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Ischaemic stroke is a heterogeneous multifactorial disorder. Epidemiological data provide substantial evidence for a genetic component to the disease, but the extent of predisposition is unknown. Large progress has been made in single-gene disorders associated with ischaemic stroke. The identification of NOTCH3 mutations in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy (CADASIL) has led to new insights on lacunar stroke and small-vessel disease. Studies of sickle-cell disease have drawn attention to the importance of modifier genes and of gene–gene interactions in determining stroke risk. They have further highlighted a potential role of genetics in predicting stroke risk. Little is known about the genes associated with complex multifactorial stroke. There are probably many alleles with small effect sizes. Genetic-association studies on a wide range of candidate pathways, such as the haemostatic and inflammatory system, homocysteine metabolism, and the renin-angiotensin aldosterone system, suggest a weak but significant effect for several at-risk alleles. Genome-wide linkage studies in extended pedigrees from Iceland led to the identification of PDE4D and ALOX5AP. Specific haplotypes in these genes have been shown to confer risk for ischaemic stroke in the Icelandic population, but their role in other populations is unclear. Advances in high-throughput genotyping and biostatistics have enabled new study designs, including genome-wide association studies. Their application to ischaemic stroke requires the collaborative efforts of multiple centres. This approach will contribute to the identification of additional genes, novel pathways, and eventually novel therapeutic approaches to ischaemic stroke.

The term cerebral small vessel disease (SVD) describes a range of neuroimaging, pathological, and associated clinical features. Clinical features range from none, to discrete focal neurological symptoms (eg, stroke), to insidious global neurological dysfunction and dementia. The burden on public health is substantial. The pathogenesis of SVD is largely unknown. Although the pathological processes leading to the arteriolar disease are associated with vascular risk factors and are believed to result from an intrinsic cerebral arteriolar occlusive disease, little is known about how these processes result in brain disease, how SVD lesions contribute to neurological or cognitive symptoms, and the association with risk factors. Pathology often shows end-stage disease, which makes identification of the earliest stages difficult. Neuroimaging provides considerable insights; although the small vessels are not easily seen themselves, the effects of their malfunction on the brain can be tracked with detailed brain imaging. We discuss potential mechanisms, detectable with neuroimaging, that might better fit the available evidence and provide testable hypotheses for future study.

Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain. VesSAP is a tissue clearing- and deep learning-based pipeline for comprehensively analyzing mouse vasculature, from large vessels to small capillaries.

Stroke, a leading cause of long-term disability and death worldwide, has a heritable component. Recent gene discovery efforts have expanded the number of known single-gene disorders associated with stroke and have linked common variants at approximately 35 genetic loci to stroke risk. These discoveries have highlighted novel mechanisms and pathways implicated in stroke related to large artery atherosclerosis, cardioembolism, and small vessel disease, and defined shared genetic influences with related vascular traits. Genetics has also successfully established causal relationships with risk factors and holds promise for prioritising targets for exploration in clinical trials. Genome-wide polygenic scores enable the identification of high-risk individuals before the emergence of vascular risk factors. Challenges ahead include a better understanding of rare variants and ancestral differences for integration of genetics into precision medicine, integration with other omics data, uncovering the genetic factors that govern stroke recurrence and stroke outcome, and the conversion of genetic discoveries to novel therapies.

Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but their predictive power has been modest compared to established stroke risk factors. Here, using a meta-scoring approach, we develop a metaGRS for ischaemic stroke (IS) and analyse this score in the UK Biobank ( n  = 395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22–1.31 per metaGRS standard deviation) doubles that of a previous GRS, identifying a subset of individuals at monogenic levels of risk: the top 0.25% of metaGRS have three-fold risk of IS. The metaGRS is similarly or more predictive compared to several risk factors, such as family history, blood pressure, body mass index, and smoking. We estimate the reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk. Stroke risk is influenced by genetic and lifestyle factors and previously a genomic risk score (GRS) for stroke was proposed, albeit with limited predictive power. Here, Abraham et al. develop a metaGRS that is composed of several stroke-related GRSs and demonstrate improved predictive power compared with individual GRS or classic risk factors.

Previous studies have identified a crucial role of the gut microbiome in modifying Alzheimer’s disease (AD) progression. However, the mechanisms of microbiome–brain interaction in AD were so far unknown. Here, we identify microbiota-derived short chain fatty acids (SCFA) as microbial metabolites which promote Aβ deposition. Germ-free (GF) AD mice exhibit a substantially reduced Aβ plaque load and markedly reduced SCFA plasma concentrations; conversely, SCFA supplementation to GF AD mice increased the Aβ plaque load to levels of conventionally colonized (specific pathogen-free [SPF]) animals and SCFA supplementation to SPF mice even further exacerbated plaque load. This was accompanied by the pronounced alterations in microglial transcriptomic profile, including upregulation of ApoE. Despite increased microglial recruitment to Aβ plaques upon SCFA supplementation, microglia contained less intracellular Aβ. Taken together, our results demonstrate that microbiota-derived SCFA are critical mediators along the gut-brain axis which promote Aβ deposition likely via modulation of the microglial phenotype.

Ischaemic stroke is a leading cause of disability and death for which no acute treatments exist beyond recanalization. The development of novel therapies has been repeatedly hindered by translational failures that have changed the way we think about tissue damage after stroke. What was initially a neuron-centric view has been replaced with the concept of the neurovascular unit (NVU), which encompasses neuronal, glial and vascular compartments, and the biphasic nature of neural–glial–vascular signalling. However, it is now clear that the brain is not the private niche it was traditionally thought to be and that the NVU interacts bidirectionally with systemic biology, such as systemic metabolism, the peripheral immune system and the gut microbiota. Furthermore, these interactions are profoundly modified by internal and external factors, such as ageing, temperature and day–night cycles. In this Review, we propose an extension of the concept of the NVU to include its dynamic interactions with systemic biology. We anticipate that this integrated view will lead to the identification of novel mechanisms of stroke pathophysiology, potentially explain previous translational failures, and improve stroke care by identifying new biomarkers of and treatment targets in stroke.In this Review, the authors summarize the interactions of the neurovascular unit with systemic biology after ischaemic stroke, consider how these interactions influence stroke outcome, and discuss how these interactions could be targeted to improve outcomes.

Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue. Traditional approaches in spatial transcriptomics often rely on preliminary data grouping, which can overlook subtle expression patterns in tissues. Here, the authors present Spatial Gradient Screening, a tool which facilitates the detection of histology-associated gene expression patterns without prior data grouping.

The recent CRISPR revolution has provided researchers with powerful tools to perform genome editing in a variety of organisms. However, recent reports indicate widespread occurrence of unintended CRISPR-induced on-target effects (OnTEs) at the edited site in mice and human induced pluripotent stem cells (iPSCs) that escape standard quality controls. By altering gene expression of targeted or neighbouring genes, OnTEs can severely affect phenotypes of CRISPR-edited cells and organisms and thus lead to data misinterpretation, which can undermine the reliability of CRISPR-based studies. Here we describe a broadly applicable framework for detecting OnTEs in genome-edited cells and organisms after non-homologous end joining-mediated and homology-directed repair-mediated editing. Our protocol enables identification of OnTEs such as large deletions, large insertions, rearrangements or loss of heterozygosity (LOH). This is achieved by subjecting genomic DNA first to quantitative genotyping PCR (qgPCR), which determines the number of intact alleles at the target site using the same PCR amplicon that has been optimized for genotyping. This combination of genotyping and quantitation makes it possible to exclude clones with monoallelic OnTEs and hemizygous editing, which are often mischaracterized as correctly edited in standard Sanger sequencing. Second, occurrence of LOH around the edited locus is detected by genotyping neighbouring single-nucleotide polymorphisms (SNPs), using either a Sanger sequencing-based method or SNP microarrays. All steps are optimized to maximize simplicity and minimize cost to promote wide dissemination and applicability across the field. The entire protocol from genomic DNA extraction to OnTE exclusion can be performed in 6–9 d. CRISPR-induced on-target effects (large deletions, large insertions, rearrangements or loss of heterozygosity) occur frequently at the edited site. This protocol describes how to identify these effects using quantitative genotyping PCR and SNP genotyping.

Background To systematically examine the clinical utility of tau-PET and Braak-staging as prognostic markers of future cognitive decline in older adults with and without cognitive impairment. Methods In this longitudinal study, we included 396 cognitively normal to dementia subjects with 18 F-Florbetapir/ 18 F-Florbetaben-amyloid-PET, 18 F-Flortaucipir-tau-PET and ~ 2-year cognitive follow-up. Annual change rates in global cognition (i.e., MMSE, ADAS13) and episodic memory were calculated via linear-mixed models. We determined global amyloid-PET (Centiloid) plus global and Braak-stage-specific tau-PET SUVRs, which were stratified as positive( + )/negative( − ) at pre-established cut-offs, classifying subjects as Braak 0 /Braak I+ /Braak I–IV+ /Braak I–VI+ /Braak atypical+ . In bootstrapped linear regression, we assessed the predictive accuracy of global tau-PET SUVRs vs. Centiloid on subsequent cognitive decline. To test for independent tau vs. amyloid effects, analyses were further controlled for the contrary PET-tracer. Using ANCOVAs, we tested whether more advanced Braak-stage predicted accelerated future cognitive decline. All models were controlled for age, sex, education, diagnosis, and baseline cognition. Lastly, we determined Braak-stage-specific conversion risk to mild cognitive impairment (MCI) or dementia. Results Baseline global tau-PET SUVRs explained more variance (partial R 2 ) in future cognitive decline than Centiloid across all cognitive tests (Cohen’s d  ~ 2, all tests p  < 0.001) and diagnostic groups. Associations between tau-PET and cognitive decline remained consistent when controlling for Centiloid, while associations between amyloid-PET and cognitive decline were non-significant when controlling for tau-PET. More advanced Braak-stage was associated with gradually worsening future cognitive decline, independent of Centiloid or diagnostic group ( p  < 0.001), and elevated conversion risk to MCI/dementia. Conclusion Tau-PET and Braak-staging are highly predictive markers of future cognitive decline and may be promising single-modality estimates for prognostication of patient-specific progression risk in clinical settings.