Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 11Μdacs Platform: a Hybrid Microfluidic Platform Using Magnetic Levitation Technique and Integrating Magnetic, Gravitational, and Drag Forces for Density-Based Rare Cancer Cell Sorting(Elsevier, 2023) Keçili, Seren; Yılmaz, Esra; Özçelik, Özge Solmaz; Anıl İnevi, Müge; Günyüz, Zehra Elif; Yalçın Özuysal, Özden; Özçivici, Engin; Tekin, Hüseyin CumhurCirculating tumor cells (CTCs) are crucial indicators of cancer metastasis. However, their rarity in the bloodstream and the heterogeneity of their surface biomarkers present challenges for their isolation. Here, we developed a hybrid microfluidic platform (microfluidic-based density-associated cell sorting (µDACS) platform) that utilizes density as a biophysical marker to sort cancer cells from the population of white blood cells (WBCs). The platform utilizes the magnetic levitation technique on a microfluidic chip to sort cells based on their specific density ranges, operating under a continuous flow condition. By harnessing magnetic, gravitational, and drag forces, the platform efficiently separates cells. This approach involves a microfluidic chip equipped with a microseparator, which directs cells into top and bottom outlets depending on their levitation heights, which are inversely proportional to their densities. Hence, low-density cancer cells are collected from the top outlet, while high-density WBCs are collected from the bottom outlet. We optimized the sorting efficiency by varying the flow rates, and concentrations of the sorting medium's paramagnetic properties using standard densities of polymeric microspheres. To demonstrate the platform's applicability, we performed hybrid microfluidic sorting on MDA-MB-231 human breast cancer cells and U-937 human monocytes. The results showed efficient sorting of rare cancer cells (≥100 cells/mL) from serum samples, achieving a sorting efficiency of ∼70% at a fast-processing speed of 1 mL h−1. This label-free approach holds promise for rapid and cost-effective CTC sorting, facilitating in-vitro diagnosis and prognosis of cancer. © 2023 The Author(s)Review Citation - WoS: 14Citation - Scopus: 14Recent Advances in Lab-On Systems for Breast Cancer Metastasis Research(Royal Society of Chemistry, 2023) Fıratlıgil Yıldırır, Burcu; Yalçın Özuysal, Özden; NonappaBreast cancer is the leading cause of cancer-related deaths in women. Multiple molecular subtypes, heterogeneity, and their ability to metastasize from the primary site to distant organs make breast cancer challenging to diagnose, treat, and obtain the desired therapeutic outcome. As the clinical importance of metastasis is dramatically increasing, there is a need to develop sustainable in vitro preclinical platforms to investigate complex cellular processes. Traditional in vitro and in vivo models cannot mimic the highly complex and multistep process of metastasis. Rapid progress in micro- and nanofabrication has contributed to soft lithography or three-dimensional printing-based lab-on-a-chip (LOC) systems. LOC platforms, which mimic in vivo conditions, offer a more profound understanding of cellular events and allow novel preclinical models for personalized treatments. Their low cost, scalability, and efficiency have resulted in on-demand design platforms for cell, tissue, and organ-on-a-chip platforms. Such models can overcome the limitations of two- and three-dimensional cell culture models and the ethical challenges involved in animal models. This review provides an overview of breast cancer subtypes, various steps and factors involved in metastases, existing preclinical models, and representative examples of LOC systems used to study and understand breast cancer metastasis and diagnosis and as a platform to evaluate advanced nanomedicine for breast cancer metastasis.Article Epithelial-Mesenchymal Transition as a Potential Route for Dapt Resistance in Breast Cancer Cells(Walter de Gruyter GmbH, 2023) Tellı, Kubra; Ozuysal, Ozden Yalcın; Telli, Kübra; Yalçın Özuysal, ÖzdenObjectives: Notch is a conserved pathway involved in cell- fate determination and homeostasis. Its dysregulation plays a role in poor prognosis and drug resistance in breast cancer. Targeting Notch signaling via inhibition of the gamma- secretase complex is in the spotlight of modern cancer treat- ments. Gamma-secretase inhibitors (GSI) have shown suc- cessful clinical activity in treating cancers, yet the possible resistance mechanism remains unstudied. Modeling the resistance and understanding culprit molecular mechanisms can improve GSI therapies. Accordingly, the aim of this study is to generate and analyze GSI-resistant breast cancer cells. Methods: Gradually increasing doses of DAPT, a well-known GSI, were applied to MCF-7 breast cancer cell lines to generate resistance. Cell viability, migration and gene expressions were assessed by MTT, wound healing and qRT-PCR analyses. Results: DAPT-resistant MCF-7 cells exhibited abnormal expression of Notch receptors, Notch targets (HES1, HES5, HEY1), and epithelial-mesenchymal transition (EMT) markers (E-cadherin, ZO-1, SNAIL2, N-cadherin) to overcome the continuous increase in DAPT toxicity by increased migration through mesenchymal transition. Conclusions: This study prospects into the role of EMT in the potential resistance mechanism against DAPT treatment for breast cancer cells. Complementary targeting of EMT should be investigated further for a possible effect to potentiate DAPT’s anti-cancer effects.Conference Object Detection and Restoration Pipeline for Phase Contrast Microscopy Time Series Images(IEEE, 2022) Iheme, Leonardo O.; Uçar, Mahmut; Önal, Sevgi; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet U.; Ünay, DevrimWe propose a pre-processing pipeline for the de-tection and restoration of distorted frames in phase-contrast microscopy time-series images. The analysis is based on the average intensity values of the frames within any given time- series image. The extent of the correction of intensity variation in frames is determined by the normalization of the difference between the current frame's average intensity and the median of average intensity of all frames. Our restoration algorithm preserves regional trans-passing pixels, does not cause new distortions, and increases the histogram similarity between the distorted and non-distorted frames. The algorithm was validated on 15,395 time-series image frames from 27 experiments and the results were found to be visually and quantitatively accurate.Article Citation - WoS: 12Citation - Scopus: 12Sema6d Differentially Regulates Proliferation, Migration, and Invasion of Breast Cell Lines(American Chemical Society, 2022) Günyüz, Zehra Elif; Sahi İlhan, Ece; Küçükköse, Cansu; İpekgil, Doğaç; Tok, Güneş; Meşe, Gülistan; Özçivici, Engin; Yalçın Özuysal, ÖzdenSemaphorin 6D (SEMA6D), a member of the class 6 semaphorin family, is a membrane-associated protein that plays a key role in the development of cardiac and neural tissues. A growing body of evidence suggests that SEMA6D is also involved in tumorigenesis. In breast cancer, high SEMA6D levels are correlated with better survival rates. However, very little is known about the functional significance of SEMA6D in breast tumorigenesis. In the present study, we aimed to investigate the effects of SEMA6D expression on the normal breast cell line MCF10A and the breast cancer cell lines MCF7 and MDA MB 231. We demonstrated that SEMA6D expression increases the proliferation of MCF10A cells, whereas the opposite effect was observed in MCF7 cells. SEMA6D expression induced anchorage-independent growth in both cancer cell lines. Furthermore, migration of MCF10A and MCF7 cells and invasion of MDA MB 231 cells were elevated in response to SEMA6D overexpression. Accordingly, the genes related to epithelial-mesenchymal transition (EMT) were altered by SEMA6D expression in MCF10A and MCF7 cell lines. Finally, we provided evidence that SEMA6D levels were associated with the expression of the cell cycle, EMT, and Notch signaling pathway-related genes in breast cancer patients' data. We showed for the first time that SEMA6D overexpression has cell-specific effects on the proliferation, migration, and invasion of normal and cancer breast cell lines, which agrees with the gene expression data of clinical samples. This study lays the groundwork for future research into understanding the functional importance of SEMA6D in breast cancerArticle Citation - WoS: 3Citation - Scopus: 6Improved Cell Segmentation Using Deep Learning in Label-Free Optical Microscopy Images(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2021) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the decoder. This alteration makes the model superconvergent yielding improved performance results on two challenging optical microscopy image series: a phase-contrast dataset of our own (MDA-MB-231) and a brightfield dataset from a well-known challenge (DSB2018). We utilized the U-Net with pretrained ResNet-18 as the encoder for the segmentation task. Hence, following the modifications, we redesign a novel skip-connection to reduce the semantic gap between the encoder and the decoder. The proposed skip-connection increases the accuracy of the model on both datasets. The proposed segmentation approach results in Jaccard Index values of 85.0% and 89.2% on the DSB2018 and MDA-MB-231 datasets, respectively. The results reveal that our method achieves competitive results compared to the state-of-the-art approaches and surpasses the performance of baseline approaches.Article Citation - WoS: 20Citation - Scopus: 22Refractive Index Sensing for Measuring Single Cell Growth(American Chemical Society, 2021) Çetin, Arif E.; Topkaya, Seda Nur; Yalçın Özuysal, Özden; Khademhosseini, AliAccessing cell growth on adhesive substrates is critical for identifying biophysical properties of cells and their therapeutic response to drug therapies. However, optical techniques have low sensitivity, and their reliability varies with cell type, whereas microfluidic technologies rely on cell suspension. In this paper, we introduced a plasmonic functional assay platform that can precisely measure cell weight and the dynamic change in real-time for adherent cells. Possessing this ability, our platform can determine growth rates of individual cells within only 10 mm to map the growth profile of populations in short time intervals. The platform could successfully determine heterogeneity within the growth profile of populations and assess subpopulations exhibiting distinct growth profiles. As a proof of principle, we investigated the growth profile of MCF-7 cells and the effect of two intracellular metabolisms critical for their proliferation. We first investigated the negative effect of serum starvation on cell growth. We then studied ornithine decarboxylase (ODC) activity, a key enzyme which is involved in proliferation, and degraded under low osmolarity that inhibits cell growth. We successfully determined the significant distinction between growth profiles of MCF-7 cells and their ODC-overproducing variants that possess strong resistance to the negative effects of low osmolarity. We also demonstrated that an exogenous parameter, putrescine, could rescue cells from ODC inhibition under hypoosmotic conditions. In addition to the ability of accessing intracellular activities through ex vivo measurements, our platform could also determine therapeutic behaviors of cancer cells in response to drug treatments. Here, we investigated difluoromethylornithine (DFMO), which has antitumor effects on MCF-7 cells by inhibiting ODC activity. We successfully demonstrated the susceptibility of MCF-7 cells to such drug treatment, while its DFMO-resistant subpopulation could survive in the presence of this antigrowth agent. By rapidly determining cell growth kinetics in small samples, our plasmonic platform may be of broad use to basic research and clinical applications.Conference Object Citation - WoS: 3Citation - Scopus: 4Deep Learning Based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images(IEEE, 2020) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Okvur, Devrim Pesen; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.Conference Object Citation - Scopus: 2Yara İyileşmesi Mikroskopi Görüntü Serilerinin Otomatik Analizi - Bir Ön-çalışma(IEEE, 2020) Mayalı, Berkay; Şaylığ, Orkun; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Ünay, DevrimCollective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase -contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their perlOrmance comparisons are carried out.Conference Object Citation - Scopus: 1A Preliminary Study on Cell Motility Analysis From Phase-Contrast Microscopy Image Series(IEEE, 2020) Kayan, Emre; Kavuşan, Tarık; Önal, Sevgi; Pesen Okvur, Devrim; Yalçın Özuysal, Özden; Töreyin, Behçet Uğur; Ünay, DevrimAnalyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.
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