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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.

SUBMITTER: Momiji H

PROVIDER: S-EPMC6592563 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Disentangling juxtacrine from paracrine signalling in dynamic tissue.

Momiji Hiroshi H Hassall Kirsty L KL Featherstone Karen K McNamara Anne V AV Patist Amanda L AL Spiller David G DG Christian Helen C HC White Michael R H MRH Davis Julian R E JRE Finkenstädt Bärbel F BF Rand David A DA

PLoS computational biology 20190613 6

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 wha ...[more]

PMID: 31194728

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Project description:BACKGROUND: Most mathematical models of biochemical pathways consider either signalling events that take place within a single cell in isolation, or an 'average' cell which is considered to be representative of a cell population. Likewise, experimental measurements are often averaged over populations consisting of hundreds of thousands of cells. This approach ignores the fact that even within a genetically-homogeneous population, local conditions may influence cell signalling and result in phenotypic heterogeneity. We have developed a multi-scale computational model that accounts for emergent heterogeneity arising from the influences of intercellular signalling on individual cells within a population. Our approach was to develop an ODE model of juxtacrine EGFR-ligand activation of the MAPK intracellular pathway and to couple this to an agent-based representation of individual cells in an expanding epithelial cell culture population. This multi-scale, multi-paradigm approach has enabled us to simulate Extracellular signal-regulated kinase (Erk) activation in a population of cells and to examine the consequences of interpretation at a single cell or population-based level using virtual assays. RESULTS: A model consisting of a single pair of interacting agents predicted very different Erk activation (phosphorylation) profiles, depending on the formation rate and stability of intercellular contacts, with the slow formation of stable contacts resulting in low but sustained activation of Erk, and transient contacts resulting in a transient Erk signal. Extension of this model to a population consisting of hundreds to thousands of interacting virtual cells revealed that the activated Erk profile measured across the entire cell population was very different and may appear to contradict individual cell findings, reflecting heterogeneity in population density across the culture. This prediction was supported by immunolabelling of an epithelial cell population grown in vitro, which confirmed heterogeneity of Erk activation. CONCLUSION: These results illustrate that mean experimental data obtained from analysing entire cell populations is an oversimplification, and should not be extrapolated to deduce the signal:response paradigm of individual cells. This multi-scale, multi-paradigm approach to biological simulation provides an important conceptual tool in addressing how information may be integrated over multiple scales to predict the behaviour of a biological system.

Project description:The physiological function of the immune system and the response to therapeutic immunomodulators may be sensitive to combinatorial cytokine micro-environments that shape the responses of specific immune cells. Previous work shows that paracrine cytokines released by virus-infected human dendritic cells (DC) can dictate the maturation state of naM-CM-/ve DCs. To understand the effects of paracrine signaling, we systematically studied the effects of combinations cytokines in this complex mixture in generating an antiviral state. After naM-CM-/ve DCs were exposed to either IFNM-NM-2 or to paracrine signaling released by DCs infected by Newcastle Disease Virus (NDV), microarray analysis revealed a large number of genes that were differently regulated by the DC-secreted paracrine signaling. In order to identify the cytokine mechanisms involved, we identified 20 cytokines secreted by NDV infected DCs for which the corresponding receptor gene is expressed in naM-CM-/ve DCs. By exposing cells to all combinations of 19 cytokines (leave-one-out studies) we identified 5 cytokines (IFNM-NM-2, TNFM-NM-1, IL-1M-oM-^AM-", TNFSF15 and IL28) as candidates for regulating DC maturation markers. Subsequent experiments identified IFNM-NM-2, TNFM-NM-1 and IL1M-oM-^AM-" as the major synergistic contributors to this antiviral state. This finding was supported by infection studies in vitro, by T cell activation studies and by in vivo infection studies in mouse. Combination of cytokines can cause response states in DCs that differ from those achieved by the individual cytokines alone. These results suggest that the cytokine microenvironment may act via a combinatorial code to direct the response state of specific immune cells. Further elucidation of this code may provide insight into responses to infection and neoplasia as well as guide the development of combinatorial cytokine immunomodulation for infectious, autoimmune and immunosurveillance-related diseases. Cells exposed to the supernatant of NDV infected cells were compared to NDV infected cells, IFNM-NM-2 treated cells and naM-CM-/ve dendritic cells after 8 hours.

Project description:BackgroundAdipose-derived stromal cells (ASCs) possess a multitude of regenerative capabilities, which include immunomodulation, angiogenesis, and stimulation of extracellular matrix (ECM) remodeling. However, the underlying mechanisms leading to ECM remodeling remain largely elusive and highlight the need for functional in vitro models for mode of action studies. Therefore, the purpose of this study was to develop an in vitro co-culture model to investigate the capabilities of ASCs to modulate fibroblasts and ECM.MethodsAn ECM in vitro model with ASCs and normal human dermal fibroblasts (NHDFs) was established utilizing macromolecular crowding, ascorbic acid, and TGF-β stimulation. Paracrine and juxtacrine co-cultures were created using transwell inserts and cell cultures with direct cell-cell contacts. The cultures were screened using RT2 PCR Profiler Arrays; the protein levels of myofibroblast differentiation marker alpha smooth muscle actin (αSMA) and ECM remodeling enzymes were analyzed using western blot on cell lysates; the formation of collagen type I, III, VI, and fibronectin was investigated using ELISA on culture supernatants; and the deposition of collagens was analyzed using immunocytochemistry.ResultsTGF-β stimulation of NHDF monocultures increased the expression of 18 transcripts relevant for ECM formation and remodeling, the protein levels of αSMA and matrix metalloproteinase-2 (MMP-2), the formation of collagen type I, III, VI, and fibronectin, and the deposition of collagen type I and VI and decreased the protein levels of MMP-14. Inclusion of ASCs in the ECM co-culture model increased the formation of collagen type I and III through paracrine mechanisms and the formation of collagen type VI through juxtacrine mechanisms.ConclusionsThe co-culture model provides effective stimulation of NHDF monocultures by TGF-β for enhanced formation and deposition of ECM. In the model, ASCs induce changes in ECM by increasing formation of collagen type I, III and VI. The obtained results could guide further investigations of ASCs' capabilities and underlying mechanisms related to ECM formation and remodeling.

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Disentangling juxtacrine from paracrine signalling in dynamic tissue.

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Disentangling juxtacrine from paracrine signalling in dynamic tissue.

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