A stochastic model dissects cell states in biological transition processes
Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assa...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Nature Publishing Group
2014
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| Online Access: | http://hdl.handle.net/1721.1/88212 |
| _version_ | 1811086179565568000 |
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| author | Armond, Jonathan W. Saha, Krishanu Rana, Anas A. Oates, Chris J. Jaenisch, Rudolf Nicodemi, Mario Mukherjee, Sach |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Armond, Jonathan W. Saha, Krishanu Rana, Anas A. Oates, Chris J. Jaenisch, Rudolf Nicodemi, Mario Mukherjee, Sach |
| author_sort | Armond, Jonathan W. |
| collection | MIT |
| description | Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations. |
| first_indexed | 2024-09-23T13:22:04Z |
| format | Article |
| id | mit-1721.1/88212 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:22:04Z |
| publishDate | 2014 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/882122022-10-01T14:46:59Z A stochastic model dissects cell states in biological transition processes Armond, Jonathan W. Saha, Krishanu Rana, Anas A. Oates, Chris J. Jaenisch, Rudolf Nicodemi, Mario Mukherjee, Sach Massachusetts Institute of Technology. Department of Biology Whitehead Institute for Biomedical Research Jaenisch, Rudolf Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations. 2014-07-08T19:40:12Z 2014-07-08T19:40:12Z 2014-01 2013-06 Article http://purl.org/eprint/type/JournalArticle 2045-2322 http://hdl.handle.net/1721.1/88212 Armond, Jonathan W., Krishanu Saha, Anas A. Rana, Chris J. Oates, Rudolf Jaenisch, Mario Nicodemi, and Sach Mukherjee. “A Stochastic Model Dissects Cell States in Biological Transition Processes.” Sci. Rep. 4 (January 17, 2014). en_US http://dx.doi.org/10.1038/srep03692 Scientific Reports Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0 application/pdf Nature Publishing Group Nature Publishing Group |
| spellingShingle | Armond, Jonathan W. Saha, Krishanu Rana, Anas A. Oates, Chris J. Jaenisch, Rudolf Nicodemi, Mario Mukherjee, Sach A stochastic model dissects cell states in biological transition processes |
| title | A stochastic model dissects cell states in biological transition processes |
| title_full | A stochastic model dissects cell states in biological transition processes |
| title_fullStr | A stochastic model dissects cell states in biological transition processes |
| title_full_unstemmed | A stochastic model dissects cell states in biological transition processes |
| title_short | A stochastic model dissects cell states in biological transition processes |
| title_sort | stochastic model dissects cell states in biological transition processes |
| url | http://hdl.handle.net/1721.1/88212 |
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