Microfluidic Immunoassays for Time-Resolved Measurement of Protein Secretion from Single Cells

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 T_H1 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 LÅ, 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]
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]