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The locus coeruleus (LC) produces most of the brain's noradrenaline (NA). Among its many roles, NA is often said to be neuroprotective and important for brain upkeep. For this reason, loss of LC integrity is thought to impact brain volume and microstructure as well as plasticity broadly. LC dysfunction is also a suspected driver in the development of neurodegenerative diseases. Nevertheless, the impact of LC dysfunction on the gross structure and microstructure of normal brains is not well-studied. We employed high-field ex vivo magnetic resonance imaging (MRI) to investigate brain volumetrics and microstructure in control (CON) mice and mice with LC ablation (LCA) at two ages, representing the developing brain and the fully matured brain. These whole-brain methods are known to be capable of detecting subtle morphological changes and brain microstructural remodeling. We found mice behavior consistent with histologically confirmed LC ablation. However, MRI showed no difference between CON and LCA groups with regard to brain size, relative regional volumes, or regional microstructural indices. Our findings suggest that LC-NA is not needed for postnatal brain maturation and growth in mice. Nor is it required for maintenance in the normal adult mouse brain, as no atrophy or microstructural aberration is detected after weeks of LC dysfunction. This adds clarity to the often-encountered notion that LC-NA is important for brain "trophic support" as it shows that such effects are likely most relevant to mechanisms related to brain plasticity and neuroprotection in the (pre)diseased brain. [ABSTRACT FROM AUTHOR]

Abstract The most effective method for mapping N6-methyladenosine (m6A) is m6A RNA immunoprecipitation sequencing (MeRIP-seq). The quality of MeRIP-seq relies on various factors, with the anti-m6A antibody being a crucial determinant. However, comprehensive research on anti-m6A antibody selection and optimal concentrations for different tissues has been limited. In this study, we optimized the concentration of five different anti-m6A antibodies across various tissues. Our findings demonstrated that 5 µg of Millipore antibodies (ABE572 and MABE1006) performed well, starting from 15 µg total RNA from the liver, while 1.25 µg of Cell Signaling Technology antibodies (CST) (#56593) was suitable for low-input total RNA. In summary, we provide a significant guideline for anti-m6A antibody selection in MeRIP sequencing for different tissues, especially in the context of low-input RNA.

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs. [ABSTRACT FROM AUTHOR]

Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future. [ABSTRACT FROM AUTHOR]

The locus coeruleus (LC) produces most of the brain’s noradrenaline (NA). Among its many roles, NA is often said to be neuroprotective and important for brain upkeep. For this reason, loss of LC integrity is thought to impact brain volume and microstructure as well as plasticity broadly. LC dysfunction is also a suspected driver in the development of neurodegenerative diseases. Nevertheless, the impact of LC dysfunction on the gross structure and microstructure of normal brains is not well-studied. We employed high-field ex vivo magnetic resonance imaging (MRI) to investigate brain volumetrics and microstructure in control (CON) mice and mice with LC ablation (LCA) at two ages, representing the developing brain and the fully matured brain. These whole-brain methods are known to be capable of detecting subtle morphological changes and brain microstructural remodeling. We found mice behavior consistent with histologically confirmed LC ablation. However, MRI showed no difference between CON and LCA groups with regard to brain size, relative regional volumes, or regional microstructural indices. Our findings suggest that LC-NA is not needed for postnatal brain maturation and growth in mice. Nor is it required for maintenance in the normal adult mouse brain, as no atrophy or microstructural aberration is detected after weeks of LC dysfunction. This adds clarity to the often-encountered notion that LC-NA is important for brain “trophic support” as it shows that such effects are likely most relevant to mechanisms related to brain plasticity and neuroprotection in the (pre)diseased brain.

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis. [ABSTRACT FROM AUTHOR]

Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation. [ABSTRACT FROM AUTHOR]

Calpains are calcium-dependent cysteine proteases activated by intracellular Ca2+. Although calpains mainly exist in the cytosol, calpain-13 is present in the mitochondria in mouse brains; however, the enzymatic properties and physiological functions of calpain-13 remain unknown. Hence, in this study, we predicted and evaluated the enzymatic properties of calpain-13. Based on our bioinformatic approaches, calpain-13 possessed a catalytic triad and EF-hand domain, similar to calpain-1, a well-studied calpain. Therefore, we hypothesized that calpain-13 had calpain-1-like enzymatic properties; however, calpain-13 was not proteolyzed in C57BL/6J mouse brains. Subsequently, cerebral ischemia/reperfusion (I/R) injury caused proteolysis of mitochondrial calpain-13. Thus, our study showed that mitochondrial calpain-13 was proteolyzed in the mitochondria of the I/R injured mouse brain. This finding could be valuable in further research elucidating the involvement of calpain-13 in cell survival or death in brain diseases, such as cerebral infarction.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell–cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Human brain organoids are emerging as translationally relevant models for the study of human brain health and disease. However, it remains to be shown whether human-specific protein processing is conserved in human brain organoids. Herein, we demonstrate that cell fate and composition of unguided brain organoids are dictated by culture conditions during embryoid body formation, and that culture conditions at this stage can be optimized to result in the presence of glia-associated proteins and neural network activity as early as three-months in vitro. Under these optimized conditions, unguided brain organoids generated from induced pluripotent stem cells (iPSCs) derived from male-female siblings are similar in growth rate, size, and total protein content, and exhibit minimal batch-to-batch variability in cell composition and metabolism. A comparison of neuronal, microglial, and macroglial (astrocyte and oligodendrocyte) markers reveals that profiles in these brain organoids are more similar to autopsied human cortical and cerebellar profiles than to those in mouse cortical samples, providing the first demonstration that human-specific protein processing is largely conserved in unguided brain organoids. Thus, our organoid protocol provides four major cell types that appear to process proteins in a manner very similar to the human brain, and they do so in half the time required by other protocols. This unique copy of the human brain and basic characteristics lay the foundation for future studies aiming to investigate human brain-specific protein patterning (e.g., isoforms, splice variants) as well as modulate glial and neuronal processes in an in situ-like environment. [ABSTRACT FROM AUTHOR]

Sleep deprivation (SD) is highly prevalent in the modern technological world. Emerging evidence shows that sleep deprivation is associated with oxidative stress. At the organelle level, the Golgi apparatus actively participates in the stress response. In this study, to determine whether SD and Golgi apparatus stress are correlated, we rationally designed and fabricated a novel Golgi apparatus–targeted ratiometric nanoprobe called Golgi dots for O2·− detection. This probe exhibits high sensitivity and selectivity in cells and brain slices of sleep-deprived mice. Golgi dots can be readily synthesized by coprecipitation of Golgi-F127, an amphiphilic polymer F127 modified with a Golgi apparatus targeting moiety, caffeic acid (CA), the responsive unit for O2·−, and red emissive carbon nanodots (CDs), which act as the reference signal. The fluorescence emission spectrum of the developed nanoprobe showed an intense peak at 674 nm, accompanied by a shoulder peak at 485 nm. As O2·− was gradually added, the fluorescence at 485 nm continuously increased; in contrast, the emission intensity at 674 nm assigned to the CDs remained constant, resulting in the ratiometric sensing of O2·−. The present ratiometric nanoprobe showed high selectivity for O2·− monitoring due to the specific recognition of O2·− by CA. Moreover, the Golgi dots exhibited good linearity with respect to the O2·− concentration within 5 to 40 μM, and the limit of detection (LOD) was ~ 0.13 μM. Additionally, the Golgi dots showed low cytotoxicity and an ability to target the Golgi apparatus. Inspired by these excellent properties, we then applied the Golgi dots to successfully monitor exogenous and endogenous O2·− levels within the Golgi apparatus. Importantly, with the help of Golgi dots, we determined that SD substantially elevated O2·− levels in the brain. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer’s disease (AD) is the most common neurodegenerative disorder affecting the elderly population worldwide. Due to the multifactorial nature of the disease, involving impairment of cholinergic neurotransmission and immune system, previous attempts to find effective treatments have faced challenges. Methods: In such scenario, we attempted to investigate the effects of alphaglyceryl-phosphoryl-choline (α-GPC), a cholinomimetic molecule, on neuroinflammation and memory outcome in the triple transgenic mouse model of AD (3xTg-AD). Mice were enrolled at 4 months of age, treated orally with α-GPC dissolved in drinking water at a concentration resulting in an average daily dose of 100 mg/kg for 8 months and sacrificed at 12 months of age. Thereafter, inflammatory markers, as well as cognitive parameters, were measured. Results: Chronic α-GPC treatment reduced accumulation of amyloid deposits and led to a substantial re-balance of the inflammatory response of resident innate immune cells, astrocytes and microglia. Specifically, fluorescent immunohistochemistry and Western blot analysis showed that α-GPC contributed to reduction of cortical and hippocampal reactive astrocytes and pro-inflammatory microglia, concurrently increasing the expression of antiinflammatory molecules. Whereas α-GPC beneficially affect the synaptic marker synaptophysin in the hippocampus. Furthermore, we observed that αGPC was effective in restoring cognitive dysfunction, as measured by the Novel Object Recognition test, wherein 3xTg-AD mice treated with α-GPC significantly spent more time exploring the novel object compared to 3xTg-AD untreated mice. Discussion: In conclusion, chronic treatment with α-GPC exhibited a significant anti-inflammatory activity and sustained the key function of hippocampal synapses, crucial for the maintenance of a regular cognitive status. In light of our results, we suggest that α-GPC could be exploited as a promising therapeutic approach in early phases of AD. [ABSTRACT FROM AUTHOR]

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions. [ABSTRACT FROM AUTHOR]

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries. [ABSTRACT FROM AUTHOR]

Brain is most cholesterol-rich organ in the body. Since cholesterol does not cross the blood brain barrier, its metabolism is provided in situ by astrocytes and neurons, and it is crucial for maintaining sterol levels and neuronal integrity and function. Recent studies have shown that the levels of cholesterol precursors and metabolites are lower in the brains of animal models of Huntington's disease (HD) while reduced levels of its catabolite are detected in the plasma of patients. In this study, we introduce a novel analytical method designed to fulfill the complex analytical requirements associated with cholesterol metabolites detection in neurodegenerative disorders. The method allows for the simultaneous quantification of a specific set of oxysterols along with cholesterol precursors in biological samples.The proposed method uses an Ultra-High-Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) system operating in multiple reaction monitoring (MRM). Since sterols can be found in biological matrices in either free form or esterified to various fatty acids, a three-step extraction procedure was devised, consisting of alkaline hydrolysis, liquid-liquid extraction and final concentration omitting the need for a solid-phase extraction (SPE) step.The validated method achieved a detection limit of 10 ng/mL in plasma and 1 ng/mg in brain tissue, reaching a comparable sensitivity to previously published LC-MS and GC–MS methods. All target analytes were separated on a reverse-phase column employing a segmented gradient and a temperature ramp. This strategy enabled the elution and separation of all selected metabolites within a 30-minutes timeframe. This innovative approach was employed to quantify cholesterol metabolites in both plasma and brain samples from wild-type (WT) and R6/2 mice, a mouse model of HD. The results obtained from the sample analysis highlighted a significant reduction in desmosterol levels in the R6/2 brain at 12 weeks.In conclusion, the proposed method paves the way for further development of high-sensitive and reproducible protocols to comprehensively investigate simultaneous alterations in both cholesterol biosynthesis and catabolism in HD samples.

Abstract Autosomal dominant pathogenic mutations in Leucine-rich repeat kinase 2 (LRRK2) cause Parkinson’s disease (PD). The most common mutation, G2019S-LRRK2, increases the kinase activity of LRRK2 causing hyper-phosphorylation of its substrates. One of these substrates, Rab10, is phosphorylated at a conserved Thr73 residue (pRab10), and is one of the most abundant LRRK2 Rab GTPases expressed in various tissues. The involvement of Rab10 in neurodegenerative disease, including both PD and Alzheimer’s disease makes pinpointing the cellular and subcellular localization of Rab10 and pRab10 in the brain an important step in understanding its functional role, and how post-translational modifications could impact function. To establish the specificity of antibodies to the phosphorylated form of Rab10 (pRab10), Rab10 specific antisense oligonucleotides were intraventricularly injected into the brains of mice. Further, Rab10 knock out induced neurons, differentiated from human induced pluripotent stem cells were used to test the pRab10 antibody specificity. To amplify the weak immunofluorescence signal of pRab10, tyramide signal amplification was utilized. Rab10 and pRab10 were expressed in the cortex, striatum and the substantia nigra pars compacta. Immunofluorescence for pRab10 was increased in G2019S-LRRK2 knockin mice. Neurons, astrocytes, microglia and oligodendrocytes all showed Rab10 and pRab10 expression. While Rab10 colocalized with endoplasmic reticulum, lysosome and trans-Golgi network markers, pRab10 did not localize to these organelles. However, pRab10, did overlap with markers of the presynaptic terminal in both mouse and human cortex, including α-synuclein. Results from this study suggest Rab10 and pRab10 are expressed in all brain areas and cell types tested in this study, but pRab10 is enriched at the presynaptic terminal. As Rab10 is a LRRK2 kinase substrate, increased kinase activity of G2019S-LRRK2 in PD may affect Rab10 mediated membrane trafficking at the presynaptic terminal in neurons in disease.

BackgroundParaquat (PQ) is one of the most widely used herbicides in the world and a risk factor for Parkinson's disease (PD), but the mechanisms underlying PD are poorly understood. Single-cell RNA sequencing (scRNA-seq) technology can study cellular heterogeneity at genetic level, providing insights into the pathogenesis of PQ-induced PD. ObjectiveTo analyze the brain cell grouping of PQ-infected mice and the biological processes involved in the subpopulation of PD-like changes cells by scRNA-seq, and to provide clues for revealing potential mechanisms of PQ-induced PD-like changes in mouse brains. MethodsSix male 6-week-old C57BL/6 mice were randomly divided into a control group and an experimental group, three mice in each group, and were intraperitoneally injected with 0 (saline) and 10.0 mg·kg−1 PD respectively, once every two days, for 10 consecutive injections for modeling. After infection, mouse brains were taken and scRNA-seq was performed. Cell segmentation was performed according to gene expression characteristics of different cell types, PD-related cell subsets were screened by bioinformatics tools, and gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein interaction network analysis, and transcription factor prediction were performed on their characteristic genes. Finally, GO and KEGG analyses were performed on the differential genes of PD-associated cell subsets between the PQ-treated group and the control group, and the biological processes in which these genes may participate were analyzed. ResultsThe sequencing data met quality control standards, a total of 55779 cells were obtained, and all cell dimensionality reduction analysis results showed that they could be further divided into 37 clusters, including 5 major cell types. Based on the KEGG analysis of the top 20 characteristic genes of each subpopulation, the specifically expressed Cluster 33 subpopulation (dopaminergic neurons) was screened and found to be significantly associated with PD. The results of GO analysis showed that the biological function of this subpopulation mainly enriched neurotransmitter transport and regulation. The results of GSEA analysis showed that the tyrosine metabolic pathway and the ligand-receptor interaction pathway of neural activity in brain tissues were significantly enriched. The analysis of transcriptional regulatory networks showed that 39 transcription factors were expressed differently. The metabolic pathway of the dopamine neuronal subset, endocytosis, Ras-associated protein 1 (Rap1) signaling pathway, and mitogen-activated protein kinase (MAPK) signaling pathway were all affected by PQ exposure, according to further analysis of its effects on this subpopulation. The GO analysis showed that differential genes were involved in biological processes such as ion transport and synaptic assembly regulation, and were involved in the cellular component formation of cytoplasm and synapses. ConclusionThis study has initially mapped the transcriptome of single cells in the mouse brain after PQ exposure, and screened out the specific expression of Cluster 33 subgroup (dopaminergic neurons), which is significantly correlated with PD, and its biological function changes may be one of the mechanisms of PD-like changes in the mouse brain induced by PQ.

Human brain organoids are emerging as translationally relevant models for the study of human brain health and disease. However, it remains to be shown whether human-specific protein processing is conserved in human brain organoids. Herein, we demonstrate that cell fate and composition of unguided brain organoids are dictated by culture conditions during embryoid body formation, and that culture conditions at this stage can be optimized to result in the presence of glia-associated proteins and neural network activity as early as three-months in vitro. Under these optimized conditions, unguided brain organoids generated from induced pluripotent stem cells (iPSCs) derived from male–female siblings are similar in growth rate, size, and total protein content, and exhibit minimal batch-to-batch variability in cell composition and metabolism. A comparison of neuronal, microglial, and macroglial (astrocyte and oligodendrocyte) markers reveals that profiles in these brain organoids are more similar to autopsied human cortical and cerebellar profiles than to those in mouse cortical samples, providing the first demonstration that human-specific protein processing is largely conserved in unguided brain organoids. Thus, our organoid protocol provides four major cell types that appear to process proteins in a manner very similar to the human brain, and they do so in half the time required by other protocols. This unique copy of the human brain and basic characteristics lay the foundation for future studies aiming to investigate human brain-specific protein patterning (e.g., isoforms, splice variants) as well as modulate glial and neuronal processes in an in situ-like environment.

Introduction: Alzheimer’s disease (AD) is the most common neurodegenerative disorder affecting the elderly population worldwide. Due to the multifactorial nature of the disease, involving impairment of cholinergic neurotransmission and immune system, previous attempts to find effective treatments have faced challenges.Methods: In such scenario, we attempted to investigate the effects of alpha-glyceryl-phosphoryl-choline (α-GPC), a cholinomimetic molecule, on neuroinflammation and memory outcome in the triple transgenic mouse model of AD (3xTg-AD). Mice were enrolled at 4 months of age, treated orally with α-GPC dissolved in drinking water at a concentration resulting in an average daily dose of 100 mg/kg for 8 months and sacrificed at 12 months of age. Thereafter, inflammatory markers, as well as cognitive parameters, were measured.Results: Chronic α-GPC treatment reduced accumulation of amyloid deposits and led to a substantial re-balance of the inflammatory response of resident innate immune cells, astrocytes and microglia. Specifically, fluorescent immunohistochemistry and Western blot analysis showed that α-GPC contributed to reduction of cortical and hippocampal reactive astrocytes and pro-inflammatory microglia, concurrently increasing the expression of anti-inflammatory molecules. Whereas α-GPC beneficially affect the synaptic marker synaptophysin in the hippocampus. Furthermore, we observed that α-GPC was effective in restoring cognitive dysfunction, as measured by the Novel Object Recognition test, wherein 3xTg-AD mice treated with α-GPC significantly spent more time exploring the novel object compared to 3xTg-AD untreated mice.Discussion: In conclusion, chronic treatment with α-GPC exhibited a significant anti-inflammatory activity and sustained the key function of hippocampal synapses, crucial for the maintenance of a regular cognitive status. In light of our results, we suggest that α-GPC could be exploited as a promising therapeutic approach in early phases of AD.

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at di [ABSTRACT FROM AUTHOR]

Nuclear pore complexes (NPCs) on the nuclear membrane surface have a crucial function in controlling the movement of small molecules and macromolecules between the cell nucleus and cytoplasm through their intricate core channel resembling a spiderweb with several layers. Currently, there are few methods available to accurately measure the dynamics of nuclear pores on the nuclear membranes at the nanoscale. The limitation of traditional optical imaging is due to diffraction, which prevents achieving the required resolution for observing a diverse array of organelles and proteins within cells. Super-resolution techniques have effectively addressed this constraint by enabling the observation of subcellular components on the nanoscale. Nevertheless, it is crucial to acknowledge that these methods often need the use of fixed samples. This also raises the question of how closely a static image represents the real intracellular dynamic system. High-speed atomic force microscopy (HS-AFM) is a unique technique used in the field of dynamic structural biology, enabling the study of individual molecules in motion close to their native states. Establishing a reliable and repeatable technique for imaging mammalian tissue at the nanoscale using HS-AFM remains challenging due to inadequate sample preparation. This study presents the rapid strainer microfiltration (RSM) protocol for directly preparing high-quality nuclei from the mouse brain. Subsequently, we promptly utilize HS-AFM real-time imaging and cinematography approaches to record the spatiotemporal of nuclear pore nano-dynamics from the mouse brain. [ABSTRACT FROM AUTHOR]

Age-related impairment of neurovascular coupling (NVC; "functional hyperemia") is a critical factor in the development of vascular cognitive impairment (VCI). Recent geroscience research indicates that cell-autonomous mechanisms alone cannot explain all aspects of neurovascular aging. Circulating factors derived from other organs, including pro-geronic factors (increased with age and detrimental to vascular homeostasis) and anti-geronic factors (preventing cellular aging phenotypes and declining with age), are thought to orchestrate cellular aging processes. This study aimed to investigate the influence of age-related changes in circulating factors on neurovascular aging. Heterochronic parabiosis was utilized to assess how exposure to young or old systemic environments could modulate neurovascular aging. Results demonstrated a significant decline in NVC responses in aged mice subjected to isochronic parabiosis (20-month-old C57BL/6 mice [A-(A)]; 6 weeks of parabiosis) when compared to young isochronic parabionts (6-month-old, [Y-(Y)]). However, exposure to young blood from parabionts significantly improved NVC in aged heterochronic parabionts [A-(Y)]. Conversely, young mice exposed to old blood from aged parabionts exhibited impaired NVC responses [Y-(A)]. In conclusion, even a brief exposure to a youthful humoral environment can mitigate neurovascular aging phenotypes, rejuvenating NVC responses. Conversely, short-term exposure to an aged humoral milieu in young mice accelerates the acquisition of neurovascular aging traits. These findings highlight the plasticity of neurovascular aging and suggest the presence of circulating anti-geronic factors capable of rejuvenating the aging cerebral microcirculation. Further research is needed to explore whether young blood factors can extend their rejuvenating effects to address other age-related cerebromicrovascular pathologies, such as blood–brain barrier integrity. [ABSTRACT FROM AUTHOR]

This research paper explores a method for addressing thermal confounds in fluorescence neuroimaging during ultrasound stimulation of the brain. The authors developed a technique that uses ultrasound delivery and widefield fluorescence to suppress thermal transients and recover neural responses. The study demonstrates the potential of this method for non-invasive stimulation of the nervous system and provides code and data for further research. The authors acknowledge support from various organizations and provide references for additional reading on ultrasound neuromodulation. [Extracted from the article]

Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to.PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to.ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times. [ABSTRACT FROM AUTHOR]

Gangliosides are specialized glycosphingolipids most abundant in the central nervous system. Their complex amphiphilic structure is essential to the formation of membrane lipid rafts and for molecular recognition. Dysfunction of lipid rafts and ganglioside metabolism has been linked to cancer, metabolic disorders, and neurodegenerative disorders. Changes in ganglioside concentration and diversity during the progression of disease have made them potential biomarkers for early detection and shed light on disease mechanisms. Chemical derivatization facilitates whole ion analysis of gangliosides while improving ionization, providing rich fragmentation spectra, and enabling multiplexed analysis schemes such as stable isotope labeling. In this work, we report improvement to our previously reported isobaric labeling methodology for ganglioside analysis by increasing buffer concentration and removing solid-phase extraction desalting for a more complete and quantitative reaction. Identification and quantification of gangliosides are automated through MS-DIAL with an in-house ganglioside derivatives library. We have applied the updated methodology to relative quantification of gangliosides in six mouse brain regions (cerebellum, pons/medulla, midbrain, thalamus/hypothalamus, cortex, and basal ganglia) with 2 mg tissue per sample, and region-specific distributions of 88 ganglioside molecular species are described with ceramide isomers resolved. This method is promising for application to comparative analysis of gangliosides in biological samples. [ABSTRACT FROM AUTHOR]

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

Information on regional variation in cell numbers and densities in the CNS provides critical insight into structure, function, and the progression of CNS diseases. However, variability can be real or a consequence of methods that do not account for technical biases, including morphologic deformations, errors in the application of cell type labels and boundaries of regions, errors of counting rules and sampling sites. We address these issues in a mouse model by introducing a workflow that consists of the following steps: 1. Magnetic resonance histology (MRH) to establish the size, shape, and regional morphology of the mouse brain in situ. 2. Light-sheet microscopy (LSM) to selectively label neurons or other cells in the entire brain without sectioning artifacts. 3. Register LSM volumes to MRH volumes to correct for dissection errors and both global and regional deformations. 4. Implement stereological protocols for automated sampling and counting of cells in 3D LSM volumes. This workflow can analyze the cell densities of one brain region in less than 1 min and is highly replicable in cortical and subcortical gray matter regions and structures throughout the brain. This method demonstrates the advantage of not requiring an extensive amount of training data, achieving a F1 score of approximately 0.9 with just 20 training nuclei. We report deformation-corrected neuron (NeuN) counts and neuronal density in 13 representative regions in 5 C57BL/6J cases and 2 BXD strains. The data represent the variability among specimens for the same brain region and across regions within the specimen. Neuronal densities estimated with our workflow are within the range of values in previous classical stereological studies. We demonstrate the application of our workflow to a mouse model of aging. This workflow improves the accuracy of neuron counting and the assessment of neuronal density on a region-by-region basis, with broad applications for studies of how genetics, environment, and development across the lifespan impact cell numbers in the CNS. [ABSTRACT FROM AUTHOR]

• MIR125B1 is not imprinted in human brain. • miR125b-1 displays distinct spatial expression in mice. • miR125b-1 is down-regulated during brain development of mice. Genomic imprinting is a predominantly brain and placenta-specific epigenetic process that contributes to parent-of-origin-specific gene expression. While microRNAs are highly expressed in the brain, their imprinting status in this tissue remains poorly studied. Previous research demonstrated that Mir125b-2 is imprinted in the human brain and regulates hippocampal circuits and functions in mice. However, the imprinting status of another isoform of miR125b, Mir125b-1 , in the human brain, as well as its spatiotemporal expression patterns in mice, have not been elucidated. Here, we show MIR125B1 is not imprinted in the human brain. Moreover, miR-125b-1 was highly expressed in the brains of mice. Furthermore, miR-125b-1 was down-regulated during brain development in mice. Specifically, miR-125b-1 displayed preferential expression in the olfactory bulb, thalamus, and hypothalamus of the mouse brain. Notably, miR-125b-1 was enriched in GABAergic neurons, particularly somatostatin-expressing GABAergic neurons, compared with glutamatergic neurons. Taken together, our findings provide the imprinting status and comprehensive spatiotemporal expression profiling of Mir125b-1 in the brain. [ABSTRACT FROM AUTHOR]

Glycosylation is one of the most common types of post-translational modifications in mammals. It is well known that N-glycans play a key role in cell adhesion, differentiation, synapsis, and myelination during the development of the mammalian central nervous system (CNS). Neuropathological symptoms (such as epilepsy and Alzheimer's disease) are usually accompanied by N-glycosylation changes. In this study, we extracted N-glycan chains from eight regions of the mouse brain, and combined high-throughput, high-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) with the Fmoc N-hydroxysuccinimide ester (Fmoc-OSU) derivatization method to improve the sensitivity of glycan detection to characterize the total N-glycans in the mouse brain. A total of 96 N-glycan moieties were detected. An exhaustive examination of the relative abundance of N-glycans, coupled with a comparative analysis of differences, has uncovered discernible variations of statistical significance, including high mannose, fucosylated, sialylated, and galactosylated N-glycans. According to our investigations, a thorough and regionally specific cartography of glycans within the brain can facilitate the investigation of glycan-mediated mechanisms related to both the developmental trajectory and functional output of the brain. Additionally, this approach may serve as a basis for identifying potential biomarkers that are relevant to various brain-associated pathologies. [ABSTRACT FROM AUTHOR]

We investigated the effect of isoflurane and ketamine-xylazine anesthesia on the positron emission tomography (PET) tracer [18F]SynVesT-1 in the mouse brain. [18F]SynVesT-1 PET scans were performed in C57BL/6J mice in five conditions: isoflurane anesthesia (ANISO), ketamine-xylazine (ANKX), awake freely moving (AW), awake followed by isoflurane administration (AW/ANISO) or followed by ketamine-xylazine (AW/ANKX) 20 min post tracer injection. ANISO, ANKX and AW scans were also performed in mice administered with levetiracetam (LEV, 200 mg/kg) to assess non-displaceable binding. Metabolite analysis was performed in ANISO, ANKX and AW mice. Finally, in vivo autoradiography in ANISO, ANKX and AW mice at 30 min post-injection was performed for validation. Kinetic modeling, with a metabolite corrected image derived input function, was performed to calculate total and non-displaceable volume of distribution (V T(IDIF)). V T(IDIF) was higher in ANISO compared to AW (p < 0.0001) while V T(IDIF) in ANKX was lower compared with AW (p < 0.0001). Non-displaceable V T(IDIF) was significantly different between ANISO and AW, but not between ANKX and AW. Change in the TAC washout was observed after administration of either isoflurane or ketamine-xylazine. Observed changes in tracer kinetics and volume of distribution might be explained by physiological changes due to anesthesia, as well as by induced cellular effects. [ABSTRACT FROM AUTHOR]

Neuromuscular diseases with primary muscle wasting symptoms may also display multisystemic changes in the body and exhibit secondary pathophysiological alterations in various non-muscle tissues. In some cases, this includes proteome-wide alterations and/or adaptations in the central nervous system. Thus, in order to provide an improved bioanalytical basis for the comprehensive evaluation of animal models that are routinely used in muscle research, this report describes the mass spectrometry-based proteomic characterization of the mouse brain. Crude tissue extracts were examined by bottom-up proteomics and detected 4558 distinct protein species. The detailed analysis of the brain proteome revealed the presence of abundant cellular proteoforms in the neuronal cytoskeleton, as well as various brain region enriched proteins, including markers of the cerebral cortex, cerebellum, hippocampus and the olfactory bulb. Neuroproteomic markers of specific cell types in the brain were identified in association with various types of neurons and glia cells. Markers of subcellular structures were established for the plasmalemma, nucleus, endoplasmic reticulum, mitochondria and other crucial organelles, as well as synaptic components that are involved in presynaptic vesicle docking, neurotransmitter release and synapse remodelling. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-tonoise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.

Immunohistochemistry (IHC) is a well-established and widely used protocol used to visualize tissue architecture, protein expression and localization. Free-floating methods for IHC employ tissue sections that are cut from a cryostat or vibratome. The limitations of these tissue sections are tissue fragility, poor morphology, and the need to use sections of 20-50 mm. In addition, there is a void of information regarding the use of free floating immunohistochemical techniques on paraffin embedded tissue. To address this, we developed a free-float IHC protocol with paraffin embedded tissue (PFFP) that saves time, resources, and tissues. PFFP localized GFAP, olfactory marker protein, tyrosine hydroxylase, and Nestin expression in mouse hippocampal, olfactory bulb, striatum, and cortical tissue. Successful localization of these antigens was achieved using PFFP with and without antigen retrieval, with subsequent chromogenic DAB (3,30-diaminobenzidine) development and immunofluorescence detection methods. The application of the PFFP in combination with methodologies of in situ hybridization, protein/protein interactions, laser capture dissection, and pathological diagnosis expands the versatility of paraffin embedded tissues. [ABSTRACT FROM AUTHOR]

The Locus Coeruleus (LC) is in the brainstem and supplies key brain structures with noradrenaline, including the forebrain and hippocampus. The LC impacts specific behaviors such as anxiety, fear, and motivation, as well as physiological phenomena that impact brain functions in general, including sleep, blood flow regulation, and capillary permeability. Nevertheless, the short- and long-term consequences of LC dysfunction remain unclear. The LC is among the brain structures first affected in patients suffering from neurodegenerative diseases such as Parkinson's disease and Alzheimer's Disease, hinting that LC dysfunction may play a central role in disease development and progression. Animal models with modified or disrupted LC function are essential to further our understanding of LC function in the normal brain, the consequences of LC dysfunction, and its putative roles in disease development. For this, well-characterized animal models of LC dysfunction are needed. Here, we establish the optimal dose of selective neurotoxin N-(2-chloroethyl)-N-ethyl-bromo-benzylamine (DSP-4) for LC ablation. Using histology and stereology, we compare LC volume and neuron number in LC ablated (LCA) mice and controls to assess the efficacy of LC ablation with different numbers of DSP-4 injections. All LCA groups show a consistent decrease in LC cell count and LC volume. We then proceed to characterize the behavior of LCA mice using a light-dark box test, Barnes maze test, and noninvasive sleep-wakefulness monitoring. Behaviorally, LCA mice differ subtly from control mice, with LCA mice generally being more curious and less anxious compared to controls consistent with known LC function and projections. We note an interesting contrast in that control mice have varying LC size and neuron count but consistent behavior whereas LCA mice (as expected) have consistently sized LC but erratic behavior. Our study provides a thorough characterization of an LC ablation model, firmly consolidating it as a valid model system for the study of LC dysfunction. [ABSTRACT FROM AUTHOR]

We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 µm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 µm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 µm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics. [ABSTRACT FROM AUTHOR]

Temozolomide is a Food and Drug Administration‐approved anticancer drug that has poor drug delivery via oral or intravenous routes. A potential strategy to combat this problem is investigating alternative routes of administration, requiring quantitation of the drug in the brain tissues by liquid chromatography‐mass spectrometry. However, current methods used to extract the drug from brain tissues resulted in poor recovery and substantial matrix effects. Herein, we reported a new two‐step extraction method that involves the use of Proteinase K to lyse tumor tissues to efficiently release the drug, followed by ethanol protein precipitation. The extracts were then separated on a C18 column and analyzed in positive electrospray ionization, a multiple reaction monitoring mode of the triple quadrupole. We found this new method led to a recovery of 82% with negligible matrix effects. The method has been validated in accordance with Food and Drug Administration guidance for linearity, specificity, selectivity, accuracy, precision, carry‐over, stability, and lower limit of quantitation. In conclusion, we have developed and validated a liquid chromatography‐mass spectrometry method with a novel sample preparation method that was able to efficiently extract temozolomide from mouse brain tissue with high recovery. [ABSTRACT FROM AUTHOR]

For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatial-normalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans before and after the administration of human immunoglobin or antibody-based treatments. To train the CNN, PET images were used as inputs and MR iSN-based target VOIs as labels. Our devised methods achieved decent performance in terms of not only VOI agreements (i.e., Dice similarity coefficient) but also the correlation of mean counts and SUVR, and CNN-based VOIs was highly accordant with ground-truth (the corresponding MR and MR template-based VOIs). Moreover, the performance metrics were comparable to that of VOI generated by MR-based deep CNN. In conclusion, we established a novel quantitative analysis method both MR-less and SN-less fashion to generate individual brain space VOIs using MR template-based VOIs for PET image quantification. [ABSTRACT FROM AUTHOR]

Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing. [ABSTRACT FROM AUTHOR]

Immunohistochemistry (IHC) is a well-established and widely used protocol used to visualize tissue architecture, protein expression and localization. Free-floating methods for IHC employ tissue sections that are cut from a cryostat or vibratome. The limitations of these tissue sections are tissue fragility, poor morphology, and the need to use sections of 20–50 μm. In addition, there is a void of information regarding the use of free floating immunohistochemical techniques on paraffin embedded tissue. To address this, we developed a free-float IHC protocol with paraffin embedded tissue (PFFP) that saves time, resources, and tissues. PFFP localized GFAP, olfactory marker protein, tyrosine hydroxylase, and Nestin expression in mouse hippocampal, olfactory bulb, striatum, and cortical tissue. Successful localization of these antigens was achieved using PFFP with and without antigen retrieval, with subsequent chromogenic DAB (3,3′-diaminobenzidine) development and immunofluorescence detection methods. The application of the PFFP in combination with methodologies of in situ hybridization, protein/protein interactions, laser capture dissection, and pathological diagnosis expands the versatility of paraffin embedded tissues.

The mouse brain is by far the most intensively studied among mammalian brains, yet basic measures of its cytoarchitecture remain obscure. For example, quantifying cell numbers, and the interplay of sex, strain, and individual variability in cell density and volume is out of reach for many regions. The Allen Mouse Brain Connectivity project produces high-resolution full brain images of hundreds of brains. Although these were created for a different purpose, they reveal details of neuroanatomy and cytoarchitecture. Here, we used this population to systematically characterize cell density and volume for each anatomical unit in the mouse brain. We developed a DNN-based segmentation pipeline that uses the autofluorescence intensities of images to segment cell nuclei even within the densest regions, such as the dentate gyrus. We applied our pipeline to 507 brains of males and females from C57BL/6J and FVB.CD1 strains. Globally, we found that increased overall brain volume does not result in uniform expansion across all regions. Moreover, region-specific density changes are often negatively correlated with the volume of the region; therefore, cell count does not scale linearly with volume. Many regions, including layer 2/3 across several cortical areas, showed distinct lateral bias. We identified strain-specific or sex-specific differences. For example, males tended to have more cells in extended amygdala and hypothalamic regions (MEA, BST, BLA, BMA, and LPO, AHN) while females had more cells in the orbital cortex (ORB). Yet, inter-individual variability was always greater than the effect size of a single qualifier. We provide the results of this analysis as an accessible resource for the community.

Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.

The Locus Coeruleus (LC) is in the brainstem and supplies key brain structures with noradrenaline, including the forebrain and hippocampus. The LC impacts specific behaviors such as anxiety, fear, and motivation, as well as physiological phenomena that impact brain functions in general, including sleep, blood flow regulation, and capillary permeability. Nevertheless, the short- and long-term consequences of LC dysfunction remain unclear. The LC is among the brain structures first affected in patients suffering from neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s Disease, hinting that LC dysfunction may play a central role in disease development and progression. Animal models with modified or disrupted LC function are essential to further our understanding of LC function in the normal brain, the consequences of LC dysfunction, and its putative roles in disease development. For this, well-characterized animal models of LC dysfunction are needed. Here, we establish the optimal dose of selective neurotoxin N-(2-chloroethyl)-N-ethyl-bromo-benzylamine (DSP-4) for LC ablation. Using histology and stereology, we compare LC volume and neuron number in LC ablated (LCA) mice and controls to assess the efficacy of LC ablation with different numbers of DSP-4 injections. All LCA groups show a consistent decrease in LC cell count and LC volume. We then proceed to characterize the behavior of LCA mice using a light-dark box test, Barnes maze test, and non-invasive sleep-wakefulness monitoring. Behaviorally, LCA mice differ subtly from control mice, with LCA mice generally being more curious and less anxious compared to controls consistent with known LC function and projections. We note an interesting contrast in that control mice have varying LC size and neuron count but consistent behavior whereas LCA mice (as expected) have consistently sized LC but erratic behavior. Our study provides a thorough characterization of an LC ablation model, firmly consolidating it as a valid model system for the study of LC dysfunction.

Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.

Autism is a neurodevelopmental disorder for which the cause and treatment have yet not been determined. The polyunsaturated fatty acid (PUFA) levels change rapidly in the blood or cerebrospinal fluid of autistic children and PUFAs are closely related to autism spectrum disorder (ASD). This finding suggests that changes in lipid metabolism are associated with ASD and result in an altered distribution of phospholipids in cell membranes. To further understand ASD, it is necessary to analyze phospholipids in organs consisting of nerve cells, such as the brain. In this study, we investigated the phospholipid distribution in the brain tissue of valproic acid-induced autistic mice using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Phospholipids including phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine were identified in each brain region and exhibited differences between the ASD and control groups. These phospholipids contain docosahexaenoic acid and arachidonic acid, which are important PUFAs for cell signaling and brain growth. We expect that the differences in phospholipids identified in the brain tissue of the ASD model with MALDI-MSI, in conjunction with conventional biological fluid analysis, will help to better understand changes in lipid metabolism in ASD. [ABSTRACT FROM AUTHOR]