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Annual Review of Chemical and Biomolecular Engineering

Volume 5, 2014

A New Toolbox for Assessing Single Cells

Abstract

Unprecedented access to the biology of single cells is now feasible, enabled by recent technological advancements that allow us to manipulate and measure sparse samples and achieve a new level of resolution in space and time. This review focuses on advances in tools to study single cells for specific areas of biology. We examine both mature and nascent techniques to study single cells at the genomics, transcriptomics, and proteomics level. In addition, we provide an overview of tools that are well suited for following biological responses to defined perturbations with single-cell resolution. Techniques to analyze and manipulate single cells through soluble and chemical ligands, the microenvironment, and cell-cell interactions are provided. For each of these topics, we highlight the biological motivation, applications, methods, recent advances, and opportunities for improvement. The toolbox presented in this review can function as a starting point for the design of single-cell experiments.

Keyword(s): cell-cell interaction, genomics, microenvironment, proteomics, single-cell analysis, soluble factors, transcriptomics
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2014-06-07
2024-05-23
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Literature Cited

  1. Wang D, Bodovitz S. 1.  2010. Single cell analysis: the new frontier in “omics.”. Trends Biotechnol. 28:6281–90 [Google Scholar]
  2. Navin N, Hicks J. 2.  2011. Future medical applications of single-cell sequencing in cancer. Genome Med. 3:51–12 [Google Scholar]
  3. Love KR, Bagh S, Choi J, Love JC. 3.  2013. Microtools for single-cell analysis in biopharmaceutical development and manufacturing. Trends Biotechnol. 31:280–86 [Google Scholar]
  4. Bird A. 4.  2007. Perceptions of epigenetics. Nature 447:7143396–98 [Google Scholar]
  5. Buganim Y, Faddah DA, Cheng AW, Itskovich E, Markoulaki S. 5.  et al. 2012. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150:61209–22 [Google Scholar]
  6. Cai L, Friedman N, Xie XS. 6.  2006. Stochastic protein expression in individual cells at the single molecule level. Nature 440:7082358–62 [Google Scholar]
  7. Paulsson J. 7.  2005. Models of stochastic gene expression. Phys. Life Rev. 2:2157–75 [Google Scholar]
  8. Munsky B, Neuert G, van Oudenaarden A. 8.  2012. Using gene expression noise to understand gene regulation. Science 336:6078183–87 [Google Scholar]
  9. Yang Q, Pando BF, Dong G, Golden SS, van Oudenaarden A. 9.  2010. Circadian gating of the cell cycle revealed in single cyanobacterial cells. Science 327:59721522–26 [Google Scholar]
  10. Håkanson M, Textor M, Charnley M. 10.  2011. Engineered 3D environments to elucidate the effect of environmental parameters on drug response in cancer. Integr. Biol. 3:131–38 [Google Scholar]
  11. Collins JM, Lam RTS, Yang Z, Semsarieh B, Smetana AB, Nettikadan S. 11.  2012. Targeted delivery to single cells in precisely controlled microenvironments. Lab Chip 12:152643–48 [Google Scholar]
  12. White AK, VanInsberghe M, Petriv I, Hamidi M, Sikorski D. 12.  et al. 2011. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl. Acad. Sci. USA 108:3413999–4004 [Google Scholar]
  13. Tang F, Barbacioru C, Nordman E, Bao S, Lee C. 13.  et al. 2011. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS ONE 6:6e21208 [Google Scholar]
  14. Slota M, Lim J-B, Dang Y, Disis ML. 14.  2011. ELISpot for measuring human immune responses to vaccines. Expert Rev. Vaccines 10:3299–306 [Google Scholar]
  15. Freer G, Rindi L. 15.  2013. Intracellular cytokine detection by fluorescence-activated flow cytometry: basic principles and recent advances. Methods 61:130–38 [Google Scholar]
  16. Love JC. 16.  2010. Integrated process design for single-cell analytical technologies. AIChE J. 56:102496–502 [Google Scholar]
  17. Lindström S, Andersson-Svahn H. 17.  2010. Overview of single-cell analyses: microdevices and applications. Lab Chip 10:243363–72 [Google Scholar]
  18. Schmid A, Kortmann H, Dittrich PS, Blank LM. 18.  2010. Chemical and biological single cell analysis. Curr. Opin. Biotechnol. 21:112–20 [Google Scholar]
  19. Yamanaka YJ, Gierahn TM, Love JC. 19.  2012. The dynamic lives of T cells: new approaches and themes. Trends Immunol. 34:59–66 [Google Scholar]
  20. Fritzsch FSO, Dusny C, Frick O, Schmid A. 20.  2012. Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu. Rev. Chem. Biomol. Eng. 3:129–55 [Google Scholar]
  21. Borland LM, Kottegoda S, Phillips KS, Allbritton NL. 21.  2008. Chemical analysis of single cells. Annu. Rev. Anal. Chem. 1:191–227 [Google Scholar]
  22. Powell AA, Talasaz AH, Zhang H, Coram MA, Reddy A. 22.  et al. 2012. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7:5e33788 [Google Scholar]
  23. Pantel K, Alix-Panabières C. 23.  2010. Circulating tumour cells in cancer patients: challenges and perspectives. Trends Mol. Med. 16:9398–406 [Google Scholar]
  24. Navin N, Kendall J, Troge J, Andrews P, Rodgers L. 24.  et al. 2011. Tumour evolution inferred by single-cell sequencing. Nature 472:734190–94 [Google Scholar]
  25. Leversha MA, Han J, Asgari Z, Danila DC, Lin O. 25.  et al. 2009. Fluorescence in situ hybridization analysis of circulating tumor cells in metastatic prostate cancer. Clin. Cancer Res. 15:62091–97 [Google Scholar]
  26. Anderson JB, Webb AJ. 26.  1987. Fine-needle aspiration biopsy and the diagnosis of thyroid cancer. Br. J. Surg. 74:4292–96 [Google Scholar]
  27. Mardis ER. 27.  2013. Next-generation sequencing platforms. Annu. Rev. Anal. Chem. 6:287–303 [Google Scholar]
  28. Blainey PC. 28.  2013. The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol. Rev. 37:407–27 [Google Scholar]
  29. Lovett M. 29.  2013. The applications of single-cell genomics. Hum. Mol. Genet. 22:R22–26 [Google Scholar]
  30. Kalisky T, Blainey P, Quake SR. 30.  2011. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45:431–45 [Google Scholar]
  31. Shapiro E, Biezuner T, Linnarsson S. 31.  2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14:618–30 [Google Scholar]
  32. Lasken RS, Egholm M. 32.  2003. Whole genome amplification: abundant supplies of DNA from precious samples or clinical specimens. Trends Biotechnol. 21:12531–35 [Google Scholar]
  33. Dean FB, Nelson JR, Giesler TL, Lasken RS. 33.  2001. Rapid amplification of plasmid and phage DNA using Phi29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11:61095–99 [Google Scholar]
  34. Spits C, Le Caignec C, De Rycke M, Van Haute L, Van Steirteghem A. 34.  et al. 2006. Whole-genome multiple displacement amplification from single cells. Nat. Protoc. 1:41965–70 [Google Scholar]
  35. Paez JG, Lin M, Beroukhim R, Lee JC, Zhao X. 35.  et al. 2004. Genome coverage and sequence fidelity of Φ29 polymerase-based multiple strand displacement whole genome amplification. Nucleic Acids Res. 32:9e71 [Google Scholar]
  36. Zong C, Lu S, Chapman AR, Xie XS. 36.  2012. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338:61141622–26 [Google Scholar]
  37. Lasken RS. 37.  2013. Single-cell sequencing in its prime. Nat. Biotechnol. 31:3211–12 [Google Scholar]
  38. Russnes HG, Navin N, Hicks J, Borresen-Dale A-L. 38.  2011. Insight into the heterogeneity of breast cancer through next-generation sequencing. J. Clin. Investig. 121:103810–18 [Google Scholar]
  39. Xu X, Hou Y, Yin X, Bao L, Tang A. 39.  et al. 2012. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148:5886–95 [Google Scholar]
  40. Heitzer E, Auer M, Gasch C, Pichler M, Ulz P. 40.  et al. 2013. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing. Cancer Res. 73:102965–75 [Google Scholar]
  41. Auer M, Heitzer E, Ulz P, Geigl JB, Speicher MR. 41.  2013. Single circulating tumor cell sequencing for monitoring. Oncotarget 4:6812 [Google Scholar]
  42. Mathiesen RR, Fjelldal R, Liestøl K, Due EU, Geigl JB. 42.  et al. 2012. High-resolution analyses of copy number changes in disseminated tumor cells of patients with breast cancer. Int. J. Cancer 131:4E405–15 [Google Scholar]
  43. Navin N, Krasnitz A, Rodgers L, Cook K, Meth J. 43.  et al. 2010. Inferring tumor progression from genomic heterogeneity. Genome Res. 20:168–80 [Google Scholar]
  44. Hou Y, Song L, Zhu P, Zhang B, Tao Y. 44.  et al. 2012. Single-cell exome sequencing and monoclonal evolution of a jak2-negative myeloproliferative neoplasm. Cell 148:5873–85 [Google Scholar]
  45. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. 45.  2012. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31:146–53 [Google Scholar]
  46. Licatalosi DD, Darnell RB. 46.  2010. RNA processing and its regulation: global insights into biological networks. Nat. Rev. Genet. 11:175–87 [Google Scholar]
  47. VanGuilder HD, Vrana KE, Freeman WM. 47.  2008. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44:5619–26 [Google Scholar]
  48. Hoheisel JD. 48.  2006. Microarray technology: beyond transcript profiling and genotype analysis. Nat. Rev. Genet. 7:3200–10 [Google Scholar]
  49. Kulkarni MM. 49.  2011. Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr. Protoc. Mol. Biol. 94:25B.10.1–25B.10.17 [Google Scholar]
  50. Guo G, Huss M, Tong GQ, Wang C, Sun LL. 50.  et al. 2010. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18:4675–85 [Google Scholar]
  51. Esumi S, Wu S-X, Yanagawa Y, Obata K, Sugimoto Y, Tamamaki N. 51.  2008. Method for single-cell microarray analysis and application to gene-expression profiling of GABAergic neuron progenitors. Neurosci. Res. 60:4439–51 [Google Scholar]
  52. Kurimoto K, Yabuta Y, Ohinata Y, Ono Y, Uno KD. 52.  et al. 2006. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34:5e42 [Google Scholar]
  53. Ozsolak F, Milos PM. 53.  2010. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12:287–98 [Google Scholar]
  54. Wang Z, Gerstein M, Snyder M. 54.  2009. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:157–63 [Google Scholar]
  55. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G. 55.  et al. 2010. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28:5511–15 [Google Scholar]
  56. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. 56.  2008. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5:7621–28 [Google Scholar]
  57. Roberts A, Pimentel H, Trapnell C, Pachter L. 57.  2011. Identification of novel transcripts in annotated genomes using RNA-seq. Bioinformatics 27:172325–29 [Google Scholar]
  58. Trapnell C, Pachter L, Salzberg SL. 58.  2009. Tophat: discovering splice junctions with RNA-seq. Bioinformatics 25:91105–11 [Google Scholar]
  59. Tang F, Barbacioru C, Nordman E, Li B, Xu N. 59.  et al. 2010. RNA-seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5:3516–35 [Google Scholar]
  60. Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE. 60.  et al. 2009. Direct RNA sequencing. Nature 461:7265814–18 [Google Scholar]
  61. Tang F, Lao K, Surani MA. 61.  2011. Development and applications of single-cell transcriptome analysis. Nat. Methods 8:S6–11 [Google Scholar]
  62. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD. 62.  2001. Reverse transcriptase template switching: a SMART™ approach for full-length cDNA library construction. Biotechniques 30:4892–97 [Google Scholar]
  63. Ramsköld D, Luo S, Wang Y-C, Li R, Deng Q. 63.  et al. 2012. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30:8777–82 [Google Scholar]
  64. Hinkle D, Glanzer J, Sarabi A, Pajunen T, Zielinski J. 64.  et al. 2004. Single neurons as experimental systems in molecular biology. Prog. Neurobiol. 72:2129–42 [Google Scholar]
  65. Pan X, Durrett RE, Zhu H, Tanaka Y, Li Y. 65.  et al. 2013. Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc. Natl. Acad. Sci. USA 110:2594–99 [Google Scholar]
  66. Snijder B, Sacher R, Rämö P, Liberali P, Mench K. 66.  et al. 2012. Single-cell analysis of population context advances RNAi screening at multiple levels. Mol. Syst. Biol. 8:1579 [Google Scholar]
  67. Hebenstreit D. 67.  2012. Methods, challenges and potentials of single cell RNA-seq. Biology 1:3658–67 [Google Scholar]
  68. Islam S, Kjällquist U, Moliner A, Zajac P, Fan JB. 68.  et al. 2011. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21:71160–67 [Google Scholar]
  69. Levsky JM, Shenoy SM, Pezo RC, Singer RH. 69.  2002. Single-cell gene expression profiling. Science 297:5582836–40 [Google Scholar]
  70. Islam S, Kjällquist U, Moliner A, Zajac P, Fan JB. 70.  et al. 2012. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7:5813–28 [Google Scholar]
  71. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT. 71.  et al. 2013. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–40 [Google Scholar]
  72. Le TT, Harlepp S, Guet CC, Dittmar K, Emonet T. 72.  et al. 2005. Real-time RNA profiling within a single bacterium. Proc. Natl. Acad. Sci. USA 102:269160–64 [Google Scholar]
  73. Baerlocher GM, Vulto I, de Jong G, Lansdorp PM. 73.  2006. Flow cytometry and FISH to measure the average length of telomeres (flow FISH). Nat. Protoc. 1:52365–76 [Google Scholar]
  74. Hashimshony T, Wagner F, Sher N, Yanai I. 74.  2012. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2:666–73 [Google Scholar]
  75. Amantonico A, Urban PL, Zenobi R. 75.  2010. Analytical techniques for single-cell metabolomics: state of the art and trends. Anal. Bioanal. Chem. 398:62493–504 [Google Scholar]
  76. Klepárník K, Foret F. 76.  2013. Recent advances in the development of single cell analysis—a review. Anal. Chim. Acta 800:12–21 [Google Scholar]
  77. Wu M, Singh AK. 77.  2012. Single-cell protein analysis. Curr. Opin. Biotechnol. 23:183–88 [Google Scholar]
  78. Ni Q, Titov DV, Zhang J. 78.  2006. Analyzing protein kinase dynamics in living cells with FRET reporters. Methods 40:3279–86 [Google Scholar]
  79. Bendall SC, Simonds EF, Qiu P, Amir ED, Krutzik PO. 79.  et al. 2011. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:6030687–96 [Google Scholar]
  80. Urban PL, Amantonico A, Zenobi R. 80.  2011. Lab-on-a-plate: extending the functionality of MALDI-MS and LDI-MS targets. Mass Spectrom. Rev. 30:3435–78 [Google Scholar]
  81. Zhang H, Jin W. 81.  2006. Single-cell analysis by intracellular immuno-reaction and capillary electrophoresis with laser-induced fluorescence detection. J. Chromatogr. A 11041346–51
  82. Rubakhin SS, Romanova EV, Nemes P, Sweedler JV. 82.  2011. Profiling metabolites and peptides in single cells. Nat. Methods 8:4sS20–29 [Google Scholar]
  83. Rubakhin SS, Sweedler JV. 83.  2008. Quantitative measurements of cell-cell signaling peptides with single-cell MALDI MS. Anal. Chem. 80:187128–36 [Google Scholar]
  84. Lorey DR, Morrison GH, Chandra S. 84.  2001. Dynamic secondary ion mass spectrometry analysis of boron from boron neutron capture therapy drugs in co-cultures: single-cell imaging of two different cell types within the same ion microscopy field of imaging. Anal. Chem. 73:163947–53 [Google Scholar]
  85. Chandra S, Lorey DR 2nd. 85.  2001. SIMS ion microscopy in cancer research: single cell isotopic imaging for chemical composition, cytotoxicity and cell cycle recognition. Cell. Mol. Biol. 47:3503–18 [Google Scholar]
  86. Kulp KS, Berman ES, Knize MG, Shattuck DL, Nelson EJ. 86.  et al. 2006. Chemical and biological differentiation of three human breast cancer cell types using time-of-flight secondary ion mass spectrometry. Anal. Chem. 78:113651–58 [Google Scholar]
  87. Passarelli MK, Ewing AG. 87.  2013. Single-cell imaging mass spectrometry. Curr. Opin. Chem. Biol. 17:854–59 [Google Scholar]
  88. El-Ali J, Sorger PK, Jensen KF. 88.  2006. Cells on chips. Nature 442:7101403–11 [Google Scholar]
  89. Joensson HN, Andersson Svahn H. 89.  2012. Droplet microfluidics—a tool for single-cell analysis. Angew. Chem. Int. Ed. 51:4912176–92 [Google Scholar]
  90. Guo MT, Rotem A, Heyman JA, Weitz DA. 90.  2012. Droplet microfluidics for high-throughput biological assays. Lab Chip 12:122146–55 [Google Scholar]
  91. Tajiri K, Kishi H, Ozawa T, Sugiyama T, Muraguchi A. 91.  2009. SFMAC: a novel method for analyzing multiple parameters on lymphocytes with a single fluorophore in cell-microarray system. Cytometry 75A:4282–88 [Google Scholar]
  92. Roach KL, King KR, Uygun BE, Kohane IS, Yarmush ML, Toner M. 92.  2009. High throughput single cell bioinformatics. Biotechnol. Prog. 25:61772–79 [Google Scholar]
  93. Lindström S, Andersson-Svahn H. 93.  2011. Miniaturization of biological assays—overview on microwell devices for single-cell analyses. Biochim. Biophys. Acta 1810:3308–16 [Google Scholar]
  94. Asphahani F, Wang K, Thein M, Veiseh O, Yung S. 94.  et al. 2011. Single-cell bioelectrical impedance platform for monitoring cellular response to drug treatment. Phys. Biol. 8:1015006 [Google Scholar]
  95. Kinoshita K, Ozawa T, Tajiri K, Kadowaki S, Kishi H, Muraguchi A. 95.  2009. Identification of antigen-specific B cells by concurrent monitoring of intracellular Ca2+ mobilization and antigen binding with microwell array chip system equipped with a CCD imager. Cytometry 75A:8682–87 [Google Scholar]
  96. Jin A, Ozawa T, Tajiri K, Obata T, Kondo S. 96.  et al. 2009. A rapid and efficient single-cell manipulation method for screening antigen-specific antibody–secreting cells from human peripheral blood. Nat. Med. 15:91088–92 [Google Scholar]
  97. Zhu H, Stybayeva G, Silangcruz J, Yan J, Ramanculov E. 97.  et al. 2009. Detecting cytokine release from single T-cells. Anal. Chem. 81:198150–56 [Google Scholar]
  98. Love JC, Ronan JL, Grotenbreg GM, van der Veen AG, Ploegh HL. 98.  2006. A microengraving method for rapid selection of single cells producing antigen-specific antibodies. Nat. Biotechnol. 24:6703–7 [Google Scholar]
  99. Han Q, Bradshaw EM, Nilsson B, Hafler DA, Love JC. 99.  2010. Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving. Lab Chip 10:111391–400 [Google Scholar]
  100. Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA, Love JC. 100.  2012. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl. Acad. Sci. USA 109:51607–12 [Google Scholar]
  101. Rettig JR, Folch A. 101.  2005. Large-scale single-cell trapping and imaging using microwell arrays. Anal. Chem. 77:175628–34 [Google Scholar]
  102. Choi JH, Ogunniyi AO, Du M, Du M, Kretschmann M. 102.  et al. 2010. Development and optimization of a process for automated recovery of single cells identified by microengraving. Biotechnol. Prog. 26:3888–95 [Google Scholar]
  103. Khademhosseini A, Langer R, Borenstein J, Vacanti JP. 103.  2006. Microscale technologies for tissue engineering and biology. Proc. Natl. Acad. Sci. USA 103:82480–87 [Google Scholar]
  104. Debs B, Utharala R, Balyasnikova IV, Griffiths AD, Merten CA. 104.  El 2012. Functional single-cell hybridoma screening using droplet-based microfluidics. Proc. Natl. Acad. Sci. USA 109:2911570–75 [Google Scholar]
  105. Park JY, Morgan M, Sachs AN, Samorezov J, Teller R. 105.  et al. 2010. Single cell trapping in larger microwells capable of supporting cell spreading and proliferation. Microfluid. Nanofluidics 8:2263–68 [Google Scholar]
  106. Engler AJ, Sen S, Sweeney HL, Discher DE. 106.  2006. Matrix elasticity directs stem cell lineage specification. Cell 126:4677–89 [Google Scholar]
  107. Hanahan D, Weinberg RA. 107.  2011. Hallmarks of cancer: the next generation. Cell 144:5646–74 [Google Scholar]
  108. Scadden DT. 108.  2006. The stem-cell niche as an entity of action. Nature 441:70971075–79 [Google Scholar]
  109. Chen CS, Mrksich M, Huang S, Whitesides GM, Ingber DE. 109.  1997. Geometric control of cell life and death. Science 276:53171425–28 [Google Scholar]
  110. Lutolf MP, Doyonnas R, Havenstrite K, Koleckar K, Blau HM. 110.  2009. Perturbation of single hematopoietic stem cell fates in artificial niches. Integr. Biol. 1:159–69 [Google Scholar]
  111. Gilbert P, Havenstrite K, Magnusson K, Sacco A, Leonardi N. 111.  et al. 2010. Substrate elasticity regulates skeletal muscle stem cell self-renewal in culture. Science 329:59951078–81 [Google Scholar]
  112. Toh Y-C, Blagović K, Voldman J. 112.  2010. Advancing stem cell research with microtechnologies: opportunities and challenges. Integr. Biol. 2:7–8305–25 [Google Scholar]
  113. Collins JM, Nettikadan S. 113.  2011. Subcellular scaled multiplexed protein patterns for single cell cocultures. Anal. Biochem. 419:2339–41 [Google Scholar]
  114. Gobaa S, Hoehnel S, Roccio M, Negro A, Kobel S, Lutolf MP. 114.  2011. Artificial niche microarrays for probing single stem cell fate in high throughput. Nat. Methods 8:11949–55 [Google Scholar]
  115. Doh J, Kim M, Krummel MF. 115.  2010. Cell-laden microwells for the study of multicellularity in lymphocyte fate decisions. Biomaterials 31:123422–28 [Google Scholar]
  116. Rosenthal A, Macdonald A, Voldman J. 116.  2007. Cell patterning chip for controlling the stem cell microenvironment. Biomaterials 28:213208–16 [Google Scholar]
  117. Andreasson-Ochsner M, Romano G, Håkanson M, Smith ML, Leckband DE. 117.  et al. 2011. Single cell 3-D platform to study ligand mobility in cell-cell contact. Lab Chip 11:172876–83 [Google Scholar]
  118. Torres AJ, Contento RL, Gordo S, Wucherpfennig KW, Love JC. 118.  2013. Functional single-cell analysis of T-cell activation by supported lipid bilayer-tethered ligands on arrays of nanowells. Lab Chip 13:190–99 [Google Scholar]
  119. Krzewski K, Coligan JE. 119.  2012. Human NK cell lytic granules and regulation of their exocytosis. Front Immunol. 3:335 [Google Scholar]
  120. Lopez JA, Jenkins MR, Rudd-Schmidt JA, Brennan AJ, Danne JC. 120.  et al. 2013. Rapid and unidirectional perforin pore delivery at the cytotoxic immune synapse. J. Immunol. 191:52328–34 [Google Scholar]
  121. Néron S, Suck G, Ma X-Z, Sakac D, Roy A. 121.  et al. 2006. B cell proliferation following CD40 stimulation results in the expression and activation of Src protein tyrosine kinase. Int. Immunol. 18:2375–87 [Google Scholar]
  122. Selimkhanov J, Hasty J, Tsimring LS. 122.  2012. Recent advances in single-cell studies of gene regulation. Curr. Opin. Biotechnol. 23:134–40 [Google Scholar]
  123. Wlodkowic D, Cooper JM. 123.  2010. Tumors on chips: oncology meets microfluidics. Curr. Opin. Chem. Biol. 14:5556–67 [Google Scholar]
  124. Lindström S, Andersson-Svahn H. 124.  2010. Overview of single-cell analyses: microdevices and applications. Lab Chip 10:243363–72 [Google Scholar]
  125. Charnley M, Textor M, Khademhosseini A, Lutolf MP. 125.  2009. Integration column: microwell arrays for mammalian cell culture. Integr. Biol. 1:11–12625 [Google Scholar]
  126. Lindström S, Larsson R, Andersson Svahn H. 126.  2008. Towards high-throughput single cell/clone cultivation and analysis. Electrophoresis 29:61219–27 [Google Scholar]
  127. Lindström S, Hammond M, Brismar H, Andersson-Svahn H, Ahmadian A. 127.  2009. PCR amplification and genetic analysis in a microwell cell culturing chip. Lab Chip 9:243465–71 [Google Scholar]
  128. Wakamoto Y, Ramsden J, Yasuda K. 128.  2005. Single-cell growth and division dynamics showing epigenetic correlations. Analyst 130:3311–17 [Google Scholar]
  129. Yamamura S, Yatsushiro S, Yamaguchi Y, Abe K, Shinohara Y. 129.  et al. 2012. Accurate detection of carcinoma cells by use of a cell microarray chip. PLoS ONE 7:3e32370 [Google Scholar]
  130. Yasuda K. 130.  2004. On-chip single-cell-based microcultivation method for analysis of genetic information and epigenetic correlation of cells. J. Mol. Recognit. 17:3186–93 [Google Scholar]
  131. Zaretsky I, Polonsky M, Shifrut E, Reich-Zeliger S, Antebi Y. 131.  et al. 2012. Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays. Lab Chip 12:235007–15 [Google Scholar]
  132. Yamanaka YJ, Berger CT, Sips M, Cheney PC, Alter G, Love JC. 132.  2012. Single-cell analysis of the dynamics and functional outcomes of interactions between human natural killer cells and target cells. Integr. Biol. 4:101175–84 [Google Scholar]
  133. Varadarajan N, Julg B, Yamanaka YJ, Chen H, Ogunniyi AO. 133.  et al. 2011. A high-throughput single-cell analysis of human CD8+ T cell functions reveals discordance for cytokine secretion and cytolysis. J. Clin. Investig. 121:114322–31 [Google Scholar]
  134. Guldevall K, Vanherberghen B, Frisk T, Hurtig J, Christakou AE. 134.  et al. 2010. Imaging immune surveillance of individual natural killer cells confined in microwell arrays. PLoS ONE 5:11e15453 [Google Scholar]
  135. Campbell KS, Hasegawa J. 135.  2013. Natural killer cell biology: an update and future directions. J. Allergy Clin. Immunol. 132:3536–44 [Google Scholar]
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A New Toolbox for Assessing Single Cells
Annual Review of Chemical and Biomolecular Engineering 5, 455 (2014); https://doi.org/10.1146/annurev-chembioeng-060713-035958
/content/journals/10.1146/annurev-chembioeng-060713-035958
/content/journals/10.1146/annurev-chembioeng-060713-035958
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  • Article Type: Review Article

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