Scaling single-cell genomics from phenomenology to mechanism

Nature

Volumn 541, Issue 7637, 2017, Pages 331-338

  Tanay, Amos ^a   Regev, Aviv ^b  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLS AND CELL COMPONENTS; EXPERIMENTAL STUDY; GENE; GENE EXPRESSION; GENOMICS; RESOLUTION;

CELL DIFFERENTIATION; GENE CONTROL; GENOMICS; HUMAN; MOLECULAR DYNAMICS; NONHUMAN; OBSERVATIONAL STUDY; PHENOMENOLOGY; PRIORITY JOURNAL; REVIEW; SINGLE CELL ANALYSIS; ANIMAL; BIOLOGICAL MODEL; GENE EXPRESSION REGULATION; PROCEDURES; SPATIOTEMPORAL ANALYSIS;

ANIMALS; GENE EXPRESSION REGULATION; GENOMICS; HUMANS; MODELS, GENETIC; SINGLE-CELL ANALYSIS; SPATIO-TEMPORAL ANALYSIS;

EID: 85017360311     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature21350     Document Type: Review

References (113)

1. Okabe, Y., Medzhitov, R. Tissue biology perspective on macrophages. Nature Immunol. 17, 9-17 (2016).
2. Mayr, E. The Growth of Biological Thought: Diversity, Evolution, Inheritance (Belknap, 1982).
3. Szathmary, E., Maynard-Smith, J. The Major Transitions in Evolution (Oxford University Press, 1995).
4. Gould, S. J. Ontogeny and Phylogeny (Belknap, 1977).
5. Richardson, L., et al. EMAGE mouse embryo spatial gene expression database: 2014 update. Nucleic Acids Res. 42, D835-D844 (2014).
6. Oh, S. W., et al. A mesoscale connectome of the mouse brain. Nature 508, 207-214 (2014).
7. Miller, J. A., et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199-206 (2014).
8. Hawrylycz, M. J., et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391-399 (2012).
9. Bakken, T. E., et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367-375 (2016).
10. Jojic, V., et al. Identification of transcriptional regulators in the mouse immune system. Nature Immunol. 14, 633-643 (2013).
11. Sanes, J. R., Masland, R. H. The types of retinal ganglion cells: Current status and implications for neuronal classification. Annu. Rev. Neurosci. 38, 221-246 (2015).
12. Chao, M. P., Seita, J., Weissman, I. L. Establishment of a normal hematopoietic and leukemia stem cell hierarchy. Cold Spring Harb. Symp. Quant. Biol. 73, 439-449 (2008).
13. Ramsköld, D., et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnol. 30, 777-782 (2012).
14. Picelli, S., et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods 10, 1096-1098 (2013).
15. Hashimshony, T., Wagner, F., Sher, N., Yanai, I. CEL-Seq: Single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666-673 (2012).
16. 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).
17. Shalek, A. K., et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236-240 (2013).
18. Zong, C., Lu, S., Chapman, A. R., Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622-1626 (2012).
19. Xu, X., et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886-895 (2012).
20. Navin, N., et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90-94 (2011).
21. Leung, M. L., Wang, Y., Waters, J., Navin, N. E. SNES: Single nucleus exome sequencing. Genome Biol. 16, 55 (2015).
22. Hou, Y., et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873-885 (2012).
23. Gao, R., et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nature Genet. 48, 1119-1130 (2016).
24. Rotem, A., et al. High-throughput single-cell labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics. PLoS ONE 10, e0116328 (2015).
25. Rotem, A., et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnol. 33, 1165-1172 (2015).
26. Buenrostro, J. D., et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486-490 (2015).
27. Cusanovich, D. A., et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910-914 (2015).
28. Smallwood, S. A., et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nature Methods 11, 817-820 (2014).
29. Mooijman, D., Dey, S. S., Boisset, J. C., Crosetto, N., van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nature Biotechnol. 34, 852-856 (2016).
30. Guo, H., et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126-2135 (2013).
31. Farlik, M., et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386-1397 (2015).
32. Kind, J., et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163, 134-147 (2015).
33. Nagano, T., et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59-64 (2013).
34. Bendall, S. C., et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687-696 (2011).
35. Bendall, S. C., et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714-725 (2014).
36. Krishnaswamy, S., et al. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).
37. Marco, E., et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. USA 111, E5643-E5650 (2014).
38. Patel, A. P., et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1401 (2014).
39. Shalek, A. K., et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363-369 (2014).
40. Trapnell, C., et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnol. 32, 381-386 (2014).
41. Arvey, A., et al. Genetic and epigenetic variation in the lineage specification of regulatory T cells. eLife 4, e07571 (2015).
42. Avraham, R., et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309-1321 (2015).
43. Gaublomme, J. T., et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400-1412 (2015).
44. Grün, D., et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251-255 (2015).
45. Kowalczyk, M. S., et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860-1872 (2015).
46. Paul, F., et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663-1677 (2015).
47. Corces, M. R., et al. Lineage-specific and single cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genet. 48, 1193-1203 (2016).
48. Habib, N., et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925-928 (2016).
49. Matcovitch-Natan, O., et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).
50. Olsson, A., et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698-702 (2016).
51. Proserpio, V., et al. Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biol. 17, 103 (2016).
52. Shekhar, K., et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308-1323 (2016).
53. Stubbington, M. J., et al. T cell fate and clonality inference from single-cell transcriptomes. Nature Methods 13, 329-332 (2016).
54. Tirosh, I., et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189-196 (2016).
55. Kumar, R. M., et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56-61 (2014).
56. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., Regev, A. Spatial reconstruction of single-cell gene expression data. Nature Biotechnol. 33, 495-502 (2015).
57. Treutlein, B., et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375 (2014).
58. Macosko, E. Z., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015).
59. Klein, A. M., et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015).
60. Hashimshony, T., et al. CEL-Seq2: Sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).
61. Nichterwitz, S., et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nature Commun. 7, 12139 (2016).
62. Thomsen, E. R., et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nature Methods 13, 87-93 (2016).
63. Lake, B. B., et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586-1590 (2016).
64. Milo, R., Jorgensen, P., Moran, U., Weber, G., Springer, M. BioNumbers-the database of key numbers in molecular and cell biology. Nucleic Acids Res. 38, D750-D753 (2010).
65. Han, F., Lillard, S. J. In-situ sampling and separation of RNA from individual mammalian cells. Anal. Chem. 72, 4073-4079 (2000).
66. Wagner, A., Regev, A., Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nature Biotechnol. 34, 1145-1160 (2016).
67. Islam, S., et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nature Methods 11, 163-166 (2014).
68. Heimberg, G., Bhatnagar, R., El-Samad, H., Thomson, M. Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2, 239-250 (2016).
69. Lavin, Y., et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312-1326 (2014).
70. Macaulay, I. C., et al. G&T-seq: Parallel sequencing of single-cell genomes and transcriptomes. Nature Methods 12, 519-522 (2015).
71. Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nature Biotechnol. 33, 285-289 (2015).
72. Angermueller, C., et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nature Methods 13, 229-232 (2016).
73. Frei, A. P., et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nature Methods 13, 269-275 (2016).
74. Darmanis, S., et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380-389 (2016).
75. Albayrak, C., et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914-924 (2016).
76. Genshaft, A. S., et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016).
77. Lu, Y., et al. Combined analysis reveals a core set of cycling genes. Genome Biol. 8, R146 (2007).
78. Bar-Joseph, Z., et al. Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc. Natl Acad. Sci. USA 105, 955-960 (2008).
79. Kafri, R., et al. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature 494, 480-483 (2013).
80. Setty, M., et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature Biotechnol. 34, 637-645 (2016).
81. Moignard, V., et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nature Biotechnol. 33, 269-276 (2015).
82. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F., Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845-848 (2016).
83. Haghverdi, L., Buettner, F., Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989-2998 (2015).
84. Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F., Poidinger, M. Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development. Nature Commun. 7, 11988 (2016).
85. Angerer, P., et al. destiny: Diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241-1243 (2016).
86. Shin, J., et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360-372 (2015).
87. Gut, G., Tadmor, M. D., Pe'er, D., Pelkmans, L., Liberali, P. Trajectories of cell-cycle progression from fixed cell populations. Nature Methods 12, 951-954 (2015).
88. Proserpio, V., et al. Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biol. 17, 103 (2016).
89. Buettner, F., et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnol. 33, 155-160 (2015).
90. Naumova, N., et al. Organization of the mitotic chromosome. Science 342, 948-953 (2013).
91. Koonin, E. V. Horizontal gene transfer: Essentiality and evolvability in prokaryotes, roles in evolutionary transitions. F1000Res. 5, 1805 (2016).
92. Kellogg, R. A., Tay, S. Noise facilitates transcriptional control under dynamic inputs. Cell 160, 381-392 (2015).
93. Afik, S., et al. Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state. Preprint at http://biorxiv.org/content/early/2016/08/31/072744 (2016).
94. McKenna, A., et al. Whole organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
95. Reizel, Y., et al. Cell lineage analysis of the mammalian female germline. PLoS Genet. 8, e1002477 (2012).
96. Shlush, L. I., et al. Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability. Blood 120, 603-612 (2012).
97. Sulston, J. E., Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110-156 (1977).
98. Alizadeh, A. A., et al. Toward understanding and exploiting tumor heterogeneity. Nature Med. 21, 846-853 (2015).
99. Wang, J., et al. Clonal evolution of glioblastoma under therapy. Nature Genet. 48, 768-776 (2016).
100. Tirosh, I., et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309-313 (2016).
101. Li, S., et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nature Med. 22, 792-799 (2016).
102. Durruthy-Durruthy, R., et al. Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution. Cell 157, 964-978 (2014).
103. Achim, K., et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nature Biotechnol. 33, 503-509 (2015).
104. Scialdone, A., et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289-293 (2016).
105. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
106. Lee, J. H., et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360-1363 (2014).
107. Giesen, C., et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods 11, 417-422 (2014).
108. Angelo, M., et al. Multiplexed ion beam imaging of human breast tumors. Nature Med. 20, 436-442 (2014).
109. Ke, R., et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nature Methods 10, 857-860 (2013).
110. Ståhl, P. L., et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82 (2016).
111. Zeisel, A., et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015).
112. Tasic, B., et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nature Neurosci. 19, 335-346 (2016).
113. Stewart-Ornstein, J., Weissman, J. S., El-Samad, H. Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae. Mol. Cell 45, 483-493 (2012).