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McSNAC: A software to approximate first-order signaling networks from mass cytometry data
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 parti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134772/ https://www.ncbi.nlm.nih.gov/pubmed/37123637 http://dx.doi.org/10.15302/j-qb-022-0308 |
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author | Wethington, Darren Mukherjee, Sayak Das, Jayajit |
author_facet | Wethington, Darren Mukherjee, Sayak Das, Jayajit |
author_sort | Wethington, Darren |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10134772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
record_format | MEDLINE/PubMed |
spelling | pubmed-101347722023-04-27 McSNAC: A software to approximate first-order signaling networks from mass cytometry data Wethington, Darren Mukherjee, Sayak Das, Jayajit Quant Biol Article 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. 2023-03 /pmc/articles/PMC10134772/ /pubmed/37123637 http://dx.doi.org/10.15302/j-qb-022-0308 Text en https://creativecommons.org/licenses/by/4.0/OPEN ACCESS This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wethington, Darren Mukherjee, Sayak Das, Jayajit McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title | McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title_full | McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title_fullStr | McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title_full_unstemmed | McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title_short | McSNAC: A software to approximate first-order signaling networks from mass cytometry data |
title_sort | mcsnac: a software to approximate first-order signaling networks from mass cytometry data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134772/ https://www.ncbi.nlm.nih.gov/pubmed/37123637 http://dx.doi.org/10.15302/j-qb-022-0308 |
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