Biology subjects

Lagache, T.

Publications and source records attributed to Lagache, T..

8 recordsLinked to original sources

bioRxiv2024

Characterizing particle dynamics in live imaging through stochastic physical models and machine learning

Particle dynamics determine the orchestration of molecular signaling in cellular processes. A wide range of subdiffusive motions has been described at the cell interior and membrane, corresponding to different environmental constraints. However, the standard methods for motion analysis, embedded in a diffusion-based framework, lack robustness for capturing the complexity of stochastic dynamics. This work develops a classification method to detect the five main stochastic laws modeling particle dynamics accurately. The method builds on machine-learning techniques that use features properly designed to capture the intrinsic geometric properties of trajectories governed by the different processes. This guarantees the accurate classification of observed dynamics in an interpretable and explainable framework. The main asset of this approach is its capability to distinguish different subdiffusive behaviors making it a privileged tool for biological investigations. The robustness to localization error and motion composition is proven, ensuring its reliability on experimental data. Moreover, the classification of composed trajectories is investigated, showing that the method can uncover the paths mono-vs bi-dynamics nature. The method is used to study the dynamics of membrane receptors CCR5, involved in HIV infection. Comparing the basal state to an agonist-bound state which displays potent anti-HIV-1 activity, we show that the latter affects the natural dynamic state of receptors, thus clarifying the link between movement and receptor activation.

biophysics↗

bioRxiv2024

Unraveling Microglial Spatial Organization in the Developing Human Brain with DeepCellMap, a Deep Learning Approach Coupled to Spatial Statistics

Mapping cellular organization in the developing brain presents significant challenges due to the multidimensional nature of the data, characterized by complex spatial patterns that are difficult to interpret without high-throughput tools. We developed DeepCellMap, a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline was designed to map microglial organization during normal and pathological brain development but can be adapted to any cell type. Using DeepCellMap, we capture the morphological diversity of microglia, identify strong coupling between proliferative and phagocytic phenotypes, and show that distinct spatial clusters rarely overlap as human brain development progresses. Additionally, we uncover a novel association between microglia and blood vessels in fetal brains exposed to maternal SARS-CoV-2. These findings offer insights into whether various microglial phenotypes form networks in the developing brain to occupy space, and in conditions involving haemorrhages, whether microglia respond to, or influence changes in blood vessel integrity. DeepCellMap is available as open-source software and is a powerful tool for extracting spatial statistics and analyzing cellular organization in large tissue sections, accommodating various imaging modalities. This platform could open new avenues for studying brain development and related pathologies.

developmental biology↗

bioRxiv2023

Automatic monitoring of whole-body neural activity in behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neurons calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

neuroscience↗

bioRxiv2021

Single-molecule imaging reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status

G protein-coupled receptors (GPCR) are present at the cell surface in different conformational and oligomeric states. However, how these states impact GPCRs biological function and therapeutic targeting remains incompletely known. Here, we investigated this issue in living cells for the CC chemokine receptor 5 (CCR5), a major receptor in inflammation and the principal entry co-receptor for Human Immunodeficiency Viruses (HIV-1). We used TIRF microscopy and an original statistical method to track and classify the motion of different receptors subpopulations. We showed a diversity of ligand-free forms of CCR5 at the cell surface constituted of various oligomeric states and exhibiting transient Brownian and restricted motions. These forms were stabilized differently by distinct ligands. In particular, agonist stimulation restricted the mobility of CCR5 and led to its clustering, a feature depending on {beta}-arrestin, while inverse agonist stimulation exhibited the opposite effect. These results suggest a link between receptor activation and immobilization. Applied to HIV-1 envelope glycoproteins gp120, our quantitative analysis revealed agonist-like properties of gp120s. Distinct gp120s influenced CCR5 dynamics differently, suggesting that they stabilize different CCR5 conformations. Then, using a dimerization-compromized mutant, we showed that dimerization (i) impacts CCR5 precoupling to G proteins, (ii) is a pre-requisite for the immobilization and clustering of receptors upon activation, and (iii) regulates receptor endocytosis, thereby impacting the fate of activated receptors. This study demonstrates that tracking the dynamic behavior of a GPCR is an efficient way to link GPCR conformations to their functions, therefore improving the development of drugs targeting specific receptor conformations.

cell biology↗

bioRxiv2020

Robust single neuron tracking of calcium imaging in behaving Hydra

Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic long-term monitoring of neuronal activity and the subsequent functional characterization of neural circuits. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. By monitoring the calcium activity of the same neuronal populations along several days in layer 2/3 of visual cortex of mice, we report important turn-over within the active neurons across days, with only few neurons that remained active across days. Then, by combining automatic tracking of single neuron activity over long time-lapse sequences with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra. We document the existence three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results prove that the EMC2 algorithm can be used as a robust platform for neuronal tracking in behaving animals.

neuroscience↗

bioRxiv2018

Tracking Activity In A Deformable Nervous System With Motion Correction And Point-Set Registration

The combination of fluorescent probes with time-lapse microscopy allows for the visualization of the entire neuronal activity of small animals, such as worms or cnidarians, over a long period of time. However, large deformations of the animal combined with the natural intermittency of neuronal activity make robust automated tracking of firing fluorescent neurons challenging. Here we present an hybrid approach where (i) a subset of very bright neurons is used as moving reference points (fiducials) to estimate the elastic deformation of the animal; (ii) deformation is frame-by-frame corrected, and firing neurons are aligned at each time with the initial mask; and (iii) point-set registration is used to robustly track the intermittent activity of all the immobilized neurons. We compare different registration strategies with manual tracking performed over {approx}620 neurons over 100 time frames in the cnidarian Hydra vulgaris.\n\nIndex TermsFluorescence imaging, wavelet detection, tracking, point-set registration, elastic deformation, Thin Plate Spline (TPS) transform, Coherent Point Drift (CPD), Hydra.

neuroscience↗

bioRxiv2018

Live Mouse Tracker: real-time behavioral analysis of groups of mice

Preclinical studies of psychiatric disorders require the use of animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we present a real-time method for behavior analysis of mice housed in groups that couples computer vision, machine learning and Triggered-RFID identification to track and monitor animals over several days in enriched environments. The system extracts a thorough list of individual and collective behavioral traits and provides a unique phenotypic profile for each animal. On mouse models, we study the impact of mutations of genes Shank2 and Shank3 involved in autism. Characterization and integration of data from behavioral profiles of mutated female mice reveals distinctive activity levels and involvement in complex social configuration.

animal behavior and cognition↗

bioRxiv2018

Electrodiffusion model of synaptic potentials in dendritic spines

When modeling electric current flow in neurons and excitable cells, traditional cable theory ignores electrodiffusion (i.e. the interaction between electric fields and ionic diffusion) as it assumes that concentration changes associated with ionic currents are negligible. This assumption, while true for large neuronal compartments, fails when applied to femto-liter size compartments such as dendritic spines - small protrusions that form the main site of synaptic inputs in the brain. Here, we use the Poisson (P) and Nernst-Planck (NP) equations, which relate electric field to charge and couple Ficks law of diffusion to the electric field, to model ion concentration dynamics in dendritic spines. We use experimentally measured voltage transients from spines with nanoelectrodes to explore these dynamics with realistic parameters. We find that (i) passive diffusion and electrodiffusion jointly affect the kinetics of spine excitatory post-synaptic potentials (EPSPs); (ii) spine geometry plays a key role in shaping EPSPs; and, (iii) the spine-neck resistance dynamically decreases during EPSPs, leading to short-term synaptic facilitation. Our formulation can be easily adopted to model ionic biophysics in a variety of nanoscale bio-compartments.

biophysics↗