in the brain
Sinha, S., Jones, B. M., Traniello, I. M., Bukhari, S. A., Halfon, M. S., Hofmann, H. A., Huang, S., Katz, P. S., Keagy, J., Lynch, V. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921625117 · Cited by 80 · 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
First proposal of minimum information about a cellular assay for regenerative medicine
Sakurai, K., Kurtz, A., Stacey, G., Sheldon, M., & Fujibuchi, W. (2016) · Stem Cells Translational Medicine · doi:10.5966/sctm.2015-0393 · Cited by 31 · see citing works
- Authors
- Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S.
- Affiliations
- Karolinska Institutet (SE)
- Illumina (United States) (US)
- Corresponding author
- Linnarsson, Sten
- Venue
- Nature Protocols
- Published
- 2012-04-05
- Volume
- 7
- Issue
- 5
- Pages
- 813-828
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2015329331
- Cited by
- 272
- References
- 27
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 15 · 2023: 19 · 2022: 17 · 2021: 23 · 2020: 20 · 2019: 31 · 2018: 16 · 2017: 23 · 2016: 33 · 2015: 21 · 2014: 23 · 2013: 11 · 2012: 2
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- 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, 5' end sequencing, strand-specific sequencing, template switching, reverse transcription, transcription start sites, gene expression quantification, single-cell transcriptomics
- MeSH terms
- Flow Cytometry, RNA, Messenger, Polymerase Chain Reaction, Sequence Analysis, RNA, DNA, Complementary, 5' Untranslated Regions, Nucleic Acid Amplification Techniques, Single-Cell Analysis
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