Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions.

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. Methods: 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. Results: 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. Conclusions: 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.

Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.

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.

Characterizing the blood-brain barrier and gut barrier with super-resolution imaging: opportunities and challenges.

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.

Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images.

The organization of proteins in the apposed nanodomains of pre- and postsynaptic compartments is thought to play a pivotal role in synaptic strength and plasticity. As such, the alignment between pre- and postsynaptic proteins may regulate, for example, the rate of presynaptic release or the strength of postsynaptic signaling. However, the analysis of these structures has mainly been restricted to subsets of synapses, providing a limited view of the diversity of synaptic protein cluster remodeling during synaptic plasticity. To characterize changes in the organization of synaptic nanodomains during synaptic plasticity over a large population of synapses, we combined STimulated Emission Depletion (STED) nanoscopy with a Python-based statistical object distance analysis (pySODA), in dissociated cultured hippocampal circuits exposed to treatments driving different forms of synaptic plasticity. The nanoscale organization, characterized in terms of coupling properties, of presynaptic (Bassoon, RIM1/2) and postsynaptic (PSD95, Homer1c) scaffold proteins was differently altered in response to plasticity-inducing stimuli. For the Bassoon - PSD95 pair, treatments driving synaptic potentiation caused an increase in their coupling probability, whereas a stimulus driving synaptic depression had an opposite effect. To enrich the characterization of the synaptic cluster remodeling at the population level, we applied unsupervised machine learning approaches to include selected morphological features into a multidimensional analysis. This combined analysis revealed a large diversity of synaptic protein cluster subtypes exhibiting differential activity-dependent remodeling, yet with common features depending on the expected direction of plasticity. The expanded palette of synaptic features revealed by our unbiased approach should provide a basis to further explore the widely diverse molecular mechanisms of synaptic plasticity.