Biology subjects

Valades-Cruz, C. A.

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

7 recordsLinked to original sources

bioRxiv2023

Spatial N-glycan rearrangement on α5β1 integrin nucleates galectin-3 oligomers to determine endocytic fate α

Membrane glycoproteins frequently adopt different conformations when altering between active and inactive states. Here, we discover a molecular switch that exploits dynamic spatial rearrangements of N-glycans during such conformational transitions to control protein function. For the conformationally switchable cell adhesion glycoprotein 5{beta}1 integrin, we find that only the bent-closed state arranges N-glycans to nucleate the formation of up to tetrameric oligomers of the glycan-binding protein galectin-3. We propose a structural model of how these galectin-3 oligomers are assembled and how they clamp the bent-closed state to prime it for endocytic uptake and subsequent retrograde trafficking to the Golgi for polarized distribution in cells. Our findings highlight an unexpectedly dynamic regulation of the glycan landscape at the cell surface to achieve oligomerization of galectin-3. Galectin-3 oligomers are thereby identified as decoders of defined spatial patterns of N-glycans and as functional extracellular interactors of specifically the bent- closed conformational state of 5{beta}1 integrin and possibly other family members.

cell biology↗

bioRxiv2023

Growth factor-induced desialylation for the fast control of endocytosis

It is commonly assumed that the glycan makeup of glycoproteins that reach the cell surface is final and static. Here, we challenge this notion by the discovery of a molecular switch that induces acute and reversible changes of glycans on the plasma membrane. We demonstrate that within minutes, the epidermal growth factor triggers the galectin-driven endocytosis of cell surface glycoproteins, such as integrins, that are key regulators of cell adhesion and migration. The onset of this process, mediated by the Na+/H+ antiporter NHE-1 and the neuraminidases Neu1/3, requires the pH-triggered enzymatic removal of sialic acids whose presence otherwise prevents galectin binding. Desialylated glycoproteins are then retrogradely transported to the Golgi apparatus where their glycan makeup is reset, and their function is repurposed to regulate EGF-dependent invasive cell migration. Glycosylation at the cell surface thereby emerges as a dynamic and reversible regulatory post-translational modification that controls a highly adaptable trafficking pathway.

cell biology↗

bioRxiv2023

DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

Mitochondria play an essential role in the life cycle of eukaryotic cells. However, we still dont know how their ultrastructure, like the cristae of the inner membrane, dynamically evolves to regulate these fundamental functions, in response to external conditions or during interaction with other cell components. Although high-resolution fluorescent microscopy coupled with recently developed innovative probes can reveal this structural organization, their long-term, fast and live 3D imaging remains challenging. To address this problem, we have developed a convolutional neural network, called DeepCristae, to restore mitochondria cristae in low spatial resolution microscopy images. Our network is trained from 2D STED images using a novel loss specifically designed for cristae restoration. To efficiently increase the size of the training set, we also developed a random image patch sampling centered on mitochondrial areas. To evaluate DeepCristae, quantitative assessments were carried out using metrics we derived by focusing on the mitochondria and cristae pixels rather than on the whole image as usual. Depending on the conditions of use indicated, DeepCristae works well on broad microscopy modalities (Stimulated Emission Depletion (STED), Live-SR, AiryScan and Lattice Light Sheet Microscopy). It is ultimately applied in the context of mitochondrial network dynamics during interaction with endo/lysosome membranes.

bioinformatics↗

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↗