1932

Abstract

Measurement of humoral factors secreted from cells has served as an indispensable method to monitor the states of a cell ensemble because humoral factors play crucial roles in cell–cell interaction and aptly reflect the states of individual cells. Although a cell ensemble consisting of a large number of cells has conventionally been the object of such measurements, recent advances in microfluidic technology together with highly sensitive immunoassays have enabled us to quantify secreted humoral factors even from individual cells in either a population or a temporal context. Many groups have reported various miniaturized platforms for immunoassays of proteins secreted from single cells. This review focuses on the current status of time-resolved assay platforms for protein secretion with single-cell resolution. We also discuss future perspectives of time-resolved immunoassays from the viewpoint of systems biology.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-091619-101212
2020-06-12
2024-12-10
Loading full text...

Full text loading...

/deliver/fulltext/anchem/13/1/annurev-anchem-091619-101212.html?itemId=/content/journals/10.1146/annurev-anchem-091619-101212&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA 2017. Single-cell transcriptomics to explore the immune system in health and disease. Science 358:635958–63
    [Google Scholar]
  2. 2. 
    Papalexi E, Satija R. 2018. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18:135–45
    [Google Scholar]
  3. 3. 
    Chen H, Ye F, Guo G 2019. Revolutionizing immunology with single-cell RNA sequencing. Cell. Mol. Immunol. 16:3242–49
    [Google Scholar]
  4. 4. 
    Kunz DJ, Gomes T, James KR 2018. Immune cell dynamics unfolded by single-cell technologies. Front. Immunol. 9:1435
    [Google Scholar]
  5. 5. 
    Rabani M, Levin JZ, Fan L, Adiconis X, Raychowdhury R et al. 2011. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29:5436–42
    [Google Scholar]
  6. 6. 
    Liu Y, Beyer A, Aebersold R 2016. On the dependency of cellular protein levels on mRNA abundance. Cell 165:3535–50
    [Google Scholar]
  7. 7. 
    Klose CSN, Mahlakõiv T, Moeller JB, Rankin LC, Flamar A-L et al. 2017. The neuropeptide neuromedin U stimulates innate lymphoid cells and type 2 inflammation. Nature 549:7671282–86
    [Google Scholar]
  8. 8. 
    Li GW, Xie XS. 2011. Central dogma at the single-molecule level in living cells. Nature 475:7356308–15
    [Google Scholar]
  9. 9. 
    Fernandez-de-Cossio-Diaz J, Mulet R, Vazquez A 2019. Cell population heterogeneity driven by stochastic partition and growth optimality. Sci. Rep. 9:9406
    [Google Scholar]
  10. 10. 
    Hodgkin PD. 2018. Modifying clonal selection theory with a probabilistic cell. Immunol. Rev. 285:1249–62
    [Google Scholar]
  11. 11. 
    Altschuler SJ, Wu LF. 2010. Cellular heterogeneity: Do differences make a difference. ? Cell 141:4559–63
    [Google Scholar]
  12. 12. 
    Satija R, Shalek AK. 2014. Heterogeneity in immune responses: from populations to single cells. Trends Immunol 35:5219–29
    [Google Scholar]
  13. 13. 
    Saelens W, Cannoodt R, Todorov H, Saeys Y 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37:5547–54
    [Google Scholar]
  14. 14. 
    Doupé DP, Perrimon N. 2014. Techniques: visualizing and manipulating temporal signalling dynamics with fluorescence-based tools. Sci. Signal. 7:319re1
    [Google Scholar]
  15. 15. 
    Duwé S, Dedecker P. 2019. Optimizing the fluorescent protein toolbox and its use. Curr. Opin. Biotechnol. 58:183–91
    [Google Scholar]
  16. 16. 
    Yeh H-W, Ai H-W. 2019. Development and applications of bioluminescent and chemiluminescent reporters and biosensors. Annu. Rev. Anal. Chem. 12:129–50
    [Google Scholar]
  17. 17. 
    Rodriguez EA, Campbell RE, Lin JY, Lin MZ, Miyawaki A et al. 2017. The growing and glowing toolbox of fluorescent and photoactive proteins. Trends Biochem. Sci. 42:2111–29
    [Google Scholar]
  18. 18. 
    Ankney JA, Xie L, Wrobel JA, Wang L, Chen X 2019. Novel secretome-to-transcriptome integrated or secreto-transcriptomic approach to reveal liquid biopsy biomarkers for predicting individualized prognosis of breast cancer patients. BMC Med. Genom. 12:178
    [Google Scholar]
  19. 19. 
    Darrah PA, Patel DT, De Luca PM, Lindsay RWB, Davey DF et al. 2007. Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major. Nat. . Med 13:7843–50
    [Google Scholar]
  20. 20. 
    Säemann MD, Haidinger M, Hecking M, Hörl WH, Weichhart T 2009. The multifunctional role of mTOR in innate immunity: implications for transplant immunity. Am. J. Transplant. 9:122655–61
    [Google Scholar]
  21. 21. 
    Chattopadhyay PK, Gierahn TM, Roederer M, Love JC 2014. Single-cell technologies for monitoring immune systems. Nat. Immunol. 15:2128–35
    [Google Scholar]
  22. 22. 
    Chessel A, Salas REC. 2019. From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Essays Biochem 63:2197–208
    [Google Scholar]
  23. 23. 
    Muzzey D, van Oudenaarden A 2009. Quantitative time-lapse fluorescence microscopy in single cells. Annu. Rev. Cell Dev. Biol. 25:1301–27
    [Google Scholar]
  24. 24. 
    Lippincott-Schwartz J. 2011. Emerging in vivo analyses of cell function using fluorescence imaging. Annu. Rev. Biochem. 80:327–32
    [Google Scholar]
  25. 25. 
    Coutu DL, Schroeder T. 2013. Probing cellular processes by long-term live imaging—historic problems and current solutions. J. Cell Sci. 126:173805–15
    [Google Scholar]
  26. 26. 
    Royer CA. 2019. Characterizing proteins in their cellular environment: examples of recent advances in quantitative fluorescence microscopy. Protein Sci 28:71210–21
    [Google Scholar]
  27. 27. 
    Shirasaki Y, Ohara O. 2018. Challenges in developing protein secretion assays at a single-cell level. Methods Mol. Biol. 1808:1–7
    [Google Scholar]
  28. 28. 
    Engvall E, Perlmann P. 1971. Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin G. Immunochemistry 8:9871–74
    [Google Scholar]
  29. 29. 
    Czerkinsky CC, Nilsson , Nygren H, Ouchterlony Ö, Tarkowski A 1983. A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of specific antibody-secreting cells. J. Immunol. Methods 65:1–2109–21
    [Google Scholar]
  30. 30. 
    Chen Z, Lu Y, Zhang K, Xiao Y, Lu J, Fan R 2019. Multiplexed, sequential secretion analysis of the same single cells reveals distinct effector response dynamics dependent on the initial basal state. Adv. Sci. 6:91801361
    [Google Scholar]
  31. 31. 
    Han Q, Bagheri N, Bradshaw EM, Hafler DA, Lauffenburger DA, Love JC 2012. Polyfunctional responses by human T cells result from sequential release of cytokines. PNAS 109:51607–12
    [Google Scholar]
  32. 32. 
    Cai W, Chiu YJ, Ramakrishnan V, Tsai Y, Chen C, Lo YH 2018. A single-cell translocation and secretion assay (TransSeA). Lab Chip 18:203154–62
    [Google Scholar]
  33. 33. 
    Chiu YJ, Cai W, Shih YR V, Lian I, Lo YH 2016. A single-cell assay for time lapse studies of exosome secretion and cell behaviors. Small 12:273658–66
    [Google Scholar]
  34. 34. 
    Riahi R, Shaegh SAM, Ghaderi M, Zhang YS, Shin SR et al. 2016. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Sci. Rep. 6:24598
    [Google Scholar]
  35. 35. 
    Junkin M, Kaestli AJ, Cheng Z, Jordi C, Albayrak C et al. 2016. High-content quantification of single-cell immune dynamics. Cell Rep 15:2411–22
    [Google Scholar]
  36. 36. 
    Kaestli AJ, Junkin M, Tay S 2017. Integrated platform for cell culture and dynamic quantification of cell secretion. Lab Chip 17:234124–33
    [Google Scholar]
  37. 37. 
    Lynch M, Ramalingam N. 2019. Integrated fluidic circuits for single-cell omics and multi-omics applications. Advances in Experimental Medicine and Biology Y Suzuki 19–26 Singapore: Springer
    [Google Scholar]
  38. 38. 
    Yu J, Zhou J, Sutherland A, Wei W, Shin YS et al. 2014. Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications. Annu. Rev. Anal. Chem. 7:275–95
    [Google Scholar]
  39. 39. 
    Li X, Soler M, Szydzik C, Khoshmanesh K, Schmidt J et al. 2018. Label-free optofluidic nanobiosensor enables real-time analysis of single-cell cytokine secretion. Small 14:26e1800698
    [Google Scholar]
  40. 40. 
    Juan-Colás J, Hitchcock IS, Coles M, Johnson S, Krauss TF 2018. Quantifying single-cell secretion in real time using resonant hyperspectral imaging. PNAS 115:5213204–9
    [Google Scholar]
  41. 41. 
    McDonald MP, Gemeinhardt A, König K, Piliarik M, Schaffer S et al. 2018. Visualizing single-cell secretion dynamics with single-protein sensitivity. Nano Lett 18:1513–19
    [Google Scholar]
  42. 42. 
    Mazutis L, Gilbert J, Ung WL, Weitz DA, Griffiths AD, Heyman JA 2013. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8:5870–91
    [Google Scholar]
  43. 43. 
    Konry T, Golberg A, Yarmush M 2013. Live single cell functional phenotyping in droplet nano-liter reactors. Sci. Rep. 3:3179
    [Google Scholar]
  44. 44. 
    Eyer K, Doineau RCL, Castrillon CE, Briseño-Roa L, Menrath V et al. 2017. Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring. Nat. Biotechnol. 35:10977–82
    [Google Scholar]
  45. 45. 
    Son KJ, Rahimian A, Shin DS, Siltanen C, Patel T, Revzin A 2016. Microfluidic compartments with sensing microbeads for dynamic monitoring of cytokine and exosome release from single cells. Analyst 141:2679–88
    [Google Scholar]
  46. 46. 
    An X, Sendra VG, Liadi I, Ramesh B, Romain G et al. 2017. Single-cell profiling of dynamic cytokine secretion and the phenotype of immune cells. PLOS ONE 12:8e0181904
    [Google Scholar]
  47. 47. 
    Sasuga Y, Tani T, Hayashi M, Yamakawa H, Ohara O, Harada Y 2006. Development of a microscopic platform for real-time monitoring of biomolecular interactions. Genome Res 16:1132–39
    [Google Scholar]
  48. 48. 
    Sasuga Y, Iwasawa T, Terada K, Oe Y, Sorimachi H et al. 2008. Single-cell chemical lysis method for analyses of intracellular molecules using an array of picoliter-scale microwells. Anal. Chem. 80:239141–49
    [Google Scholar]
  49. 49. 
    Salehi-Reyhani A, Kaplinsky J, Burgin E, Novakova M, Demello AJ et al. 2011. A first step towards practical single cell proteomics: a microfluidic antibody capture chip with TIRF detection. Lab Chip 11:71256–61
    [Google Scholar]
  50. 50. 
    Shirasaki Y, Yamagishi M, Suzuki N, Izawa K, Nakahara A et al. 2014. Real-time single-cell imaging of protein secretion. Sci. Rep. 4:4736
    [Google Scholar]
  51. 51. 
    Varma S, Voldman J. 2018. Caring for cells in microsystems: principles and practices of cell-safe device design and operation. Lab Chip 18:223333–52
    [Google Scholar]
  52. 52. 
    Han Q, Bradshaw EM, Nilsson B, Hafler DA, Love JC 2010. Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving. Lab Chip 10:111391–400
    [Google Scholar]
  53. 53. 
    Bartosh T, Ylostalo J. 2014. Macrophage inflammatory assay. Bio-Protocol 4:14e1180
    [Google Scholar]
  54. 54. 
    Satchidanandam V. 2016. Mouse BMDC-dependent T cell polarization assays. Bio-Protocol 6:3e1721
    [Google Scholar]
  55. 55. 
    Torres AJ, Hill AS, Love JC 2014. Nanowell-based immunoassays for measuring single-cell secretion: characterization of transport and surface binding. Anal. Chem. 86:2311562–69
    [Google Scholar]
  56. 56. 
    Nakahara A, Shirasaki Y, Kawai K, Ohara O, Mizuno J, Shoji S 2011. Fabrication of high-aspect-ratio amorphous perfluorinated polymer structure for total internal reflection fluorescence microscopy. Microelectron. Eng. 88:81817–20
    [Google Scholar]
  57. 57. 
    Verboogen DRJ, ter Beest M, Honigmann A, van den Bogaart G 2018. Secretory vesicles of immune cells contain only a limited number of interleukin 6 molecules. FEBS Lett 592:91535–44
    [Google Scholar]
  58. 58. 
    Galluzzi L, López-Soto A, Kumar S, Kroemer G 2016. Caspases connect cell-death signaling to organismal homeostasis. Immunity 44:2221–31
    [Google Scholar]
  59. 59. 
    Gudipaty SA, Conner CM, Rosenblatt J, Montell DJ 2018. Unconventional ways to live and die: cell death and survival in development, homeostasis, and disease. Annu. Rev. Cell Dev. Biol. 34:311–32
    [Google Scholar]
  60. 60. 
    Vénéreau E, Ceriotti C, Bianchi ME 2015. DAMPs from cell death to new life. Front. Immunol. 6:422
    [Google Scholar]
  61. 61. 
    Place DE, Kanneganti T-D. 2019. Cell death-mediated cytokine release and its therapeutic implications. J. Exp. Med. 216:71474–86
    [Google Scholar]
  62. 62. 
    Liu T, Yamaguchi Y, Shirasaki Y, Shikada K, Yamagishi M et al. 2014. Single-cell imaging of caspase-1 dynamics reveals an all-or-none inflammasome signaling response. Cell Rep 8:4974–82
    [Google Scholar]
  63. 63. 
    Murai S, Yamaguchi Y, Shirasaki Y, Yamagishi M, Shindo R et al. 2018. A FRET biosensor for necroptosis uncovers two different modes of the release of DAMPs. Nat. Commun. 9:4457
    [Google Scholar]
  64. 64. 
    Shi J, Zhao Y, Wang K, Shi X, Wang Y et al. 2015. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature 526:7575660–65
    [Google Scholar]
  65. 65. 
    Kayagaki N, Stowe IB, Lee BL, O'Rourke K, Anderson K et al. 2015. Caspase-11 cleaves gasdermin D for non-canonical inflammasome signalling. Nature 526:7575666–71
    [Google Scholar]
  66. 66. 
    Quarato G, Guy CS, Grace CR, Llambi F, Nourse A et al. 2016. Sequential engagement of distinct MLKL phosphatidylinositol-binding sites executes necroptosis. Mol. Cell 61:4589–601
    [Google Scholar]
  67. 67. 
    Zhang Y, Chen X, Gueydan C, Han J 2018. Plasma membrane changes during programmed cell deaths. Cell Res 28:9–21
    [Google Scholar]
  68. 68. 
    Polykratis A, Martens A, Eren RO, Shirasaki Y, Yamagishi M et al. 2019. A20 prevents inflammasome-dependent arthritis by inhibiting macrophage necroptosis through its ZnF7 ubiquitin-binding domain. Nat. Cell Biol. 21:6731–42
    [Google Scholar]
  69. 69. 
    Gong YN, Guy C, Olauson H, Becker JU, Yang M et al. 2017. ESCRT-III acts downstream of MLKL to regulate necroptotic cell death and its consequences. Cell 169:2286–300.e16
    [Google Scholar]
  70. 70. 
    Yoon S, Kovalenko A, Bogdanov K, Wallach D 2017. MLKL, the protein that mediates necroptosis, also regulates endosomal trafficking and extracellular vesicle generation. Immunity 47:151–65.e7
    [Google Scholar]
  71. 71. 
    Loomis WP, den Hartigh AB, Cookson BT, Fink SL 2019. Diverse small molecules prevent macrophage lysis during pyroptosis. Cell Death Dis 10:4326
    [Google Scholar]
  72. 72. 
    Kovacs SB, Miao EA. 2017. Gasdermins: effectors of pyroptosis. Trends Cell Biol 27:9673–84
    [Google Scholar]
  73. 73. 
    Wang J, Tham D, Wei W, Shin YS, Ma C et al. 2012. Quantitating cell-cell interaction functions with applications to glioblastoma multiforme cancer cells. Nano Lett 12:126101–6
    [Google Scholar]
  74. 74. 
    Elitas M, Brower K, Lu Y, Chen JJ, Fan R 2014. A microchip platform for interrogating tumor-macrophage paracrine signaling at the single-cell level. Lab Chip 14:183582–88
    [Google Scholar]
  75. 75. 
    Lu S, Dugan CE, Kennedy RT 2018. Microfluidic chip with integrated electrophoretic immunoassay for investigating cell-cell interactions. Anal. Chem. 90:85171–78
    [Google Scholar]
  76. 76. 
    Tanaka Y, Suzuki N, Mora K, Mizuno J, Shoji S et al. 2017. Widefield real-time single-cell secretion imaging with optical waveguide technique Paper presented at 19th International Conference on Solid-State Sensors, Actuators, and Microsystems (TRANSDUCERS), June 18–22 Kaohsiung, Taiwan: https://doi.org/10.1109/TRANSDUCERS.2017.7994363
    [Crossref] [Google Scholar]
  77. 77. 
    George J, Wang J. 2016. Assay of genome-wide transcriptome and secreted proteins on the same single immune cells by microfluidics and RNA sequencing. Anal. Chem. 88:2010309–15
    [Google Scholar]
  78. 78. 
    Jin A, Ozawa T, Tajiri K, Obata T, Kondo S 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]
  79. 79. 
    Ogunniyi AO, Story CM, Papa E, Guillen E, Love JC 2009. Screening individual hybridomas by microengraving to discover monoclonal antibodies. Nat. Protoc. 4:5767–82
    [Google Scholar]
  80. 80. 
    Yoshimoto N, Kida A, Jie X, Kurokawa M, Iijima M et al. 2013. An automated system for high-throughput single cell-based breeding. Sci. Rep. 3:1191
    [Google Scholar]
  81. 81. 
    Jorgolli M, Nevill T, Winters A, Chen I, Chong S et al. 2019. Nanoscale integration of single cell biologics discovery processes using optofluidic manipulation and monitoring. Biotechnol. Bioeng. 116:92393–411
    [Google Scholar]
  82. 82. 
    Xi HD, Zheng H, Guo W, Gañán-Calvo AM, Ai Y et al. 2017. Active droplet sorting in microfluidics: a review. Lab Chip 17:5751–71
    [Google Scholar]
  83. 83. 
    Kasai Y, Sakuma S, Arai F 2019. Isolation of single motile cells using a high-speed picoliter pipette. Microfluid. Nanofluid. 23:18
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-091619-101212
Loading
/content/journals/10.1146/annurev-anchem-091619-101212
Loading

Data & Media loading...

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error