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.

Author summary

Modeling how cells transfer a signal from an extracellular stimulus to different compartments within the cell is critical to understand how different stimuli result in different cellular responses. We designed a software (McSNAC) that proposes a minimal mathematical model to describe biochemical signaling events learning from time-stamped cytometry data. This software makes linear approximations which hold true in many scenarios, but can also break down under several conditions. We explore this approximation in depth to provide guidelines regarding its applicability. In addition, we provide a user interface for non-technical users to analyze their time-stamped cytometry data with this approach.

Graphical abstract

Keywords

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

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-022-0308.

SOFTWARE AVAILABILITY

The software McSNAC can be found on github (dweth/mcsnac).

AUTHOR CONTRIBUTIONS

D.W. built the parent script and GUI for McSNAC, built in silico data, created MATLAB codes, and ran the simulations described in the text. S.M. wrote the simulated annealing Fortran code and NK cell signaling BioNetGen codes. J.D. and D.W. planned the experiments and analyzed the results. All authors contributed to writing and editing the manuscript.

ACKNOWLEDGEMENTS

This work is supported by the NIH awards R01-AI 143740 and R01-AI 146581 to J.D. We would like to thank Bill Stewart for his help in developing the profile likelihood confidence interval estimator.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Darren Wethington, Sayak Mukherjee and Jayajit Das declare that they have no competing interests.

This article does not contain any studies with human participants or animals performed by any of the authors.

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2023 The Author (s). Published by Higher Education Press.