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Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi‐cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state‐of‐the‐art high‐parameter assays demonstrating its value in summarizing the information‐rich data generated from these technologies. [ABSTRACT FROM AUTHOR]

Introduction: Parkinson's disease (PD) is the most common movement disorder, and its prevalence is increasing rapidly worldwide with an ageing population. The UK Biobank is the world's largest and most comprehensive longitudinal study of ageing community volunteers. The cause of the common form of PD is multifactorial, but the degree of causal heterogeneity among patients or the relative importance of one risk factor over another is unclear. This is a major impediment to the discovery of disease-modifying therapies. Methods: We used an integrated machine learning algorithm (IDEARS) to explore the relative effects of 1,753 measured non-genetic variables in 334,062 eligible UK Biobank participants, including 2,719 who had developed PD since their recruitment into the study. Results: Male gender was the highest-ranked risk factor, followed by elevated serum insulin-like growth factor 1 (IGF-1), lymphocyte count, and neutrophil/lymphocyte ratio. A group of factors aligned with the symptoms of frailty also ranked highly. IGF-1 and neutrophil/lymphocyte ratio were also elevated in both sexes before PD diagnosis and at the point of diagnosis. Discussion: The use of machine learning with the UK Biobank provides the best opportunity to explore the multidimensional nature of PD. Our results suggest that novel risk biomarkers, including elevated IGF-1 and NLR, may play a role in, or are indicative of PD pathomechanisms. In particular, our results are consistent with PD being a central manifestation of a systemic inflammatory disease. These biomarkers may be used clinically to predict future PD risk, improve early diagnosis and provide new therapeutic avenues. [ABSTRACT FROM AUTHOR]

Introduction and aims: There is emerging evidence that heavy long‐term alcohol consumption may alter the neuroimmune profile. We conducted a meta‐analysis of the association between alcohol use disorder (AUD) and the extent of neuroinflammation using cerebrospinal (CSF), PET (Positron Emission Tomography), and postmortem studies. Design and methods: A comprehensive search of electronic databases was conducted using the Preferred Reporting Items for Systematic Review and Meta‐Analysis Protocols (PRISMA‐P) for AUD‐related terms in combination with neuroinflammatory markers and cytokine‐ and chemokine‐related terms for CSF, PET, and postmortem studies. Participants had to meet established criteria for AUD and/or heavy alcohol consumption with dependence features and be compared with healthy controls. Papers retrieved were assessed for inclusion criteria and a critical appraisal was completed using the Newcastle‐Ottawa Scale. A meta‐analysis was conducted on postmortem and PET studies. Results: Eleven papers met the inclusion criteria with CSF, PET, and postmortem studies included in the final analysis. Postmortem studies demonstrate significant heterogeneity (푄 (14) = 62.02, 푝 < 0.001), with the alcohol group showing higher levels of neuroimmune markers than controls (푑 = 1.50 [95% CI 0.56, 2.45]). PET studies demonstrated a lower [11C] PBR28 total volume of distribution (VT) for translocator protein in the hippocampus (g = −1.95 [95% CI −2.72, −1.18], p < 0.001) of the alcohol group compared to controls. Conclusion: There is emerging evidence across multiple diagnostic modalities that alcohol impacts neuroimmune signaling in the human brain. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common form of dementia and the leading risk factor, after age, is possession of the apolipoprotein E epsilon 4 allele (APOE4). Approximately 50% of AD patients carry one or two copies of APOE4 but the mechanisms by which it confers risk are still unknown. APOE4 carriers are reported to demonstrate changes in brain structure, cognition, and neuropathology, but findings have been inconsistent across studies. In the present study, we used multi-modal data to characterise the effects of APOE4 on the brain, to investigate whether AD pathology manifests differently in APOE4 carriers, and to determine if AD pathomechanisms are different between carriers and non-carriers. Brain structural differences in APOE4 carriers were characterised by applying machine learning to over 2000 brain MRI measurements from 33,384 non-demented UK biobank study participants. APOE4 carriers showed brain changes consistent with vascular dysfunction, such as reduced white matter integrity in posterior brain regions. The relationship between APOE4 and AD pathology was explored among the 1260 individuals from the Religious Orders Study and Memory and Aging Project (ROSMAP). APOE4 status had a greater effect on amyloid than tau load, particularly amyloid in the posterior cortical regions. APOE status was also highly correlated with cerebral amyloid angiopathy (CAA). Bulk tissue brain transcriptomic data from ROSMAP and a similar dataset from the Mount Sinai Brain Bank showed that differentially expressed genes between the dementia and non-dementia groups were enriched for vascular-related processes (e.g., "angiogenesis") in APOE4 carriers only. Immune-related transcripts were more strongly correlated with AD pathology in APOE4 carriers with some transcripts such as TREM2 and positively correlated with pathology severity in APOE4 carriers, but negatively in non-carriers. Overall, cumulative evidence from the largest neuroimaging, pathology, and transcriptomic studies available suggests that vascular dysfunction is key to the development of AD in APOE4 carriers. However, further studies are required to tease out non-APOE4-specific mechanisms. [ABSTRACT FROM AUTHOR]

Most neurodegenerative disorders take decades to develop, and their early detection is challenged by confounding non-pathological ageing processes. Therefore, the discovery of genes and molecular pathways in both peripheral and brain tissues that are highly predictive of disease evolution is necessary. To find genes that influence Alzheimer's disease (AD) and Parkinson's disease (PD) pathogenesis, human RNA-Seq transcriptomic data from Brodmann Area 9 (BA9) of the dorsolateral prefrontal cortex (DLPFC), whole blood (WB), and peripheral blood mononuclear cells (PBMC) were analysed using a combination of differential gene expression and a random forest-based machine learning algorithm. The results suggest that there is little overlap between PD and AD, and the AD brain signature is unique mainly compared to blood-based samples. Moreover, the AD-BA9 was characterised by changes in 'nervous system development' with Myocyte-specific enhancer factor 2C (Mef2C), encoding a transcription factor that induces microglia activation, a prominent feature. The peripheral AD transcriptome was associated with alterations in 'viral process', and FYN, which has been previously shown to link amyloid-beta and tau, was the prominent feature. However, in the absence of any overlap with the central transcriptome, it is unclear whether peripheral FYN levels reflect AD severity or progression. In PD, central and peripheral signatures are characterised by anomalies in 'exocytosis' and specific genes related to the SNARE complex, including Vesicle-associated membrane protein 2 (VAMP2), Syntaxin 1A (STX1A), and p21-activated kinase 1 (PAK1). This is consistent with our current understanding of the physiological role of alpha-synuclein and how alpha-synuclein oligomers compromise vesicle docking and neurotransmission. Overall, the results describe distinct disease-specific pathomechanisms, both within the brain and peripherally, for the two most common neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Dementia affects millions of individuals worldwide, yet there are no effective treatments. Alzheimer's disease, the most common form of dementia, is characterized by amyloid and tau pathology with amyloid accumulation thought to precipitate tau pathology, neurodegeneration, and dementia. The Religious Orders Study and Memory and Aging Project (ROSMAP) cohort is a unique resource with quantitative pathology from multiple brain regions, RNA sequencing, and longitudinal cognitive data. Our previous work applying machine learning to the RNA sequencing data identified lactoferrin (LTF) as the gene most predictive of amyloid accumulation with a potential amyloidogenic mechanism identified in vitro and with cell-culture models. In the present study, we examined which pathologies and genes were related to cognitive status (dementia, mild impairment, and no cognitive impairment) and rate of cognitive decline. Tau load in the anterior cingulate and ADAMTS2 , encoding a metallopeptidase, were the respective regional pathology and gene most associated with cognitive decline, while PRTN3 , encoding a serine protease, was the key protective feature. ADAMTS2 , but not PRTN3 , was related to amyloid and tau load in the previous study while LTF was not related to cognitive decline here. These findings confirm a general relationship between tau pathology and dementia, show the specific importance of tau pathology in the anterior cingulate cortex and identify ADAMTS2 as a potential target for slowing cognitive decline. [ABSTRACT FROM AUTHOR]

Alcohol‐related brain injury is characterized by cognitive deficits and brain atrophy with the prefrontal cortex particularly susceptible. White matter in the human brain is lipid rich and a major target of damage from chronic alcohol abuse; yet, there is sparse information on how these lipids are affected. Here, we used untargeted lipidomics as a discovery tool to describe these changes in the prefrontal, middle temporal, and visual cortices of human subjects with alcohol use disorder and controls. Significant changes to the lipidome, predominantly in the prefrontal and visual cortices, and differences between the white and grey matter of each brain region were identified. These effects include broad decreases to phospholipids and ceramide, decreased polyunsaturated fatty acids, decreased sphingadiene backbones, and selective decreases in cholesteryl ester fatty acid chains. Our findings show that chronic alcohol abuse results in selective changes to the neurolipidome, which likely reflects both the directs effects on the brain and concurrent effects on the liver. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is characterised by synaptic dysfunction accompanied by the microscopically visible accumulation of pathological protein deposits and cellular dystrophy involving both neurons and glia. Late-stage AD shows pronounced loss of synapses and neurons across several differentially affected brain regions. Recent studies of advanced AD using post-mortem brain samples have demonstrated the direct involvement of microglia in synaptic changes. Variants of the Apolipoprotein E and Triggering Receptors Expressed on Myeloid Cells gene represent important determinants of microglial activity but also of lipid metabolism in cells of the central nervous system. Here we review evidence that may help to explain how abnormal lipid metabolism, microglial activation, and synaptic pathophysiology are inter-related in AD. [ABSTRACT FROM AUTHOR]

Background: The molecular data from multiple brain regions, cohorts, experimental models are seldom integrated comprehensively, although it provides a unique opportunity to study the cellular heterogeneity and molecular changes across AD etiology and progression. We have developed a new reference model to bridge across datasets. Method: We leveraged single‐nuclei transcriptomic profiles (snRNA‐seq) of human microglia (parietal cortex from 67 donors from the Knight ADRC and DIAN brain banks; N = 15,726; Dataset1) [1], iPSC‐MLC CRISPRi/a‐based genetic screens in human iPSC‐derived microglia (N = 15,350; Dataset2) [2], and WT and R47H TREM2 xenografted microglia (xMGs), isolated from chimeric AD mice (N = 25,407; Dataset3) [3] to build a comprehensive atlas of microglia heterogeneity, that captures transcriptional states fairly well represented in each dataset. We employed Seurat v4 [4] to integrate the data, and scArches [5] to build classifiers. Result: We identified homeostatic, activated, interferon (IFN), and IL1B clusters across datasets present in all data we had integrated. In addition, we identified MHCII. We observed that some of the clusters of original states were combined into single clusters. For example, CXCL10‐IFN and INF. We also observed that the integrated data shows a more distinctive expression profile of Type I+II IFN. Furthermore, we build a multiclassifier to label novel microglia generated from hundreds of samples, and data from various experimental models. The cross‐validation analyses indicate that the overall accuracy of the classifier (scArchers) is 0.85 (Fig. 1), although it changes for the distinct datasets [5]. For human microglia in Dataset1 the accuracy was 0.79, while for iPSC‐MLC was 0.73 and 0.96 for Dataset 3. Our results indicate that this multi‐model reference can capture subtle differences better that study‐centric analyses of the data. Conclusion: We are repurposing microglia transcriptomic single‐cell data to build a cross‐species and experimental iPSC‐MLC to bridge across cohorts, species, and experiments. Our results, indicate that this new resource allows to simultaneously interrogate gene expression across different experiments and transcriptional states, and providing the foundation to integrate additional single‐cell molecular data from model organism, ATAC‐seq, and spatially resolved transcriptomics. References: [1 https://doi.org/10.1101/2021.11.30.21267072; [2] PMID: 34301296; [3] PMID: 35953545; [4] PMID: 34062119; [5] PMID: 34462589 [ABSTRACT FROM AUTHOR]

Rationale: Matrix-assisted laser desorption ionisation with mass spectrometry imaging (MSI) has seen rapid development in recent years and as such is becoming an important technique for the mapping of biomolecules from the surface of tissues. One key area of development is the optimisation of analyte extraction by using modified matrices or mixes of common ones. Methods: A series of serial sections were prepared for lipid MSI by either dry coating (sublimation) or by wet spray application of several matrices. These samples were then evaluated for analyte extraction, delocalisation and dynamic range. Results: We have shown that the spraying and sublimation methods of matrix application can be used complementarily. This creates large datasets, with each preparation method applied narrowly and then interpreted as a 'fraction' of the whole. Once combined, the dynamic range is significantly increased. We have dubbed this technique 'matrix phase fractionation'. Conclusions: We have found that, by utilising matrix phase fractionation for the detection of lipids in brain tissue, it is possible to create a significantly more comprehensive dataset than would otherwise be possible with traditional 'single-run' workflows. [ABSTRACT FROM AUTHOR]

Executive function (EF) is a set of cognitive capabilities considered essential for successful daily living, and is negatively affected by ageing and neurodegenerative conditions. Underpinning EF performance are functional nodes in the executive control network (ECN), while the structural connectivity underlying this network is not well understood. In this paper, we evaluated the structural white matter tracts that interconnect the ECN and investigated their relationship to the EF performance. Using high‐angular resolution diffusion MRI data, we performed tractography analysis of structural connectivity in a cognitively normal cohort (n = 140), specifically targeting the connectivity between ECN nodes. Our data revealed the presence of a strongly‐connected "structural core" of the ECN comprising three components: interhemispheric frontal connections, a fronto‐parietal subnetwork and fronto‐striatal connections between right dorsolateral prefrontal cortex and right caudate. These pathways were strongly correlated with EF performance (p =.003). Post‐hoc analysis of subregions within the significant ECN connections showed that these effects were driven by a highly specific subset of interconnected cortical regions. The structural core subnetwork of the functional ECN may be an important feature crucial to a better future understanding of human cognition and behaviour. [ABSTRACT FROM AUTHOR]

Microglial associations with both the major Alzheimer's disease (AD) pathognomonic entities, β‐amyloid‐positive plaques and tau‐positive neurofibrillary tangles, have been noted in previous investigations of both human tissue and mouse models. However, the precise nature of their role in the pathogenesis of AD is debated; the major working hypothesis is that pro‐inflammatory activities of activated microglia contribute to disease progression. In contrast, others have proposed that microglial dystrophy with a loss of physiological and neuroprotective activities promotes neurodegeneration. This immunohistochemical study sought to gain clarity in this area by quantifying the morphological subtypes of microglia in the mildly‐affected primary visual cortex (PVC), the moderately affected superior frontal cortex (SFC) and the severely affected inferior temporal cortex (ITC) of 8 AD cases and 15 age and gender‐matched, non‐demented controls with ranging AD‐type pathology. AD cases had increased β‐amyloid and tau levels compared to controls in all regions. Neuronal loss was observed in the SFC and ITC, and was associated with atrophy in the latter. A major feature of the ITC in AD was a decrease in ramified (healthy) microglia with image analysis confirming reductions in arborized area and skeletal complexity. Activated microglia were not associated with AD but were increased in non‐demented controls with greater AD‐type pathology. Microglial clusters were occasionally associated with β‐amyloid‐ and tau‐positive plaques but represented less than 2% of the total microglial population. Dystrophic microglia were not associated with AD, but were inversely correlated with brain pH suggesting that agonal events were responsible for this morphological subtype. Overall these novel findings suggest that there is an early microglial reaction to AD‐type pathology but a loss of healthy microglia is the prominent feature in severely affected regions of the AD brain. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) affects millions of people with no effective treatments making it vital to identify potential therapeutic targets. Genome‐wide association studies (GWAS) have consistently identified AD susceptibility loci or 'hits'. Few of these are differentially expressed in brain transcriptomic studies, meaning that their effects may be manifest via interacting or downstream transcripts. We have recently used machine learning on human brain transcriptomic data to identify and validate an association between lactoferrin and amyloid (1). Here we apply the same method to identify the transcripts associated with AD susceptibility 'loci'. Method: Analyses were performed on the disease‐linked genes from our machine learning analyses (LTF, ADAMTS2, PPDPF, PRTN3) and the combined 58 GWAS risk loci from two recent studies (2, 3). Boruta was used to find transcripts associated with each GWAS 'hit' and our genes of interest in two pathologically confirmed AD cohorts (Religious Orders Study and Memory and Aging project, Mount Sinai Brain Bank). The GWAS hits and associated transcripts were correlated with disease markers (cognition, plaque number, Braak score, CERAD score) to find direct and indirect effects. Results: TREM2 was the GWAS 'hit' most correlated with disease markers in both cohorts but most GWAS 'hits' were either not correlated or negatively correlated with disease markers. Among the GWAS 'hit'‐associated transcripts, SWAP70, EMP3, and WWTR1 were positively and VGF negatively correlated with disease markers in both cohorts. For our genes of interest, ADAMTS2 and PPDPF shared overlaps between their associated transcripts and that of GWAS 'hit' ABCA7. LTF was linked with TREM2 and associated with plaques in both cohorts. Conclusion: This study has teased apart nascent associations between known susceptibility genes and gene expression data from the AD brain, but it also hints that consideration of pathological sub‐phenotypes are required to make greater progress in understanding AD pathogenesis. 1. Tsatsanis et al. The acute phase protein lactoferrin is a key feature of AD .... Mol Psychiatry. 2021;26(10):5516‐31. 2. Yang et al. A human brain vascular atlas reveals diverse cell mediators of AD risk. BioRxiv [Preprint]. 2021. 3. Wightman et al. A GWAS with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet. 2021;53(9):1276‐82. [ABSTRACT FROM AUTHOR]

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