Biology subjects

Olivo-Marin, J.-C.

Publications and source records attributed to Olivo-Marin, J.-C..

9 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

Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ

This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in the life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained neural networks to custom data. The manuscript demonstrates a number of deepImageJ capabilities, particularly in executing complex pipelines, 3D analysis, and processing large images. A key development is the integration of the Java Deep Learning Library (JDLL), expanding deepImageJs compatibility with various deep learning frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis tasks. The manuscript details three case studies to demonstrate these capabilities. The first explores integrated image-to image translation and nuclei segmentation. The second focuses on 3D nuclei segmentation. The third case study deals with large image segmentation. These studies underscore deepImageJs versatility and power in bioimage analysis, emphasizing its role as a critical tool for life scientists and researchers. The advancements in deepImageJ bridge the gap between deep learning model developers and end-users, enabling a more accessible and efficient approach to biological image analysis. The advancements in deepImageJ, detailed in this paper, represent a significant leap in bioimage analysis, crucial for life sciences. By enhancing this Fiji/ImageJ plugin, the research bridges the gap between complex deep learning models and practical applications, making advanced bioimage analysis accessible to a broader audience. This integration of the Java Deep Learning Library (JDLL) within deepImageJ is particularly noteworthy, as it expands compatibility with leading deep learning frameworks. This allows for the seamless execution of multiple models in a single instance, simplifying the construction of complex image analysis pipelines. The implications of this research are far-reaching, extending beyond academic circles to potentially impact various sectors, including healthcare, pharmaceuticals, and biotechnology. The enhanced capabilities of deepImageJ in handling intricate pipelines, 3D analysis, and large images facilitate detailed and efficient analysis of biological data. Such advancements are vital for accelerating research and development in medical imaging, drug discovery, and understanding complex biological processes. This manuscript contribution to the field of bioimage analysis is significant, offering a tool that empowers researchers, irrespective of their computational expertise, to leverage advanced technologies in their work. The wide applicability and ease of use of deepImageJ have the potential to foster interdisciplinary collaborations, drive innovation, and facilitate discoveries across various scientific and industrial sectors.

bioinformatics↗

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↗

bioRxiv2021

HIV-1 hijacks the cell extracellular matrix to spread collectively and efficiently between T lymphocytes

Collective transmission via structures containing several virions has recently emerged as a highly efficient mode of viral spread. Here, we demonstrate that HIV-1 spreads between T lymphocytes in the form of viral particles colonies that are concentrated and sheltered in an extracellular matrix (ECM) lattice enabling their collective transmission upon cell contacts. Intrinsically, ECM-clustered viruses infect T lymphocytes more efficiently than individual viral particles. They preserve HIV-1 transmission from antiretroviral treatment (ArT) and potent broadly neutralizing antibodies. We also show that collagen induced by HIV-1 infection controls the clustering of virions and their collective spread, thereby enhancing infectivity. CD4+ T cells from HIV-1-infected patients produce and transmit ECM-virus clusters, supporting that they could be involved in vivo. This study provides new insights into modes of HIV-1 transmission and identifies a novel fundamental role for collagen in this process. HIV-1 spread via ECM-virus clusters may have important implications for viral dissemination and persistence, including during therapy.

microbiology↗

bioRxiv2019

Cell Tracking Profiler: a user-driven analysis framework for evaluating 4D live cell imaging data

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm we have established a ground truth for muSC cell behaviour. This revealed that variability in segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) operates through the freely available Icy platform, and allows user-managed cell tracking from 3D time-lapsed datasets to provide measures of cell shape and movement. Using dimensionality reduction methods, multiple correlation and regression analyses we identify myosin II-dependent parameters of muSC behaviour during regeneration. CTP and the associated statistical tools we have developed thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets. SummaryAnalysis of cell shape and movement from 3D time-lapsed datasets is currently very challenging. We therefore designed Cell Tracking Profiler for analysing cell behaviour from complex datasets and demonstrate its effectiveness by analysing stem cell behaviour during muscle regeneration in zebrafish.

cell biology↗

bioRxiv2019

Dlx5 and Dlx6 expression in GABAergic neurons controls behavior, metabolism, healthy aging and lifespan.

Dlx5 and Dlx6 encode two homeobox transcription factors expressed by developing and mature GABAergic interneurons. During development Dlx5/6 are important for the differentiation of Parvalbumin (Pvalb)-expressing neurons. Perinatal lethality of homozygous mice in which Dlx5/6 have been constitutively deleted has, so far, hindered the study of the function of these genes in adult neurons. We first show that Dlx5 and Dlx6 are expressed by all subclasses of adult cortical GABAergic neurons. Then we analyse Vgat{Delta}Dlx5-6 mice in which Dlx5 and Dlx6 are simultaneously inactivated in all GABAergic interneurons. Vgat{Delta}Dlx5-6 mice present a behavioral pattern suggesting reduction of anxiety and obsessive-compulsive activities. They rapidly access and spend more time in the central region of an open field, bury few marbles in the marble burying test and show little interest in nest building. Male and female 20-month-old Vgat{Delta}Dlx5-6 animals have the same size as their normal littermates, but present a 25% body weight reduction associated with a marked decline in white and brown adipose tissue. Remarkably, both Vgat{Delta}Dlx5-6/+ and Vgat{Delta}Dlx5-6 mice present a 33% longer median survival than their control littermates. Hallmarks of biological aging such as motility, adipose deposition and coat conditions are improved in mutant animals. Our data imply that GABAergic interneurons can regulate mammalian healthspan and lifespan through Dlx5/6-dependent mechanisms. Understanding these regulations can be an entry point to unravel the processes through which the brain affects body homeostasis and, ultimately, longevity and healthy aging.\n\nSIGNIFICANCE STATEMENTDlx5 and Dlx6 are transcription factors controlling several developmental processes, including GABAergic neuronal migration and differentiation. To study their function in adult brain, we selectively inactivated both genes in GABAergic interneurons (Vgat{Delta}Dlx5-6 mice). Mutant mice have reduced anxiety-like and obsessive-compulsive behaviors. Interestingly, Vgat{Delta}Dlx5-6 mice have a 25% body weight reduction and about 70% less white and brown adipose tissue; their general health status is excellent. Vgat{Delta}Dlx5-6 mice have a median survival about 33% longer than their control littermates and hallmarks of biological aging are improved. Dlx5/6-dependent regulations in GABAergic neurons could be an entry point to understand how the brain determines the psychophysiological status of the body and, ultimately, longevity and healthy aging.

physiology↗

bioRxiv2019

DypFISH: Dynamic Patterned FISH to Interrogate RNA and Protein Spatial and Temporal Subcellular Distribution

Advances in single cell RNA sequencing have allowed for the identification and characterization of cellular subtypes based on quantification of the number of transcripts in each cell. However, cells may differ not only in the number of mRNA transcripts that they exhibit, but also in their spatial and temporal distribution, intrinsic to the definition of their cellular state. Here we describe DypFISH, an approach to quantitatively investigate the spatial and temporal subcellular localization of RNA and protein, by combining micropatterning of cells with fluorescence microscopy at high resolution. We introduce a range of analytical techniques for quantitatively interrogating single molecule RNA FISH data in combination with protein immunolabeling over time. Strikingly, our results show that constraining cellular architecture reduces variation in subcellular mRNA and protein distributions, allowing the characterization of their localization and dynamics with high reproducibility. Many tissues contain cells that exist in similar constrained architectures. Thus DypFISH reveals reproducible patterns of clustering, strong correlative influences of mRNA-protein localization on MTOC orientation when they are present and interdependent dynamics globally and at specific subcellular locations which can be extended to physiological systems.

systems biology↗

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↗