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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 Ca 2 + 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.

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. Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide range of imaging tasks.

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.

The organization of proteins in the apposed nanodomains of pre- and post-synaptic 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 remodelling 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 remodelling 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 remodelling, 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.

The development of deep learning approaches to detect, segment or classify structures of interest has transformed the field of quantitative microscopy. High-throughput quantitative image analysis presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. Methods capable of reducing the annotation burden associated with the training of a deep neural network on microscopy images becomes primordial. Here we introduce a weakly supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple more complex tasks such as semantic segmentation. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to established architectures when no precisely annotated dataset is available. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate detailed feature maps of the biological structures of interest. We demonstrate how MICRA-Net substantially alleviates the expert annotation process on various microscopy datasets and can be used for high-throughput quantitative analysis of microscopy images. An important problem in microscopy analysis is to reduce the need for manual annotation of datasets. Bilodeau et al. develop a neural network method that can be trained on a simple main classification task using image-level annotations to solve more complex tasks such as semantic segmentation.

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.

Lens-based optical microscopy failed to discern fluorescent features closer than 200 nm for decades, but the recent breaking of the diffraction resolution barrier by sequentially switching the fluorescence capability of adjacent features on and off is making nanoscale imaging routine. Reported fluorescence nanoscopy variants switch these features either with intense beams at defined positions or randomly, molecule by molecule. Here we demonstrate an optical nanoscopy that records raw data images from living cells and tissues with low levels of light. This advance has been facilitated by the generation of reversibly switchable enhanced green fluorescent protein (rsEGFP), a fluorescent protein that can be reversibly photoswitched more than a thousand times. Distributions of functional rsEGFP-fusion proteins in living bacteria and mammalian cells are imaged at <40-nanometre resolution. Dendritic spines in living brain slices are super-resolved with about a million times lower light intensities than before. The reversible switching also enables all-optical writing of features with subdiffraction size and spacings, which can be used for data storage. GFP memory a bright prospect The usefulness of the fluorescent proteins used in super-resolution microscopy is limited by the fact that they generally don't survive more than about ten on–off cycles. Stefan Hell and colleagues have developed a reversibly switchable fluorescent protein that can endure more a thousand switching cycles. In proof-of-principle experiments, the new material, based on enhanced green fluorescent protein (GFP) and termed rsEGFP, was effective at high resolution in live-cell nanoscopy. In a data storage task, 25 Grimm's Fairy Tales were coded in ASCII on a 17 × 17-micrometre layer of rsEGFP.

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. The mechanisms of neurovascular adaptations underlying stress resilience remain unclear. Here the authors show that the astrocytic endocannabinoid system modulates the blood–brain barrier changes during stress in adult mice.

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.