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

Quantitative Biology 2023年第1期11卷 59-71页

作者： Darren Wethington Sayak Mukherjee Jayajit Das Steve and Cindy Rasmussen Institute for Genomic Medicine Abigail Wexner Research InstituteNationwide Children’s HospitalColumbusOhio 43205United States Biomedical Sciences Graduate Program College of MedicineThe Ohio State UniversityColumbusOhio 43210United States Department of Pediatrics College of MedicineThe Ohio State UniversityColumbusOhio 43210United States Pelotonia Institute for Immuno-Oncology College of MedicineThe Ohio State UniversityColumbusOhio 43210United States DepartmentofBiomedicalInformatics CollegeofMedicineTheOhioStateUniversityColumbusOhio43210United States Biophysics Graduate Program The Ohio State UniversityColumbusOhio 43210United States Battelle Memorial Institute ColumbusOhio 43201United States

Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 *** high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular *** particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling ***,computational analysis is required to reconstruct such networks with a mechanistic ***: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 *** approximates signaling networks as a network of first-order reactions between *** assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear ***,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 ***: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 ***: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

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