Browsing by Author "Boyvat, Dudu"

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Comparative Proteomic Analysis of Dental-Origin Stem Cells: Insights Into Regenerative Potential
(Springer, 2025) Tez, Banu Cicek; Durukan, Sebahat Melike; Yildir, Selin Kubra; Cokkececi, Murat; Boyvat, Dudu; Altinsoy, Nilay; Ozcan, Servet
Teeth are a significant source of stem cells and have clinical importance for regenerative medicine. A human tooth harbors different kinds of stem cells in the dental pulp (DPSC) or the periodontal ligament (PDLSC). Also exfoliated teeth in childhood contain a special type of stem cells in their pulp called Stem cells from Human Exfoliated Deciduous teeth (SHED). All these stem cells have features and capacities that vary depending on their niche. Here we investigated the proteomic properties of three types of stem cells that originated from human teeth. We isolated and cultured the DPSCs, PDLSCs, and SHED cells. After validating MSC populations via immunophenotyping, we performed a mass spectrometry-based proteomic approach to identify and relatively quantify whole cell and secreted proteins. Identified proteins were evaluated by using Gene Ontology and Reactome pathway analysis tools. Our data reveal that SHED cells represented inflammation, hypoxia, and nutrient deficiency-associated ontologies in both their secretome and whole-cell proteomes. The whole-cell proteome of PDLSCs consisted of differentiation and proliferation-associated molecules while their secretory molecules were mainly associated with inflammation, ECM organization, and immune response. Among dental-originated stem cells, DPSCs appeared to be the healthiest and clinically relevant in terms of proteomic properties with their proliferation, growth factor signaling, and stemness-associated molecules in their secretome and whole-cell proteome. Obtained results demonstrated that every type of stem cell from dental origin has unique proteomic features that are altered by their location and physiological conditions. The findings may help researchers improve the dental stem-cell-based regenerative medicine approaches.
Citation - WoS: 3
Citation - Scopus: 3
Improved Senescent Cell Segmentation on Bright-Field Microscopy Images Exploiting Representation Level Contrastive Learning
(Wiley, 2024) Celebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, Kutay; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
Mesenchymal 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.
Citation - WoS: 3
Citation - Scopus: 3
Protocol for Cell Surface Biotinylation of Magnetic Labeled and Captured Human Peripheral Blood Mononuclear Cells
(Elsevier, 2022) Ayaz Güner, Şerife; Acar, Mustafa Burak; Boyvat, Dudu; Güner, Hüseyin; Bozalan, Habibe; Güzel, Melis; Yıldır, Selin Kübra; Altınsoy, Nilay; Fındık, Fatma; Karakükçü, Musa; Özcan, Servet; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
Analysis of the surfaceome of a blood cell subset requires cell sorting, followed by surface protein enrichment. Here, we present a protocol combining magnetically activated cell sorting (MACS) and surface biotinylation of the target cell subset from human peripheral blood mononuclear cells (PBMCs). We describe the steps for isolating target cells and their in-column surface biotinylation, followed by isolation and mass spectrometry analysis of biotinylated proteins. The protocol enables in-column surface biotinylation of specific cell subsets with minimal membrane disruption.