Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
2 results
Search Results
Article Citation - WoS: 25Citation - Scopus: 27Progression of Irradiated Mesenchymal Stromal Cells From Early to Late Senescence: Changes in SASP Composition and Anti-Tumour Properties(Wiley, 2023-03-22) Alessio, Nicola; Acar, Mustafa Burak; Squillaro, Tiziana; Aprile, Domenico; Ayaz-Guner, Serife; Di Bernardo, Giovanni; Galderisi, UmbertoGenotoxic injuries converge on senescence-executive program that promotes production of a senescence-specific secretome (SASP). The study of SASP is particularly intriguing, since through it a senescence process, triggered in a few cells, can spread to many other cells and produce either beneficial or negative consequences for health. We analysed the SASP of quiescent mesenchymal stromal cells (MSCs) following stress induced premature senescence (SIPS) by ionizing radiation exposure. We performed a proteome analysis of SASP content obtained from early and late senescent cells. The bioinformatics studies evidenced that early and late SASPs, besides some common ontologies and signalling pathways, contain specific factors. In spite of these differences, we evidenced that SASPs can block in vitro proliferation of cancer cells and promote senescence/apoptosis. It is possible to imagine that SASP always contains core components that have an anti-tumour activity, the progression from early to late senescence enriches the SASP of factors that may promote SASP tumorigenic activity only by interacting and instructing cells of the immune system. Our results on Caco-2 cancer cells incubated with late SASP in presence of peripheral white blood cells strongly support this hypothesis. We evidenced that quiescent MSCs following SIPS produced SASP that, while progressively changed its composition, preserved the capacity to block cancer growth by inducing senescence and/or apoptosis only in an autonomous manner.Article Citation - WoS: 6Citation - Scopus: 7Improved Senescent Cell Segmentation on Bright-Field Microscopy Images Exploiting Representation Level Contrastive Learning(Wiley, 2024-03) Celebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, KutayMesenchymal stem cells (MSCs) are stromal cells which have multi-lineage differentiation and self-renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright-field microscope is time-consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well-performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning-based automated counting process impractically expensive. To address this challenge, self-supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.