Biology subjects

Tekpinar, M.

Publications and source records attributed to Tekpinar, M..

3 recordsLinked to original sources

bioRxiv2025

Enabling Real-Time Fluctuation-Based Super ResolutionImaging

Live-cell imaging captures dynamic cellular processes, yet many structures remain beyond the diffraction limit. Fluctuation-based super-resolution techniques overcome this limit by exploiting correlations in fluorescence blinking, but they typically require hundreds of frames and computationally intensive post-processing, prohibiting real-time imaging of fast cellular events. Recent deep learning approaches aim to increase the temporal resolution; however, many rely on extensive pre-processing or large, complex models that increase training cost and inference latency, preventing real-time deployment. To address this, we employ a light-weight recurrent neural network model, which integrates sequential low-resolution frames to extract spatio-temporally correlated signals. Our method is taylored for live-cell imaging under extreme signal-to-noise ratio conditions. It significantly improves temporal resolution by reducing the required number of frames down to as few as 8 frames while doubling the spatial resolution in an inference time below 30 ms. By combining simulation based training with an efficient network architecture, we introduce RESURF, a deep-learning based real-time super-resolution fluctuation imaging framework. We demonstrate that RESURF generalizes across different biological structures and can be readily adapted to various microscope setups using transfer learning. The accompanying dataset, comprising simulations and experiments across multiple subcellular structures and labeling strategies, establishes a benchmarking platform for fluctuation-based super-resolution techniques. RESURF offers a practical, low-latency deep-learning framework for high-throughput and real-time live-cell super-resolution imaging.

biophysics↗

bioRxiv2024

High-throughput single molecule microscopy with adaptable spatial resolution using exchangeable oligonucleotide labels

Super-resolution microscopy facilitates the visualization of cellular structures at resolutions approaching the molecular level. Especially, super-resolution techniques based on the localization of single-molecules have relatively modest instrument requirements and are thus good candidates for adoption in bioimaging. However, their low throughput nature hampers their applicability in biomolecular research and screening. Here, we propose a workflow for more efficient data collection, starting with scanning of large areas using fast fluctuation based imaging, followed by single-molecule localization microscopy of selected cells. To achieve this workflow, we exploit the versatility of DNA oligo hybridization kinetics with DNA-PAINT probes to tailor the fluorescent blinking towards high-throughput and high-resolution imaging. Additionally, we employ super-resolution optical fluctuation imaging (SOFI) to analyze statistical fluctuations in the DNA-PAINT binding kinetics, thereby tolerating much denser blinking and facilitating accelerated imaging speeds. Thus, we demonstrate 30-300-fold faster imaging of different cellular structures compared to conventional DNA-PAINT imaging, albeit at a lower resolution. Notably, by tuning image medium and data processing though, we can flexibly switch between high-throughput SOFI (scanning a FOV of 0.65mm x 0.52mm within 4 minutes of total acquisition time) and super-resolution DNA-PAINT microscopy and thereby demonstrate that combining DNA-PAINT and SOFI enables to adapt image resolution and acquisition time based to the imaging needs. We envision this approach to be especially powerful when combined with multiplexing and 3D imaging.

biophysics↗

bioRxiv2024

Comprehensive Mutational Landscape Analysis of Monkeypox Virus Proteome

In this study, we present a comprehensive computational analysis of the single point mutational landscapes of the Monkeypox virus (MPXV) proteome. We reconstructed full single-point mutational landscapes of 171 MPXV proteins using an advanced mutational effect predictor, ESCOTT, selected for its superior performance on viral proteins. ESCOTT performance was assessed by benchmarking against the experimental data in the ProteinGym (v1.0.0) dataset that contains 48917 multiple and 173502 single point mutations. A recent MPXV strain sequenced in July 2024 was used as the reference genome. Multiple sequence alignments and protein structures were generated using Colabfold v1.5.5, and the predicted structures were evaluated with pLDDT metric, secondary structure predictions, and comparisons with available experimental data, ensuring high confidence in the structural models. We determined mutational sensitivity of all positions in a protein utilizing ESCOTT scores and demonstrated their functional implications on cysteine proteinase and helicase of MPXV. Moreover, we created an interactive visualization tool to visualize mutational landscapes and sensitivities in a publicly available Google Colab. Furthermore, we introduced a novel, interpretable metric (Average Gene Mutation Sensitivity) to prioritize the most mutation-sensitive proteins within the large MPXV proteome as prime candidates for drug or vaccine development. Among the top 20 proteins identified with this metric, several were membrane-associated proteins, proven to be important for viral interactions with the hosts in other viruses. This analysis provides a valuable resource for assessing the impact of new MPXV variants. This pioneering study underscores the significance of understanding MPXV evolution in the context of the ongoing global health crisis and offers a robust computational framework to support this effort.

microbiology↗