Biology subjects

Valades-Cruz, C. A.

Publications and source records attributed to Valades-Cruz, C. A..

4 recordsLinked to original sources

bioRxiv2022

Genome-wide analysis of the dynamic and biophysical properties of chromatin and nuclear proteins in living cells with Hi-D

To understand the dynamic nature of the genome in real-time, the localization and rearrangement of DNA and DNA-binding proteins must be analyzed across the entire nucleus of single living cells. Recently, we developed a new computational light microscopy technique, called high-resolution diffusion mapping (Hi-D), that can accurately detect, classify, and map the types of diffusion and biophysical parameters such as the diffusion constant, anomalous exponent, drift velocity, and physical diffusion models at a high spatial resolution over the entire genome in living cells. Hi-D combines dense optical flow to detect and track local chromatin and protein motion, and Bayesian inference to characterize this local movement at nanoscale resolution. The initial implementation requires solid experience using MATLAB (MathWorks) and computational resources, for instance, access to a computer cluster, to perform the Hi-D analysis. In addition, this implementation takes [~]18-24 hours to analyze a typical imaging stack. To avoid these limitations and emphasize high-performance implementation, we present a customized version called Hi-D-Py. The new implementation is written in the open-source Python programming language and has an option for parallelizing the calculations to run on multi-core CPUs. The functionality of Hi-D-Py is exposed to the users via user-friendly documented Python notebooks. Our efficient implementation reduces the analysis time to less than one hour using a multi-core CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, H2B, and RNA Pol II sequences acquired with spinning disk confocal and super-resolution structured illumination microscopy.

biophysics↗

bioRxiv2022

STracking: a free and open-source python library for particle tracking and analysis

SummaryAnalysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. Designing a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking...) requiring a lot of manual operations, and therefore leading to poorly reproducible results. A good synergy between particle detector with a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking a python library that allows to combine algorithms into standardized particle tracking pipelines. Availability and ImplementationSTracking is available as a python library using "pip install" and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using "pip install". The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). Contactsylvain.prigent@inria.fr, cesar-augusto.valades-cruz@curie.fr

bioinformatics↗

bioRxiv2022

SPITFIR(e): A supermaneuverable algorithm for restoring 2D-3D fluorescence images and videos,and background subtraction

While fluorescent microscopy imaging has become the spearhead of modern biology as it is able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by the optical aberrations and the photon budget available in the specimen and to some extend to photo-toxicity. A direct consequence is the necessity to develop flexible and "off-road" algorithms in order to recover structural details and improve spatial resolution, which is critical when pushing the illumination to the low levels in order to limit photo-damages. Moreover, as the processing of very large temporal series of images considerably slows down the analysis, special attention must be paid to the feasibility and scalability of the developed restoration algorithms. To address these specifications, we present a very flexible method designed to restore 2D-3D+Time fluorescent images and subtract undesirable out-of-focus background. We assume that the images are sparse and piece-wise smooth, and are corrupted by mixed Poisson-Gaussian noise. To recover the unknown image, we consider a novel convex and non-quadratic regularizer Sparse Hessian Variation) defined as the mixed norms which gathers image intensity and spatial second-order derivatives. This resulting restoration algorithm named SPITFIR(e) (SParse fIT for Fluorescence Image Restoration) utilizes the primal-dual optimization principle for energy minimization and can be used to process large images acquired with varied fluorescence microscopy modalities. It is nearly parameter-free as the practitioner needs only to specify the amount of desired sparsity (weak, moderate, high). Experimental results in lattice light sheet, stimulated emission depletion, multifocus microscopy, spinning disk confocal, and wide-field microscopy demonstrate the generic ability of the SPITFIR(e) algorithm to efficiently reduce noise and blur, and to subtract undesirable fluorescent background, while avoiding the emergence of deconvolution artifacts.

bioinformatics↗

bioRxiv2021

BioImageIT: Open-source framework for integration of image data-management with analysis

Open science and FAIR principles have become major topics in the field of bioimaging. This is due to both new data acquisition technologies that generate large datasets, and new analysis approaches that automate data mining with high accuracy. Nevertheless, data are rarely shared and rigorously annotated because it requires a lot of manual and tedious management tasks and software packaging. We present BioImageIT, an open-source framework for integrating data management according to FAIR principles with data processing.

bioinformatics↗