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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2023-04-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.221057 |
| _version_ | 1797852062443110400 |
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| author | Songhao Luo Zhenquan Zhang Zihao Wang Xiyan Yang Xiaoxuan Chen Tianshou Zhou Jiajun Zhang |
| author_facet | Songhao Luo Zhenquan Zhang Zihao Wang Xiyan Yang Xiaoxuan Chen Tianshou Zhou Jiajun Zhang |
| author_sort | Songhao Luo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T19:26:52Z |
| format | Article |
| id | doaj.art-99e47427ff81441fada2441766e33d6a |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-04-09T19:26:52Z |
| publishDate | 2023-04-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-99e47427ff81441fada2441766e33d6a2023-04-05T07:05:24ZengThe Royal SocietyRoyal Society Open Science2054-57032023-04-0110410.1098/rsos.221057Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph modelSonghao Luo0Zhenquan Zhang1Zihao Wang2Xiyan Yang3Xiaoxuan Chen4Tianshou Zhou5Jiajun Zhang6Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaGuangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaGuangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaSchool of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, People's Republic of ChinaGuangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaGuangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaGuangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of ChinaGene 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.https://royalsocietypublishing.org/doi/10.1098/rsos.221057inferencetranscriptional burstingsingle-cell snapshotnon-Markoviangene expression |
| spellingShingle | Songhao Luo Zhenquan Zhang Zihao Wang Xiyan Yang Xiaoxuan Chen Tianshou Zhou Jiajun Zhang Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model Royal Society Open Science inference transcriptional bursting single-cell snapshot non-Markovian gene expression |
| 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 | inference transcriptional bursting single-cell snapshot non-Markovian gene expression |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.221057 |
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