Biology subjects

Cimen, S.

Publications and source records attributed to Cimen, S..

3 recordsLinked to original sources

bioRxiv2025

Development of a Genetically Encoded Live-Cell Iron Mapping System

Any disturbances in cellular iron regulation can lead to dysfunctions and various diseases. However, our current understanding of iron dynamics in living cells is hampered by the lack of high-resolution iron reporter systems capable of monitoring the iron status of individual cells dynamically. To address this challenge, we have developed the Live-Cell Iron Mapping System (LIMS), which consists of two distinct genetically encoded fluorescent iron reporter systems: the rFeRepS and IronFist. The rFeRepS (ratiometric Iron Reporter Systems) code for two genetically encoded constructs that translate the iron status of individual cells into a ratiometric fluorescent signal. The ratio signal reflects the cellular iron status, based on endogenous iron-responsive proteins. A high rFeRepS ratio indicates ferritin-based cellular iron storage, while a low ratio implies transferrin receptor (TfR)-based active cellular iron uptake. The IronFist (Iron Fluorescent Indicators based on Stability and Translation) is derived from the hemerythrin-like domain (Hr) of the F-box and leucine-rich protein 5 (FXBL5), which undergoes a structural rearrangement upon binding to ferrous iron, preventing its ubiquitination and subsequent degradation. In the IronFist design, we fused either the blue or green fluorescent protein (FP) variant, mTagBFP2 or mNeonGreen, to the C-terminus of Hr and co-expressed mCherry via a ribosomal skipping sequence as a reference protein for normalization. Thus, IronFist dynamically translates fluctuations of the labile iron pool into a ratiometric fluorescent signal. We demonstrated the functionality of both iron reporter systems by treating cells with different iron formulations including iron carbohydrate nanoparticles. Our results show clear cell-to-cell variations in the response to high iron treatments.

biochemistry↗

bioRxiv2022

Hexokinase 1b is a novel target for non-small-cell lung cancer

Deregulation of glycolysis is common in non-small cell lung cancer (NSCLC). Hexokinase (HK) enzymes catalyze the phosphoryl-group-transfer in glucose metabolism. There are a very few studies that have begun to reveal the connections between glucose metabolism and splicing programs. Unlike HK2 gene, which is expressed as a single transcript, there are several transcripts of the HK1 gene due to alternative splicing. However, the functional differential roles of HK1 isoforms in glucose metabolism and tumor progression are still elusive. Here, we show that primary NSCLC patient tumor cells metabolically differ from the normal lung epithelium where they display predominant expression of one of the HK1 transcripts, hexokinase1b (HK1b). We utilized CRISPR-Cas9 system to selectively target specific HK1b isoform in NSCLC and show that silencing HK1b in NSCLC cells inhibits tumorigenesis through diminishing glycolysis and proliferation. Our findings constitute the first demonstration of the first biochemical distinction between the HK1 splice variants. Finally, HK1b deletion sensitizes NSCLC cells to standard-of-care, cisplatin treatment, and the combination therapy synergistically increases both apoptotic cell death by cisplatin and autophagic cell death by increased formation of LC3-II associated autophagic vesicles and myelinoid bodies. Notably, loss of HK1b leads to cellular DNA damage, further combination with cisplatin therapy showed significantly increased levels of DNA damage. Importantly, we showed that glycolysis and cisplatin resistance can be restored by adding-back HK1b in HK1b knock-out cells. Our findings reveal that targeting HK1b isoform alone or in combination with cisplatin may represent a novel strategy for NSCLC patients.

cancer biology↗

bioRxiv2018

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.

neuroscience↗