Understanding the nervous system: lessons from Frontiers in Neurophotonics.

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.

Special Section Guest Editorial: Frontiers in Neurophotonics.

The editorial presents the two-part Special Section on Frontiers in Neurophotonics.

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.

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.

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.

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition.

Super-resolution fluorescence microscopy methods enable the characterization of nanostructures in living and fixed biological tissues. However, they require the adjustment of multiple imaging parameters while attempting to satisfy conflicting objectives, such as maximizing spatial and temporal resolution while minimizing light exposure. To overcome the limitations imposed by these trade-offs, post-acquisition algorithmic approaches have been proposed for resolution enhancement and image-quality improvement. Here we introduce the task-assisted generative adversarial network (TA-GAN), which incorporates an auxiliary task (for example, segmentation, localization) closely related to the observed biological nanostructure characterization. We evaluate how the TA-GAN improves generative accuracy over unassisted methods, using images acquired with different modalities such as confocal, bright-field, stimulated emission depletion and structured illumination microscopy. The TA-GAN is incorporated directly into the acquisition pipeline of the microscope to predict the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to automatically select the imaging modality and regions of interest, optimizing the acquisition sequence by reducing light exposure. Data-driven microscopy methods like the TA-GAN will enable the observation of dynamic molecular processes with spatial and temporal resolutions that surpass the limits currently imposed by the trade-offs constraining super-resolution microscopy.

Nano-positioning and tubulin conformation contribute to axonal transport regulation of mitochondria along microtubules.

Correct spatiotemporal distribution of organelles and vesicles is crucial for healthy cell functioning and is regulated by intracellular transport mechanisms. Controlled transport of bulky mitochondria is especially important in polarized cells such as neurons that rely on these organelles to locally produce energy and buffer calcium. Mitochondrial transport requires and depends on microtubules that fill much of the available axonal space. How mitochondrial transport is affected by their position within the microtubule bundles is not known. Here, we found that anterograde transport, driven by kinesin motors, is susceptible to the molecular conformation of tubulin in neurons both in vitro and in vivo. Anterograde velocities negatively correlate with the density of elongated tubulin dimers like guanosine triphosphate (GTP)-tubulin. The impact of the tubulin conformation depends primarily on where a mitochondrion is positioned, either within or at the rim of microtubule bundle. Increasing elongated tubulin levels lowers the number of motile anterograde mitochondria within the microtubule bundle and increases anterograde transport speed at the microtubule bundle rim. We demonstrate that the increased kinesin velocity and density on microtubules consisting of elongated dimers add to the increased mitochondrial dynamics. Our work indicates that the molecular conformation of tubulin contributes to the regulation of mitochondrial motility and as such to the local distribution of mitochondria along axons.

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.

A postzygotic de novo NCDN mutation identified in a sporadic FTLD patient results in neurochondrin haploinsufficiency and altered FUS granule dynamics.

Frontotemporal dementia (FTD) is a heterogeneous clinical disorder characterized by progressive abnormalities in behavior, executive functions, personality, language and/or motricity. A neuropathological subtype of FTD, frontotemporal lobar degeneration (FTLD)-FET, is characterized by protein aggregates consisting of the RNA-binding protein fused in sarcoma (FUS). The cause of FTLD-FET is not well understood and there is a lack of genetic evidence to aid in the investigation of mechanisms of the disease. The goal of this study was to identify genetic variants contributing to FTLD-FET and to investigate their effects on FUS pathology. We performed whole-exome sequencing on a 50-year-old FTLD patient with ubiquitin and FUS-positive neuronal inclusions and unaffected parents, and identified a de novo postzygotic nonsense variant in the NCDN gene encoding Neurochondrin (NCDN), NM_014284.3:c.1206G > A, p.(Trp402*). The variant was associated with a ~ 31% reduction in full-length protein levels in the patient's brain, suggesting that this mutation leads to NCDN haploinsufficiency. We examined the effects of NCDN haploinsufficiency on FUS and found that depleting primary cortical neurons of NCDN causes a reduction in the total number of FUS-positive cytoplasmic granules. Moreover, we found that these granules were significantly larger and more highly enriched with FUS. We then examined the effects of a loss of FUS function on NCDN in neurons and found that depleting cells of FUS leads to a decrease in NCDN protein and mRNA levels. Our study identifies the NCDN protein as a likely contributor of FTLD-FET pathophysiology. Moreover, we provide evidence for a negative feedback loop of toxicity between NCDN and FUS, where loss of NCDN alters FUS cytoplasmic dynamics, which in turn has an impact on NCDN expression.

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.

Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions.

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.

Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons.

The nanoscale organization of the F-actin cytoskeleton in neurons comprises membrane-associated periodical rings, bundles, and longitudinal fibers. The F-actin rings have been observed predominantly in axons but only sporadically in dendrites, where fluorescence nanoscopy reveals various patterns of F-actin arranged in mixed patches. These complex dendritic F-actin patterns pose a challenge for investigating quantitatively their regulatory mechanisms. We developed here a weakly supervised deep learning segmentation approach of fluorescence nanoscopy images of F-actin in cultured hippocampal neurons. This approach enabled the quantitative assessment of F-actin remodeling, revealing the disappearance of the rings during neuronal activity in dendrites, but not in axons. The dendritic F-actin cytoskeleton of activated neurons remodeled into longitudinal fibers. We show that this activity-dependent remodeling involves [Formula: see text] and NMDA receptor-dependent mechanisms. This highly dynamic restructuring of dendritic F-actin based submembrane lattice into longitudinal fibers may serve to support activity-dependent membrane remodeling, protein trafficking and neuronal plasticity.

Underestimated Color Centers: Defects as Useful Reducing Agents in Lanthanide-Activated Luminescent Materials.

Inorganic hosts, such as SrB4 O7 or certain nitrides, intrinsically stabilize Eu2+ even when the dopant is an Eu3+ -based precursor and reducing conditions are not employed in the synthesis. Although this concept is well known in the synthesis of phosphorescent materials, the mechanistic details are scarcely understood. Herein, we demonstrate that trapped charge carriers, such as color centers, can also act as redox partners to stabilize certain oxidation states of activators. Eu-activated CsMgCl3 and CsMgBr3 are used as examples. Upon doping with EuCl3 and in the absence of reducing conditions during the synthesis, dominant cyan or green luminescence from Eu2+ ions was observed. Photoluminescence spectroscopy at 10 K revealed that the reduction is correlated to color centers localized at defects. Although defects are typically undesired in phosphors, we have shown that their role may be underestimated and they could be used on purpose in the preparation of selected inorganic phosphors.

Fluorescence lifetime imaging nanoscopy for measuring Förster resonance energy transfer in cellular nanodomains.

Microscopy methods used to measure Förster resonance energy transfer (FRET) between fluorescently labeled proteins can provide information on protein interactions in cells. However, these methods are diffraction-limited, thus do not enable the resolution of the nanodomains in which such interactions occur in cells. To overcome this limitation, we assess FRET with an imaging system combining fluorescence lifetime imaging microscopy with stimulated emission depletion, termed fluorescence lifetime imaging nanoscopy (FLIN). The resulting FRET-FLIN approach utilizes immunolabeling of proteins in fixed cultured neurons. We demonstrate the capacity to discriminate nanoclusters of synaptic proteins exhibiting variable degrees of interactions with labeled binding partners inside dendritic spines of hippocampal neurons. This method enables the investigation of FRET within nanodomains of cells, approaching the scale of molecular signaling.

A machine learning approach for online automated optimization of super-resolution optical microscopy.

Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.

Comment on "Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics".

Li et al (Research Articles, 28 August 2015, aab3500) purport to present solutions to long-standing challenges in live-cell microscopy, reporting relatively fast acquisition times in conjunction with improved image resolution. We question the methods' reliability to visualize specimen features at sub-100-nanometer scales, because the mandatory mathematical processing of the recorded data leads to artifacts that are either difficult or impossible to disentangle from real features. We are also concerned about the chosen approach of subjectively comparing images from different super-resolution methods, as opposed to using quantitative measures.

Gold nanoparticle-assisted all optical localized stimulation and monitoring of Ca²âº signaling in neurons.

Light-assisted manipulation of cells to control membrane activity or intracellular signaling has become a major avenue in life sciences. However, the ability to perform subcellular light stimulation to investigate localized signaling has been limited. Here, we introduce an all optical method for the stimulation and the monitoring of localized Ca(2+) signaling in neurons that takes advantage of plasmonic excitation of gold nanoparticles (AuNPs). We show with confocal microscopy that 800 nm laser pulse application onto a neuron decorated with a few AuNPs triggers a transient increase in free Ca(2+), measured optically with GCaMP6s. We show that action potentials, measured electrophysiologically, can be induced with this approach. We demonstrate activation of local Ca(2+) transients and Ca(2+) signaling via CaMKII in dendritic domains, by illuminating a single or few functionalized AuNPs specifically targeting genetically-modified neurons. This NP-Assisted Localized Optical Stimulation (NALOS) provides a new complement to light-dependent methods for controlling neuronal activity and cell signaling.

Coordinate-targeted and coordinate-stochastic super-resolution microscopy with the reversibly switchable fluorescent protein Dreiklang.

Diffraction-unlimited far-field super-resolution fluorescence (nanoscopy) methods typically rely on transiently transferring fluorophores between two states, whereby this transfer is usually laid out as a switch. However, depending on whether this is induced in a spatially controlled manner using a pattern of light (coordinate-targeted) or stochastically on a single-molecule basis, specific requirements on the fluorophores are imposed. Therefore, the fluorophores are usually utilized just for one class of methods only. In this study we demonstrate that the reversibly switchable fluorescent protein Dreiklang enables live-cell recordings in both spatially controlled and stochastic modes. We show that the Dreiklang chromophore entails three different light-induced switching mechanisms, namely a reversible photochemical one, off-switching by stimulated emission, and a reversible transfer to a long-lived dark state from the S1 state, all of which can be utilized to overcome the diffraction barrier. We also find that for the single-molecule-based stochastic GSDIM approach (ground-state depletion followed by individual molecule return), Dreiklang provides a larger number of on-off localization events as compared to its progenitor Citrine. Altogether, Dreiklang is a versatile probe for essentially all popular forms of live-cell fluorescence nanoscopy.

Two-color RESOLFT nanoscopy with green and red fluorescent photochromic proteins.

Up to now, all demonstrations of reversible saturable optical fluorescence transitions (RESOLFT) superresolution microscopy of living cells have relied on the use of reversibly switchable fluorescent proteins (RSFP) emitting in the green spectral range. Here we show RESOLFT imaging with rsCherryRev1.4, a new red-emitting RSFP enabling a spatial resolution up to four times higher than the diffraction barrier. By co-expressing green and red RSFPs in living cells we demonstrate two-color RESOLFT imaging both for single ("donut") beam scanning and for parallelized versions of RESOLFT nanoscopy where an array of >23,000 "donut-like" minima are scanned simultaneously.