Microfluidics as a Strategic Player to Decipher Single-Cell Omics?

Trends in Biotechnology

Volumn 35, Issue 8, 2017, Pages 713-727

  Caen, Ouriel ^a   Lu, Heng ^a   Nizard, Philippe ^a   Taly, Valerie ^a  

^a INSERM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; CELL PROLIFERATION; MOLECULAR BIOLOGY; NORMAL DISTRIBUTION; STEM CELLS; THROUGHPUT;

AUTOMATED ANALYSIS; BIOLOGICAL DOMAIN; CELL POPULATION HETEROGENEITY; DROPLET-BASED MICROFLUIDICS; MICROFLUIDIC TECHNOLOGIES; SINGLE CELL STUDIES; SINGLECELL ANALYSIS; STEM CELL RESEARCH;

MICROFLUIDICS;

GENOMIC DNA;

CELL COMPARTMENTALIZATION; CELL HETEROGENEITY; CELL ISOLATION; DNA METHYLATION; EPIGENETICS; FLOW CYTOMETRY; GENE EXPRESSION; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLOMICS; MICROFLUIDICS; NONHUMAN; OMICS; PHARMACOGENOMICS; PHENOTYPIC VARIATION; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; PROTEIN SECRETION; PROTEOMICS; REVIEW; RNA SEQUENCE; SINGLE CELL ANALYSIS; TRANSCRIPTOMICS; WHOLE GENOME SEQUENCING; ANIMAL; DEVICES; LAB ON A CHIP; MICROFLUIDIC ANALYSIS; PROCEDURES;

ANIMALS; HUMANS; LAB-ON-A-CHIP DEVICES; MICROFLUIDIC ANALYTICAL TECHNIQUES;

EID: 85020532249     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2017.05.004     Document Type: Review

References (105)

1. Turner, W., The cell theory, past and present. J. Anat. Physiol., 24, 1890, 253.
2. Haselgrubler, T., et al. High-throughput, multiparameter analysis of single cells. Anal. Bioanal. Chem. 406 (2014), 3279–3296.
3. Goodell, M.A., et al. Somatic stem cell heterogeneity: diversity in the blood, skin and intestinal stem cell compartments. Nat. Rev. Mol. Cell Biol. 16 (2015), 299–309.
4. McGranahan, N., Swanton, C., Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27 (2015), 15–26.
5. Heath, J.R., et al. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug. Discov. 15 (2016), 204–216.
6. Gawad, C., et al. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17 (2016), 175–188.
7. Crosetto, N., et al. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16 (2015), 57–66.
8. Schwartzman, O., Tanay, A., Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16 (2015), 716–726.
9. Elowitz, M.B., et al. Stochastic gene expression in a single cell. Science 297 (2002), 1183–1186.
10. Kim, J.K., et al. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun., 6, 2015, 8687.
11. Shalek, A.K., et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510 (2014), 363–369.
12. Pinard, R., et al. Assessment of whole genome amplification-induced bias through high-throughput, massively parallel whole genome sequencing. BMC Genom., 7, 2006 216–216.
13. Ding, B., et al. Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics 31 (2015), 2225–2227.
14. He, J.L., et al. Digital microfluidics for manipulation and analysis of a single cell. Int. J. Mol. Sci. 16 (2015), 22319–22332.
15. Whitesides, G.M., The origins and the future of microfluidics. Nature 442 (2006), 368–373.
16. Esmaeilsabzali, H., et al. Detection and isolation of circulating tumor cells: principles and methods. Biotechnol. Adv. 31 (2013), 1063–1084.
17. Lindström, S., et al. High-density microwell chip for culture and analysis of stem cells. PLoS One, 4, 2009, e6997.
18. He, J.L., et al. Digital microfluidics for manipulation and analysis of a single cell. Int. J. Mol. Sci. 16 (2015), 22319–22332.
19. Saliba, A.E., et al. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42 (2014), 8845–8860.
20. Lim, J., et al. Parallelized ultra-high throughput microfluidic emulsifier for multiplex kinetic assays. Biomicrofluidics, 9, 2015, 034101.
21. Kim, M., et al. Optofluidic ultrahigh-throughput detection of fluorescent drops. Lab Chip 15 (2015), 1417–1423.
22. Brouzes, E., et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl. Acad. Sci. U. S. A. 106 (2009), 14195–14200.
23. Collins, D.J., et al. The Poisson distribution and beyond: methods for microfluidic droplet production and single cell encapsulation. Lab Chip 15 (2015), 3439–3459.
24. Koster, S., et al. Drop-based microfluidic devices for encapsulation of single cells. Lab Chip 8 (2008), 1110–1115.
25. Shembekar, N., et al. Droplet-based microfluidics in drug discovery, transcriptomics and high-throughput molecular genetics. Lab Chip 16 (2016), 1314–1331.
26. Sciambi, A., Abate, A.R., Accurate microfluidic sorting of droplets at 30 kHz. Lab Chip 15 (2015), 47–51.
27. Sukovich, D.J., et al. Sequence specific sorting of DNA molecules with FACS using 3dPCR. Sci. Rep., 7, 2017, 39385.
28. Fu, Y., et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl. Acad. Sci. U. S. A. 112 (2015), 11923–11928.
29. Hammond, M., et al. Picodroplet partitioned whole genome amplification of low biomass samples preserves genomic diversity for metagenomic analysis. Microbiome, 4, 2016, 52.
30. Sidore, A.M., et al. Enhanced sequencing coverage with digital droplet multiple displacement amplification. Nucleic Acids Res., 44, 2016, e66.
31. Fu, Y., et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl. Acad. Sci. U. S. A. 112 (2015), 11923–11928.
32. Gawad, C., et al. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17 (2016), 175–188.
33. Wang, D., Bodovitz, S., Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol. 28 (2010), 281–290.
34. Gawad, C., et al. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl. Acad. Sci. U. S. A. 111 (2014), 17947–17952.
35. Novak, R., et al. Single-cell multiplex gene detection and sequencing with microfluidically generated agarose emulsions. Angew. Chem. Int. Ed. Engl. 50 (2011), 390–395.
36. Leung, K., et al. A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 7665–7670.
37. Kumaresan, P., et al. High-throughput single copy DNA amplification and cell analysis in engineered nanoliter droplets. Anal. Chem. 80 (2008), 3522–3529.
38. Cheow, L.F., et al. Multiplexed locus-specific analysis of DNA methylation in single cells. Nat. Protoc. 10 (2015), 619–631.
39. Consortium, E.P., An integrated encyclopedia of DNA elements in the human genome. Nature 489 (2012), 57–74.
40. Buenrostro, J.D., et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10 (2013), 1213–1218.
41. Buenrostro, J.D., et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523 (2015), 486–490.
42. Rotem, A., et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33 (2015), 1165–1172.
43. Wu, A.R., et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11 (2014), 41–46.
44. 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 (2014), 1053–1058.
45. Johnson, M.B., et al. Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex. Nat. Neurosci. 18 (2015), 637–646.
46. Treutlein, B., et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509 (2014), 371–375.
47. Kimmerling, R.J., et al. A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun., 7, 2016, 10220.
48. Fan, H.C., et al. Combinatorial labeling of single cells for gene expression cytometry. Science, 347, 2015, 1258367.
49. Gierahn, T.M., et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14 (2017), 395–398.
50. Mary, P., et al. Analysis of gene expression at the single-cell level using microdroplet-based microfluidic technology. Biomicrofluidics, 5, 2011, 24109.
51. Eastburn, D.J., et al. Ultrahigh-throughput mammalian single-cell reverse-transcriptase polymerase chain reaction in microfluidic drops. Anal. Chem. 85 (2013), 8016–8021.
52. Eastburn, D.J., et al. Identification and genetic analysis of cancer cells with PCR-activated cell sorting. Nucleic Acids Res., 42, 2014, e128.
53. Zilionis, R., et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12 (2017), 4–73.
54. Macosko, E.Z., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
55. Klein, A.M., et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161 (2015), 1187–1201.
56. Adamson, B., et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167 (2016), 1867–1882.e21.
57. Zheng, G.X.Y., et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun., 8, 2017, 14049.
58. Dixit, A., et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167 (2016), 1853–1866.e17.
59. Kellogg, R.A., Tay, S., Noise facilitates transcriptional control under dynamic inputs. Cell 160 (2015), 381–392.
60. Kellogg, R.A., et al. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat. Protoc. 9 (2014), 1713–1726.
61. Wang, J., Yang, F., Emerging single-cell technologies for functional proteomics in oncology. Expert Rev. Proteomics 13 (2016), 805–815.
62. Gavasso, S., et al. Single-cell proteomics: potential implications for cancer diagnostics. Expert Rev. Mol. Diagn. 16 (2016), 579–589.
63. Shin, Y.S., et al. Protein signaling networks from single cell fluctuations and information theory profiling. Biophys. J. 100 (2011), 2378–2386.
64. Bendall, S.C., et al. A deep profiler's guide to cytometry. Trends Immunol. 33 (2012), 323–332.
65. O'Donnell, E.A., et al. Multiparameter flow cytometry: advances in high resolution analysis. Immune Network 13 (2013), 43–54.
66. Bendall, S.C., et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332 (2011), 687–696.
67. Furman, D., et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat. Med. 23 (2017), 174–184.
68. Hughes, A.J., et al. Single-cell western blotting. Nat. Methods 11 (2014), 749–755.
69. Kang, C.-C., et al. Single-cell western blotting after whole-cell imaging to assess cancer chemotherapeutic response. Anal. Chem. 86 (2014), 10429–10436.
70. Love, J.C., et al. A microengraving method for rapid selection of single cells producing antigen-specific antibodies. Nat. Biotechnol. 24 (2006), 703–707.
71. + T cells using microengraving. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 3885–3890.
72. Han, Q., et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 1607–1612.
73. Lu, Y., et al. High-throughput secretomic analysis of single cells to assess functional cellular heterogeneity. Anal. Chem. 85 (2013), 2548–2556.
74. Lu, Y., et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc. Natl. Acad. Sci. U. S. A. 112 (2015), E607–E615.
75. Genshaft, A.S., et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol., 17, 2016, 188.
76. Fredriksson, S., et al. Protein detection using proximity-dependent DNA ligation assays. Nat. Biotech. 20 (2002), 473–477.
77. Ma, C., et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17 (2011), 738–743.
78. Shi, Q., et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 419–424.
79. + T cell function. J. Immunol. 193 (2014), 940–949.
80. Chokkalingam, V., et al. Probing cellular heterogeneity in cytokine-secreting immune cells using droplet-based microfluidics. Lab Chip 13 (2013), 4740–4744.
81. Darmanis, S., et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14 (2016), 380–389.
82. Lundberg, M., et al. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res., 39, 2011 e102–e102.
83. Ibáñez, A.J., et al. Mass spectrometry-based metabolomics of single yeast cells. Proc. Natl. Acad. Sci. U. S. A. 110 (2013), 8790–8794.
84. Zenobi, R., Single-cell metabolomics: analytical and biological perspectives. Science, 342, 2013.
85. Acar, M., et al. Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet. 40 (2008), 471–475.
86. Meyer, A., et al. Optimization of a whole-cell biocatalyst by employing genetically encoded product sensors inside nanolitre reactors. Nat. Chem. 7 (2015), 673–678.
87. Ferraro, D., et al. Microfluidic platform combining droplets and magnetic tweezers: application to HER2 expression in cancer diagnosis. Sci. Rep., 6, 2016, 25540.
88. Fornell, A., et al. Controlled lateral positioning of microparticles inside droplets using acoustophoresis. Anal. Chem. 87 (2015), 10521–10526.
89. Gruner, P., et al. Stabilisers for water-in-fluorinated-oil dispersions: key properties for microfluidic applications. Curr. Opin. Colloid Interface Sci. 20 (2015), 183–191.
90. Churski, K., et al. Rapid screening of antibiotic toxicity in an automated microdroplet system. Lab Chip 12 (2012), 1629–1637.
91. Eduati, F., et al. Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidics. bioRxiv, 2016, 093906.
92. Klein, A.M., et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161 (2015), 1187–1201.
93. Shapiro, E., et al. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14 (2013), 618–630.
94. Unger, M.A., et al. Monolithic microfabricated valves and pumps by multilayer soft lithography. Science 288 (2000), 113–116.
95. Araci, I.E., Brisk, P., Recent developments in microfluidic large scale integration. Curr. Opin. Biotechnol. 25 (2014), 60–68.
96. Thorsen, T., et al. Microfluidic large-scale integration. Science 298 (2002), 580–584.
97. Di Carlo, D., et al. Single-cell enzyme concentrations, kinetics, and inhibition analysis using high-density hydrodynamic cell isolation arrays. Anal. Chem. 78 (2006), 4925–4930.
98. Joensson, H.N., Andersson Svahn, H., Droplet microfluidics – a tool for single-cell analysis. Angew. Chem. Int. Ed. Engl. 51 (2012), 12176–12192.
99. Gruner, P., et al. Stabilisers for water-in-fluorinated-oil dispersions: key properties for microfluidic applications. Curr. Opin. Colloid Interface Sci. 20 (2015), 183–191.
100. Clausell-Tormos, J., et al. Droplet-based microfluidic platforms for the encapsulation and screening of mammalian cells and multicellular organisms. Chem. Biol. 15 (2008), 427–437.
101. Wang, B.L., et al. Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption. Nat. Biotechnol. 32 (2014), 473–478.
102. Lu, H., et al. High throughput single cell counting in droplet-based microfluidics. Sci. Rep., 7, 2017, 1366.
103. Taly, V., et al. Detecting biomarkers with microdroplet technology. Trends Mol. Med. 18 (2012), 405–416.
104. Brouzes, E., et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl. Acad. Sci. U. S. A. 106 (2009), 14195–14200.
105. Fan, H.C., et al. Combinatorial labeling of single cells for gene expression cytometry. Science, 347, 2015 1258367.