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Quantifying Enzyme Activity and Gene Expression Within Single Cells Using a Multiplexed Capillary Electrophoresis Platform

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Single Cell ‘Omics of Neuronal Cells

Part of the book series: Neuromethods ((NM,volume 184))

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Abstract

To date, numerous chemical cytometry platforms have been developed to study a wide range of biologically relevant analytes in single cells by employing creative combinations of chemical sensors with separation methods and detection instrumentation. However, despite many impressive developments over the past decade, the scope of chemical cytometry methods has remained limited, in part due to a reliance on the use of chemical sensors for analyte detection. Traditionally, these chemical sensors have been restricted to detecting a single analyte or a closely related subset of analytes. Additionally, the use of multiple sensors simultaneously can quickly increase the hardware and experimental complexity, thereby restricting the technology to a handful of academic laboratories. In contrast, single-cell sequencing technologies have seen widespread adoption over the past decade and facilitate measurements of thousands of unique genes simultaneously, yielding a rich data set describing the cellular genotype or RNA expression pattern. Furthermore, sequencing methods and hardware have grown increasingly more robust and cost-effective over time. While these developments are impressive, methods relying on nucleic acid sequencing alone (genotype and RNA expression) provide a limited view into the biochemical processes and overall cellular phenotype. Therefore, to improve upon current single-cell sequencing methods, our lab has developed a combined platform that integrates capillary electrophoresis-based chemical cytometry measurements with the power of single-cell sequencing. In this manuscript, we describe the background and theory behind our multiplexed CE-based, single-cell analysis platform and provide detailed instruction as to its construction and operation when performing simultaneous measurements of enzyme activity and gene expression in single cells.

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References

  1. Milo R, Philips R. How big is a human cell? http://book.bionumbers.org/how-big-is-a-human-cell/. Accessed 28 Aug 2020

  2. Characteristic cell volume – Human Homo sapiens – BNID 100434. https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=16&id=100434. Accessed 28 Aug 2020

  3. Cohen D et al (2008) Chemical cytometry: fluorescence-based single-cell analysis. Annu Rev Anal Chem 1:165–190. https://doi.org/10.1146/annurev.anchem.1.031207.113104

    Article  CAS  Google Scholar 

  4. Lähnemann D et al (2020) Eleven grand challenges in single-cell data science. Genome Biol 21(31):1–35. https://doi.org/10.1186/s13059-020-1926-6

    Article  Google Scholar 

  5. Arvaniti E, Claassen M (2017) Sensitive detection of rare disease-associated cell subsets via representation learning. Nat Commun 8(14825):1–10. https://10.1038/ncomms14825

    Google Scholar 

  6. Hodzic E (2016) Single-cell analysis: advances and future perspectives. Bosnian J Basic Med Sci 16(4):313–314. https://doi.org/10.17305/bjbms.2016.1371

    Article  Google Scholar 

  7. Abdallah BY et al (2013) Single cell heterogeneity. Cell Cycle 12:3640–3649. https://doi.org/10.4161/cc.26580

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Goldman SL et al (2019) The impact of heterogeneity on single-cell sequencing. Front Genet 10:1–8. https://doi.org/10.3389/fgene.2019.00008

    Article  CAS  Google Scholar 

  9. Qian Z, Bao L (2019) Cellular heterogeneity and single-cell omics. In: Single-cell omics. Elsevier, Amsterdam, Netherlands, pp 35–44

    Google Scholar 

  10. Sottoriva A et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci 110:4009–4014. https://doi.org/10.1073/pnas.1219747110

    Article  PubMed  PubMed Central  Google Scholar 

  11. Qazi MA, Bakhshinyan D, Singh SK (2019) Deciphering brain tumor heterogeneity, one cell at a time. Nat Med 25:1474–1476. https://doi.org/10.1038/s41591-019-0605-1

    Article  CAS  PubMed  Google Scholar 

  12. Perus LJM, Walsh LA (2019) Microenvironmental heterogeneity in brain malignancies. Front Immunol 10:1–15. https://doi.org/10.3389/fimmu.2019.02294/full

    Article  Google Scholar 

  13. Nowotarski HL, Attayek PJ, Allbritton NL (2020) Automated platform for cell selection and separation based on four-dimensional motility and matrix degradation. Analyst 145:2731–2742. https://doi.org/10.1039/c9an02224d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15:81–94. https://doi.org/10.1038/nrclinonc.2017.166

    Article  CAS  PubMed  Google Scholar 

  15. Vembadi A, Menachery A, Qasaimeh MA (2019) Cell cytometry: review and perspective on biotechnological advances. Front Bioeng Biotechnol 7:1–20. https://doi.org/10.3389/fbioe.2019.00147

    Article  Google Scholar 

  16. Doan M et al (2018) Diagnostic potential of imaging flow cytometry. Trends Biotechnol 36:649–652. https://doi.org/10.1016/j.tibtech.2017.12.008

    Article  CAS  PubMed  Google Scholar 

  17. Spitzer M, Nolan GP (2016) Mass cytometry: single cells, many features. Cell 165:780–791. https://doi.org/10.1016/j.cell.2016.04.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Vickerman BM et al (2018) Design and application of sensors for chemical cytometry. ACS Chem Biol 13:1741–1751. https://doi.org/10.1021/acschembio.7b01009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Neumann EK et al (2019) Lipid heterogeneity between astrocytes and neurons revealed by single-cell MALDI-MS combined with Immunocytochemical classification. Angew Chem Int Ed 58:5910–5914. https://doi.org/10.1002/anie.201812892

    Article  CAS  Google Scholar 

  20. Hsu C-C et al (2020) A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons. Proc Natl Acad Sci 117:18412–18423. https://doi.org/10.1073/pnas.2001906117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Baker LA (2018) Perspective and prospectus on single-entity electrochemistry. J Am Chem Soc 140:15549–15559. https://doi.org/10.1021/jacs.8b09747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chan J, Dodani SC, Chang CJ (2012) Reaction-based small-molecule fluorescent probes for chemoselective bioimaging. Nat Chem 4:973–984. https://doi.org/10.1038/nchem.1500

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mullassery D, Horton C, Wood C, White M (2008) Single live cell imaging for systems biology. Essays Biochem 45:121–133. https://doi.org/10.1042/BSE0450121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Komatsu T, Urano Y (2015) Evaluation of enzymatic activities in living systems with small-molecular fluorescent substrate probes. Anal Sci 31:257–265. https://doi.org/10.2116/analsci.31.257

    Article  CAS  PubMed  Google Scholar 

  25. Ou Y, Wilson RE, Weber SG (2018) Methods of measuring enzyme activity ex vivo and in vivo. Annu Rev Anal Chem 11:509–533. https://doi.org/10.1146/annurev-anchem-061417-125619

    Article  CAS  Google Scholar 

  26. Mainz ER et al (2016) Single cell chemical cytometry of Akt activity in rheumatoid arthritis and Normal fibroblast-like Synoviocytes in response to tumor necrosis factor α. Anal Chem 88:7786–7792. https://doi.org/10.1021/acs.analchem.6b01801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mainz ER et al (2016) An integrated chemical cytometry method: shining a light on Akt activity in single cells. Angew Chem Int Ed 55:13095–13098. https://doi.org/10.1002/anie.201606914

    Article  CAS  Google Scholar 

  28. Turner AH et al (2016) Rational Design of a Dephosphorylation-Resistant Reporter Enables Single-Cell Measurement of tyrosine kinase activity. ACS Chem Biol 11:355–362. https://doi.org/10.1021/acschembio.5b00667

    Article  CAS  PubMed  Google Scholar 

  29. Phillips RM et al (2013) Measurement of protein tyrosine phosphatase activity in single cells by capillary electrophoresis. Anal Chem 85:6136–6142. https://doi.org/10.1021/ac401106e

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Phillips RM et al (2014) Ex vivo chemical cytometric analysis of protein tyrosine phosphatase activity in single human airway epithelial cells. Anal Chem 86:1291–1297. https://doi.org/10.1021/ac403705c

    Article  CAS  PubMed  Google Scholar 

  31. Dickinson AJ et al (2014) Single-cell sphingosine kinase activity measurements in primary leukemia. Anal Bioanal Chem 406:7027–7036. https://doi.org/10.1007/s00216-014-7974-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dickinson AJ et al (2015) Analysis of sphingosine kinase activity in single natural killer cells from peripheral blood. Integr Biol (Camb) 7:392–401. https://doi.org/10.1039/c5ib00007f

    Article  CAS  Google Scholar 

  33. Proctor A et al (2014) Measurement of protein kinase B activity in single primary human pancreatic cancer cells. Anal Chem 86:4573–4580. https://doi.org/10.1021/ac500616q

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mainz ER, Dobes NC, Allbritton NL (2015) Pronase E-based generation of fluorescent peptide fragments: tracking intracellular peptide fate in single cells. Anal Chem 87:7987–7995. https://doi.org/10.1021/acs.analchem.5b01929

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Proctor A et al (2016) Development of a protease-resistant reporter to quantify BCR–ABL activity in intact cells. Analyst 141:6008–6017. https://doi.org/10.1039/C6AN01378C

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Keithley RB et al (2013) Capillary electrophoresis with three-color fluorescence detection for the analysis of glycosphingolipid metabolism. Analyst 138:164–170. https://doi.org/10.1039/C2AN36286D

    Article  CAS  PubMed  Google Scholar 

  37. Essaka DC et al (2012) Metabolic cytometry: capillary electrophoresis with two-color fluorescence detection for the simultaneous study of two glycosphingolipid metabolic pathways in single primary neurons. Anal Chem 84:2799–2804. https://doi.org/10.1021/ac2031892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Essaka DC et al (2012) Single cell ganglioside catabolism in primary cerebellar neurons and glia. Neurochem Res 37:1308–1314. https://doi.org/10.1007/s11064-012-0733-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Keithley RB et al (2013) Manipulating ionic strength to improve single cell electrophoretic separations. Talanta 111:206–214. https://doi.org/10.1016/j.talanta.2013.03.012

    Article  CAS  PubMed  Google Scholar 

  40. Voeten RLC et al (2018) Capillary electrophoresis: trends and recent advances. Anal Chem 90:1464–1481. https://doi.org/10.1021/acs.analchem.8b00015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sims CE et al (1998) Laser−micropipet combination for single-cell analysis. Anal Chem 70:4570–4577. https://doi.org/10.1021/ac9802269

    Article  CAS  PubMed  Google Scholar 

  42. Dickinson AJ, Armistead PM, Allbritton NL (2013) Automated capillary electrophoresis system for fast single-cell analysis. Anal Chem 85:4797–4804. https://doi.org/10.1021/ac4005887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Abraham DH et al (2019) Design of an automated capillary electrophoresis platform for single-cell analysis. In: Enzyme activity in single cells. Elsevier, Cambridge, pp 191–122

    Chapter  Google Scholar 

  44. Jorgenson JW, Lukacs KD (1983) Capillary zone electrophoresis. Science 222:266–272. http://www.jstor.org/stable/1691606

    Article  CAS  Google Scholar 

  45. Grossman PD, Colburn JC (2012) Capillary electrophoresis: theory and practice. Academic Press, San Diego

    Google Scholar 

  46. Blackney DM, Foley JP (2017) Dual-opposite injection capillary electrophoresis: principles and misconceptions. Electrophoresis 38:607–616. https://doi.org/10.1002/elps.201600337

    Article  CAS  PubMed  Google Scholar 

  47. Whitmore CD et al (2007) Yoctomole analysis of ganglioside metabolism in PC12 cellular homogenates. Electrophoresis 28:3100–3104. https://doi.org/10.1002/elps.200700202

    Article  CAS  PubMed  Google Scholar 

  48. Ding J et al (2020) Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38:737–746. https://doi.org/10.1038/s41587-020-0465-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Peterson VM et al (2017) Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol 35:936–939. https://doi.org/10.1038/nbt.3973

    Article  CAS  PubMed  Google Scholar 

  50. Becker K et al (2018) Quantifying post-transcriptional regulation in the development of Drosophila melanogaster. Nat Commun 4970:1–14. https://doi.org/10.1038/s41467-018-07455-9

    Article  CAS  Google Scholar 

  51. Hamidi H et al (2014) KRAS mutational subtype and copy number predict in vitro response of human pancreatic cancer cell lines to MEK inhibition. Br J Cancer 111:1788–1801. https://doi.org/10.1038/bjc.2014.475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Martin KC, Ephrussi A (2009) mRNA localization: gene expression in the spatial dimension. Cell 136:719–730. https://doi.org/10.1016/j.cell.2009.01.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Haque A et al (2017) A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med 75:1–12. https://doi.org/10.1186/s13073-017-0467-4

    Article  CAS  Google Scholar 

  54. Stewart MP, Langer R, Jensen KF (2018) Intracellular delivery by membrane disruption: mechanisms, strategies, and concepts. Chem Rev 118:7409–7531. https://doi.org/10.1021/acs.chemrev.7b00678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meacham JM et al (2014) Physical methods for intracellular delivery: practical aspects from laboratory use to industrial-scale processing. J Lab Autom 19:1–18. https://doi.org/10.1177/2211068213494388

    Article  CAS  PubMed  Google Scholar 

  56. Klán P et al (2013) Photoremovable protecting groups in chemistry and biology: reaction mechanisms and efficacy. Chem Rev 113:119–191. https://doi.org/10.1021/cr300177k

    Article  CAS  PubMed  Google Scholar 

  57. Thakur S, Cattoni DI, Nöllmann M (2015) The fluorescence properties and binding mechanism of SYTOX green, a bright, low photo-damage DNA intercalating agent. Eur Biophys J 44:337–348. https://doi.org/10.1007/s00249-015-1027-8

    Article  CAS  PubMed  Google Scholar 

  58. Breadmore MC (2009) Electrokinetic and hydrodynamic injection: making the right choice for capillary electrophoresis. Bioanalysis 1:889–894. https://doi.org/10.4155/bio.09.73

    Article  CAS  PubMed  Google Scholar 

  59. Hellman AN et al (2008) Biophysical response to pulsed laser microbeam-induced cell lysis and molecular delivery. J Biophotonics 1:24–35. https://doi.org/10.1002/jbio.200710010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rau KR et al (2006) Pulsed laser microbeam-induced cell lysis: time-resolved imaging and analysis of hydrodynamic effects. Biophys J 91:317–329. https://doi.org/10.1529/biophysj.105.079921

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kanda T, Sullivan KF, Wahl GM (1998) Histone–GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. Curr Biol 8:377–385. https://doi.org/10.1016/S0960-9822(98)70156-3

    Article  CAS  PubMed  Google Scholar 

  62. Hows MEP, Perrett D (1998) Effects of buffer depletion in capillary electrophoresis: development of a continuous flow cathode. Chromatographia 48:355–359. https://doi.org/10.1007/bf02467703

    Article  CAS  Google Scholar 

  63. Edelstein A et al (2010) Computer control of microscopes using μManager. Curr Protoc Mol Biol 14(20):1–22. https://doi.org/10.1002/0471142727.mb1420s92

    Article  Google Scholar 

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Acknowledgments

Sensors C48F and 48F were synthesized and characterized in collaboration with Brianna Vickerman, Professor David Lawrence, and Dr. Qunzhao Wang at the University of North Carolina at Chapel Hill. Additional thanks to David Lawrence for reviewing the chemical structures in this manuscript. This work is supported by the National Institutes of Health grants R01 CA224763 (N.L.A.), R01 CA233811 (N.L.A.), F31 HL147500-01 (M.M.A.), and 5F31CA243312-02 (L.A.G.). The authors declare no conflicts of interest.

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Authors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Matthew M. Anttila & Luke A. Gallion

  2. University of Washington, Seattle, WA, USA

    Matthew M. Anttila, Brae V. Petersen, Luke A. Gallion, Manibarathi Vaithiyanathan & Nancy L. Allbritton

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  1. Matthew M. Anttila
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  2. Brae V. Petersen
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  3. Luke A. Gallion
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  4. Manibarathi Vaithiyanathan
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  5. Nancy L. Allbritton
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Correspondence to Nancy L. Allbritton .

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Editors and Affiliations

  1. The Beckman Institute, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Jonathan V. Sweedler

  2. Dept Pharmacology, 38 John Morgan Bldg, University of Pennsylvania, Philadelphia, PA, USA

    James Eberwine

  3. Bridge Institute, University of Southern California, Los Angeles, CA, USA

    Scott E. Fraser

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Anttila, M.M., Petersen, B.V., Gallion, L.A., Vaithiyanathan, M., Allbritton, N.L. (2022). Quantifying Enzyme Activity and Gene Expression Within Single Cells Using a Multiplexed Capillary Electrophoresis Platform. In: Sweedler, J.V., Eberwine, J., Fraser, S.E. (eds) Single Cell ‘Omics of Neuronal Cells. Neuromethods, vol 184. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2525-5_8

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  • DOI: https://doi.org/10.1007/978-1-0716-2525-5_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2524-8

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