Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

Permanent URI for this collectionhttps://hdl.handle.net/11147/9

Browse

Filters

20232

Settings

Publisher: search.filters.publisher.ElsevierWoS Q: search.filters.wosQuartile.N/A

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - Scopus: 11
    Μdacs 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 Cumhur
    Circulating 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)
  • Book Part
    Citation - Scopus: 1
    Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer
    (Elsevier, 2023) Erdem, Yusuf Sait; Ayanzadeh, Aydın; Mayalı, Berkay; Balıkçı, Muhammed; Belli, Özge Nur; Uçar, Mahmut; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Morani, Kenan; Iheme, Leonardo Obinna; Töreyin, Behçet Uğur
    This chapter describes a workflow for analyzing phase-contrast microscopy (PCM) data from two fundamental types of biomedical assays: assays for cell motility and assays for wound healing. The workflow of the analysis is composed of the methods for acquiring, restoring, segmenting, and quantifying biomedical data. In the literature, there have been separate methods aimed at specific stages of PCM data analysis. Nonetheless, there has never been a complete workflow for all stages of analysis. This work is an innovation that proposes an end-to-end workflow for image pre-processing, deep learning segmentation, tracking, and quantification stages in cell motility and wound healing assay analyses. The findings indicate that domain knowledge can be used to make simple but significant improvements to the results of cutting-edge methods. Furthermore, even for deep learning-based methods, pre-processing is clearly a necessary step in the workflow. © 2023 Elsevier Inc. All rights reserved.