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

作　　者：Darren Wethington Sayak Mukherjee Jayajit Das 

机构地区：[1]Steve and Cindy Rasmussen Institute for Genomic Medicine,Abigail Wexner Research Institute,Nationwide Children’s Hospital,Columbus,Ohio 43205,United States [2]Biomedical Sciences Graduate Program,College of Medicine,The Ohio State University,Columbus,Ohio 43210,United States [3]Department of Pediatrics,College of Medicine,The Ohio State University,Columbus,Ohio 43210,United States [4]Pelotonia Institute for Immuno-Oncology,College of Medicine,The Ohio State University,Columbus,Ohio 43210,United States [5]DepartmentofBiomedicalInformatics,CollegeofMedicine,TheOhioStateUniversity,Columbus,Ohio43210,United States [6]Biophysics Graduate Program,The Ohio State University,Columbus,Ohio 43210,United States [7]Battelle Memorial Institute,Columbus,Ohio 43201,United States

出　　处：《Quantitative Biology》2023年第1期59-71,共13页定量生物学（英文版）

摘　　要：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.

关 键 词：SINGLE-CELL CyTOF data signaling network kinetics ODE McSNAC 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]