Scaling single-cell genomics from phenomenology to mechanism (original) (raw)

• Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nature Immunol. 17, 9–17 (2016).
  CAS Google Scholar
• Mayr, E. The Growth of Biological Thought: Diversity, Evolution, and Inheritance (Belknap, 1982).
  Google Scholar
• Szathmary, E. & Maynard-Smith, J. The Major Transitions in Evolution (Oxford University Press, 1995).
  Google Scholar
• Gould, S. J. Ontogeny and Phylogeny (Belknap, 1977).
  Google Scholar
• Richardson, L. et al. EMAGE mouse embryo spatial gene expression database: 2014 update. Nucleic Acids Res. 42, D835–D844 (2014).
  CAS PubMed Google Scholar
• Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
  ADS CAS PubMed PubMed Central Google Scholar
• Bakken, T. E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).
  ADS CAS PubMed PubMed Central Google Scholar
• Jojic, V. et al. Identification of transcriptional regulators in the mouse immune system. Nature Immunol. 14, 633–643 (2013).
  CAS Google Scholar
• 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).
  CAS PubMed Google Scholar
• 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).
  CAS PubMed Google Scholar
• 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). Refs 13–17 are initial reports on the development and scaling of single-cell RNA-seq.
  Google Scholar
• Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods 10, 1096–1098 (2013).
  CAS PubMed Google Scholar
• Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
  CAS PubMed Google Scholar
• 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).
  ADS CAS PubMed PubMed Central Google Scholar
• Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
  ADS CAS PubMed PubMed Central Google Scholar
• 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). Refs 18–23 introduce and develop single-cell genome sequencing, which has applications in the inference of cancer evolution.
  ADS CAS PubMed PubMed Central Google Scholar
• Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).
  CAS PubMed PubMed Central Google Scholar
• Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
  ADS CAS PubMed PubMed Central Google Scholar
• Leung, M. L., Wang, Y., Waters, J. & Navin, N. E. SNES: single nucleus exome sequencing. Genome Biol. 16, 55 (2015).
  PubMed PubMed Central Google Scholar
• Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).
  CAS PubMed Google Scholar
• Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nature Genet. 48, 1119–1130 (2016).
  CAS PubMed Google Scholar
• Rotem, A. et al. High-throughput single-cell labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics. PLoS ONE 10, e0116328 (2015).
  PubMed PubMed Central Google Scholar
• Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnol. 33, 1165–1172 (2015). This paper develops the technique single-cell chromatin immunoprecipitation followed by sequencing (ChIP-seq) as well as the concept of single-cell pooling for the analysis of sparse epigenomic data.
  CAS Google Scholar
• Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). Refs 26 and 27 develop two methods of single-cell assay for transposase-accessible chromatin using sequencing (ATAC-seq) and discuss their application to the identification of cell subtypes and to pooling information across loci.
  ADS CAS PubMed PubMed Central Google Scholar
• Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
  ADS CAS PubMed PubMed Central Google Scholar
• Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nature Methods 11, 817–820 (2014). Refs 28, 30 and 31 develop variants of single-cell DNA methylation profiling, which are applied to sparse single-cell epigenomic analysis.
  CAS PubMed PubMed Central Google Scholar
• 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). This paper uses restriction enzymes to profile hydroxymethylation and perform very short-term lineage reconstruction.
  CAS Google Scholar
• 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).
  CAS PubMed PubMed Central Google Scholar
• Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).
  CAS PubMed PubMed Central Google Scholar
• Kind, J. et al. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163, 134–147 (2015).
  CAS PubMed PubMed Central Google Scholar
• Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013). A proof-of-concept report on single-cell Hi-C (a high-throughput derivative of chromosome conformation capture), which demonstrates the variability of T-cell chromosomal architectures.
  ADS CAS PubMed Google Scholar
• 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).
  ADS CAS PubMed PubMed Central Google Scholar
• Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).
  CAS PubMed PubMed Central Google Scholar
• Krishnaswamy, S. et al. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).
  PubMed PubMed Central Google Scholar
• Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. USA 111, E5643–E5650 (2014).
  CAS PubMed PubMed Central Google Scholar
• Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• 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).
  CAS Google Scholar
• Arvey, A. et al. Genetic and epigenetic variation in the lineage specification of regulatory T cells. eLife 4, e07571 (2015).
  PubMed PubMed Central Google Scholar
• Avraham, R. et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309–1321 (2015).
  CAS PubMed PubMed Central Google Scholar
• Gaublomme, J. T. et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).
  CAS PubMed PubMed Central Google Scholar
• Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).
  ADS PubMed Google Scholar
• 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).
  CAS PubMed PubMed Central Google Scholar
• Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
  CAS PubMed Google Scholar
• Corces, M. R. et al. Lineage-specific and single cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genet. 48, 1193–1203 (2016).
  CAS PubMed Google Scholar
• Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016). This paper adapts RNA-seq to the nuclei of single cells, with applications in rare cells and other challenging samples.
  ADS CAS PubMed PubMed Central Google Scholar
• Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).
  PubMed Google Scholar
• Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016).
  ADS CAS PubMed PubMed Central Google Scholar
• 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).
  PubMed PubMed Central Google Scholar
• Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 (2016).
  CAS PubMed PubMed Central Google Scholar
• Stubbington, M. J. et al. T cell fate and clonality inference from single-cell transcriptomes. Nature Methods 13, 329–332 (2016).
  PubMed PubMed Central Google Scholar
• Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
  ADS CAS PubMed PubMed Central Google Scholar
• Kumar, R. M. et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• 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).
  CAS Google Scholar
• Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). Refs 58 and 59 enhance throughputs in single-cell RNA-seq using droplet technology.
  CAS PubMed PubMed Central Google Scholar
• Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
  CAS PubMed PubMed Central Google Scholar
• Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).
  PubMed PubMed Central Google Scholar
• Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nature Commun. 7, 12139 (2016).
  ADS CAS Google Scholar
• Thomsen, E. R. et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nature Methods 13, 87–93 (2016).
  CAS PubMed Google Scholar
• Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).
  ADS CAS PubMed PubMed Central Google Scholar
• 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).
  CAS PubMed Google Scholar
• Han, F. & Lillard, S. J. In-situ sampling and separation of RNA from individual mammalian cells. Anal. Chem. 72, 4073–4079 (2000).
  CAS PubMed Google Scholar
• Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nature Biotechnol. 34, 1145–1160 (2016).
  CAS Google Scholar
• Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nature Methods 11, 163–166 (2014).
  CAS PubMed Google Scholar
• 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).
  CAS PubMed PubMed Central Google Scholar
• Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).
  CAS PubMed PubMed Central Google Scholar
• Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nature Methods 12, 519–522 (2015). Refs 70–76 introduce techniques for the simultaneous profiling of RNA and DNA, RNA and DNA methylation, or RNA and proteins.
  CAS PubMed Google Scholar
• 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).
  CAS Google Scholar
• Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nature Methods 13, 229–232 (2016).
  CAS PubMed PubMed Central Google Scholar
• Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nature Methods 13, 269–275 (2016).
  CAS PubMed PubMed Central Google Scholar
• Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).
  CAS PubMed Google Scholar
• Albayrak, C. et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914–924 (2016).
  CAS PubMed Google Scholar
• Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016).
  PubMed PubMed Central Google Scholar
• Lu, Y. et al. Combined analysis reveals a core set of cycling genes. Genome Biol. 8, R146 (2007).
  PubMed PubMed Central Google Scholar
• 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).
  ADS CAS PubMed PubMed Central Google Scholar
• Kafri, R. et al. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature 494, 480–483 (2013).
  ADS CAS PubMed PubMed Central Google Scholar
• Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature Biotechnol. 34, 637–645 (2016). Refs 80–87 introduce techniques for inferring dynamics from the steady state sampling of single-cell profiles.
  CAS Google Scholar
• Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nature Biotechnol. 33, 269–276 (2015).
  CAS Google Scholar
• Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848 (2016).
  CAS PubMed Google Scholar
• Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).
  CAS PubMed Google Scholar
• 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).
  ADS CAS Google Scholar
• Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).
  CAS PubMed Google Scholar
• Shin, J. et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015).
  CAS PubMed PubMed Central Google Scholar
• 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).
  CAS PubMed PubMed Central Google Scholar
• 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).
  PubMed PubMed Central Google Scholar
• 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).
  CAS Google Scholar
• Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).
  ADS CAS PubMed PubMed Central Google Scholar
• Koonin, E. V. Horizontal gene transfer: essentiality and evolvability in prokaryotes, and roles in evolutionary transitions. F1000Res. 5, 1805 (2016).
  Google Scholar
• Kellogg, R. A. & Tay, S. Noise facilitates transcriptional control under dynamic inputs. Cell 160, 381–392 (2015).
  CAS PubMed Google Scholar
• 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).
• McKenna, A. et al. Whole organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
  PubMed PubMed Central Google Scholar
• Reizel, Y. et al. Cell lineage analysis of the mammalian female germline. PLoS Genet. 8, e1002477 (2012).
  CAS PubMed PubMed Central Google Scholar
• 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).
  CAS PubMed Google Scholar
• Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977).
  CAS PubMed Google Scholar
• Alizadeh, A. A. et al. Toward understanding and exploiting tumor heterogeneity. Nature Med. 21, 846–853 (2015).
  CAS PubMed Google Scholar
• Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nature Genet. 48, 768–776 (2016).
  CAS PubMed Google Scholar
• Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
  ADS PubMed PubMed Central Google Scholar
• Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nature Med. 22, 792–799 (2016).
  CAS PubMed Google Scholar
• Durruthy-Durruthy, R. et al. Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution. Cell 157, 964–978 (2014).
  CAS PubMed PubMed Central Google Scholar
• Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nature Biotechnol. 33, 503–509 (2015).
  CAS Google Scholar
• Scialdone, A. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016).
  ADS CAS PubMed PubMed Central Google Scholar
• 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). Refs 105 and 106 develop multiplexed RNA-FISH (ref. 105) or in situ sequencing (ref. 106) to enable the spatial mapping of a large number of different transcripts at single-cell resolution.
  PubMed PubMed Central Google Scholar
• Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
  ADS CAS PubMed PubMed Central Google Scholar
• Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods 11, 417–422 (2014).
  ADS CAS PubMed Google Scholar
• Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nature Med. 20, 436–442 (2014).
  CAS PubMed Google Scholar
• Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nature Methods 10, 857–860 (2013).
  CAS PubMed Google Scholar
• Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
  ADS PubMed Google Scholar
• Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
  ADS CAS PubMed Google Scholar
• Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nature Neurosci. 19, 335–346 (2016).
  CAS PubMed Google Scholar
• Stewart-Ornstein, J., Weissman, J. S. & El-Samad, H. Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae. Mol. Cell 45, 483–493 (2012).
  CAS PubMed PubMed Central Google Scholar