Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Article Blank Frame and Intensity Variation Distortion Detection and Restoration Pipeline for Phase-Contrast Microscopy Time-Lapse Images(Aves, 2024) Ucar, Mahmut; Iheme, Leonardo O.; Onal, Sevgi; Pesen-Okvur, Devrim; Yalcin-Ozuysal, Ozden; Toreyin, Behcet U.; Unay, DevrimIn this study, we propose a preprocessing pipeline for the detection and correction of distorted frames in time-lapse images obtained from phase-contrast microscopy. The proposed pipeline employs the average intensities of frames as a foundational element for the analysis. In order to evaluate the degree of correction required for intensity variance, a normalization technique is applied to the difference between the average intensity of a specific frame and the median average intensity of all frames within the study. Our restoration method increases the histogram similarity between the distorted and non-distorted frames, preserves trans-passing pixels in regions of interest, and mitigates the development of additional distortions. The efficacy of the proposed method was evaluated using 15 395 time-lapse image frames from 27 experiments using our own dataset and 830 time-lapse images from four different experiments obtained from the cell tracking challenge. The results of the validation demonstrate a high degree of numerical and visual accuracy of the proposed pipeline.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 Citation - WoS: 2Citation - Scopus: 2Plasmonic Functional Assay Platform Determines the Therapeutic Profile of Cancer Cells(American Chemical Society, 2023) Çetin, Arif E.; Topkaya, Seda Nur; Yazıcı, Ziya Ata; Yalçın Özuysal, ÖzdenFunctional assay platforms could identify the biophysicalpropertiesof cells and their therapeutic response to drug treatments. Despitetheir strong ability to assess cellular pathways, functional assaysrequire large tissue samples, long-term cell culture, and bulk measurements.Even though such a drawback is still valid, these limitations didnot hinder the interest in these platforms for their capacity to revealdrug susceptibility. Some of the limitations could be overcome withsingle-cell functional assays by identifying subpopulations usingsmall sample volumes. Along this direction, in this article, we developeda high-throughput plasmonic functional assay platform to identifythe growth profile of cells and their therapeutic profile under therapiesusing mass and growth rate statistics of individual cells. Our technologycould determine populations' growth profiles using the growthrate data of multiple single cells of the same population. Evaluatingspectral variations based on the plasmonic diffraction field intensityimages in real time, we could simultaneously monitor the mass changefor the cells within the field of view of a camera with the capacityof > & SIM;500 cells/h scanning rate. Our technology could determinethe therapeutic profile of cells under cancer drugs within few hours,while the classical techniques require days to show reduction in viabilitydue to antitumor effects. The platform could reveal the heterogeneitywithin the therapeutic profile of populations and determine subpopulationsshowing resistance to drug therapies. As a proof-of-principle demonstration,we studied the growth profile of MCF-7 cells and their therapeuticbehavior to standard-of-care drugs that have antitumor effects asshown in the literature, including difluoromethylornithine (DFMO),5-fluorouracil (5-FU), paclitaxel (PTX), and doxorubicin (Dox). Wesuccessfully demonstrated the resistant behavior of an MCF-7 variantthat could survive in the presence of DFMO. More importantly, we couldprecisely identify synergic and antagonistic effects of drug combinationsbased on the order of use in cancer therapy. Rapidly assessing thetherapeutic profile of cancer cells, our plasmonic functional assayplatform could be used to reveal personalized drug therapies for cancerpatients.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: 7Citation - Scopus: 7Connexin 32 Overexpression Increases Proliferation, Reduces Gap Junctional Intercellular Communication, Motility and Epithelial-To Transition in Hs578t Breast Cancer Cells(Springer, 2022) Uğur, Deniz; Güngül, Taha Buğra; Yücel, Simge; Özçivici, Engin; Yalçın Özuysal, Özden; Meşe Özçivici, GülistanConnexins (Cx) are primary components of gap junctions that selectively allow molecules to be exchanged between adjacent cells, regulating multiple cellular functions. Along with their channel forming functions, connexins play a variety of roles in different stages of tumorigenesis and their roles in tumor initiation and progression is isoform- and tissue-specific. While Cx26 and Cx43 were downregulated during breast tumorigenesis, Cx32 was accumulated in the cytoplasm of the cells in lymph node metastasis of breast cancers and Cx32 was further upregulated in metastasis. Cx32's effect on cell proliferation, gap junctional communication, hemichannel activity, cellular motility and epithelial-to-mesenchymal transition (EMT) were investigated by overexpressing Cx32 in Hs578T and MCF7 breast cancer cells. Additionally, the expression and localization of Cx26 and Cx43 upon Cx32 overexpression were examined by Western blot and immunostaining experiments, respectively. We observed that MCF7 cells had endogenous Cx32 while Hs578T cells did not and when Cx32 was overexpressed in these cells, it caused a significant increase in the percentages of Hs578T cells at the S phase in addition to increasing their proliferation. Further, while Cx32 overexpression did not induce hemichannel activity in either cell, it decreased gap junctional communication between Hs578T cells. Additionally, Cx32 was mainly observed in the cytoplasm in both cells, where it did not form gap junction plaques but Cx32 overexpression reduced Cx43 levels without affecting Cx26. Moreover, migration and invasion potentials of Hs578T and migration in MCF7 were reduced upon Cx32 overexpression. Finally, the protein level of mesenchymal marker N-cadherin decreased while epithelial marker ZO-1 and E-cadherin increased in Hs578T cells. We observed that Cx32 overexpression altered cell proliferation, communication, migration and EMT in Hs578T, suggesting a tumor suppressor role in these cells while it had minor effects on MCF7 cells.Article 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.
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