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Disentangling juxtacrine from paracrine signalling in dynamic tissue
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Momiji, Hiroshi, Hassall, Kirsty, Featherstone, Karen, McNamara, Anne, Patist, Amanda, Spiller, David G., Christian, Helen, White, Michael R. H., Davis, Julian R. E., Finkenstädt, Bärbel and Rand, D. A. (David A.) (2019) Disentangling juxtacrine from paracrine signalling in dynamic tissue. PLoS Computational Biology, 15 (6). e1007030. doi:10.1371/journal.pcbi.1007030
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Official URL: https://doi.org/10.1371/journal.pcbi.1007030
Abstract
Prolactin is a major hormone product of the pituitary gland, the central endocrine regulator. Despite its physiological importance, the cell-level mechanisms of prolactin production are not well understood. Having significantly improved the resolution of real-time-single-cell-GFP-imaging, the authors recently revealed that prolactin gene transcription is highly dynamic and stochastic yet shows space-time coordination in an intact tissue slice. However, it still remains an open question as to what kind of cellular communication mediates the observed space-time organization. To determine the type of interaction between cells we developed a statistical model. The degree of similarity between two expression time series was studied in terms of two distance measures, Euclidean and geodesic, the latter being a network-theoretic distance defined to be the minimal number of edges between nodes, and this was used to discriminate between juxtacrine from paracrine signalling. The analysis presented here suggests that juxtacrine signalling dominates. To further determine whether the coupling is coordinating transcription or post-transcriptional activities we used stochastic switch modelling to infer the transcriptional profiles of cells and estimated their similarity measures to deduce that their spatial cellular coordination involves coupling of transcription via juxtacrine signalling. We developed a computational model that involves an inter-cell juxtacrine coupling, yielding simulation results that show space-time coordination in the transcription level that is in agreement with the above analysis. The developed model is expected to serve as the prototype for the further study of tissue-level organised gene expression for epigenetically regulated genes, such as prolactin.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology Q Science > QP Physiology |
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| Divisions: | Faculty of Science > Mathematics | ||||||
| Library of Congress Subject Headings (LCSH): | Cell interaction -- Mathematical models, Pituitary gland, Paracrine mechanisms, Endocrinology | ||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||
| Publisher: | Public Library of Science | ||||||
| ISSN: | 1553-7358 | ||||||
| Official Date: | 13 June 2019 | ||||||
| Dates: |
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| Volume: | 15 | ||||||
| Number: | 6 | ||||||
| Article Number: | e1007030 | ||||||
| DOI: | 10.1371/journal.pcbi.1007030 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
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