Introduction

Das, Biplab; Jagadish, Jagadish

doi:10.1007/978-3-031-27635-4_1

Cited by 1 publication

(1 citation statement)

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“…Since biochemical reaction propensities depend on concentrations of participating molecules, and biochemical reactions are intrinsically stochastic in nature due to thermal fluctuations [7], single cells in clonal cell populations stimulated by the same ligand can show large variations in responses making it difficult to determine key regulators in the face of large variations. Recent advances in single cell cytometry, such as Mass Cytometry, or Cytometry by Time-of-Flight (CyTOF), are currently capable of measuring up to 40 proteins simultaneously in thousands to millions of single cells, with a theoretical limit as high as 100 proteins [8,9]. Thus, measuring signaling protein abundances at different points post stimulation using CyTOF provides time-stamped snapshot data regarding signaling kinetics and cell-cell variation of the kinetics.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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Section: Introductionmentioning

confidence: 99%