Browsing by Author "Boyvat, Dudu"

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IDENTIFICATION OF SURFACE PROTEOME OF B CELL ACUTE LYMPHOBLASTIC LEUKEMIA CELL LINE
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü / Biyomühendislik Ana Bilim Dalı, 2022) Boyvat, Dudu; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı
B-cell acute lymphoblastic leukemia is characterized by over and uncontrolled expression of B lymphocytes. B-ALL may occur as a result of aberrant cytosolic signal transduction and molecular abnormalities such as gene mutations, abnormal protein interactions, and an un-arrested cell cycle. Due to these abnormalities, surface proteins that compromised one-third of the proteome show different expressions compared to the healthy cells. These differences are currently in use for diagnostic and treatment approaches. Here, we aimed to isolate and identify the surface proteins of the CCRF-SB cell line to identify new, additional possible target antigens with the mass spectrometrybased proteomics approach using two different surface protein isolation strategies. The surface proteins of CCRF-SB cells were isolated with the surface biotinylation method and N-linked glycoprotein enrichment methods. With the biotinylation method, we isolated 782 proteins with 1% FDR. Gene Ontology Cellular Compartment analysis showed that 467 of these isolated proteins are annotated as ‘Membrane’. 263 of those proteins are annotated as ‘Extracellular Space’. These isolated cell surface proteins include HLA protein complexes and well-known CD19 surface markers. With the Nlinked glycosylation enrichment method 229 protein identified with 1% FDR rate. Gene Ontology Cellular Compartment analysis showed that 155 of these isolated proteins are annotated as ‘Membrane’, 132 of those proteins are annotated as ‘Extracellular Space’. Both methods identified different proteins from each other. This result showed that to map the surfaceome of CCRF-SB cell line, it is required to combine these two enrichment methods.
Improved senescent cell segmentation on bright-field microscopy images exploiting representation level contrastive learning
(WILEY Online Library, 2024) Çelebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, Kutay; 0000-0003-3157-6806; 0000-0002-1052-0961; 0000-0002-0947-6166; 0000-0001-7472-8297; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Çelebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Icoz, Kutay
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 timeconsuming 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.
Protocol for cell surface biotinylation of magnetic labeled and captured human peripheral blood mononuclear cells
(Cell Press, 2022) Ayaz-Guner, Serife; Acar, Mustafa Burak; Boyvat, Dudu; Guner, Huseyin; Bozalan, Habibe; Güzel, Melis; Yıldır, Selin Kübra; Altınsoy, Nilay; Fındık, Fatma; Karakükçü, Musa; Özcan, Servet; 0000-0002-1052-0961; 0000-0002-0220-5224; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Moleküler Biyoloji ve Genetik Bölümü; Boyvat, Dudu; Ayaz-Guner, Şerife; Guner, Huseyin
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.