MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing.
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
07
09
2018
accepted:
12
03
2019
pubmed:
19
5
2019
medline:
11
7
2019
entrez:
19
5
2019
Statut:
ppublish
Résumé
Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4-9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2-3 d. Sequencing and processing the data through the analytical pipeline take another 1-2 d.
Identifiants
pubmed: 31101904
doi: 10.1038/s41596-019-0164-4
pii: 10.1038/s41596-019-0164-4
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1841-1862Références
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
doi: 10.1038/nmeth.1315
Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535 (2010).
doi: 10.1038/nprot.2009.236
Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
doi: 10.1038/nature12172
Moignard, V. et al. Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis. Nat. Cell Biol. 15, 363–372 (2013).
doi: 10.1038/ncb2709
Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
doi: 10.1126/science.1245316
Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
doi: 10.1038/nature13173
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
doi: 10.1126/science.1254257
Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
doi: 10.1126/science.aaa1934
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
doi: 10.1016/j.cell.2015.11.013
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
doi: 10.1126/science.1247651
Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).
doi: 10.1038/nbt.2967
Grun, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).
doi: 10.1038/nature14966
Petropoulos, S. et al. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 167, 285 (2016).
doi: 10.1016/j.cell.2016.08.009
Grun, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).
doi: 10.1016/j.cell.2015.10.039
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
doi: 10.1038/nrg3833
Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).
doi: 10.1038/nrg3542
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. & Teichmann, S. A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).
doi: 10.1016/j.molcel.2015.04.005
Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).
doi: 10.1101/gr.110882.110
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
doi: 10.1016/j.celrep.2012.08.003
Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012).
doi: 10.1038/nprot.2012.022
Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
doi: 10.1038/nbt.2282
Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).
doi: 10.1186/gb-2013-14-4-r31
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
doi: 10.1038/nmeth.2639
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
doi: 10.1038/nprot.2014.006
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
doi: 10.1038/nmeth.2772
Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).
doi: 10.1038/nmeth.2694
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
doi: 10.1016/j.cell.2015.05.047
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
doi: 10.1038/nmeth.4407
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
doi: 10.1038/nmeth.4179
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
doi: 10.1016/j.cell.2015.05.002
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
doi: 10.1016/j.cell.2015.04.044
Regev, A. et al. The Human Cell Atlas. Elife 6, e27041 (2017).
doi: 10.7554/eLife.27041
Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).
doi: 10.1101/gr.190595.115
Ballas, S. K. Erythrocyte concentration and volume are inversely related. Clin. Chim. Acta 164, 243–244 (1987).
doi: 10.1016/0009-8981(87)90078-7
Giladi, A. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat. Cell Biol. 20, 836–846 (2018).
doi: 10.1038/s41556-018-0121-4
Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017).
doi: 10.1126/science.aao4277
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
doi: 10.1038/nmeth.4380
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016).
doi: 10.1016/j.cell.2016.11.039
Paul, F. & Amit, I. Plasticity in the transcriptional and epigenetic circuits regulating dendritic cell lineage specification and function. Curr. Opin. Immunol. 30, 1–8 (2014).
doi: 10.1016/j.coi.2014.04.004
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s Disease. Cell 169, 1276–1290.e17 (2017).
doi: 10.1016/j.cell.2017.05.018
Bornstein, C. et al. Single-cell mapping of the thymic stroma identifies IL-25-producing tuft epithelial cells. Nature 559, 622–626 (2018).
doi: 10.1038/s41586-018-0346-1
Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044.e18 (2018).
doi: 10.1016/j.cell.2018.09.009
Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).
doi: 10.1126/science.aad8670
Steuerman, Y. et al. Dissection of influenza infection in vivo by single-cell RNA sequencing. Cell Syst. 6, 679–691.e4 (2018).
doi: 10.1016/j.cels.2018.05.008
Sebe-Pedros, A. et al. Cnidarian cell type diversity and regulation revealed by whole-organism single-cell RNA-seq. Cell 173, 1520–1534.e20 (2018).
doi: 10.1016/j.cell.2018.05.019
Sebe-Pedros, A. et al. Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nat. Ecol. Evol. 2, 1176–1188 (2018).
doi: 10.1038/s41559-018-0575-6
Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).
doi: 10.1038/s41591-018-0269-2
Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).
doi: 10.1016/j.cell.2017.04.014
Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).
doi: 10.1016/j.cell.2018.11.043
Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).
pubmed: 30222169
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
doi: 10.1038/nature21065
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
doi: 10.1038/nbt.4042
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
doi: 10.1038/ncomms14049
Han, K. Y. et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 28, 75–87 (2018).
doi: 10.1101/gr.223263.117
Biase, F. H. et al. Rainbow-Seq: combining cell lineage tracing with single-cell RNA sequencing in preimplantation embryos. iScience 7, 16–29 (2018).
doi: 10.1016/j.isci.2018.08.009
Giladi, A. & Amit, I. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172, 14–21 (2018).
doi: 10.1016/j.cell.2017.11.011
Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329.e6 (2017).
doi: 10.1016/j.neuron.2017.09.026
Grun, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).
doi: 10.1038/nmeth.2930
Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2011).
doi: 10.1038/nmeth.1778
Rais, Y. et al. Deterministic direct reprogramming of somatic cells to pluripotency. Nature 502, 65–70 (2013).
doi: 10.1038/nature12587