McSNAC: A software to approximate first-order signaling networks from mass cytometry data

Abstract

Background: Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.

Methods: We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.

Results: We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.

Conclusions: These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Graphical abstract

Keywords

single-cell / CyTOF data / signaling network / kinetics / ODE / McSNAC

References

[1] Kholodenko, B. (2006). Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol., 7: 165–176

[2] MurphyK.. (2016) Janeway’s immunobiology. New York: Garland science

[3] Chakraborty, A. K. (2010). Pairing computation with experimentation: a powerful coupling for understanding T cell signalling. Nat. Rev. Immunol., 10: 59–71

[4] Das, J. Lanier, L. (2019). Data analysis to modeling to building theory in NK cell biology and beyond: how can computational modeling contribute? J. Leukoc. Biol., 105: 1305–1317

[5] Kim, M. Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S. . (2014). A draft map of the human proteome. Nature, 509: 575–581

[6] Salman, H., Brenner, N., Tung, C. K., Elyahu, N., Stolovicki, E., Moore, L., Libchaber, A. (2012). Universal protein fluctuations in populations of microorganisms. Phys. Rev. Lett., 108: 238105

[7] Swain, P. S., Elowitz, M. B. Siggia, E. (2002). Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci.U.S.A., 99: 12795–12800

[8] Spitzer, M. H. Nolan, G. (2016). Mass cytometry: single cells, many features. Cell, 165: 780–791

[9] Bandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E. Tanner, S. (2009). Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem., 81: 6813–6822

[10] DasJ.. (2018) Systems immunology: an introduction to modeling methods for scientists. Cleveland: CRC Press

[11] Goldstein, B., Faeder, J. R. Hlavacek, W. (2004). Mathematical and computational models of immune-receptor signalling. Nat. Rev. Immunol., 4: 445–456

[12] Veglia, F., Perego, M. (2018). Myeloid-derived suppressor cells coming of age. Nat. Immunol., 19: 108–119

[13] Mukherjee, S., Jensen, H., Stewart, W., Stewart, D., Ray, W. C., Chen, S. Y., Nolan, G. P., Lanier, L. L. (2017). In silico modeling identifies CD45 as a regulator of IL-2 synergy in the NKG2D-mediated activation of immature human NK cells. Sci. Signal., 10: eaai9062

[14] Yuan, B., Shen, C., Luna, A., Korkut, A., Marks, D. S., Ingraham, J. (2021). CellBox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy. Cell Syst., 12: 128–140.e4

[15] Mukherjee, S., Stewart, D., Stewart, W., Lanier, L. L. (2017). Connecting the dots across time: reconstruction of single-cell ignaling trajectories using time-stamped data. R. Soc. Open Sci., 4: 170811

[16] Faeder, J. R., Blinov, M. L. Hlavacek, W. (2009). Rule-based modeling of biochemical systems with BioNetGen. methods. Mol. Biol., 500: 113–167

[17] Harris, L. A., Hogg, J. S., Tapia, J. J., Sekar, J. A., Gupta, S., Korsunsky, I., Arora, A., Barua, D., Sheehan, R. P. Faeder, J. (2016). BioNetGen 2. 2: advances in rule-based modeling. Bioinformatics, 32: 3366–3368

[18] JohnW.,. (2022) Generalized Method of Moments improves parameter estimation in biochemical signaling models of time-stamped single-cell snapshot data. bioRxiv, p. 2022.03.17.484491

[19] ck, A. (2016). Generalized method of moments for estimating parameters of stochastic reaction networks. BMC Syst. Biol., 10: 98

[20] Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., ller, U. (2009). Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25: 1923–1929

[21] PressW.. (1992) Numerical recipes in C: the art of scientific computing. 2nd ed. New York: Cambridge University Press. xxvi, 994 p

[22] Das, J. (2010). Activation or tolerance of natural killer cells is modulated by ligand quality in a nonmonotonic manner. Biophys. J., 99: 2028–2037

[23] Jiang, K., Zhong, B., Gilvary, D. L., Corliss, B. C., Hong-Geller, E., Wei, S. Djeu, J. (2000). Pivotal role of phosphoinositide-3 kinase in regulation of cytotoxicity in natural killer cells. Nat. Immunol., 1: 419–425

RIGHTS & PERMISSIONS

The Author (s). Published by Higher Education Press.