Biology subjects

Beghin, A.

Publications and source records attributed to Beghin, A..

5 recordsLinked to original sources

bioRxiv2026

Microdomes: Micro-engineered Microdome arrays enable standardised and shear-free 3D biology with full-spectrum optical imaging compatibility

Three-dimensional cellular models such as organoids and spheroids hold major promise for developmental biology, disease modelling and precision medicine, yet their large-scale production and analysis remain constrained by handling-induced shear stress, sample fragility, positional instability and limited compatibility with advanced imaging workflows. Here, we introduce Microdomes, dense arrays of open-top, dome-shaped microcavities. Each cavity holds a single specimen in its own miniature aquarium, accessed through a narrow apical opening that admits cells, medium, matrix and staining reagents while shielding it from the shear generated during routine pipetting. Each Microdomes chip accommodates more than 100 spheroids or organoids, supporting diverse cell types, co-culture formats and both matrix-free and matrix-embedded culture. All specimens are retained through prolonged culture, repeated medium exchange, fixation and immunostaining. Because every specimen occupies a fixed, addressable position against a thin transparent film, the same spheroid can be relocated and re-imaged over weeks of culture and across microscopes, from array-wide overviews to subcellular details, without transfer, embedding or other perturbation. Microdomes also support AI-based automated segmentation, from whole-spheroid outlines to individual nuclei in 3D, using common image analysis software. Microdomes thereby turn each array into a self-contained quality control unit, providing specimen-level traceability that organoid production pipelines currently lack.

cell biology↗

bioRxiv2023

Digitalized organoids: integrated pipeline for 3D high-speed analysis of organoid structures using multilevel segmentation and cellular topology

Analysing the tissue morphogenesis and function is crucial for unravelling the underlying mechanisms of tissue development and disease. Organoids, 3D in vitro models that mimic the architecture and function of human tissues, offer a unique opportunity to study effects of external perturbators that are difficult to replicate in vivo. However, large-scale screening procedures for studying the effects of different stress on cellular morphology and topology of these 3D tissue-like system face significant challenges, including limitations in high-resolution 3D imaging, and accessible 3D analysis platforms. These limitations impede the scale and throughput necessary to accurately quantify the effects of mechanical and chemical cues. Here, we present a novel, fine-tuned pipeline for screening morphology and topology modifications in 3D cell culture using multilevel segmentations and cellular topology, based on confocal microscopy and validated across different image qualities. Our pipeline incorporates advanced image analysis algorithms and artificial intelligence (AI) for multiscale 3D segmentation, enabling quantification of morphology changes at both the nuclear and cytoplasmic levels, as well as at the organoid scale. Additionally, we investigate cell relative position and employ neighbouring topology analysis to identify tissue patterning and their correlations with organoid microniches. Eventually, we have organized all the extracted features, 3D segmented masks and raw images into a single database to allow statistical and data mining approaches to facilitate data analysis, in a biologist-friendly way. We validate our approach through proof-of-concept experiments, including well-characterized conditions and poorly explored mechanical stressors such as microgravity, showcasing the versatility of our pipeline. By providing a powerful tool for discovery-like assays in screening 3D organoid models, our pipeline has wide-ranging interests from biomedical applications in development and aging-related pathologies to tissue engineering and regenerative medicine.

cell biology↗

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↗

bioRxiv2020

AutoNeuriteJ: An ImageJ plugin for measurement and classification of neuritic extensions

Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.

bioinformatics↗