Biology subjects

Vidi, P.-A.

Publications and source records attributed to Vidi, P.-A..

4 recordsLinked to original sources

bioRxiv2026

Mechanotransduction tuned by intermediate filaments promotes DNA repair in stiff environments

High mammographic density increases breast cancer risk. Dense tissues are rich in fibrillar collagen, forming a rigid extracellular matrix around the epithelium. Although cancer initiation and progression are closely tied to DNA damage and to the DNA damage response, if and how tissue stiffness affects genome maintenance is not fully elucidated. We found that repair factor recruitment is more efficient, and that DNA breaks are repaired faster by cells on rigid substrates. Mechanistically, hemidesmosomal integrins, intermediate filaments, the LINC complex, and YAP are key mediators of this mechanogenomic response. Rigid tissue contexts favor survival of cells with DNA damage, which may lead to the accumulation of somatic mutations, as identified here with RNAseq analyses of normal breast tissue. Our findings provide new insights into how tissue rigidity influences cancer risk.

cell biology↗

bioRxiv2020

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

biophysics↗

bioRxiv2017

NuMA is a negative regulator of 53BP1 in DNA double-strand break repair

Accumulation of 53BP1 at DNA breaks determines DNA repair pathway choice and promotes checkpoint activation. Here, we show regulation of 53BP1 beyond repair foci. 53BP1 movements are constrained in the nucleoplasm and increase in response to DNA damage. 53BP1 interacts with the structural protein NuMA, which controls 53BP1 diffusion. This interaction, and colocalization between the two proteins in vitro and in breast tissues, is reduced after DNA damage. In cell lines and breast carcinoma, NuMA prevents 53BP1 accumulation at DNA breaks and high NuMA expression predicts better patient outcomes. Manipulating NuMA expression alters PARP inhibitor sensitivity of BRCA1-null cells, end-joining activity, and immunoglobulin class switching that rely on 53BP1. We propose a new mechanism that involves the sequestration of 53BP1 by NuMA in the absence of DNA damage. Such mechanism may have evolved to disable repair functions and may be a decisive factor for tumor responses to genotoxic treatments.

cell biology↗

bioRxiv2017

Structured illumination to spatially map chromatin motions

We describe a simple optical method that creates structured illumination of a photoactivatable probe and apply this method to characterize chromatin motions in the nuclei of live cells. A laser beam coupled to a diffractive optical element at the back focal plane of an excitation objective generates an array of near diffraction-limited beamlets with FWHM of 340{+/-}30 nm, which simultaneously photoactivate a 7x7 matrix pattern of GFP-labeled histones, with spots 1.70 m apart. From the movements of the photoactivated spots, we map chromatin diffusion coefficients at multiple microdomains of the cell nucleus. The results show correlated motions of nearest chromatin microdomain neighbors, whereas chromatin movements are uncorrelated at the global scale of the nucleus. The method also reveals DNA damage-dependent decrease in chromatin diffusion. The DOE instrumentation can easily and cheaply be implemented on commercial inverted fluorescence microscopes to analyze adherent cell culture models. A protocol to measure chromatin motions in non-adherent human hematopoietic stem and progenitor cells is also described. We anticipate that the method will contribute to the identification of the mechanisms regulating chromatin mobility, which influences most genomic processes and may underlie the biogenesis of genomic translocations associated with hematologic malignancies.

cell biology↗