Brain Extracts Research Articles

Polyserine peptides are toxic and exacerbate tau pathology in mice.

Polyserine domains mediate the association of nuclear RNA binding proteins with cytoplasmic tau aggregates that occurs across tauopathy models and patient samples. In cell lines, polyserine peptides co-localize with and promote formation of tau aggregates suggesting the cytoplasmic mislocalization of polyserine-containing proteins might contribute to human disease. Moreover, polyserine can be produced by repeat associated non-AUG translation in CAG repeat expansion diseases. However, whether polyserine expressed in a mammalian brain is toxic and/or can exacerbate tau pathology is unknown. Here, we used AAV9-mediated delivery to express a 42-repeat polyserine protein in wild-type and tau transgenic mouse models. We observe that polyserine expression has toxic effects in wild-type animals indicated by reduced weight, behavioral abnormalities and a striking loss of Purkinje cells. Moreover, in the presence of a pathogenic variant of human tau, polyserine exacerbates disease markers such as phosphorylated and insoluble tau levels and the seeding capacity of brain extracts. These findings demonstrate that polyserine domains can promote tau-mediated pathology in a mouse model and are consistent with the hypothesis that cytoplasmic mislocalization of polyserine containing proteins might contribute to the progression of human tauopathies.

Functional characterization of CCHamides and deorphanization of their receptors in the yellow fever mosquito, Aedes aegypti

As a widely distributed anthropophilic mosquito species and vector of various arboviruses, Aedes aegypti poses a significant threat to human health on a global scale. Investigating mosquito neuropeptides allows us to better understand their physiology. The neuropeptides CCHamide1 (CCHa1) and CCHamide2 (CCHa2) along with their associated G protein-coupled receptors (CCHa1R and CCHa2R) were recently identified and studied across insects. However, expression profiles and physiological roles of CCHamides and their receptors in many other insects, including A. aegypti, remain unclear. This research aimed to quantify and localize the expression of CCHamides along with their receptors and gain insight on their physiological function in the yellow fever mosquito. RT-qPCR analysis revealed transcript abundance of CCHamides and receptors changes over development. Differential expression was also observed in tissues/organs of adult mosquitoes indicating CCHa1 and CCHa2 transcripts are enriched in the midgut, while receptors are expressed across various tissues. CCHamide immunoreactivity was observed in neurons in the brain and ventral nerve cord along with enteroendocrine cells in the posterior midgut adjacent to the midgut-hindgut junction, corroborating their transcript expression profiles. Using different mass spectrometrical approaches, presence of CCHamides were confirmed in the brain of both sexes, including the pars intercerebralis of female mosquitoes, as well as in the gut of adult mosquitoes. For chemical identification of predicted CCHamides, we analyzed brain and gut extracts by ESI-Q Exactive Orbitrap MS and resulting fragmentations confirmed CCHa1 and CCHa2 in brain and midgut samples of both male and female mosquitoes. A heterologous functional assay was used to confirm the specificity and sensitivity of the two CCHamide receptors by assessing their activation in response to diverse mosquito peptides, which confirmed CCHa1 and CCHa2 as natural ligands. Finally, using a capillary feeder (CAFE) bioassay, our results suggest that CCHa2 modulates feeding behaviour in female mosquitoes.

Targeting the TDP-43 low complexity domain blocks spreading of pathology in a mouse model of ALS/FTD

Abnormal cytoplasmic localization and accumulation of pathological transactive response DNA binding protein of 43 kDa (TDP-43) underlies several devastating diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP). A key element is the correlation between disease progression and spatio-temporal propagation of TDP-43-mediated pathology in the central nervous system. Several lines of evidence support the concept of templated aggregation and cell to cell spreading of pathological TDP-43. To further investigate this mechanism in vivo, we explored the efficacy of capturing and masking the seeding-competent region of extracellular TDP-43 species. For this, we generated a novel monoclonal antibody (mAb), ACI-6677, that targets the pathogenic protease-resistant amyloid core of TDP-43. ACI-6677 has a picomolar binding affinity for TDP-43 and is capable of binding to all C-terminal TDP-43 fragments. In vitro, ACI-6677 inhibited TDP-43 aggregation and boosted removal of pathological TDP-43 aggregates by phagocytosis. When injecting FTLD-TDP brain extracts unilaterally in the CamKIIa-hTDP-43NLSm mouse model, ACI-6677 significantly limited the induction of phosphorylated TDP-43 (pTDP-43) inclusions. Strikingly, on the contralateral side, the mAb significantly prevented pTDP-43 inclusion appearance exemplifying blocking of the spreading process. Taken together, these data demonstrate for the first time that an immunotherapy targeting the protease-resistant amyloid core of TDP-43 has the potential to restrict spreading, substantially slowing or stopping progression of disease.

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.

Fructose-2,6-bisphosphate restores DNA repair activity of PNKP and ameliorates neurodegenerative symptoms in Huntington’s disease

Huntington's disease (HD) and spinocerebellar ataxia type 3 (SCA3) are the two most prevalent polyglutamine (polyQ) neurodegenerative diseases, caused by CAG (encoding glutamine) repeat expansion in the coding region of the huntingtin (HTT) and ataxin-3 (ATXN3) proteins, respectively. We have earlier reported that the activity, but not the protein level, of an essential DNA repair enzyme, polynucleotide kinase 3'-phosphatase (PNKP), is severely abrogated in both HD and SCA3 resulting in accumulation of double-strand breaks in patients' brain genome. While investigating the mechanistic basis for the loss of PNKP activity and accumulation of DNA double-strand breaks leading to neuronal death, we observed that PNKP interacts with the nuclear isoform of 6-phosphofructo-2-kinase fructose-2,6-bisphosphatase 3 (PFKFB3). Depletion of PFKFB3 markedly abrogates PNKP activity without changing its protein level. Notably, the levels of both PFKFB3 and its product fructose-2,6 bisphosphate (F2,6BP), an allosteric modulator of glycolysis, are significantly lower in the nuclear extracts of postmortem brain tissues of HD and SCA3 patients. Supplementation of F2,6BP restored PNKP activity in the nuclear extracts of patients' brain. Moreover, intracellular delivery of F2,6BP restored both the activity of PNKP and the integrity of transcribed genome in neuronal cells derived from the striatum of the HD mouse. Importantly, supplementing F2,6BP rescued the HD phenotype in Drosophila, suggesting F2,6BP to serve in vivo as a cofactor for the proper functionality of PNKP and thereby, of brain health. Our results thus provide a compelling rationale for exploring the therapeutic use of F2,6BP and structurally related compounds for treating polyQ diseases.

Co-opting templated aggregation to degrade pathogenic tau assemblies and improve motor function

Protein aggregation causes a wide range of neurodegenerative diseases. Targeting and removing aggregates, but not the functional protein, is a considerable therapeutic challenge. Here, we describe a therapeutic strategy called "RING-Bait," which employs an aggregating protein sequence combined with an E3 ubiquitin ligase. RING-Bait is recruited into aggregates, whereupon clustering dimerizes the RING domain and activates its E3 function, resulting in the degradation of the aggregate complex. We exemplify this concept by demonstrating the specific degradation of tau aggregates while sparing soluble tau. Unlike immunotherapy, RING-Bait is effective against both seeded and cell-autonomous aggregation. RING-Bait removed tau aggregates seeded from Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) brain extracts and was also effective in primary neurons. We used a brain-penetrant adeno-associated virus (AAV) to treat P301S tau transgenic mice, reducing tau pathology and improving motor function. A RING-Bait strategy could be applied to other neurodegenerative proteinopathies by replacing the Bait sequence to match the target aggregate.

Comparative analysis of in situ and ex situ postmortem brain MRI: Evaluating volumetry, DTI, and relaxometry

AbstractPurposeTo compare postmortem in situ with ex situ MRI parameters, including volumetry, diffusion tensor imaging (DTI), and relaxometry for assessing methodology‐induced alterations, which is a crucial prerequisite when performing MRI biomarker validation.MethodsMRI whole‐brain scans of five deceased patients with amyotrophic lateral sclerosis were performed at 3 T. In situ scans were conducted within 32 h after death (SD 18 h), and ex situ scans after brain extraction and 3 months of formalin fixation. The imaging protocol included MP2RAGE, DTI, and multi‐contrast spin‐echo and multi‐echo gradient‐echo sequences. Volumetry, fractional anisotropy, mean diffusivity, T1, T2, and have been assessed for specific brain regions.ResultsWhen comparing ex situ to in situ values, the following results were obtained. Deep gray matter as well as the thalamus and the hippocampus showed a reduced volume. Fractional anisotropy was reduced in the cortex and the whole brain. Mean diffusivity was decreased in white matter and deep gray matter. T1 and T2 were reduced in all investigated structures, whereas was increased in the cortex.ConclusionThe results of this study show that the volumes and MRI parameters of several brain regions are potentially affected by tissue extraction and subsequent formalin fixation, suggesting that methodological alterations are present in ex situ MRI. To avoid overlap of indistinguishable methodological and disease‐related changes, we recommend performing in situ postmortem MRI as an additional intermediate step for in vivo MRI biomarker validation.

Mouse Brain Extractor: Brain segmentation of mouse MRI using global positional encoding and SwinUNETR.

In spite of the great progress that has been made towards automating brain extraction in human magnetic resonance imaging (MRI), challenges remain in the automation of this task for mouse models of brain disorders. Researchers often resort to editing brain segmentation results manually when automated methods fail to produce accurate delineations. However, manual corrections can be labor-intensive and introduce interrater variability. This motivated our development of a new deep-learning-based method for brain segmentation of mouse MRI, which we call Mouse Brain Extractor. We adapted the existing SwinUNETR architecture (Hatamizadeh et al., 2021) with the goal of making it more robust to scale variance. Our approach is to supply the network model with supplementary spatial information in the form of absolute positional encoding. We use a new scheme for positional encoding, which we call Global Positional Encoding (GPE). GPE is based on a shared coordinate frame that is relative to the entire input image. This differs from the positional encoding used in SwinUNETR, which solely employs relative pairwise image patch positions. GPE also differs from the conventional absolute positional encoding approach, which encodes position relative to a subimage rather than the entire image. We trained and tested our method on a heterogeneous dataset of N=223 mouse MRI, for which we generated a corresponding set of manually-edited brain masks. These data were acquired previously in other studies using several different scanners and imaging protocols and included in vivo and ex vivo images of mice with heterogeneous brain structure due to different genotypes, strains, diseases, ages, and sexes. We evaluated our method's results against those of seven existing rodent brain extraction methods and two state-of-the art deep-learning approaches, nnU-Net (Isensee et al., 2018) and SwinUNETR. Overall, our proposed method achieved average Dice scores on the order of 0.98 and average HD95 measures on the order of 100 μm when compared to the manually-labeled brain masks. In statistical analyses, our method significantly outperformed the conventional approaches and performed as well as or significantly better than the nnU-Net and SwinUNETR methods. These results suggest that Global Positional Encoding provides additional contextual information that enables our Mouse Brain Extractor to perform competitively on datasets containing multiple resolutions.

Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.

Deep learning has proven highly effective in various medical imaging scenarios, yet the lack of an efficient distribution platform hinders developers from sharing models with end-users. Here, we describe brainchop, a fully functional web application that allows users to apply deep learning models developed with Python to local neuroimaging data from within their browser. While training artificial intelligence models is computationally expensive, applying existing models to neuroimaging data can be very fast; brainchop harnesses the end user's graphics card such that brain extraction, tissue segmentation, and regional parcellation require only seconds and avoids privacy issues that impact cloud-based solutions. The integrated visualization allows users to validate the inferences, and includes tools to annotate and edit the resulting segmentations. Our pure JavaScript implementation includes optimized helper functions for conforming volumes and filtering connected components with minimal dependencies. Brainchop provides a simple mechanism for distributing models for additional image processing tasks, including registration and identification of abnormal tissue, including tumors, lesions and hyperintensities. We discuss considerations for other AI model developers to leverage this open-source resource.

Learning to integrate parts for whole through correlated neural variability.

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.

Patient-derived tau and amyloid-β facilitate long-term depression in vivo: role of tumour necrosis factor-α and the integrated stress response.

Alzheimer's disease is characterized by a progressive cognitive decline in older individuals accompanied by the deposition of two pathognomonic proteins amyloid-β and tau. It is well documented that synaptotoxic soluble amyloid-β aggregates facilitate synaptic long-term depression, a major form of synaptic weakening that correlates with cognitive status in Alzheimer's disease. Whether synaptotoxic tau, which is also associated strongly with progressive cognitive decline in patients with Alzheimer's disease and other tauopathies, also causes facilitation remains to be clarified. Young male adult and middle-aged rats were employed. Synaptotoxic tau and amyloid-β were obtained from different sources including (i) aqueous brain extracts from patients with Alzheimer's disease and Pick's disease tauopathy; (ii) the secretomes of induced pluripotent stem cell-derived neurons from individuals with trisomy of chromosome 21; and (iii) synthetic amyloid-β. In vivo electrophysiology was performed in urethane anaesthetized animals. Evoked field excitatory postsynaptic potentials were recorded from the stratum radiatum in the CA1 area of the hippocampus with electrical stimulation to the Schaffer collateral-commissural pathway. To study the enhancement of long-term depression, relatively weak low-frequency electrical stimulation was used to trigger peri-threshold long-term depression. Synaptotoxic forms of tau or amyloid-β were administered intracerebroventricularly. The ability of agents that inhibit the cytokine tumour necrosis factor-α or the integrated stress response to prevent the effects of amyloid-β or tau on long-term depression was assessed after local or systemic injection, respectively. We found that diffusible tau from Alzheimer's disease or Pick's disease patients' brain aqueous extracts or the secretomes of trisomy of chromosome 21 induced pluripotent stem cell-derived neurons, like Alzheimer's disease brain-derived amyloid-β and synthetic oligomeric amyloid-β, potently enhanced synaptic long-term depression in live rats. We further demonstrated that long-term depression facilitation by both tau and amyloid-β was age-dependent, being more potent in middle-aged compared with young animals. Finally, at the cellular level, we provide pharmacological evidence that tumour necrosis factor-α and the integrated stress response are downstream mediators of long-term depression facilitation by both synaptotoxic tau and amyloid-β. Overall, these findings reveal the promotion of an age-dependent synaptic weakening by both synaptotoxic tau and amyloid-β. Pharmacologically targeting shared mechanisms of tau and amyloid-β synaptotoxicity, such as tumour necrosis factor-α or the integrated stress response, provides an attractive strategy to treat early Alzheimer's disease.

Ex-Vivo 13C NMR Spectroscopy of Rodent Brain: TNF Restricts Neuronal Utilization of Astrocyte-Derived Metabolites.

Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using 13C NMR spectroscopy on brain extracts following intravenous [1,2-13C]-glucose (to probe glia and neuron metabolism), [2-13C]-acetate (probing astrocyte-specific metabolites), or [3-13C]-lactate. An increase in [4,5-13C]-glutamine and [2,3-13C]-lactate coupled with a decrease in [4,5-13C]-glutamate was observed in the [1,2-13C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-13C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-13C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-13C]-lactate and decrease in [4,5-13C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-13C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.

Convergent generation of atypical prions in knockin mouse models of genetic prion disease.

Most cases of human prion disease arise due to spontaneous misfolding of WT or mutant prion protein, yet recapitulating this event in animal models has proven challenging. It remains unclear whether spontaneous prion generation can occur within the mouse lifespan in the absence of protein overexpression and how disease-causing mutations affect prion strain properties. To address these issues, we generated knockin mice that express the misfolding-prone bank vole prion protein (BVPrP). While mice expressing WT BVPrP (I109 variant) remained free from neurological disease, a subset of mice expressing BVPrP with mutations (D178N or E200K) causing genetic prion disease developed progressive neurological illness. Brains from spontaneously ill knockin mice contained prion disease-specific neuropathological changes as well as atypical protease-resistant BVPrP. Moreover, brain extracts from spontaneously ill D178N- or E200K-mutant BVPrP-knockin mice exhibited prion seeding activity and transmitted disease to mice expressing WT BVPrP. Surprisingly, the properties of the D178N- and E200K-mutant prions appeared identical before and after transmission, suggesting that both mutations guide the formation of a similar atypical prion strain. These findings imply that knockin mice expressing mutant BVPrP spontaneously develop a bona fide prion disease and that mutations causing prion diseases may share a uniform initial mechanism of action.

MULTIMEDIA CONTENT ANALYSIS FOR ALZHEIMER’S DISEASE DIAGNOSIS USING MRI SCANS AND DEEP LEARNING

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early diagnosis being crucial for effective intervention. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting structural brain changes associated with AD. However, accurate and automated analysis of MRI scans remains a challenge due to the complexity and variability in brain structures. Traditional methods for analyzing MRI scans for AD diagnosis often rely on manual interpretation or basic image processing techniques, which can be time-consuming and prone to variability. There is a need for advanced automated methods that can accurately segment brain structures and extract relevant features for reliable diagnosis. This study proposes a novel approach for AD diagnosis using MRI scans, combining Conditional Attention U-Net for segmentation and Ant Colony Optimization (ACO) for feature extraction. The Conditional Attention U-Net enhances segmentation accuracy by incorporating conditional attention mechanisms to focus on relevant features while minimizing background noise. ACO is employed to optimize feature extraction by simulating the foraging behavior of ants, which efficiently selects and refines key features related to AD. The proposed model was evaluated on a dataset of 500 MRI scans, comparing performance with traditional methods using metrics such as Dice Similarity Coefficient (DSC) and classification accuracy. The Conditional Attention U-Net achieved an average DSC of 0.89 for segmentation of key brain regions, outperforming conventional methods by 10%. The ACO-enhanced feature extraction resulted in a classification accuracy of 92% for AD diagnosis, representing a 7% improvement over baseline methods. The combination of these techniques demonstrated a significant enhancement in both segmentation precision and diagnostic accuracy, showcasing the effectiveness of the proposed approach for early AD detection.

Transmembrane and coiled-coil 2 associates with Alzheimer's disease pathology in the human brain.

Transmembrane and coiled-coil 2 (TMCC2) is a human orthologue of the Drosophila gene dementin, mutant alleles of which cause neurodegeneration with features of Alzheimer's disease (AD). TMCC2 and Dementin further have an evolutionarily conserved interaction with the amyloid protein precursor (APP), a protein central to AD pathogenesis. To investigate if human TMCC2 might also participate in mechanisms of neurodegeneration, we examined TMCC2 expression in late onset AD human brain and age-matched controls, familial AD cases bearing a mutation in APP Val717, and Down syndrome AD. Consistent with previous observations of complex formation between TMCC2 and APP in the rat brain, the dual immunocytochemistry of control human temporal cortex showed highly similar distributions of TMCC2 and APP. In late onset AD cases stratified by APOE genotype, TMCC2 immunoreactivity was associated with dense core senile plaques and adjacent neuronal dystrophies, but not with Aβ surrounding the core, diffuse Aβ plaques or tauopathy. In Down syndrome AD, we observed in addition TMCC2-immunoreactive and methoxy-X04-positive pathological features that were morphologically distinct from those seen in the late onset and familial AD cases, suggesting enhanced pathological alteration of TMCC2 in Down syndrome AD. At the protein level, western blots of human brain extracts revealed that human brain-derived TMCC2 exists as at least three isoforms, the relative abundance of which varied between the temporal gyrus and cerebellum and was influenced by APOE and/or dementia status. Our findings thus implicate human TMCC2 in AD via its interactions with APP, its association with dense core plaques, as well as its alteration in Down syndrome AD.

Discovery of a novel Xanthone derivative P24 for anti-AD via targeting sTGFBR3

Soluble transforming growth factor beta receptor 3 (sTGFBR3) antagonist is a new focus in the research and development of Alzheimer's disease (AD) drugs. Our previous studies have identified sTGFBR3 as a promising new target for AD, with few targeted antagonists identified. In this study, we performed structural modeling of sTGFBR3 using AlphaFold2, followed by high-throughput virtual screening and surface plasmon resonance assays. which collectively identified Xanthone as potential compounds for targeting sTGFBR3. After optimizing the sTGFBR3-Xanthone complex using molecular dynamics (MD) simulations, we prepared a series of novel Xanthone derivatives and evaluated their anti-inflammatory activity, toxicity, and structure-activity relationship in BV2 cell model induced by lipopolysaccharides (LPS) or APP/PS1/tau mouse brain extract (BE). Several derivatives with the most potent anti-inflammatory activity were tested for blood-brain barrier permeability and sTGFBR3 affinity. Derivative P24, selected for its superior properties, was further evaluated in vitro. The results indicated that P24 increased the activation of TGF-β signaling and decreased the activation of IκBα/NF-κB signaling by targeting sTGFBR3, thereby regulating the inflammation-phagocytosis balance in microglia. Moreover, the low acute toxicity, long half-life, and low plasma clearance of P24 suggest that it can be sustained in vivo. This property may render P24 a more effective treatment modality for chronic diseases, particularly AD. The study demonstrates P24 serve as potential novel candidates for the treatment of AD via antagonizing sTGFBR3.

Gz Enhanced Signal Transduction assaY (GZESTY) for GPCR deorphanization.

G protein-coupled receptors (GPCRs) are key pharmacological targets, yet many remain underutilized due to unknown activation mechanisms and ligands. Orphan GPCRs, lacking identified natural ligands, are a high priority for research, as identifying their ligands will aid in understanding their functions and potential as drug targets. Most GPCRs, including orphans, couple to Gi/o/z family members, however current assays to detect their activation are limited, hindering ligand identification efforts. We introduce GZESTY, a highly sensitive, cell-based assay developed in an easily deliverable format designed to study the pharmacology of Gi/o/z-coupled GPCRs and assist in deorphanization. We optimized assay conditions and developed an all-in-one vector employing novel cloning methods to ensure the correct expression ratio of GZESTY components. GZESTY successfully assessed activation of a library of ligand-activated GPCRs, detecting both full and partial agonism, as well as responses from endogenous GPCRs. Notably, with GZESTY we established the presence of endogenous ligands for GPR176 and GPR37 in brain extracts, validating its use in deorphanization efforts. This assay enhances the ability to find ligands for orphan GPCRs, expanding the toolkit for GPCR pharmacologists.

FAIMS Shotgun Lipidomics for Enhanced Class- and Charge-State Separation Complemented by Automated Ganglioside Annotation.

The analysis of gangliosides is extremely challenging, given their structural complexity, lack of reference standards, databases, and software solutions. Here, we introduce a fast 6 min high field asymmetric ion mobility spectrometry (FAIMS) shotgun lipidomics workflow, along with a dedicated software solution for ganglioside detection. By ramping FAIMS compensation voltages, ideal ranges for different ganglioside classes were obtained. FAIMS revealed both class- and charge-state separation behavior based on the glycan headgroup moiety. The number of sialic acids attached to the glycan moiety correlates positively with their preferred charge states, i.e., trisialylated gangliosides were mainly present as [M - 3H]3- ions, whereas [M - 4H]4- and [M - 5H]5- ions were observed for GQ1 and GP1. For data evaluation, we developed a shotgun/FAIMS extension for the open-source Lipid Data Analyzer (LDA), enabling automated annotation of gangliosides up to the molecular lipid species level. This extension utilized combined orthogonal fragmentation spectra from CID, HCD, and 213 nm UVPD ion activation methods and covers 29 ganglioside classes, including acetylated and fucosylated modifications. With our new workflow and software extension 117 unique gangliosides species were identified in porcine brain extracts. While conventional shotgun lipidomics favored the observation of singly charged ganglioside species, the utilization of FAIMS made multiply charged lipid species accessible, resulting in an increased number of detected species, primarily due to an improved signal-to-noise ratio arising from FAIMS charge state filtering. Therefore, this FAIMS-driven workflow, complemented by new software capabilities, offers a promising strategy for complex ganglioside and glycosphingolipid characterization in shotgun lipidomics.

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Background:Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Method:Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. Results:deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method’s output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. Conclusions:In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via “pip install deepbet” and is publicly accessible at https://github.com/wwu-mmll/deepbet.

Improved Imaging Surface for Quantitative Single-Molecule Microscopy.

Preventing nonspecific binding is essential for sensitive surface-based quantitative single-molecule microscopy. Here we report a much-simplified RainX-F127 (RF-127) surface with improved passivation. This surface achieves up to 100-fold less nonspecific binding from protein aggregates compared to commonly used polyethylene glycol (PEG) surfaces. The method is compatible with common single-molecule techniques including single-molecule pull-down (SiMPull), super-resolution imaging, antibody-binding screening and single exosome visualization. This method is also able to specifically detect alpha-synuclein (α-syn) and tau aggregates from a wide range of biofluids including human serum, brain extracts, cerebrospinal fluid (CSF) and saliva. The simplicity of this method further allows the functionalization of microplates for robot-assisted high-throughput single-molecule experiments. Overall, this simple but improved surface offers a versatile platform for quantitative single-molecule microscopy without the need for specialized equipment or personnel.