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Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain tr...

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Autores principales: Luo, Songhao, Zhang, Zhenquan, Wang, Zihao, Yang, Xiyan, Chen, Xiaoxuan, Zhou, Tianshou, Zhang, Jiajun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073913/
https://www.ncbi.nlm.nih.gov/pubmed/37035293
http://dx.doi.org/10.1098/rsos.221057
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author Luo, Songhao
Zhang, Zhenquan
Wang, Zihao
Yang, Xiyan
Chen, Xiaoxuan
Zhou, Tianshou
Zhang, Jiajun
author_facet Luo, Songhao
Zhang, Zhenquan
Wang, Zihao
Yang, Xiyan
Chen, Xiaoxuan
Zhou, Tianshou
Zhang, Jiajun
author_sort Luo, Songhao
collection PubMed
description Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.
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spelling pubmed-100739132023-04-06 Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model Luo, Songhao Zhang, Zhenquan Wang, Zihao Yang, Xiyan Chen, Xiaoxuan Zhou, Tianshou Zhang, Jiajun R Soc Open Sci Biochemistry, Cellular and Molecular Biology Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data. The Royal Society 2023-04-05 /pmc/articles/PMC10073913/ /pubmed/37035293 http://dx.doi.org/10.1098/rsos.221057 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Biochemistry, Cellular and Molecular Biology
Luo, Songhao
Zhang, Zhenquan
Wang, Zihao
Yang, Xiyan
Chen, Xiaoxuan
Zhou, Tianshou
Zhang, Jiajun
Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title_full Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title_fullStr Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title_full_unstemmed Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title_short Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
title_sort inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model
topic Biochemistry, Cellular and Molecular Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073913/
https://www.ncbi.nlm.nih.gov/pubmed/37035293
http://dx.doi.org/10.1098/rsos.221057
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