| 1. |
- Hudhud, Lina, et al.
(författare)
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Transient receptor potential Ankyrin 1 Ion channel is expressed in Osteosarcoma and its activation reduces viability
- 2024
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Ingår i: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 25:7
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Tidskriftsartikel (refereegranskat)abstract
- Osteosarcoma is a highly malignant, painful cancer with poor treatment opportunities and a bad prognosis. Transient receptor potential ankyrin 1 (TRPA1) and vanilloid 1 (TRPV1) receptors are non-selective cation channels that have been of great interest in cancer, as their expression is increased in some malignancies. In our study we aim to characterize the expression and functionality of the TRPA1 and TRPV1 channels in human and mouse osteosarcoma tissues and in a mouse cell line. TRPA1/Trpa1 and TRPV1/Trpv1 mRNA expressions were demonstrated by PCR gel electrophoresis and RNAscope in situ hybridization. The function of these channels was confirmed by their radioactive 45Ca2+ uptake in response to the TRPA1 agonist, Allyl-isothiocyanate (AITC), and TRPV1 agonist, capsaicin, in K7M2 cells. An ATP-based K2M7 cell viability luminescence assay was used to determine cell viability after AITC or capsaicin treatments. Both TRPA1/Trpa1 and TRPV1/Trpv1 were expressed similarly in human and mouse osteosarcoma tissues, while Trpa1 transcripts were more abundantly present in K7M2 cells. TRPA1 activation with 200 µM AITC induced a significant 45Ca2+ influx into K7M2 cells, and the antagonist attenuated this effect. In accordance with the lower Trpv1 expression, capsaicin induced a moderate 45Ca2+ uptake, which did not reach the level of statistical significance. Both AITC and capsaicin significantly reduced K7M2 cell viability, demonstrating EC50 values of 22 µM and 74 µM. The viability-decreasing effect of AITC was significantly but only partially antagonized by HC-030031, but the action of capsaicin was not affected by the TRPV1 antagonist capsazepine. We provide here the first data on the functional expression of the TRPA1 and TRPV1 ion channels in osteosarcoma, suggesting novel diagnostic and/or therapeutic perspectives.
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| 2. |
- Yáñez-Mó, María, et al.
(författare)
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Biological properties of extracellular vesicles and their physiological functions.
- 2015
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Ingår i: Journal of extracellular vesicles. - : Wiley. - 2001-3078. ; 4
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Forskningsöversikt (refereegranskat)abstract
- In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.
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| 3. |
- Piccinini, Filippo, et al.
(författare)
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Advanced Cell Classifier : User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data
- 2017
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 4:6, s. 651-
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Tidskriftsartikel (refereegranskat)abstract
- High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.
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