637 results found
Venkataraman AV, Bai W, Whittington A, et al., 2021, Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support, Alzheimer's Research and Therapy, Vol: 13, Pages: 1-12, ISSN: 1758-9193
BackgroundAmyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD.MethodsVoxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology.ResultsThis classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr.ConclusionsThe diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.
Venkataraman A, Bishop C, Mansur A, et al., 2021, Imaging synaptic microstructure and synaptic loss in vivo in early Alzheimer’s Disease, Publisher: Cold Spring Harbor Laboratory
Background Synaptic loss and neurite dystrophy are early events in Alzheimer’s Disease (AD). We aimed to characterise early synaptic microstructural changes in vivo.Methods MRI neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI) were used to image cortical microstructure in both sporadic, late onset, amyloid PET positive AD patients and healthy controls (total n = 28). We derived NODDI measures of grey matter extracellular free water (FISO), neurite density (NDI) and orientation dispersion (ODI), which provides an index of neurite branching and orientation, as well as more conventional DTI measures of fractional anisotropy (FA), mean/axial/radial diffusivity (MD, AD, RD, respectively). We also performed [11C]UCB-J PET, which provides a specific measure of the density of pre-synaptic vesicular protein SV2A. Both sets of measures were compared to regional brain volumes.Results The AD patients showed expected relative decreases in regional brain volumes (range, -6 to - 23%) and regional [11C]UCB-J densities (range, -2 to -25%). Differences between AD and controls were greatest in the hippocampus. NODDI microstructural measures showed greater FISO (range, +26 to +44%) in AD, with little difference in NDI (range, -1 to +7%) and mild focal changes in ODI (range, -4 to +3%). Regionally greater FISO and lower [11C]UCB-J binding were correlated across grey matter in patients (most strongly in the caudate, r2 = 0.37, p = 0.001). FISO and DTI RD were strongly positively associated, particularly in the hippocampus (r2 = 0.98, p < 7.4 × 10−9). After 12-18 months we found a 5% increase in FISO in the temporal lobe, but little change across all ROIs in NDI and ODI. An exploratory analysis showed higher parietal lobe FISO was associated with lower language scores in people with AD.Conclusions We interpreted the increased extracellular free water as a possible consequence of glial activation. The dynamic range of disease
Smith AM, Davey K, Tsartsalis S, et al., 2021, Diverse human astrocyte and microglial transcriptional responses to Alzheimer's pathology, ACTA NEUROPATHOLOGICA, Vol: 143, Pages: 75-91, ISSN: 0001-6322
Dobson R, Craner M, Waddingham E, et al., 2021, OPTIMISE: MS study protocol: a pragmatic, prospective observational study to address the need for, and challenges with, real world pharmacovigilance in multiple sclerosis, BMJ OPEN, Vol: 11, ISSN: 2044-6055
Nutma E, Gebro E, Marzin MC, et al., 2021, Activated microglia do not increase 18 kDa translocator protein (TSPO) expression in the multiple sclerosis brain, GLIA, Vol: 69, Pages: 2447-2458, ISSN: 0894-1491
To monitor innate immune responses in the CNS, the 18 kDa Translocator protein (TSPO) is a frequently used target for PET imaging. The frequent assumption that increased TSPO expression in the human CNS reflects pro-inflammatory activation of microglia has been extrapolated from rodent studies. However, TSPO expression does not increase in activated human microglia in vitro. Studies of multiple sclerosis (MS) lesions reveal that TSPO is not restricted to pro-inflammatory microglia/macrophages, but also present in homeostatic or reparative microglia. Here, we investigated quantitative relationships between TSPO expression and microglia/macrophage phenotypes in white matter and lesions of brains with MS pathology. In white matter from brains with no disease pathology, normal appearing white matter (NAWM), active MS lesions and chronic active lesion rims, over 95% of TSPO+ cells are microglia/macrophages. Homeostatic microglial markers in NAWM and control tissue are lost/reduced in active lesions and chronic active lesion rims, reflecting cell activation. Nevertheless, pixel analysis of TSPO+ cells (n = 12,225) revealed that TSPO expression per cell is no higher in active lesions and chronic active lesion rims (where myeloid cells are activated) relative to NAWM and control. This data suggests that whilst almost all the TSPO signal in active lesions, chronic active lesion rims, NAWM and control is associated with microglia/macrophages, their TSPO expression predominantly reflects cell density and not activation phenotype. This finding has implications for the interpretation of TSPO PET signal in MS and other CNS diseases, and further demonstrates the limitation of extrapolating TSPO biology from rodents to humans.
Khozoie C, Fancy N, Marjaneh MM, et al., 2021, scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
<jats:title>Abstract</jats:title><jats:p>Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.</jats:p>
Venkataraman A, Mansur A, Rizzo G, et al., 2021, Widespread cell stress and mitochondrial dysfunction in early Alzheimer’s Disease, Publisher: MedRxiv
Cell stress and impaired oxidative phosphorylation are central to mechanisms of synaptic loss and neurodegeneration in the cellular pathology of Alzheimer’s disease (AD). We quantified the in vivo density of the endoplasmic reticulum stress marker, the sigma 1 receptor (S1R) using [11C]SA4503 PET, as well as that of mitochondrial complex I (MC1) with [18F]BCPP-EF and the pre-synaptic vesicular protein SV2A with [11C]UCB-J in 12 patients with early AD and in 16 cognitively normal controls. We integrated these molecular measures with assessments of regional brain volumes and brain perfusion (CBF) measured with MRI arterial spin labelling. 8 AD patients were followed longitudinally to estimate rates of change with disease progression over 12-18 months. The AD patients showed widespread increases in S1R (≤ 27%) and regional decreases in MC1 (≥ -28%), SV2A (≥ -25%), brain volume (≥ -23%), and CBF (≥ -26%). [18F]BCPP-EF PET MC1 density (≥ -12%) and brain volumes (≥ -5%) were further reduced at follow up in brain regions consistent with the differences between AD patients and controls at baseline. Exploratory analyses showing associations of MC1, SV2A and S1R density with cognitive changes at baseline and longitudinally with AD, but not in controls, suggested a loss of metabolic functional reserve with disease. Our study thus provides novel in vivo evidence for widespread cellular stress and bioenergetic abnormalities in early AD and that they may be clinically meaningful.
Feleke R, Reynolds RH, Smith AM, et al., 2021, Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases, Acta Neuropathologica, Vol: 142, Pages: 449-474, ISSN: 0001-6322
Parkinson's disease (PD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.
Calsolaro V, Matthews PM, Donat CK, et al., 2021, Astrocyte reactivity with late onset cognitive impairment assessed in-vivo using 11C-BU99008 PET and its relationship with amyloid load, Molecular Psychiatry, ISSN: 1359-4184
11C-BU99008 is a novel positron emission tomography (PET) tracer that enables selective imaging of astrocyte reactivity in vivo. To explore astrocyte reactivity associated with Alzheimer’s disease, 11 older, cognitively impaired (CI) subjects and 9 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging (MRI), 18F-florbetaben and 11C-BU99008 PET. The 8 amyloid (Aβ)-positive CI subjects had higher 11C-BU99008 uptake relative to HC across the whole brain, but particularly in frontal, temporal, medial temporal and occipital lobes. Biological parametric mapping demonstrated a positive voxel-wise neuroanatomical correlation between 11C-BU99008 and 18F-florbetaben. Autoradiography using 3H-BU99008 with post-mortem Alzheimer’s brains confirmed through visual assessment that increased 3H-BU99008 binding localised with the astrocyte protein glial fibrillary acid protein and was not displaced by PiB or florbetaben. This proof-of-concept study provides direct evidence that 11C-BU99008 can measure in vivo astrocyte reactivity in people with late-life cognitive impairment and Alzheimer’s disease. Our results confirm that increased astrocyte reactivity is found particularly in cortical regions with high Aβ load. Future studies now can explore how clinical expression of disease varies with astrocyte reactivity.
Wei GZ, Martin KA, Xing PY, et al., 2021, Tryptophan-metabolizing gut microbes regulate adult neurogenesis via the aryl hydrocarbon receptor, Proceedings of the National Academy of Sciences, Vol: 118, Pages: 1-10, ISSN: 0027-8424
While modulatory effects of gut microbes on neurological phenotypes have been reported, the mechanisms remain largely unknown. Here, we demonstrate that indole, a tryptophan metabolite produced by tryptophanase-expressing gut microbes, elicits neurogenic effects in the adult mouse hippocampus. Neurogenesis is reduced in germ-free (GF) mice and in GF mice monocolonized with a single-gene tnaA knockout (KO) mutant Escherichia coli unable to produce indole. External administration of systemic indole increases adult neurogenesis in the dentate gyrus in these mouse models and in specific pathogen-free (SPF) control mice. Indole-treated mice display elevated synaptic markers postsynaptic density protein 95 and synaptophysin, suggesting synaptic maturation effects in vivo. By contrast, neurogenesis is not induced by indole in aryl hydrocarbon receptor KO (AhR−/−) mice or in ex vivo neurospheres derived from them. Neural progenitor cells exposed to indole exit the cell cycle, terminally differentiate, and mature into neurons that display longer and more branched neurites. These effects are not observed with kynurenine, another AhR ligand. The indole-AhR–mediated signaling pathway elevated the expression of β-catenin, Neurog2, and VEGF-α genes, thus identifying a molecular pathway connecting gut microbiota composition and their metabolic function to neurogenesis in the adult hippocampus. Our data have implications for the understanding of mechanisms of brain aging and for potential next-generation therapeutic opportunities.
Evangelou E, Suzuki H, Bai W, et al., 2021, Alcohol consumption in the general population is associated with structural changes in multiple organ systems., eLife, Vol: 10, Pages: 1-15, ISSN: 2050-084X
Background:Excessive alcohol consumption is associated with damage to various organs, but its multi-organ effects have not been characterised across the usual range of alcohol drinking in a large general population sample.Methods:We assessed global effect sizes of alcohol consumption on quantitative magnetic resonance imaging phenotypic measures of the brain, heart, aorta, and liver of UK Biobank participants who reported drinking alcohol.Results:We found a monotonic association of higher alcohol consumption with lower normalised brain volume across the range of alcohol intakes (–1.7 × 10−3 ± 0.76 × 10−3 per doubling of alcohol consumption, p=3.0 × 10−14). Alcohol consumption was also associated directly with measures of left ventricular mass index and left ventricular and atrial volume indices. Liver fat increased by a mean of 0.15% per doubling of alcohol consumption.Conclusions:Our results imply that there is not a ‘safe threshold’ below which there are no toxic effects of alcohol. Current public health guidelines concerning alcohol consumption may need to be revisited.
Lally P, Matthews P, Bangerter N, 2021, Unbalanced SSFP for super-resolution in MRI, Magnetic Resonance in Medicine, Vol: 85, Pages: 2477-2489, ISSN: 0740-3194
Purpose: To achieve rapid, low SAR super-resolution imaging by exploiting the characteristic magnetization off-resonance profile in SSFP.Theory and Methods: In the presented technique, low flip angle unbalanced SSFP imaging is used to acquire a series of images at a low nominal resolution which are then combined in a super-resolution strategy analogous to non-linear structured illumination microscopy. This is demonstrated in principle via Bloch simulations and synthetic phantoms, and the performance is quantified in terms of point-spread function (PSF) and signal-to-noise ratio (SNR) for gray and white matter from field strengths of 0.35T to 9.4T. A k-space reconstruction approach is proposed to account for B0 effects. This was applied to reconstruct super-resolution images from a test object at 9.4T.Results: Artifact-free super-resolution images were produced after incorporating sufficient preparation time for the magnetization to approach the steady state. High-resolution images of a test object were obtained at 9.4T, in the presence of considerable B0 inhomogeneity. For gray matter, the highest achievable resolution ranges from 3% of the acquired voxel dimension at 0.35T, to 9% at 9.4T. For white matter, this corresponds to 3% and 10% respectively. Compared to an equivalent segmented gradient echo acquisition at the optimal flip angle, with a fixed TR of 8ms, gray matter has up to 34% of the SNR at 9.4T while using a x10 smaller flip angle. For white matter, this corresponds to 29% with a x11 smaller flip angle.Conclusion: This approach achieves high degrees of super-resolution enhancement with minimal RF power requirements.
CNNs achieve high levels of performance by leveraging deep, over-parametrized neural architectures, trained on large datasets. However, they exhibit limited generalization abilities outside their training domain and lack robustness to corruptions such as noise and adversarial attacks. To improve robustness and obtain more computationally and memory efficient models, better inductive biases are needed. To provide such inductive biases, tensor layers have been successfully proposed to leverage multi-linear structure through higher-order computations. In this paper, we propose tensor dropout, a randomization technique that can be applied to tensor factorizations, such as those parametrizing tensor layers. In particular, we study tensor regression layers, parametrized by low-rank weight tensors and augmented with our proposed tensor dropout. We empirically show that our approach improves generalization for image classification on ImageNet and CIFAR-100. We also establish state-of-the-art accuracy for phenotypic trait prediction on the largest available dataset of brain MRI (U.K. Biobank), where multi-linear structure is paramount. In all cases, we demonstrate superior performance and significantly improved robustness, both to noisy inputs and to adversarial attacks. We establish the theoretical validity of our approach and the regularizing effect of tensor dropout by demonstrating the link between randomized tensor regression with tensor dropout and deterministic regularized tensor regression.
Rajewsky N, Almouzni G, Gorski SA, et al., 2021, Publisher Correction: LifeTime and improving European healthcare through cell-based interceptive medicine., Nature, Vol: 592
Ware J, Tadros R, Francis C, et al., 2021, Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, Nature Genetics, Vol: 53, Pages: 128-134, ISSN: 1061-4036
The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young otherwise healthy individuals. We conducted genome-wide association studies (GWAS) and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases), and nine left ventricular (LV) traits in 19,260 UK Biobank participants with structurally-normal hearts. We identified 16 loci associated with HCM, 13 with DCM, and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased contractility with HCM risk. A polygenic risk score (PRS) explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that PRS may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.
Chua SYL, Lascaratos G, Atan D, et al., 2021, Relationships between retinal layer thickness and brain volumes in the UK Biobank cohort, EUROPEAN JOURNAL OF NEUROLOGY, Vol: 28, Pages: 1490-1498, ISSN: 1351-5101
Sargurupremraj M, Suzuki H, Jian X, et al., 2020, Cerebral small vessel disease genomics and its implications across the lifespan, Nature Communications, Vol: 11, ISSN: 2041-1723
White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
Kolbeinsson A, Filippi S, Panagakis I, et al., 2020, Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders, Scientific Reports, Vol: 10, ISSN: 2045-2322
Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.
Rajewsky N, Almouzni G, Gorski SA, et al., 2020, LifeTime and improving European healthcare through cell-based interceptive medicine., Nature, Vol: 587, Pages: 377-386
Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.
Huang J, Zuber V, Matthews P, et al., 2020, Sleep, major depressive disorder and Alzheimer’s disease: a Mendelian randomisation study, Neurology, Vol: 95, ISSN: 0028-3878
ObjectiveTo explore the causal relationships between sleep, major depressive disorder (MDD), and Alzheimer’s disease (AD).MethodsWe conducted bi-directional two-sample Mendelian randomisation analyses. Genetic associations were obtained from the largest genome-wide association studies currently available in UK Biobank (N=446,118), the Psychiatric Genomics Consortium (N=18,759), and the International Genomics of Alzheimer’s Project (N=63,926). We used the inverse variance weighted Mendelian randomisation method to estimate causal effects, and weighted median and MR-Egger for sensitivity analyses to test for pleiotropic effects. ResultsWe found that higher risk of AD was significantly associated with being a “morning person” (odds ratio (OR)=1.01, P=0.001), shorter sleep duration (self-reported: β=-0.006, P=1.9×10-4; accelerometer-based: β=-0.015, P=6.9×10-5), less likely to report long sleep (β=-0.003, P=7.3×10-7), earlier timing of the least active 5 hours (β=-0.024, P=1.7×10-13), and a smaller number of sleep episodes (β=-0.025, P=5.7×10-14) after adjusting for multiple comparisons. We also found that higher risk of AD was associated with lower risk of insomnia (OR=0.99, P=7×10-13). However, we did not find evidence either that these abnormal sleep patterns were causally related to AD or for a significant causal relationship between MDD and risk of AD. ConclusionWe found that AD may causally influence sleep patterns. However, we did not find evidence supporting a causal role of disturbed sleep patterns for AD or evidence for a causal relationship between MDD and AD.
Bai W, Suzuki H, Huang J, et al., 2020, A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956
Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
Thrupp N, Frigerio CS, Wolfs L, et al., 2020, Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans, Cell Reports, Vol: 32, Pages: 1-13, ISSN: 2211-1247
Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.
Meyer H, Dawes T, Serrani M, et al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836
The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease
Waddingham E, Matthews PM, Ashby D, 2020, Exploiting relationships between outcomes in Bayesian multivariate network meta-analysis with an application to relapsing-remitting multiple sclerosis, Statistics in Medicine, Vol: 39, Pages: 3329-3346, ISSN: 0277-6715
In multivariate network meta‐analysis (NMA), the piecemeal nature of the evidence base means that there may be treatment‐outcome combinations for which no data is available.Most existing multivariate evidence synthesis models are either unable to estimate the missing treatment‐outcome combinations, or can only do so under particularly strong assumptions, such as perfect between‐study correlations between outcomes or constant effect size across outcomes. Many existing implementations are also limited to two treatments or two outcomes, or rely on model specification that is heavily tailored to the dimensions of the dataset. We present a Bayesian multivariate NMA model that estimates the missing treatment‐outcome combinations via mappings between the population mean effects, while allowing the study‐specific effects to be imperfectly correlated. The method is designed for aggregate‐level data (rather than individual patient data) and is likely to be useful when modeling multiple sparsely reported outcomes, or when varying definitions of the same underlying outcome are adopted by different studies. We implement the model via a novel decomposition of the treatment effect variance, which can be specified efficiently for an arbitrary dataset given some basic assumptions regarding the correlation structure. The method is illustrated using data concerning the efficacy and liver‐related safety of eight active treatments for relapsing‐remitting multiple sclerosis. The results indicate that fingolimod and interferon beta‐1b are the most efficacious treatments but also have some of the worst effects on liver safety. Dimethyl fumarate and glatiramer acetate perform reasonably on all of the efficacy and safety outcomes in the model.
Popescu SG, Whittington A, Gunn RN, et al., 2020, Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease, Human Brain Mapping, Vol: 41, Pages: 4406-4418, ISSN: 1065-9471
Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for per
Gafson AR, Barthelemy NR, Bomont P, et al., 2020, Neurofilaments: neurobiological foundations for biomarker applications, BRAIN, Vol: 143, Pages: 1975-1998, ISSN: 0006-8950
Littlejohns TJ, Holliday J, Gibson LM, et al., 2020, The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions, Nature Communications, Vol: 11, ISSN: 2041-1723
UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.
Faergeman SL, Evans H, Attfield KE, et al., 2020, A novel neurodegenerative spectrum disorder in patients with MLKL deficiency, Cell Death and Disease, Vol: 11, ISSN: 2041-4889
Mixed lineage kinase domain-like (MLKL) is the main executor of necroptosis, an inflammatory form of programmed cell death. Necroptosis is implicated in combating infections, but also in contributing to numerous other clinical conditions, including cardiovascular diseases and neurodegenerative disorders. Inhibition of necroptosis is therefore of therapeutic interest. Here we report two siblings both of whom over the course of 35 years developed a similar progressive, neurodegenerative spectrum disorder characterized by paresis, ataxia and dysarthria. Magnetic resonance imaging of their central nervous system (CNS) revealed severe global cerebral volume loss and atrophy of the cerebellum and brainstem. These brothers are homozygous for a rare haplotype identified by whole genome sequencing carrying a frameshift variant in MLKL, as well as an in-frame deletion of one amino acid in the adjacent fatty acid 2-hydroxylase (FA2H) gene. Functional studies of patient-derived primary cells demonstrated that the variant in MLKL leads to a deficiency of MLKL protein resulting in impairment of necroptosis. Conversely, shotgun lipidomic analysis of the variant in FA2H shows no impact on either the abundance or the enzymatic activity of the encoded hydroxylase. To our knowledge, this is the first report of complete necroptosis deficiency in humans. The findings may suggest that impaired necroptosis is a novel mechanism of neurodegeneration, promoting a disorder that shares some clinical features with primary progressive multiple sclerosis (PPMS) and other neurodegenerative diseases. Importantly, the necroptotic deficiency does not cause symptoms outside the nervous system, nor does it confer susceptibility to infections. Given the current interest in pharmacological inhibition of necroptosis by targeting MLKL and its associated pathways, this strategy should be developed with caution, with careful consideration of the possible development of adverse neurological effects.
The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure ‘lab’ using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.
Matthews PM, Block VJ, Leocani L, 2020, E-health and multiple sclerosis., Current Opinion in Neurology, Vol: 33, Pages: 271-276, ISSN: 1080-8248
PURPOSE OF REVIEW: To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis. RECENT FINDINGS: The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence. SUMMARY: Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system.
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