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Malacrida B, Nichols S, Maniati E, Jones R, Delanie-Smith R, Roozitalab R, Tyler EJ, Thomas M, Boot G, Mackerodt J, Lockley M, Knight MM, Balkwill FR, Pearce OM. A human multi-cellular model shows how platelets drive production of diseased extracellular matrix and tissue invasion. iScience 2021; 24:102676. [PMID: 34189439 PMCID: PMC8215303 DOI: 10.1016/j.isci.2021.102676] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/08/2021] [Accepted: 05/27/2021] [Indexed: 12/14/2022] Open
Abstract
Guided by a multi-level "deconstruction" of omental metastases, we developed a tetra (four cell)-culture model of primary human mesothelial cells, fibroblasts, adipocytes, and high-grade serous ovarian cancer (HGSOC) cell lines. This multi-cellular model replicated key elements of human metastases and allowed malignant cell invasion into the artificial omental structure. Prompted by findings in patient biopsies, we used the model to investigate the role of platelets in malignant cell invasion and extracellular matrix, ECM, production. RNA (sequencing and quantitative polymerase-chain reaction), protein (proteomics and immunohistochemistry) and image analysis revealed that platelets stimulated malignant cell invasion and production of ECM molecules associated with poor prognosis. Moreover, we found that platelet activation of mesothelial cells was critical in stimulating malignant cell invasion. Whilst platelets likely activate both malignant cells and mesothelial cells, the tetra-culture model allowed us to dissect the role of both cell types and model the early stages of HGSOC metastases.
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Affiliation(s)
- Beatrice Malacrida
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Sam Nichols
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Eleni Maniati
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Roanne Jones
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Robin Delanie-Smith
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- School of Engineering and Materials Science, Queen Mary University of London, Mile End, London E1 4NS, UK
| | - Reza Roozitalab
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Eleanor J. Tyler
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Morgan Thomas
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Gina Boot
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Jonas Mackerodt
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Michelle Lockley
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Martin M. Knight
- School of Engineering and Materials Science, Queen Mary University of London, Mile End, London E1 4NS, UK
| | - Frances R. Balkwill
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Oliver M.T. Pearce
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
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Dony L, Mackerodt J, Ward S, Filippi S, Stumpf MPH, Liepe J. PEITH(Θ): perfecting experiments with information theory in Python with GPU support. Bioinformatics 2018; 34:1249-1250. [PMID: 29228182 PMCID: PMC5998942 DOI: 10.1093/bioinformatics/btx776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 12/04/2017] [Indexed: 01/27/2023] Open
Abstract
Motivation Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. Availability and implementation https://github.com/MichaelPHStumpf/Peitho
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Affiliation(s)
| | | | | | - Sarah Filippi
- Faculty of Medicine, School of Public Health.,Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | | | - Juliane Liepe
- Department of Life Sciences.,Max-Planck-Institute for Biophysical Chemistry, 37077 Göttingen, Germany
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