Cancer is arguably one of the most dangerous diseases and the major causes of death in the modern day. It becomes increasingly harder to treat and cure the disease as it makes progress. Detecting cancer at an early stage can help in preventing it from affecting an organism. However, it is very hard to detect at an early stage. The best possible way to tackle this disease is to first study it at a cellular level. This study aims at identifying various phenotypic traits of these cells in the Dielectrophoresis (DEP) based microfluidic device experimental setup and subsequently classifying the cells from the rest. A general framework for automatic labeling, identifying and classifying the malignant from the dead cells is developed in this work. The framework shows a top-down approach starting from static background subtraction, tracking, automatic labeling, feature extraction and finally classification. The data used in this work are videos of live and dead human prostate cancer (PC-3) cells flowing through the microfluidic device. Previous studies have shown that there are significant differences in morphological attributes between cancerous and non-cancerous cells. We focus mainly on shape, texture and geometry as the prominent attribute in our work and subsequently use them for classification. In this work we obtain good tracking results through optical flow as compared to previous work. For classification, linear classifiers such as logistic regression and linear Support Vector Machine (SVM) showed decent results. The machine learning algorithms use Histogram of Oriented Gradient (HOG) features plus the elliptical features as a combined feature vector. The elliptic features branch out this study to another direction that is useful in calculation of physical properties such as the cell elasticity through video processing and we propose a model for the same for the given setup. Currently, the elasticity of a single cell is calculated using expensive and time consuming procedures such as the atomic force microscopy (AFM). Using our framework, we can potentially obtain elasticity for a batch of cells in much less time. Also, our cell classification algorithm procedure is suitable for real time applications and can be a proposed futuristic concept for selective killing of cells. / Master of Science / Cancer is one of the most dangerous disease and a major cause of death in the modern day. It becomes increasingly harder to treat and cure the disease as it makes progress. Detecting cancer at an early stage can help in preventing it from affecting an organism. However, it is very hard to detect at an early stage. The best possible way to tackle this disease is to first study it at the cellular scale. Our study aims at identifying various characteristics of these cells from videos recorded in a dielectrophoresis (DEP) based cell sorting setup. A general framework for identifying and classifying the malignant from the dead cells is developed in this work. We use computer vision algorithms for detection, tracking and analyzing characteristics of the cells. We use these characteristics to classify the cells into live and dead with an accuracy of 95% using standard classification algorithms used in machine learning such as support vector machine and logistic regression. The study of such properties of the cells also enables us to propose a model to estimate Young’s modulus of elasticity of the cells. Currently, time consuming techniques such as the atomic force microscopy (AFM) are being used to determine the elasticity of a single cell at a time. Using our work, we can potentially obtain elasticity for a batch of cells in much less time. Our cell classification algorithm procedure is suitable for real time applications and can be a proposed futuristic concept for selective killing of cells.

Yes / Polysialic acid (PolySia) is an important carbohydrate bio-polymer that is commonly over-expressed on tumours of neuroendocrine origin and plays a key role in tumour progression. PolySia exclusively decorates the neural cell adhesion molecule (NCAM) on tumour cell membranes, modulating cell-cell interactions, motility and invasion. In this preliminary study, we examine the nano-mechanical properties of isogenic C6 rat glioma cells - transfected cells engineered to express the enzyme polysialyltransferase ST8SiaII, which synthesises polySia (C6-STX cells) and wild type cells (C6-WT). We demonstrate that polySia expression leads to reduced elastic and adhesive properties but also more visco-elastic compared to non-expressing wild type cells. Whilst differences in cell elasticity between healthy and cancer cells is regularly assigned to changes in the cytoskeleton, we show that in this model system the change in properties at the nano-level is due to the polySia on the transfected cell membrane surface.

Myosin 1c (Myo1c) is a molecular motor involved in regulation of tension-gated ion channels, exocytosis, endocytosis, motility and other membrane-related events. Moreover, it acts as a dynamic linker between the cell membrane and cortical actin network, contributing to the maintenance of plasma membrane tension. In contrast, nuclear myosin 1 (NM1), an isoform of Myo1c, has been described only in the nucleus where it participates in various nuclear processes, including transcription or chromatin remodeling. However, although traditionally regarded as exclusively cytoplasmic or nuclear, all myosin 1c isoforms participate in nuclear functions and they are present in the cytoplasm as well. The main focus of this study was to characterize the functional significance of NM1 in the cytoplasm. We have found that NM1 localizes to plasma membrane and shows a uniform punctuated distribution with a high concentration at the cell periphery. Moreover, atomic force microscopy measurements of mouse NM1 KO fibroblasts revealed a significant increase in an overall plasma membrane elasticity in comparison to WT cells, indicating a disruption in the regulation of plasma membrane tension caused by the loss of NM1. Since a higher membrane elasticity and deformability is a characteristic marker of cancer cells,...