Biology subjects

Galland, R.

Publications and source records attributed to Galland, R..

7 recordsLinked to original sources

bioRxiv2025

Extracellular matrix deposition controls early differentiation patterns in human cardiac gastruloids

Amongst the various factors that affect differentiation and tissue organization, the autonomous deposition of extracellular matrix (ECM) has hardly been considered in the context of gastruloids. Using biofunctionalized colloidal particles as artificial organizing centers (aOCs), we patterned differentiation loci within 3D embryonic bodies. Our findings reveal that the spatial distribution of the aOCs induces various pattern organizations, mediated by endogenous deposition of ECM. We investigated how the interplay between meso-endoderm cell epithelial-to-mesenchymal transition, migration into germ layers, and the prolonged maintenance of local stem-cell niches orchestrates gastruloid structure and composition. These factors collectively regulate the complexity of emergent cardiac structures observed at later time points. This work uncovers a critical feedback loop between cell differentiation rates and endogenous ECM deposition patterns, which governs the differentiation paths in scaffolded cardiac gastruloids.

cell biology↗

bioRxiv2024

Fast volumetric fluorescence lifetime imaging of multicellular systems using single-objective light-sheet microscopy

Fluorescence lifetime imaging (FLIM) is widely used for functional and multiplexed bioimaging. The lifetime of autofluorescence or fluorescent sensors encodes physiologically relevant parameters. Thus, FLIM is especially relevant for the investigation of living systems. However, application of FLIM to live specimen is hampered by its slow speed and high phototoxicity. To enable faster and gentler FLIM, we integrated single-objective light-sheet microscopy with pulsed excitation and time-resolved detection on a novel SPAD array detector. We achieved 10-100-fold acceleration compared to confocal FLIM, down to 100 ms acquisition time per image, with excellent quantitative agreement. The massively enhanced speed enables volumetric FLIM acquisitions on live multicellular specimens, which we demonstrate with lifetime-based multiplexing in 3D and time-lapse FLIM of tension probes on living embryonic organoids. We benchmark both scanned and static light-sheet modalities to facilitate adding FLIM capability to a large variety of light-sheet microscopes.

biophysics↗

bioRxiv2023

3D Nuclei Segmentation by Combining GAN Based Image Synthesis and Existing 3D Manual Annotations

Nuclei segmentation is an important task in cell biology analysis that requires accurate and reliable methods, especially within complex low signal to noise ratio images with crowded cells populations. In this context, deep learning-based methods such as Stardist have emerged as the best performing solutions for segmenting nucleus. Unfortunately, the performances of such methods rely on the availability of vast libraries of ground truth hand-annotated data-sets, which become especially tedious to create for 3D cell cultures in which nuclei tend to overlap. In this work, we present a workflow to segment nuclei in 3D in such conditions when no specific ground truth exists. It combines the use of a robust 2D segmentation method, Stardist 2D, which have been trained on thousands of already available ground truth datasets, with the generation of pair of 3D masks and synthetic fluorescence volumes through a conditional GAN. It allows to train a Stardist 3D model with 3D ground truth masks and synthetic volumes that mimic our fluorescence ones. This strategy allows to segment 3D data that have no available ground truth, alleviating the need to perform manual annotations, and improving the results obtained by training Stardist with the original ground truth data.

bioinformatics↗

bioRxiv2022

Selective volumetric excitation and imaging for single molecule localization microscopy in multicellular systems

Light sheet fluorescence microscopy (LSFM) has become a leading standard in high-resolution imaging of living samples in 2- and 3-dimensions. Biological samples are however not restricted to a single observation plane and several molecular processes evolve rapidly in 3D. The conventional mechanical scanning required in LSFM limits the range of observable dynamics and are usually restricted in resolution. Here we introduce a new strategy for instantaneous volumetric excitation and volumetric imaging of single-molecules in cell aggregates. The technique combines, for the first time, the use of light sheet microscopy and multifocus microscopy (MFM) and enables a volumetric 4D imaging of biological samples with single-molecule resolution. We engineered the excitation beam to yield a modular and uniform excitation matching the observable detection range of MFM. The strength of the method is highlighted with examples of single-molecule 3D tracking and 3D super-resolution imaging in multicellular samples.

biophysics↗

bioRxiv2022

SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

Automatic segmentation of nuclei in low-light microscopy images remains a difficult task, especially for high-throughput experiments where need for automation is strong. Low saliency of nuclei with respect to the background, variability of their intensity together with low signal-to-noise ratio in these images constitute a major challenge for mainstream algorithms of nuclei segmentation. In this work we introduce SalienceNet, an unsupervised deep learning-based method that uses the style transfer properties of cycleGAN to transform low saliency images into high saliency images, thus enabling accurate segmentation by downstream analysis methods, and that without need for any parameter tuning. We have acquired a novel dataset of organoid images with soSPIM, a microscopy technique that enables the acquisition of images in low-light conditions. Our experiments show that SalienceNet increased the saliency of these images up to the desired level. Moreover, we evaluated the impact of SalienceNet on segmentation for both Otsu thresholding and StarDist and have shown that enhancing nuclei with SalienceNet improved segmentation results using Otsu thresholding by 30% and using StarDist by 26% in terms of IOU when compared to segmentation of non-enhanced images. Together these results show that SalienceNet can be used as a common preprocessing step to automate nuclei segmentation pipelines for low-light microscopy images.

bioinformatics↗

bioRxiv2021

High content 3D imaging method for quantitative characterization of organoid development and phenotype.

Quantitative analysis on a large number of organoids can provide meaningful information from the morphological variability observed in 3D organotypic cultures, called organoids. Yet, gathering statistics of growing organoids is currently limited by existing imaging methods and subsequent image analysis workflows that are either restricted to 2D, limited in resolution, or with a low throughput. Here, we present an automated high content imaging platform synergizing high density organoid cultures with 3D live light-sheet imaging. The platform is an add-on to a standard inverted microscope. We demonstrate our capacity to collect libraries of 3D images at a rate of 300 organoids per hour, enabling training of artificial intelligence-based algorithms to quantify the organoid morphogenetic organization at multiple scales with subcellular resolution. We validate our approach on different organotypic cell cultures (stem, primary, and cancer), and quantify the development of hundreds of neuroectoderm organoids (from human Embryonic Stem Cells) at cellular, multicellular and whole organoid scales.

biophysics↗

bioRxiv2019

Mammalian Amoeboid Swimming is propelled by molecular and not protrusion-based paddling in Lymphocytes

Mammalian cells developed two main migration modes. The slow mesenchymatous mode, like fibroblasts crawling, relies on maturation of adhesion complexes and actin fiber traction, while the fast amoeboid mode, observed exclusively for leukocytes and cancer cells, is characterized by weak adhesion, highly dynamic cell shapes, and ubiquitous motility on 2D and in 3D solid matrix. In both cases, interactions with the substrate by adhesion or friction are widely accepted as a prerequisite for mammalian cell motility, which precludes swimming. We show here experimentally and computationally that leukocytes do swim, and that propulsion is not fueled by waves of cell deformation but by a rearward and inhomogeneous treadmilling of the cell envelope. We model the propulsion as a molecular paddling by transmembrane proteins linked to and advected by the actin cortex, whereas freely diffusing transmembrane proteins hinder swimming. This mechanism explains that swimming is five times slower than the cortex retrograde flow. Resultantly the ubiquitous ability of mammalian amoeboid cells to migrate in various environments can be explained for lymphocytes by a single machinery of envelope treadmilling.

biophysics↗