Biology subjects

Bernatchez, R.

Publications and source records attributed to Bernatchez, R..

3 recordsLinked to original sources

bioRxiv2026

PhenoMe: An interactive AI framework for interpretable, microscopy-driven phenotyping

Extracting meaningful biological patterns from microscopy image datasets requires tools that quantitatively connect visual similarities and experimental context. Deep learning models, and in particular novel foundation models, have the capacity to translate images into high-dimensional embeddings that can better represent complex visual patterns. Using these complex representations analytically and linking them to experimental information, it is possible to build advanced exploratory and phenotyping capabilities. Here, we present PhenoMe, an open-source, model-agnostic framework to handle pretrained foundation model-derived image representations alongside quantitative image properties and experimental metadata. PhenoMe integrates multiple scriptable and interactive workflows for embedding space visualisation, dimensionality reduction and clustering, similarity-based comparison and retrieval of representative images for each phenotype, among others. It also supports correlation analyses that link the embedding structure to interpretable image properties. We demonstrate PhenoMe's capabilities on seven public microscopy datasets, ranging from cell painting assays, nanoscale synaptic images, drug-response analyses and dynamic cell-phenotyping. These case studies show how readily available image representations can support data contextualisation, curation and analysis. Through guided notebooks, reproducible configurations, extensible model wrappers and a napari plugin, PhenoMe makes representation-based microscopy exploration accessible to researchers without dedicated technical expertise. PhenoMe lays the groundwork for applying foundation models and representation learning to microscopy data analysis.

bioinformatics↗

bioRxiv2025

A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy

Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a variety of tasks. The performance increase they offer is obtained without elaborated specific trainings for domains such as natural language processing and computer vision. However, their application in specialized fields like biomedical imaging and fluorescence microscopy remains difficult due to distribution shifts and the scarcity of high-quality annotated datasets. The high cost of data acquisition and the requirement for in-domain expertise further exacerbate this challenge in microscopy. To address this we introduce STED-FM, a foundation model specifically designed for super-resolution STimulated Emission Depletion (STED) microscopy. STED-FM leverages a Vision Transformer architecture trained at scale with Masked Autoencoding on a new dataset of nearly one million STED images. STED-FM learns expressive latent representations without requiring extensive annotations, yielding robust performance across diverse downstream microscopy image analysis tasks. Unsupervised experiments demonstrate the discriminative structure of its learned latent space. These representations can be leveraged for multiple downstream applications, including fully supervised classification and segmentation with reduced annotation requirements. Moreover, STED-FM representations enhance the performance of deep learning-based image denoising and improve the quality of images generated by diffusion models, enabling latent attribute manipulation for the data-driven discovery of subtle nanostructures and phenotypes, as well as algorithmic super-resolution. Moreover, its powerful structure retrieval capabilities are integrated into automated STED microscopy acquisition pipelines, paving the way for smart microscopy. In sum, we demonstrate that STED-FM lays a robust foundation for state-of-the-art algorithms across a wide array of tasks, establishing it as a highly valuable and scalable resource for researchers in super-resolution microscopy.

bioinformatics↗

bioRxiv2022

Chronic stress exposure alters the gut barrier: sex-specific effects on microbiota and jejunum tight junctions

Major depressive disorder (MDD) is the leading cause of disability worldwide. However, 30-50% of patients are unresponsive to commonly prescribed antidepressants, highlighting untapped causal biological mechanisms. Dysfunction in the microbiota-gut-brain axis, the bidirectional communications between the central nervous system and gastrointestinal tract that are modulated by gut microorganisms, has been implicated in MDD pathogenesis. Exposure to chronic stress disrupts blood-brain barrier integrity, still, little is known about intestinal barrier function in these conditions particularly for the small intestine where most food and drug absorption takes place. Thus, here we investigate how chronic social or variable stress, two mouse models of depression, impact the jejunum (JEJ) intestinal barrier in males and females. Mice were subjected to stress paradigms followed by analysis of gene expression profiles of intestinal barrier-related targets, fecal microbial composition, and blood-based markers. Altered microbial populations as well as changes in gene expression of JEJ tight junctions were observed depending on the type and duration of stress, with sex-specific effects. We took advantage of machine learning to characterize in detail morphological tight junction properties identifying a cluster of ruffled junctions in stressed animals. Junctional ruffling is associated with inflammation, so we evaluated if LPS injection recapitulates stress-induced changes in the JEJ and observed profound sex differences. Finally, LPS-binding protein (LBP), a marker of gut barrier leakiness, was associated with stress vulnerability in mice and translational value was confirmed on blood samples from women with MDD. Our results provide evidence that chronic stress disrupts intestinal barrier homeostasis in conjunction with the manifestation of depressive-like behaviors in a sex-specific manner in mice and possibly, human depression.

neuroscience↗