Biology subjects

Saguy, A.

Publications and source records attributed to Saguy, A..

3 recordsLinked to original sources

bioRxiv2024

Monitoring DNA double strand break repair in Saccharomyces cerevisiae at high throughput and high resolution

DNA repair is critical for cellular function and genomic stability across organisms. Yeast mating-type switching serves as an established model for studying DNA break repair and chromatin dynamics. However, real-time tracking of mating-type switching in live cells remains challenging due to resolution limitations of existing techniques. Here, we use high-throughput methods, including three-dimensional imaging, to follow the dynamics of DNA damage and repair and to quantify mating-type switching occurrences at the single live cell level, with unprecedented resolution. We reveal chromatin reconfiguration for both single- and double-strand breaks following switching induction. Our findings provide new observation of the correlation between chromatin folding and single-strand breaks.

cell biology↗

bioRxiv2023

This microtubule does not exist: Super-resolution microscopy image generation by a diffusion model

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, we explore the adaptation and training of a diffusion model on super-resolution microscopy images from publicly available databases. We show that the generated images resemble experimental images, and that the generation process does not memorize existing images from the training set. Additionally, we compare the performance of a deep learning-based deconvolution method trained using our generated high-resolution data versus training using high-resolution data acquired by mathematical modeling of the sample. We obtain superior reconstruction quality in terms of spatial resolution using a small real training dataset, showing the potential of accurate virtual image generation to overcome the limitations of collecting and annotating image data for training. Finally, we make our pipeline publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.

bioengineering↗

bioRxiv2022

DBlink: Dynamic localization microscopy in super spatiotemporal resolution via deep learning

Single molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques depend on accumulation of many localizations over thousands of recorded frames to yield a single super-resolved image, which is time consuming. Hence, the capability of SMLM to observe dynamics has always been limited. Typically, a few minutes of data acquisition are needed to reconstruct a single super-resolved frame. In this work, we present DBlink, a novel deep-learning-based algorithm for super spatiotemporal resolution reconstruction from SMLM data. The input to DBlink is a recorded video of single molecule localization microscopy data and the output is a super spatiotemporal resolution video reconstruction. We use bi-directional long short term memory (LSTM) network architecture, designed for capturing long term dependencies between different input frames. We demonstrate DBlink performance on simulated data of random filaments and mitochondria-like structures, on experimental SMLM data in controlled motion conditions, and finally on live cell dynamic SMLM. Our neural network based spatiotemporal interpolation method constitutes a significant advance in super-resolution imaging of dynamic processes in live cells.

bioengineering↗