Review Article | Published:

Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices

Nature Reviews Genetics volume 18, pages 345361 (2017) | Download Citation

This article has been updated

Abstract

Recent advances in cellular profiling have demonstrated substantial heterogeneity in the behaviour of cells once deemed 'identical', challenging fundamental notions of cell 'type' and 'state'. Not surprisingly, these findings have elicited substantial interest in deeply characterizing the diversity, interrelationships and plasticity among cellular phenotypes. To explore these questions, experimental platforms are needed that can extensively and controllably profile many individual cells. Here, microfluidic structures — whether valve-, droplet- or nanowell-based — have an important role because they can facilitate easy capture and processing of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency. In this article, we review the current state-of-the-art methodologies with respect to microfluidics for mammalian single-cell 'omics' and discuss challenges and future opportunities.

Key points

  • There has been a renaissance in single-cell biology, facilitated in part by the rise of microfluidic devices that can facilitate easy capture, processing and profiling of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency.

  • The three most common classes of microfluidic device are defined by their fundamental elements: valves, droplets or nanowells. Valve-based microfluidic devices afford control but have limited scale; droplet-based devices have high throughput but limited control; and nanowell-based methods have intermediate scale and control, but greater simplicity in operation. These factors influence the costs and benefits of porting any existing assay to each microfluidic device.

  • Each of these three classes has been used to profile several cellular 'omics' — including the genome, epigenome, transcriptome and proteome — achieving different levels of throughput and efficiency, while leaving opportunities for future development.

  • Emerging efforts are beginning to focus on measuring multiple cellular properties at once, such as the transcriptome and the proteome or the transcriptome and the epigenome, to obtain a more comprehensive picture of cellular phenotype and its drivers.

  • Such comprehensive profiling is especially important when studying single cells owing to technical and biological noise sources, which limit the utility of any given measurement from any given cell.

  • Sequencing is increasingly becoming the de facto method for profiling information from single cells given its bandwidth relative to the information content of a single cell and the growing ease of mapping information in a nucleic acid readout. Yet, given fixed sequencing bandwidth and the often limited utility of any one measurement, it is important to carefully consider how to most judiciously allocate reads over cells and their variables.

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Change history

  • 19 April 2017

    In the acknowledgements section of this article, the number of an NIH grant awarded to A.K.S. was corrected from U24AI11862 to U24AI118672.

References

  1. 1.

    et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  2. 2.

    et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

  3. 3.

    et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

  4. 4.

    et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013). A demonstration of how covariation in gene expression in scRNA-Seq data can be used to identify cellular circuits and molecular drivers of behaviour in seemingly identical immune cells. It also nicely demonstrates the use of microfluidic devices for targeted single-cell mRNA measurements (STA).

  5. 5.

    et al. Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Nat. Biotechnol. 32, 479–484 (2014).

  6. 6.

    et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

  7. 7.

    et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008).

  8. 8.

    et al. Single-cell quantification of IL-2 response by effector and regulatory T cells reveals critical plasticity in immune response. Mol. Syst. Biol. 6, 437 (2010).

  9. 9.

    et al. A generic and cell-type-specific wound response precedes regeneration in planarians. Dev. Cell 35, 632–645 (2015).

  10. 10.

    et al. The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing. Nat. Immunol. 17, 451–460 (2016).

  11. 11.

    , , , & Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

  12. 12.

    , , & Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002). A fundamental work that describes how to think about the two non-technical sources (intrinsic and extrinsic) of noise that influence single-cell measurements and their interpretation.

  13. 13.

    , , , & Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

  14. 14.

    , & Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

  15. 15.

    et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016). These authors used commercial valve-based microfluidics to perform multiplexed single-cell transcriptome and protein profiling from the same cell, an important demonstration of simultaneous single-cell multi-omic profiling.

  16. 16.

    et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).

  17. 17.

    , & Stand-sit microchip for high-throughput, multiplexed analysis of single cancer cells. Sci. Rep. 6, 32505 (2016).

  18. 18.

    et al. Rapid, efficient functional characterization and recovery of HIV-specific human CD8+ T cells using microengraving. Proc. Natl Acad. Sci. USA 109, 3885–3890 (2012).

  19. 19.

    The origins and the future of microfluidics. Nature 442, 368–373 (2006).

  20. 20.

    , , & Whole-genome molecular haplotyping of single cells. Nat. Biotechnol. 29, 51–57 (2011).

  21. 21.

    & Integrated nanoliter systems. Nat. Biotechnol. 21, 1179–1183 (2003). A review on the mechanics, operation and utility of valve-based microfluidic platforms, with a detailed discussion of applications in many fields, including biology.

  22. 22.

    , & Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl Acad. Sci. USA 111, 17947–17952 (2014).

  23. 23.

    , , & Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402–412 (2012). An early demonstration of single-cell genome sequencing applied to microfluidic devices, in which de novo mutations in human sperm were identified and quantified with a custom valve-based device.

  24. 24.

    et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

  25. 25.

    et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).

  26. 26.

    , & Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016). A review of the current state of the art in single-cell genomics, including key insights into the limitations of handling genetic material from a single cell and important work in the field.

  27. 27.

    , , & Single-cell technologies for monitoring immune systems. Nat. Immunol. 15, 128–135 (2014).

  28. 28.

    , , , & High-performance single cell genetic analysis using microfluidic emulsion generator arrays. Anal. Chem. 82, 3183–3190 (2010).

  29. 29.

    , , & Droplet microfluidics for high-throughput biological assays. Lab Chip 12, 2146–2155 (2012).

  30. 30.

    , , , & Massively parallel single-molecule and single-cell emulsion reverse transcription polymerase chain reaction using agarose droplet microfluidics. Anal. Chem. 84, 3599–3606 (2012).

  31. 31.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). A high-throughput scRNA-Seq platform that uses droplet-based capture and early bead-based barcoding to profile thousands of single cells in a cost-efficient manner.

  32. 32.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). A high-throughput scRNA-Seq platform, distinguished from the platform in reference 31 through its use of hydrogel beads that enable more efficient bead loading and linear amplification.

  33. 33.

    , , , & A microengraving method for rapid selection of single cells producing antigen-specific antibodies. Nat. Biotechnol. 24, 703–707 (2006). Pioneering work that applies nanowell arrays to enable high-throughput screening of hybridomas for specific antibodies.

  34. 34.

    et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

  35. 35.

    et al. Functional analysis of single cells identifies a rare subset of circulating tumor cells with malignant traits. Integr. Biol. (Camb.) 6, 388–398 (2014).

  36. 36.

    , , , & Comprehensive qPCR profiling of gene expression in single neuronal cells. Nat. Protoc. 7, 118–127 (2011).

  37. 37.

    et al. Association of reactive oxygen species levels and radioresistance in cancer stem cells. Nature 458, 780–783 (2009).

  38. 38.

    et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419–424 (2012).

  39. 39.

    et al. Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing. Nat. Commun. 6, 5940 (2015).

  40. 40.

    , & Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358–362 (2006). An early microfluidic assay that confined secreted cellular products to effectively probe the abundance of β-galactosidase in E. coli with single-molecule resolution and sensitivity.

  41. 41.

    et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods 10, 1003–1005 (2013).

  42. 42.

    et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610 (2011).

  43. 43.

    et al. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl Acad. Sci. USA 108, 13999–14004 (2011).

  44. 44.

    , , & Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc. Natl Acad. Sci. USA 103, 17807–17812 (2006). An early demonstration of single-cell transcriptomics performed in microfluidic devices, with RT–qPCR used to perform in-device quantification of transcripts.

  45. 45.

    et al. Multicolor combinatorial probe coding for real-time PCR. PLoS ONE 6, e16033 (2011).

  46. 46.

    , & TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

  47. 47.

    et al. Site-specific DNA-antibody conjugates for specific and sensitive immuno-PCR. Proc. Natl Acad. Sci. USA 109, 3731–3736 (2012).

  48. 48.

    , , , & Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes. Mol. Metab. 5, 233–244 (2016).

  49. 49.

    et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

  50. 50.

    , , , & Photocleavable DNA barcode-antibody conjugates allow sensitive and multiplexed protein analysis in single cells. J. Am. Chem. Soc. 134, 18499–18502 (2012).

  51. 51.

    et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014).

  52. 52.

    , & Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq. Genome Med. 8, 80 (2016).

  53. 53.

    , & Microfluidic whole genome amplification device for single cell sequencing. Anal. Chem. 86, 9386–9390 (2014).

  54. 54.

    et al. Single-cell whole genome sequencing reveals no evidence for common aneuploidy in normal and Alzheimer's disease neurons. Genome Biol. 17, 116 (2016).

  55. 55.

    , , , & Parallel single cancer cell whole genome amplification using button-valve assisted mixing in nanoliter chambers. PLoS ONE 9, e107958 (2014).

  56. 56.

    et al. Single-cell genetic analysis using automated microfluidics to resolve somatic mosaicism. PLoS ONE 10, e0135007 (2015).

  57. 57.

    et al. Quantitative assessment of single-cell whole genome amplification methods for detecting copy number variation using hippocampal neurons. Sci. Rep. 5, 11415 (2015).

  58. 58.

    et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc. Natl Acad. Sci. USA 110, 21083–21088 (2013).

  59. 59.

    , , , & High-throughput single copy DNA amplification and cell analysis in engineered nanoliter droplets. Anal. Chem. 80, 3522–3529 (2008).

  60. 60.

    et al. Highly sensitive and quantitative detection of rare pathogens through agarose droplet microfluidic emulsion PCR at the single-cell level. Lab Chip 12, 3907–3913 (2012).

  61. 61.

    et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl Acad. Sci. USA 112, 11923–11928 (2015). Single-cell genome profiling with droplet-based capture that demonstrates the utility of isolating components from individual cells to perform amplification in droplet, improving coverage relative to amplification en masse.

  62. 62.

    et al. Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates. Proc. Natl Acad. Sci. USA 113, 8484–8489 (2016). High-throughput single-cell MDA performed in nanolitre volumes with commercial liquid dispensers, highlighting robust coverage of single-cell genomes in ovarian cancer cell lines.

  63. 63.

    et al. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013).

  64. 64.

    et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).

  65. 65.

    , , , & Lineage-specific gene expansions in bacterial and archaeal genomes. Genome Res. 11, 555–565 (2001).

  66. 66.

    et al. Diversity and clonal selection in the human T-cell repertoire. Proc. Natl Acad. Sci. USA 111, 13139–13144 (2014).

  67. 67.

    et al. Single-cell genotyping demonstrates complex clonal diversity in acute myeloid leukemia. Sci. Transl Med. 7, 281re2 (2015).

  68. 68.

    et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).

  69. 69.

    et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

  70. 70.

    et al. Antiviral CD8+ T cells restricted by human leukocyte antigen class II exist during natural HIV infection and exhibit clonal expansion. Immunity 45, 917–930 (2016).

  71. 71.

    et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

  72. 72.

    , & Epigenetic reprogramming in cancer. Science 339, 1567–1570 (2013).

  73. 73.

    et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

  74. 74.

    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).

  75. 75.

    et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science 341, 1110–1112 (2013).

  76. 76.

    et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).

  77. 77.

    et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

  78. 78.

    et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat. Methods 13, 833–836 (2016).

  79. 79.

    et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). These authors used a commercial valve-based microfluidic platform to perform single-cell ATAC-seq, a transposase-based assay that generates NGS libraries of open chromatin regions with single-base-pair resolution.

  80. 80.

    et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

  81. 81.

    & Micro- and nanoscale devices for the investigation of epigenetics and chromatin dynamics. Nat. Nanotechnol. 8, 709–718 (2013).

  82. 82.

    et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

  83. 83.

    et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  84. 84.

    et al. RNA sequencing of pancreatic circulating tumour cells implicates WNT signalling in metastasis. Nature 487, 510–513 (2012).

  85. 85.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  86. 86.

    et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

  87. 87.

    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).

  88. 88.

    , , & Low Dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2, 239–250 (2016).

  89. 89.

    & Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 17, 63 (2016).

  90. 90.

    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).

  91. 91.

    et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

  92. 92.

    et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

  93. 93.

    et al. Comparative analysis of single-cell RNA-sequencing methods. Mol. Cell 65, 631–643 (2017).

  94. 94.

    et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

  95. 95.

    et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  96. 96.

    et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods (2017).

  97. 97.

    et al. High-throughput sequencing of the paired human immunoglobulin heavy and light chain repertoire. Nat. Biotechnol. 31, 166–169 (2013).

  98. 98.

    et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 16, 120 (2015).

  99. 99.

    & An automated microwell platform for large-scale single cell RNA-Seq. Sci. Rep. 6, 33883 (2016).

  100. 100.

    , , & Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003).

  101. 101.

    et al. Integrated barcode chips for rapid, multiplexed analysis of proteins in microliter quantities of blood. Nat. Biotechnol. 26, 1373–1378 (2008). A single-cell barcoded chip that used antibodies labelled with nucleotides to enable detection of a panel of secreted proteins from individual cells isolated from whole blood.

  102. 102.

    et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17, 738–743 (2011).

  103. 103.

    et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc. Natl Acad. Sci. USA 112, E607–E615 (2015).

  104. 104.

    et al. Quantitating cell–cell interaction functions with applications to glioblastoma multiforme cancer cells. Nano Lett. 12, 6101–6106 (2012).

  105. 105.

    et al. Counting low-copy number proteins in a single cell. Science 315, 81–84 (2007).

  106. 106.

    et al. Proximity ligation assay for high-content profiling of cell signaling pathways on a microfluidic chip. Mol. Cell. Proteomics 12, 3898–3907 (2013).

  107. 107.

    et al. Protein detection using proximity-dependent DNA ligation assays. Nat. Biotechnol. 20, 473–477 (2002).

  108. 108.

    et al. Opportunities for sensitive plasma proteome analysis. Anal. Chem. 84, 1824–1830 (2012).

  109. 109.

    et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9, e95192 (2014).

  110. 110.

    et al. Cancer cell profiling by barcoding allows multiplexed protein analysis in fine-needle aspirates. Sci. Transl Med. 6, 219ra9 (2014).

  111. 111.

    et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914–924 (2016).

  112. 112.

    et al. Single-cell western blotting. Nat. Methods 11, 749–755 (2014).

  113. 113.

    , & Development of a high-throughput functional screen using nanowell-assisted cell patterning. Small 11, 4643–4650 (2015).

  114. 114.

    et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl Acad. Sci. USA 109, 1607–1612 (2012).

  115. 115.

    et al. Single-cell detection of secreted Aβ and sAPPα from human IPSC-derived neurons and astrocytes. J. Neurosci. 36, 1730–1746 (2016).

  116. 116.

    et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 10, 427–436 (2016).

  117. 117.

    et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016).

  118. 118.

    Emerging features of mRNA decay in bacteria. RNA 6, 1079–1090 (2000).

  119. 119.

    , , , & Reconstructing each cell's genome within complex microbial communities — dream or reality? Front. Microbiol. 5, 771 (2015).

  120. 120.

    The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol. Rev. 37, 407–427 (2013).

  121. 121.

    & Single cell genomics of bacterial pathogens: outlook for infectious disease research. Genome Med. 6, 108 (2014).

  122. 122.

    et al. Mechanistic evaluation of the pros and cons of digital RT-LAMP for HIV-1 viral load quantification on a microfluidic device and improved efficiency via a two-step digital protocol. Anal. Chem. 85, 1540–1546 (2013).

  123. 123.

    et al. Single-cell analysis of the dynamics and functional outcomes of interactions between human natural killer cells and target cells. Integr. Biol. (Camb.) 4, 1175–1184 (2012).

  124. 124.

    et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

  125. 125.

    & Heterogeneity in immune responses: from populations to single cells. Trends Immunol. 35, 219–229 (2014).

  126. 126.

    et al. Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens. Science 347, 1259038 (2015).

  127. 127.

    , , , & A silicon-glass microwell platform for high-resolution imaging and high-content screening with single cell resolution. Biomed. Microdevices 13, 683–693 (2011).

  128. 128.

    et al. Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays. Lab Chip 12, 5007–5015 (2012).

  129. 129.

    et al. Analysis of transient migration behavior of natural killer cells imaged in situ and in vitro. Integr. Biol. (Camb.) 3, 770–778 (2011).

  130. 130.

    et al. Microfluidic device for single-cell analysis. Anal. Chem. 75, 3581–3586 (2003).

  131. 131.

    et al. A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun. 7, 10220 (2016).

  132. 132.

    et al. High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat. Biotechnol. 34, 1052–1059 (2016).

  133. 133.

    et al. Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate. Nat. Biotechnol. 34, 1161–1167 (2016).

  134. 134.

    et al. Using buoyant mass to measure the growth of single cells. Nat. Methods 7, 387–390 (2010).

  135. 135.

    et al. Weighing of biomolecules, single cells and single nanoparticles in fluid. Nature 446, 1066–1069 (2007).

  136. 136.

    et al. Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proc. Natl Acad. Sci. USA 107, 4004–4009 (2010). Droplet-based screening of single cells undergoing directed evolution, with most active cells selected based on their fluorescence on exposure to substrates that initiate turnover of proteins on their surface.

  137. 137.

    et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

  138. 138.

    et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

  139. 139.

    et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167, 1883–1896.e15 (2016).

  140. 140.

    et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

  141. 141.

    , & Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016). This review highlights computational frameworks for inferring cellular states from single-cell genomic profiling data, with a detailed survey of current methods and the computational challenges that accompany improved experimental throughput.

  142. 142.

    et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360.e4 (2016).

  143. 143.

    , , , & Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015).

  144. 144.

    et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10, 1986–2003 (2015).

  145. 145.

    & Suspended microchannel resonators for biomolecular detection. Appl. Phys. Lett. 83, 2698–2700 (2003).

  146. 146.

    et al. Cell-surface sensors for real-time probing of cellular environments. Nat. Nanotechnol. 6, 524–531 (2011).

  147. 147.

    et al. Programmed synthesis of three-dimensional tissues. Nat. Methods 12, 975–981 (2015).

  148. 148.

    Initial impact of the sequencing of the human genome. Nature 470, 187–197 (2011).

  149. 149.

    et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  150. 150.

    et al. A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities. Proc. Natl Acad. Sci. USA 109, 7665–7670 (2012).

  151. 151.

    , , & DNA methylation in human epigenomes depends on local topology of CpG sites. Nucleic Acids Res. 44, 5123–5132 (2016).

  152. 152.

    et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

  153. 153.

    , , & Open chromatin reveals the functional maize genome. Proc. Natl Acad. Sci. USA 113, E3177–E3184 (2016).

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Acknowledgements

The authors thank J. C. Love, A. S Genshaft, K. E. Kolb, C. G. K. Ziegler and S. L. Carroll for helpful comments and suggestions. This work was supported by the Searle Scholars Program (A.K.S.), the Beckman Young Investigator Program (A.K.S.), a US National Institutes of Health (NIH) New Innovator Award DP2OD020839 (A.K.S.), NIH grants U24AI118672 (A.K.S.), P50HG006193 (A.K.S.), P01GM096971 (D.A.W.), P01HL120839 (D.A.W.) and R01EB014703 (D.A.W.), US National Science Foundation (NSF) Materials Research Science and Engineering Center grant DMR-1420570 (D.A.W.) and NSF grant DMR-1310266 (D.A.W.).

Author information

Affiliations

  1. Institute for Medical Engineering & Science (IMES) and Department of Chemistry, MIT, Cambridge, Massachusetts 02139, USA.

    • Sanjay M. Prakadan
    •  & Alex K. Shalek
  2. Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139, USA.

    • Sanjay M. Prakadan
    •  & Alex K. Shalek
  3. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

    • Sanjay M. Prakadan
    •  & Alex K. Shalek
  4. School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.

    • David A. Weitz
  5. Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA.

    • David A. Weitz

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  2. Search for Alex K. Shalek in:

  3. Search for David A. Weitz in:

Competing interests

D.A.W. is a founder of 1-CellBio, a company that provides equipment for preparing samples for single-cell sequencing, as well as Hunter BioDiscovery and SphereBio, companies that employ single-cell analysis for diagnostic and therapeutic applications. D.A.W. and A.K.S. have both filed patents on work that can be used to prepare samples for single-cell sequencing. S.M.P. declares no competing interests.

Corresponding authors

Correspondence to Alex K. Shalek or David A. Weitz.

Glossary

Spatial encoding

Stratifying analytes (cells or multiple cellular products) by physical confinement on a microfluidic chip.

Spectral encoding

Stratifying analytes (multiple cellular products) using different colours of fluorescence.

Temporal encoding

Stratifying analytes (cells or multiple cellular products) by measuring them sequentially.

Exome sequencing

Selective amplification and sequencing of the protein- coding regions of the genome using exon-specific priming.

Direct deterministic phasing

(DDP). The chromosomes of individual cells are partitioned, isolated and amplified using multiple displacement amplification. The products are then flushed and analysed by molecular haplotyping.

Transposase

An enzyme that catalyses the movement of a transposable DNA element into another DNA sequence (for example, a genome) by a cut and paste mechanism. A hyperactive variant of the Tn5 transposase is now commonly used to insert adaptor sequences for next-generation sequencing library preparation. If performed on native chromosomes, the transposase can only bind to exposed DNA, revealing accessible DNA regions.

Zero inflation

Owing to inefficiencies in detection, the distribution of counts for several detected genes can be artificially inflated by the abundance of 'zeros' (detection failures) during normalization.

Micrococcal nuclease

(MNase). An endo-exo nuclease derived from Staphylococcus aureus. When applied to single-stranded and double-stranded DNA, MNase will digest all accessible DNA, enabling the study of which DNA regions are occluded with chromatin.

Mass cytometry

Cells are tagged with antibodies that have been labelled with rare earth metal isotopes, nebulized and passed through a quadrapole mass spectrometer for detection. The abundance of a rare earth metal can thus be used as a proxy for the level of the protein in the single cell. Importantly, whereas the number of proteins that can be detected at once with fluorescent labels is limited owing to overlap between emission spectra, mass cytometry can detect tens of proteins in parallel because individual isotopes of rare earth metals have well-defined, non-overlapping masses.

Directed evolution

A technique in which a series of genetic variants is introduced into a cell population, which are then screened with a fitness assay to isolate a desired phenotype. The genetic variants responsible for the greatest fitness are identified by sequencing the surviving cell population for the target gene.

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nrg.2017.15

Further reading