Disentangling juxtacrine from paracrine signalling in dynamic tissueCitation formats

  • External authors:
  • Hiroshi Momiji
  • Karen Featherstone
  • Kirsty Hassall
  • Anne Mcnamara
  • Amanda Patist
  • Helen C. Christian
  • Michael White
  • Barbel Finkenstadt
  • David Rand

Standard

Disentangling juxtacrine from paracrine signalling in dynamic tissue. / Momiji, Hiroshi; Featherstone, Karen; Hassall, Kirsty; Mcnamara, Anne; Patist, Amanda; Spiller, David; Christian, Helen C.; White, Michael; Davis, Julian; Finkenstadt, Barbel; Rand, David.

In: PL o S Computational Biology, Vol. 15, No. 6, e1007030, 13.06.2019.

Research output: Contribution to journalArticlepeer-review

Harvard

Momiji, H, Featherstone, K, Hassall, K, Mcnamara, A, Patist, A, Spiller, D, Christian, HC, White, M, Davis, J, Finkenstadt, B & Rand, D 2019, 'Disentangling juxtacrine from paracrine signalling in dynamic tissue', PL o S Computational Biology, vol. 15, no. 6, e1007030. https://doi.org/10.1371/journal.pcbi.1007030

APA

Momiji, H., Featherstone, K., Hassall, K., Mcnamara, A., Patist, A., Spiller, D., Christian, H. C., White, M., Davis, J., Finkenstadt, B., & Rand, D. (2019). Disentangling juxtacrine from paracrine signalling in dynamic tissue. PL o S Computational Biology, 15(6), [e1007030]. https://doi.org/10.1371/journal.pcbi.1007030

Vancouver

Momiji H, Featherstone K, Hassall K, Mcnamara A, Patist A, Spiller D et al. Disentangling juxtacrine from paracrine signalling in dynamic tissue. PL o S Computational Biology. 2019 Jun 13;15(6). e1007030. https://doi.org/10.1371/journal.pcbi.1007030

Author

Momiji, Hiroshi ; Featherstone, Karen ; Hassall, Kirsty ; Mcnamara, Anne ; Patist, Amanda ; Spiller, David ; Christian, Helen C. ; White, Michael ; Davis, Julian ; Finkenstadt, Barbel ; Rand, David. / Disentangling juxtacrine from paracrine signalling in dynamic tissue. In: PL o S Computational Biology. 2019 ; Vol. 15, No. 6.

Bibtex

@article{7c55e16b789f43dfab884fc5547d012e,
title = "Disentangling juxtacrine from paracrine signalling in dynamic tissue",
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.",
author = "Hiroshi Momiji and Karen Featherstone and Kirsty Hassall and Anne Mcnamara and Amanda Patist and David Spiller and Christian, {Helen C.} and Michael White and Julian Davis and Barbel Finkenstadt and David Rand",
year = "2019",
month = jun,
day = "13",
doi = "10.1371/journal.pcbi.1007030",
language = "English",
volume = "15",
journal = "PL o S Computational Biology",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "6",

}

RIS

TY - JOUR

T1 - Disentangling juxtacrine from paracrine signalling in dynamic tissue

AU - Momiji, Hiroshi

AU - Featherstone, Karen

AU - Hassall, Kirsty

AU - Mcnamara, Anne

AU - Patist, Amanda

AU - Spiller, David

AU - Christian, Helen C.

AU - White, Michael

AU - Davis, Julian

AU - Finkenstadt, Barbel

AU - Rand, David

PY - 2019/6/13

Y1 - 2019/6/13

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1007030

DO - 10.1371/journal.pcbi.1007030

M3 - Article

VL - 15

JO - PL o S Computational Biology

JF - PL o S Computational Biology

SN - 1553-7358

IS - 6

M1 - e1007030

ER -