SLC38A10 Regulate Glutamate Homeostasis and Modulate the AKT/TSC2/mTOR Pathway in Mouse Primary Cortex Cells

Glutamate acts as a critical regulator of neurotransmitter balance, recycling, synaptic function and homeostasis in the brain and glutamate transporters control glutamate levels in the brain. SLC38A10 is a member of the SLC38 family and regulates protein synthesis and cellular stress responses. Here, we uncover the role of SLC38A10 as a transceptor involved in glutamate-sensing signaling pathways that control both the glutamate homeostasis and mTOR-signaling. The culture of primary cortex cells from SLC38A10 knockout mice had increased intracellular glutamate. In addition, under nutrient starvation, KO cells had an impaired response in amino acid-dependent mTORC1 signaling. Combined studies from transcriptomics, protein arrays and metabolomics established that SLC38A10 is involved in mTOR signaling and that SLC38A10 deficient primary cortex cells have increased protein synthesis. Metabolomic data showed decreased cholesterol levels, changed fatty acid synthesis, and altered levels of fumaric acid, citrate, 2-oxoglutarate and succinate in the TCA cycle. These data suggests that SLC38A10 may act as a modulator of glutamate homeostasis, and mTOR-sensing and loss of this transceptor result in lower cholesterol, which could have implications in neurodegenerative diseases.

Machine learning for cell classification and neighborhood analysis in glioma tissue

Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.

ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis

ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Studying cell morphology and cell migration in time-lapse datasets using TrackMate (Fiji) and CellProfiler Basic Protocol 2: Creating whole plate montages to easily assess adaptability of segmentation parameters.

Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists

NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.

Automated identification of the mouse brain's spatial compartments from in situ sequencing data

Background

Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments.

Results

Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles.

Conclusion

We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time

Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system's performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spotted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.

Deep Learning in Image Cytometry: A Review

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Estimating hemodynamic shear stress in murine peripheral collateral arteries by two-photon line scanning

Changes in wall shear stress of blood vessels are assumed to be an important component of many physiological and pathophysiological processes. However, due to technical limitations experimental in vivo data are rarely available. Here, we investigated two-photon excitation fluorescence microscopy as an option to measure vessel diameter as well as blood flow velocities in a murine hindlimb model of arteriogenesis (collateral artery growth). Using line scanning at high frequencies, we measured the movement of blood cells along the vessel axis. We found that peak systolic blood flow velocity averaged 9 mm/s and vessel diameter 42 µm in resting collaterals. Induction of arteriogenesis by femoral artery ligation resulted in a significant increase in centerline peak systolic velocity after 1 day with an average of 51 mm/s, whereas the averaged luminal diameter of collaterals (52 µm) changed much less. Thereof calculations revealed a significant fourfold increase in hemodynamic wall shear rate. Our results indicate that two-photon line scanning is a suitable tool to estimate wall shear stress e.g., in experimental animal models, such as of arteriogenesis, which may not only help to understand the relevance of mechanical forces in vivo, but also to adjust wall shear stress in ex vivo investigations on isolated vessels as well as cell culture experiments.

Metaphase kinetochore movements are regulated by kinesin-8 motors and microtubule dynamic instability

During metaphase, sister chromatids are connected to microtubules extending from the opposite spindle poles via kinetochores to protein complexes on the chromosome. Kinetochores congress to the equatorial plane of the spindle and oscillate around it, with kinesin-8 motors restricting these movements. Yet, the physical mechanism underlying kinetochore movements is unclear. We show that kinetochore movements in the fission yeast Schizosaccharomyces pombe are regulated by kinesin-8-promoted microtubule catastrophe, force-induced rescue, and microtubule dynamic instability. A candidate screen showed that among the selected motors only kinesin-8 motors Klp5/Klp6 are required for kinetochore centering. Kinesin-8 accumulates at the end of microtubules, where it promotes catastrophe. Laser ablation of the spindle resulted in kinetochore movement toward the intact spindle pole in wild-type and klp5Δ cells, suggesting that kinetochore movement is driven by pulling forces. Our theoretical model with Langevin description of microtubule dynamic instability shows that kinesin-8 motors are required for kinetochore centering, whereas sensitivity of rescue to force is necessary for the generation of oscillations. We found that irregular kinetochore movements occur for a broader range of parameters than regular oscillations. Thus, our work provides an explanation for how regulation of microtubule dynamic instability contributes to kinetochore congression and the accompanying movements around the spindle center.

Pumilio2-deficient mice show a predisposition for epilepsy

Epilepsy is a neurological disease that is caused by abnormal hypersynchronous activities of neuronal ensembles leading to recurrent and spontaneous seizures in human patients. Enhanced neuronal excitability and a high level of synchrony between neurons seem to trigger these spontaneous seizures. The molecular mechanisms, however, regarding the development of neuronal hyperexcitability and maintenance of epilepsy are still poorly understood. Here, we show that pumilio RNA-binding family member 2 (Pumilio2; Pum2) plays a role in the regulation of excitability in hippocampal neurons of weaned and 5-month-old male mice. Almost complete deficiency of Pum2 in adult Pum2 gene-trap mice (Pum2 GT) causes misregulation of genes involved in neuronal excitability control. Interestingly, this finding is accompanied by the development of spontaneous epileptic seizures in Pum2 GT mice. Furthermore, we detect an age-dependent increase in Scn1a (Na_v1.1) and Scn8a (Na_v1.6) mRNA levels together with a decrease in Scn2a (Na_v1.2) transcript levels in weaned Pum2 GT that is absent in older mice. Moreover, field recordings of CA1 pyramidal neurons show a tendency towards a reduced paired-pulse inhibition after stimulation of the Schaffer-collateral-commissural pathway in Pum2 GT mice, indicating a predisposition to the development of spontaneous seizures at later stages. With the onset of spontaneous seizures at the age of 5 months, we detect increased protein levels of Na_v1.1 and Na_v1.2 as well as decreased protein levels of Na_v1.6 in those mice. In addition, GABA receptor subunit alpha-2 (Gabra2) mRNA levels are increased in weaned and adult mice. Furthermore, we observe an enhanced GABRA2 protein level in the dendritic field of the CA1 subregion in the Pum2 GT hippocampus. We conclude that altered expression levels of known epileptic risk factors such as Na_v1.1, Na_v1.2, Na_v1.6 and GABRA2 result in enhanced seizure susceptibility and manifestation of epilepsy in the hippocampus. Thus, our results argue for a role of Pum2 in epileptogenesis and the maintenance of epilepsy.

Summary: Epileptogenic risk factors are misregulated in Pumilio2-deficient mice, determining a predisposition to develop seizures. This article has an associated First Person interview with the first author of the paper as part of the supplementary information.

Focal adhesion kinase stabilizes the cytoskeleton

Focal adhesion kinase (FAK) is a central focal adhesion protein that promotes focal adhesion turnover, but the role of FAK for cell mechanical stability is unknown. We measured the mechanical properties of wild-type (FAKwt), FAK-deficient (FAK-/-), FAK-silenced (siFAK), and siControl mouse embryonic fibroblasts by magnetic tweezer, atomic force microscopy, traction microscopy, and nanoscale particle tracking microrheology. FAK-deficient cells showed lower cell stiffness, reduced adhesion strength, and increased cytoskeletal dynamics compared to wild-type cells. These observations imply a reduced stability of the cytoskeleton in FAK-deficient cells. We attribute the reduced cytoskeletal stability to rho-kinase activation in FAK-deficient cells that suppresses the formation of ordered stress fiber bundles, enhances cortical actin distribution, and reduces cell spreading. In agreement with this interpretation is that cell stiffness and cytoskeletal stability in FAK-/- cells is partially restored to wild-type level after rho-kinase inhibition with Y27632.

The influence of Pyk2 on the mechanical properties in fibroblasts

The cell surface receptor integrin is involved in signaling mechanical stresses via the focal adhesion complex (FAC) into the cell. Within FAC, the focal adhesion kinase (FAK) and Pyk2 are believed to act as important scaffolding proteins. Based on the knowledge that many signal transducing molecules are transiently immobilized within FAC connecting the cytoskeleton with integrins, we applied magnetic tweezer and atomic force microscopic measurements to determine the influence of FAK and Pyk2 in cells mechanically. Using mouse embryonic fibroblasts (MEF; FAK(+/+), FAK(-/-), and siRNA-Pyk2 treated FAK(-/-) cells) provided a unique opportunity to describe the function of FAK and Pyk2 in more detail and to define their influence on FAC and actin distribution.

Mechano-chemical signaling in F9 cells

We investigated the molecular mechanism by which cells recognize and respond to physical forces in their local environment. Using a model system, to study wild type mouse F9 embryonic carcinoma cells, we examined how these cells sense mechanical stresses and translate them into biochemical responses through their cell surface receptor integrin and via the focal adhesion complex (FAC). Based on studies that show that many signal transducing molecules are immobilized on the cytoskeleton at the site of integrin binding within the focal adhesion complex, we found a time-dependent increase of focal adhesion kinase (pp125(FAK)) phosphorylation possibly due to protein kinase C (PKC) activation as well as protein kinase A (PKA) activity increase upon cell adhesion/spreading. These studies provide some insight into intracellular mechano-chemical signaling.

CapZ-lipid membrane interactions: a computer analysis

Background

CapZ is a calcium-insensitive and lipid-dependent actin filament capping protein, the main function of which is to regulate the assembly of the actin cytoskeleton. CapZ is associated with membranes in cells and it is generally assumed that this interaction is mediated by polyphosphoinositides (PPI) particularly PIP₂, which has been characterized in vitro.

Results

We propose that non-PPI lipids also bind CapZ. Data from computer-aided sequence and structure analyses further suggest that CapZ could become partially buried in the lipid bilayer probably under mildly acidic conditions, in a manner that is not only dependent on the presence of PPIs. We show that lipid binding could involve a number of sites that are spread throughout the CapZ molecule i.e., alpha- and beta-subunits. However, a beta-subunit segment between residues 134–151 is most likely to be involved in interacting with and inserting into lipid membrane due to a slighly higher ratio of positively to negatively charged residues and also due to the presence of a small hydrophobic helix.

Conclusion

CapZ may therefore play an essential role in providing a stable membrane anchor for actin filaments.