@article{SchaanProfes2026,

title = {Autophagy Is Required for Dopaminergic Axon Development and Confers Their Responsiveness to Guidance Cues},

author = {Marcos Schaan Profes and Charles Gora and Flavie Lavoie-Cardinal and Armen Saghatelyan and Martin Lévesque},

doi = {10.1523/jneurosci.1224-25.2026},

issn = {1529-2401},

year  = {2026},

date = {2026-05-27},

journal = {J. Neurosci.},

volume = {46},

number = {21},

publisher = {Society for Neuroscience},

abstract = {Midbrain dopamine (mDA) neurons play a wide range of brain functions, but the molecular mechanisms driving the formation of mDA circuits remain largely unknown. Here, we show that autophagy, the main cellular recycling pathway, is present in the growth cones of developing mDA neurons, and its level changes dynamically in response to guidance cues. To characterize the role of autophagy in mDA axon growth and guidance, we knocked out essential autophagy genes (Atg12, Atg5) specifically in mDA neurons in mice of either sex. Autophagy-deficient mDA axons exhibit axonal swellings and reduced branching both in vitro and in vivo. Strikingly, deletion of autophagy-related genes completely blunted the response of mDA neurons to both chemorepulsive and chemoattractive guidance cues. Our data demonstrate that autophagy plays a central role in regulating mDA neuron development by orchestrating axonal growth and guidance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40887786,

title = {Surface-Enhanced Raman Spectroscopy Semi-Quantitative Molecular Profiling with a Convolutional Neural Network},

author = {Alexis Lebrun and Flavie Lavoie-Cardinal and Denis Boudreau},

doi = {10.1177/00037028251377474},

issn = {1943-3530},

year  = {2025},

date = {2025-08-01},

journal = {Appl Spectrosc},

pages = {37028251377474},

abstract = {Surface-enhanced Raman scattering (SERS) spectroscopy represents a powerful analytical platform that combines non-destructive, label-free molecular identification with exceptional sensitivity for trace-level detection. Its capacity to generate information-rich spectral fingerprints makes SERS particularly advantageous for simultaneous multi-analyte analysis across diverse sample matrices, including complex biological systems. This study addresses the analytical challenges associated with identifying and quantifying multiple molecular species in complex environments by integrating SERS with advanced machine learning methodologies. We developed a hierarchical analytical framework that leverages the complementary strengths of deep learning and regression techniques: A multi-label convolutional neural network (CNN) for discriminating structurally similar analytes from SERS spectral data, coupled with a support vector regression (SVR) model for semi-quantitative determination of relative concentration ratios among identified species. The methodology was systematically validated using binary mixtures of short-chain fatty acids (SCFAs) as representative biomolecular targets, with performance rigorously benchmarked against established multivariate statistical methods and conventional machine learning approaches. Experimental validation demonstrated robust classification accuracy for both analytes at physiologically relevant concentrations, maintaining consistent performance across simple aqueous media and complex cell culture environments. These results establish the viability of the integrated SERS-CNN-SVR approach for advanced mixture analysis applications where precise identification and quantification of multiple biomarkers is essential.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40016352,

title = {Astrocytic cannabinoid receptor 1 promotes resilience by dampening stress-induced blood-brain barrier alterations},

author = {Katarzyna A Dudek and Sam E J Paton and Luisa Bandeira Binder and Adeline Collignon and Laurence Dion-Albert and Alice Cadoret and Manon Lebel and Olivier Lavoie and Jonathan Bouchard and Fernanda Neutzling Kaufmann and Valerie Clavet-Fournier and Claudia Manca and Manuel Guzmán and Matthew Campbell and Gustavo Turecki and Naguib Mechawar and Nicolas Flamand and Flavie Lavoie-Cardinal and Cristoforo Silvestri and Vincenzo Di Marzo and Caroline Menard},

doi = {10.1038/s41593-025-01891-9},

issn = {1546-1726},

year  = {2025},

date = {2025-04-01},

journal = {Nat Neurosci},

volume = {28},

number = {4},

pages = {766--782},

abstract = {Blood-brain barrier (BBB) alterations contribute to stress vulnerability and the development of depressive behaviors. In contrast, neurovascular adaptations underlying stress resilience remain unclear. Here we report that high expression of astrocytic cannabinoid receptor 1 (CB1) in the nucleus accumbens (NAc) shell, particularly in the end-feet ensheathing blood vessels, is associated with resilience during chronic social stress in adult male mice. Viral-mediated overexpression of Cnr1 in astrocytes of the NAc shell results in baseline anxiolytic effects and dampens stress-induced anxiety- and depression-like behaviors in male mice. It promotes the expression of vascular-related genes and reduces astrocyte inflammatory response and morphological changes following an immune challenge with the cytokine interleukin-6, linked to stress susceptibility and mood disorders. Physical exercise and antidepressant treatment increase the expression of astrocytic Cnr1 in the perivascular region in male mice. In human tissue from male donors with major depressive disorder, we observe loss of CNR1 in the NAc astrocytes. Our findings suggest a role for the astrocytic endocannabinoid system in stress responses via modulation of the BBB.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39919748,

title = {Neural stem cell quiescence and activation dynamics are regulated by feedback input from their progeny under homeostatic and regenerative conditions},

author = {Alina Marymonchyk and Raquel Rodriguez-Aller and Ashleigh Willis and Frédéric Beaupré and Sareen Warsi and Marina Snapyan and Valérie Clavet-Fournier and Flavie Lavoie-Cardinal and David R Kaplan and Freda D Miller and Armen Saghatelyan},

doi = {10.1016/j.stem.2025.01.001},

issn = {1875-9777},

year  = {2025},

date = {2025-03-01},

journal = {Cell Stem Cell},

volume = {32},

number = {3},

pages = {445--462.e9},

abstract = {Life-long maintenance of stem cells implies that feedback mechanisms from the niche regulate their quiescence/activation dynamics. Here, in the mouse adult subventricular neural stem cell (NSC) niche, we charted a precise spatiotemporal map of functional responses in NSCs induced by multiple niche cells and used machine learning to predict NSC interactions with specific niche cell types. We revealed a feedback mechanism whereby the NSC proliferative state is directly repressed by transient amplifying cells (TAPs), their rapidly dividing progeny. NSC processes wrap around TAPs and display hotspots of Ca activity at their points of contact, mediated by ephrin (Efn) signaling. The modulation of Efn signaling or TAP ablation altered the Ca signature of NSCs, leading to their activation. In vivo optogenetic modulation of Ca dynamics abrogated NSC activation and prevented niche replenishment. Thus, TAP-to-NSC feedback signaling controls stem cell quiescence and activation, providing a mechanism to maintain stem cell pools throughout life.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38306213,

title = {Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions},

author = {Ellen Doney and Laurence Dion-Albert and Francois Coulombe-Rozon and Natasha Osborne and Renaud Bernatchez and Sam E J Paton and Fernanda Neutzling Kaufmann and Roseline Olory Agomma and José L Solano and Raphael Gaumond and Katarzyna A Dudek and Joanna Kasia Szyszkowicz and  and Manon Lebel and Alain Doyen and Audrey Durand and Flavie Lavoie-Cardinal and Marie-Claude Audet and Caroline Menard},

doi = {10.1016/j.bpsgos.2023.04.007},

issn = {2667-1743},

year  = {2024},

date = {2024-01-01},

journal = {Biol Psychiatry Glob Open Sci},

volume = {4},

number = {1},

pages = {213--228},

abstract = {BACKGROUND: Major depressive disorder (MDD) is the leading cause of disability worldwide. Of individuals with MDD, 30% to 50% are unresponsive to common antidepressants, highlighting untapped causal biological mechanisms. Dysfunction in the microbiota-gut-brain axis 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 absorption of most foods and drugs takes place.nnMETHODS: We investigated how chronic social or variable stress, two mouse models of depression, impact the jejunum 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.nnRESULTS: Altered microbial populations and changes in gene expression of jejunum tight junctions were observed depending on the type and duration of stress, with sex-specific effects. We used 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 whether lipopolysaccharide injection recapitulates stress-induced changes in the jejunum and observed profound sex differences. Finally, lipopolysaccharide-binding protein, 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.nnCONCLUSIONS: 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, in human depression.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38550388,

title = {Special Section Guest Editorial: Frontiers in Neurophotonics},

author = {Yves De Koninck and Paul De Koninck and Anna Devor and Flavie Lavoie-Cardinal},

doi = {10.1117/1.NPh.11.1.014401},

issn = {2329-423X},

year  = {2024},

date = {2024-01-01},

journal = {Neurophotonics},

volume = {11},

number = {1},

pages = {014401},

abstract = {The editorial presents the two-part Special Section on Frontiers in Neurophotonics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39440349,

title = {Development of AI-assisted microscopy frameworks through realistic simulation with pySTED},

author = {Anthony Bilodeau and Albert Michaud-Gagnon and Julia Chabbert and Benoit Turcotte and Jörn Heine and Audrey Durand and Flavie Lavoie-Cardinal},

doi = {10.1038/s42256-024-00903-w},

issn = {2522-5839},

year  = {2024},

date = {2024-01-01},

journal = {Nat Mach Intell},

volume = {6},

number = {10},

pages = {1197--1215},

abstract = {The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38545127,

title = {Understanding the nervous system: lessons from Frontiers in Neurophotonics},

author = {Yves De Koninck and Johanna Alonso and Stéphane Bancelin and Jean-Claude Béïque and Erik Bélanger and Catherine Bouchard and Marco Canossa and Johan Chaniot and Daniel Choquet and Marie-Ève Crochetière and Nanke Cui and Lydia Danglot and Paul De Koninck and Anna Devor and Mathieu Ducros and Angela M Getz and Mohamed Haouat and Iván Coto Hernández and Nate Jowett and Iason Keramidis and Céline Larivière-Loiselle and Flavie Lavoie-Cardinal and Harold D MacGillavry and Asiye Malkoç and Mattia Mancinelli and Pierre Marquet and Steven Minderler and Maxime Moreaud and U Valentin Nägerl and Katerina Papanikolopoulou and Marie-Eve Paquet and Lorenzo Pavesi and David Perrais and Romain Sansonetti and Martin Thunemann and Beatrice Vignoli and Jenny Yau and Clara Zaccaria},

doi = {10.1117/1.NPh.11.1.014415},

issn = {2329-423X},

year  = {2024},

date = {2024-01-01},

journal = {Neurophotonics},

volume = {11},

number = {1},

pages = {014415},

abstract = {The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37799760,

title = {Characterizing the blood-brain barrier and gut barrier with super-resolution imaging: opportunities and challenges},

author = {Ellen Doney and Renaud Bernatchez and Valérie Clavet-Fournier and Katarzyna A Dudek and Laurence Dion-Albert and Flavie Lavoie-Cardinal and Caroline Menard},

doi = {10.1117/1.NPh.10.4.044410},

issn = {2329-423X},

year  = {2023},

date = {2023-10-01},

journal = {Neurophotonics},

volume = {10},

number = {4},

pages = {044410},

abstract = {Brain and gut barriers have been receiving increasing attention in health and diseases including in psychiatry. Recent studies have highlighted changes in the blood-brain barrier and gut barrier structural properties, notably a loss of tight junctions, leading to hyperpermeability, passage of inflammatory mediators, stress vulnerability, and the development of depressive behaviors. To decipher the cellular processes actively contributing to brain and gut barrier function in health and disease, scientists can take advantage of neurophotonic tools and recent advances in super-resolution microscopy techniques to complement traditional imaging approaches like confocal and electron microscopy. Here, we summarize the challenges, pros, and cons of these innovative approaches, hoping that a growing number of scientists will integrate them in their study design exploring barrier-related properties and mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37636490,

title = {Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics},

author = {Catherine Bouchard and Renaud Bernatchez and Flavie Lavoie-Cardinal},

doi = {10.1117/1.NPh.10.4.044405},

issn = {2329-423X},

year  = {2023},

date = {2023-10-01},

journal = {Neurophotonics},

volume = {10},

number = {4},

pages = {044405},

abstract = {Machine learning has revolutionized the way data are processed, allowing information to be extracted in a fraction of the time it would take an expert. In the field of neurophotonics, machine learning approaches are used to automatically detect and classify features of interest in complex images. One of the key challenges in applying machine learning methods to the field of neurophotonics is the scarcity of available data and the complexity associated with labeling them, which can limit the performance of data-driven algorithms. We present an overview of various strategies, such as weakly supervised learning, active learning, and domain adaptation that can be used to address the problem of labeled data scarcity in neurophotonics. We provide a comprehensive overview of the strengths and limitations of each approach and discuss their potential applications to bioimaging datasets. In addition, we highlight how different strategies can be combined to increase model performance on those datasets. The approaches we describe can help to improve the accessibility of machine learning-based analysis with limited number of annotated images for training and can enable researchers to extract more meaningful insights from small datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}