Label-free microfluidic biosensor for quantification for neutrophil extracellular traps

• 主要作者: Wong, Siong Onn
• 其他作者: Hou Han Wei
• 格式: Final Year Project
• 語言: English
• 出版: Nanyang Technological University 2023
• 主題: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Science::Biological sciences::Molecular biology
• 在線閱讀: https://hdl.handle.net/10356/168242

Neutrophils are integral in our innate immune system. One of the neutrophils critical functions includes release of DNA strands, also known as neutrophil extracellular traps (NETs), which is affected in many diseases including cancer, type 2 diabetes mellitus and infectious diseases. Currently, conventional methods to quantify the formation of NETs (NETosis) relies on fluorescence antibodies-based labelling or NETs-associated protein detection using ELISA, which are expensive laborious, and time consuming. In this report, the development of a microfluidic biosensor for label-free NETs quantification is demonstrated. By combining NETs trapping pillar arrays with Dean Fractionation Flow (DFF) spiral module for size-based cell sorting, circulating NETs can be continuously concentrated and trapped from purified neutrophils or diluted blood. Next, we also developed a novel “virtual staining” concept for NETs quantification using deep learning neural networks. By training and deploying convolutional neural networks (CNNs) to learn the fundamental morphological features of the trapping arrays and NETs using brightfield images, the model can generate images that virtually stain the DNA content through inference networks, thus eliminating the need for antibodies staining. We first characterised the microfluidic technology using purified neutrophils treated with NETosis inducers including phorbol 12-myristate 13-acetate (PMA), and Calcium Ionophore. Images were used to train the CNN models used and showed high structural similarity and minimal pixel-wise error with actual stained DNA images. Further work includes testing of NETs-specific biomarkers such as myeloperoxidase (MPO). H3Cit, and cell staining using different biofluids (blood, urine etc.).