Biology subjects

Arslanturk, S.

Publications and source records attributed to Arslanturk, S..

5 recordsLinked to original sources

bioRxiv2025

Mechanism of clinically relevant neurosecretory and metabolic impairment in Alzheimers and identification of engineered nanotherapy

A central challenge in Alzheimers disease is understanding the mechanism of neuronal secretory dysfunction. By exploiting AI, we reveal how beta-amyloid disrupts key protein interactions within the neuronal secretory machinery. In a combinatorial strategy of reprogramming the neuronal secretory and metabolic components, we established a dual-target therapeutic framework that repairs synaptic and mitochondrial defects to counteract neurodegeneration in Alzheimers.

neuroscience↗

bioRxiv2020

Skeletal Muscle Remodeling in Immobilized Patients: Determined Using a Parameter Estimation Histomorphometric Approach

Skeletal muscle biopsy commonly used for light microscopic, electron microscopic and biochemical and transcriptional evaluation remains the gold standard for establishing the etiology of a myopathy. While most myopathies exhibit one or more phenotypes, early stages or several metabolic myopathies often exhibit normal muscle morphology, making diagnosis difficult. In such cases where standard staining techniques fail to offer definitive diagnostic information, a combination of expensive and time-consuming electron microscopy and biochemical testing is required to provide definitive diagnosis. As a step toward overcoming these limitations in diagnostic pathology of skeletal muscle tissue, here we report the application of parameter estimation machine learning approaches on immunofluorescent images of human skeletal muscle tissue acquired using fluorescent microscopy. The machine learning morphometric approach enables the recognition of fine cellular changes in skeletal muscle tissue, allowing determination of skeletal muscle remodeling as a consequence of immobilization.

cell biology↗

bioRxiv2020

Cystic fibrosis transmembrane conductance regulator (CFTR) inhibition results in mucus accumulation in human airway epithelia Calu-3 cells: Experimental and Machine Learning Studies

Porosomes are cup-shaped lipoprotein structures at the cell plasma membrane involved in fractional release of intra-vesicular contents during secretion. At the base of the porosome facing the cell cytoplasm, secretory vesicles dock, fuse and swell, to release intra-vesicular content during secretion. Earlier studies demonstrate the cystic fibrosis trans-membrane conductance regulator (CFTR) associated with the porosome in human airways epithelial Calu-3 mucous-secreting cells, suggesting its possible involvement in porosome-mediated mucus secretion. The current study was undertaken to test this hypothesis. Electron microscopy followed by morphometric analysis using manual and computational machine learning approaches were used to assess changes in secretory vesicle size and content, following stimulation of secretion in the absence and presence of CFTR inhibitors. Results from the study demonstrate that stimulated Calu-3 cells pre-exposed to CFTR inhibitors, demonstrate attenuation of secretory vesicle swelling and the release of mucin. Consequently, accumulation of intracellular mucin is observed in cells exposed to CFTR inhibitors. These results further suggest that mucin secretion from Calu-3 cells involve CFTR both at the secretory vesicle membrane to regulate vesicle volume and hydration, and at the porosome to facilitate mucin hydration and secretion. These new findings progress our understanding of the involvement of CFTR on mucus hydration and secretion, providing critical insights into the etiology of CF disease.

cell biology↗

bioRxiv2019

Deep Learning Strategies for Differential Expansion Microscopy

Differential expansion microscopy (DiExM) achieves greater than 500-fold volumetric expansion of biological specimens without loss of cellular antigens. The anisotropic character of this expansion, which affects tissues, cells, organelles and even sub-organelle features, requires the application of novel machine learning approaches to extract accurate and meaningful morphological and biochemical information from DiExM images. Here we describe current strategies to achieve this goal.

cell biology↗

bioRxiv2019

Differential expansion microscopy

Expansion microscopy (ExM) involves the use of hydration-competent polymers to physically expand biological specimens approximately 4-fold linear increase to achieve 70 nanometer resolution using an ordinary diffraction limited optical microscope. Optimal conditions however for antigen retention during the expansion process and the relative expansion between organelles within cells has remained unclear. It is reported that different tissues expand to different extents, suggesting that although isotropic expansion is believed to occur, different subcellular compartments with different composition would undergo anisotropic or differential expansion (DiEx). Consequently, there would be distortion of the native shape and size of subcellular compartments upon expansion, parameters which are critical in assessing cellular states in health and disease. Here we report optimal fixation and expansion conditions that retain structural integrity of cells while exhibiting up to 8-fold linear and therefore 512-fold volumetric expansion. Anisotropic expansion is observed not just between tissues, but between different subcellular compartments and even within subcellular compartments. Combining image analysis and machine learning, we provide an approach for the rapid and precise measurement of cellular and subcellular structures in expanded tissue. Using both manual and computation assessment of morphometric parameters, we demonstrate expansion to be anisotropic and name this method differential expansion microscopy (DiExM).

cell biology↗