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Nonspecific probe binding and automatic gating in flow cytometry and fluorescence activated cell sorting (FACS)

1 Department of Biomathematics, UCLA, Los Angeles, CA, 90095-1766, USA
2 Department of Mathematics, UCLA, Los Angeles, CA, 90095-1555, USA

Special Issues: Mathematical Methods in the Biosciences

Flow cytometry is extensively used in cell biology to differentiate cells of interest (mutants) from control cells (wild-types). For mutant cells characterized by expression of a distinct membrane surface structure, fluorescent marker probes can be designed to bind specifically to these structures while the cells are in suspension, resulting in a sufficiently high fluorescence intensity measurement by the cytometer to identify a mutant cell. However, cell membranes may have relatively weak, nonspecific binding affinity to the probes, resulting in false positive results. Furthermore, the same effect would be present on mutant cells, allowing both specific and nonspecific binding to a single cell. We derive and analyze a kinetic model of fluorescent probe binding dynamics by tracking populations of mutant and wild-type cells with differing numbers of probes bound specifically and nonspecifically. By assuming the suspension is in chemical equilibrium prior to cytometry, we use a two-species Langmuir adsorption model to analyze the confounding effects of non-specific binding on the assay. Furthermore, we analytically derive an expectation maximization method to infer an appropriate estimate of the total number of mutant cells as an alternative to existing, heuristic methods. Lastly, using our model, we propose a new method to infer physical and experimental parameters from existing protocols. Our results provide improved ways to quantitatively analyze flow cytometry data
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Keywords FACS; flow cytometry; automatic gating; fluorescing antibodies; Langmuir adsorption; mixture model; serial dilution

Citation: Bhaven A. Mistry, Tom Chou. Nonspecific probe binding and automatic gating in flow cytometry and fluorescence activated cell sorting (FACS). Mathematical Biosciences and Engineering, 2019, 16(5): 4477-4490. doi: 10.3934/mbe.2019223

References

  • 1. I. A. Zuleta, A. Aranda-Díaz, H. Li, et al., Dynamic characterization of growth and gene expression, Nat. Meth., 11 (2014), 443–450.
  • 2. H. Abe and E. T. Kool, Flow cytometric detection of specific RNAs in native human cells with quenched autoligating FRET probes, Proc. Nat. Acad. Sci., 103 (2006), 263–268.
  • 3. S. B. Joseph and K. T. Arrildt, A. E. Swanstrom, et al., Quantification of entry phenotypes of macrophage-tropic HIV-1 across a wide range of CD4 densities, J. Virol., 88 (2014), 1858–1869.
  • 4. N.E.Webb and B.Lee, Quantifying CD4/CCR5 usage efficiency of HIV-1 Env using the Affinofile system, HIV Prot., 1st edition, Springer, New York, 2016, 3–20.
  • 5. T. M. Ashhurst, A. L. Smith and N. J. C. King, High-dimensional fluorescence cytometry, Curr. Prot. Immun., 119 (2017), 1–38.
  • 6. B. Bourdin, E. Segura, M. Téreault, et al., Determination of the relative cell surface and total expression of recombinant ion channels using flow cytometry, J. Vis. Exp., 115 (2016), 54732.
  • 7. A. Adan, G. Alizada, Y. Kiraz, et al., Flow cytometry: basic principles and applications, Crit. Rev. Biotech., 37 (2017), 163–176.
  • 8. L. A. Herzenberg, D. Parks, B. Sahaf, et al., The history and future of the fluorescence activated cell sorter and flow cytometry: A view from Stanford, Clin. Chem., 48 (2002), 1819–1827.
  • 9. K. Lo, R. R. Brinkman and R. Gottardo, Automated gating of flow cytometry data via robust model-based clustering, J. Intl. Soc. Anal. Cyt., 73 (2008), 321–332.
  • 10. J. G. Kenna, G. N. Major and R. S. Williams, Methods for reducing non-specific antibody binding in enzyme-linked immunosorbent assays, J. Immun. Meth., 85 (1985), 409–419.
  • 11. C. P. Verschoor, A. Lelic, J. L. Bramson, et al., An introduction to automated flow cytometry gating tools and their implementation, Front. Immun., 6 (2015), 380.
  • 12. R.A.Burns, M.Y.El-Sayed and M.F.Roberts, Kinetic model for surface-active enzymes based on the Langmuir adsorption isotherm: phospholipase C (bacillus cereus) activity toward dimyristoyl phosphatidylcholine/detergent micelles, Natl. Acad. Sci. USA, 79 (1982), 4902–4906.
  • 13. K. Lange, Mathematical and statistical methods for genetic analysis, 1st edition, Springer-Verlag, New York, 1997.
  • 14. S. A. Mutch, B. S. Fujimoto, C. L. Kuyper, et al., Deconvolving single-molecule intensity distributions for quantitative microscopy measurements, Biophys J., 92 (2007), 2926–2943.
  • 15. European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases, Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution, Clin. Microbio. Infect., 9 (2003), 9–15.
  • 16. B. A. Mistry, M. R. D'Orsogna and T. Chou, The effects of statistical multiplicity of infection on virus quantification and infectivity assays, Biophysi J., 114 (2018), 2974–2985.
  • 17. J. P. Awe, P. C. Lee, C. Ramathal, et al., Generation and characterization of transgene-free human induced pluripotent stem cells and conversion to putative clinical-grade status, Stem Cell Res. Ther., 4 (2013), 87.

 

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