Biology subjects

Scott, C.

Publications and source records attributed to Scott, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

An automated pipeline for extracting quantitative histological metrics for voxelwise MRI-histology comparisons

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on heavy manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology coregistrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of certain tissue features (e.g. myelin and neurofilaments). Further, we find activated microglia to consistently be the strongest predictor of DTI FA, which may suggest sensitivity of diffusion MRI to neuroinflammation. Taken together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

neuroscience↗

bioRxiv2021

Lower Aβ-PET signal in white matter lesions relates to higher extracellular free water in mixed small vessel disease and Alzheimer's pathology

White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (A{beta}-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered A{beta}-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to A{beta}-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in A{beta}-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in A{beta}-PET related more closely to higher cortical A{beta} (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and A{beta}-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.

neuroscience↗

bioRxiv2021

The Digital Brain Bank: an open access platform for post-mortem datasets

Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes - Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our labs investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides crossscale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

neuroscience↗

bioRxiv2021

In an "ecological-belonging" intervention to reduce inequities in STEM, context matters

Doubts about belonging in the classroom are often shouldered disproportionately by students from historically marginalized groups, which can lead to underperformance. Ecological-belonging interventions use a classroom-based activity to instill norms that adversity is normal, temporary and surmountable. Building on prior studies, we sought to identify the conditions under which such interventions are effective. In a chemistry course (Study 1), students from underrepresented ethnic backgrounds underperformed relative to their peers in the absence of the intervention. This performance gap was eliminated by the intervention. In an introductory biology course (Study 2), there were no large performance gaps in the absence of the intervention, and the intervention had no effect. Study 2 also explored the role of the instructor that delivers the intervention. The intervention boosted scores in classrooms of instructors with a fixed (versus growth-oriented) intelligence mindset. Our results suggest that ecological-belonging interventions are more effective in more threatening classroom contexts.

scientific communication and education↗

bioRxiv2020

Genome-wide binding analyses of HOXB1 revealed a novel DNA binding motif associated with gene repression

Knowledge of the diverse DNA binding specificities of transcription factors is important for understanding their specific regulatory functions in animal development and evolution. We have examined the genome-wide binding properties of the mouse HOXB1 protein in ES cells differentiated into neural fates. Unexpectedly, only a small number of HOXB1 bound regions (7%) correlate with binding of the known HOX cofactors PBX and MEIS. In contrast, 22% of the HOXB1 binding peaks display co-occupancy with the transcriptional repressor REST. Analyses revealed that co-binding of HOXB1 with PBX correlates with active histone marks and high levels of expression, while co-occupancy with REST correlates with repressive histone marks and repression of the target genes. Analysis of HOXB1 bound regions uncovered enrichment of a novel 15 base pair HOXB1 binding motif HB1RE (HOXB1 response element). In vitro template binding assays showed that HOXB1, PBX1 and MEIS can bind to this motif. In vivo, this motif is sufficient to direct expression of a reporter gene and over-expression of HOXB1 selectively represses this activity. Our analyses suggest that HOXB1 has evolved an association with REST in gene regulation and the novel HB1RE motif contributes to HOXB1 function in part through a repressive role in gene expression.

developmental biology↗

bioRxiv2020

Ovarian carcinosarcoma genomics and pre-clinical models highlight the N-MYC pathway as a key driver and susceptibility to EMT-targeting therapy

Ovarian carcinosarcoma (OCS) is an aggressive and rare tumour type with limited treatment options. OCS is hypothesised to develop via the combination theory from a single progenitor, resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). We show OCS from 18 women to be monoclonal through analysis of DNA variants from isolated carcinoma and sarcoma components. RNA sequencing indicated the carcinoma components were more mesenchymal when compared with pure ovarian carcinomas, supporting the conversion theory. We used pre-clinical OCS models to test the efficacy of microtubule-targeting drugs, including eribulin, which has been shown to reverse EMT characteristics. We demonstrated that microtubule inhibitors, vinorelbine and eribulin, were more effective than standard-of-care platinum-based chemotherapy. Eribulin reduced mesenchymal characteristics, N-MYC expression and cholesterol biosynthesis. Finally, eribulin induced a strong immune response, supporting immunotherapy combinations in the clinic.

cancer biology↗

bioRxiv2020

Temporal emergence of age-associated changes in cognitive and physical function in vervets (Chlorocebus aethiops sabaeus)

Dual declines in gait speed and cognitive performance are associated with increased risk of developing dementia. Characterizing the patterns of such impairments therefore is paramount to distinguishing healthy from pathological aging. Nonhuman primates such as vervet/African green monkeys (Chlorocebus aethiops sabaeus) are important models of human neurocognitive aging, yet the trajectory of dual decline has not been characterized. We therefore 1) assessed whether cognitive and physical performance (i.e., gait speed) are lower in older aged animals; 2) explored the relationship between performance in a novel task of executive function (Wake Forest Maze Task - WFMT) and a well-established assessment of working memory (Delayed Response Task - DR Task); and 3) examined the association between baseline gait speed with executive function and working memory at one-year follow-up. We found 1) physical and cognitive declines with age; 2) strong agreement between performance in the novel WFMT and DR task; and 3) that slow gait predicted poor cognitive performance in both domains. Our results suggest that older-aged vervets exhibit a coordinated suite of traits consistent with human aging and that slow gait may be a risk factor for cognitive decline. This integrative approach provides evidence that gait speed and cognitive function differ across the lifespan in female vervet monkeys, which advances them as a model that could be used to evaluate the trajectory of dual decline over time.

animal behavior and cognition↗

bioRxiv2020

Genetic ablation of serotonin receptor 2B improves aortic valve hemodynamics in a high-cholesterol diet mouse model

Calcific aortic valve disease (CAVD) is a deadly disease that is rising in prevalence due to population aging. While the disease is complex and poorly understood, one well-documented driver of valvulopathy is serotonin agonism. Both serotonin overexpression, as seen with carcinoid tumors and drug-related agonism, such as with Fenfluramine use, are linked with various diseases of the valves. Thus, the objective of this study was to determine if genetic ablation or pharmacological antagonism of the 5-HT2B serotonin receptor (gene: Htr2b) could improve the hemodynamic and histological progression of calcific aortic valve disease. Htr2b mutant mice were crossed with Notch1+/- mice, an established small animal model of CAVD, to determine if genetic ablation affects CAVD progression. To assess the effect of pharmacological inhibition on CAVD progression, Notch1+/- mice were treated with the 5-HT2B receptor antagonist SB204741. Mice were analyzed using echocardiography, histology, immunofluorescence, and real-time quantitative polymerase chain reaction. Htr2b mutant mice showed lower aortic valve peak velocity and mean pressure gradient - classical hemodynamic indicators of aortic valve stenosis - without concurrent left ventricle change. 5-HT2B receptor antagonism, however, did not affect hemodynamic progression. Leaflet thickness, collagen density, and CAVD-associated transcriptional markers were not significantly different in any group. This study reveals that genetic ablation of Htr2b attenuates hemodynamic development of CAVD in the Notch1+/- mice, but pharmacological antagonism may require high doses or long-term treatment to slow progression.

pharmacology and toxicology↗

bioRxiv2020

Proteomic and Transcriptomic Analyses of the Hippocampus and Cortex in SUDEP and High-Risk SUDEP Cases

Sudden unexpected death in epilepsy (SUDEP) is the leading type of epilepsy-related death. Severely depressed brain activity in these cases may impair respiration, arousal, and protective reflexes, occurring as a prolonged postictal generalized EEG suppression (PGES) and resulting in a high-risk for SUDEP. In autopsy hippocampus and cortex, we observed no proteomic differences between SUDEP and epilepsy cases, contrasting our previously reported robust differences between epilepsy and controls. Transcriptomics in hippocampus and cortex from surgical epilepsy cases segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 lncRNAs, three pending) in hippocampus. Overall, the SUDEP proteome and high-risk SUDEP transcriptome largely reflected other epilepsy cases in the brain regions analyzed, consistent with diverse epilepsy syndromes and comorbidities associated with SUDEP. Thus, studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions may identify molecular mechanisms of SUDEP.

neuroscience↗

bioRxiv2020

Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes

We dissect metabolic variability of mononuclear phagocyte (MNP) subpopulations across different tissues through integrative analysis of three large scale datasets. Specifically, we introduce ImmGen MNP Open Source dataset that profiled 337 samples and extended previous ImmGen effort which included 202 samples of mononuclear phagocytes and their progenitors. Next, we analysed Tabula Muris Senis dataset to extract data for 51,364 myeloid cells from 18 tissues. Taken together, a compendium of data assembled in this work covers phagocytic populations found across 38 different tissues. To analyse common metabolic features, we developed novel network-based computational approach for unbiased identification of key metabolic subnetworks based on cellular transcriptional profiles in large-scale datasets. Using ImmGen MNP Open Source dataset as baseline, we define 9 metabolic subnetworks that encapsulate the metabolic differences within mononuclear phagocytes, and demonstrate that these features are robustly found across all three datasets, including lipid metabolism, cholesterol biosynthesis, glycolysis, and a set of fatty acid related metabolic pathways, as well as nucleotide and folate metabolism. We systematically define major features specific to macrophage and dendritic cell subpopulations. Among other things, we find that cholesterol synthesis appears particularly active within the migratory dendritic cells. We demonstrate that interference with this pathway through statins administration diminishes migratory capacity of the dendritic cells in vivo. This result demonstrates the power of our approach and highlights importance of metabolic diversity among mononuclear phagocytes.

immunology↗

bioRxiv2020

Streamlining Data-Intensive Biology With Workflow Systems

As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis, and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of practices and strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these strategies in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field. Author SummaryWe present a guide for workflow-enabled biological sequence data analysis, developed through our own teaching, training and analysis projects. We recognize that this is based on our own use cases and experiences, but we hope that our guide will contribute to a larger discussion within the open source and open science communities and lead to more comprehensive resources. Our main goal is to accelerate the research of scientists conducting sequence analyses by introducing them to organized workflow practices that not only benefit their own research but also facilitate open and reproducible science.

bioinformatics↗

bioRxiv2020

Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N=528 subjects for ICV, N=501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer), as well as a ventricular segmentation tool (FreeSurfer). Our multi-contrast models outperformed other methods across all evaluation metrics, with average Dice coefficients of 0.98 and 0.94 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.

neuroscience↗

bioRxiv2019

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. DW-SSFP datasets were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise across the brain. We compared one and two flip-angle DW-SSFP data using a diffusion tensor model that incorporates the full DW-SSFP Buxton signal model. The two-flip angle data were subsequently fitted using a modified DW-SSFP signal model that incorporates a Gamma distribution of diffusivities. This allowed us to generate tensor maps at a single, SNR-optimal effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments. HighlightsO_LIB1 inhomogeneity at 7T leads to spatially varying SNR & ADC estimates in DW-SSFP C_LIO_LI2-flip angle DW-SSFP data can address B1 effects in a cohort of post-mortem brains C_LIO_LIOur approach reduces degradations in PDD estimates & improves whole brain coverage C_LIO_LIOur approach provides a means to define ADCs at an SNR-optimal effective b-value C_LI

neuroscience↗

bioRxiv2019

An integrated platform to systematically identify causal variants and genes for polygenic human traits.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

genetics↗

bioRxiv2019

Modelling erythropoiesis in congenital dyserythropoietic anaemia type I (CDA-I)

We employ and extensively characterise an ex vivo culture system to study terminal erythroid maturation of CD34+ progenitors from the peripheral blood of normal individuals and patients with Congenital Dyserythropoietic Anaemia type 1 (CDA-I). Using morphological analysis, FACS analysis and the proteomic approach CyTOF, we analysed patient-derived erythroblasts stage-matched with those from healthy donors during the expansion phase and into early differentiation. In patient cells, aspects of disordered erythropoiesis manifest midway through differentiation, including increased proliferation and changes in the DNA accessibility profile. We also show that cultured erythroblasts from CDA-I patients recapitulate the pathognomic feature of this erythroid disorder with up to 40% of the cells having abnormal spongy chromatin morphology by electron microscopy, as well as upregulation of GDF15, a marker of ineffective erythropoiesis. In the tertiary phase of culture, patient cells show significantly less enucleation and there is persistence of earlier erythroid precursors. Furthermore, the enucleation defect appears to be more severe in patients with mutations in C15orf41, as compared to the other known causative gene CDAN1, indicating a genotype/phenotype correlation in CDA-I. Such erythroblasts are a valuable resource for investigating the pathogenesis of this disease and provide the opportunity for streamlining diagnosis for CDA-I patients and ultimately other forms of unexplained anaemia.

genetics↗

bioRxiv2019

Native and synthetic methanol assimilation in Saccharomyces cerevisiae

Microbial fermentation for chemical production is becoming more broadly adopted as an alternative to petrochemical refining. Fermentation typically relies on sugar as a feedstock, however, one-carbon compounds like methanol are an attractive alternative as they can be derived from organic waste and natural gas. This study focused on engineering methanol assimilation in the yeast Saccharomyces cerevisiae. Three methanol assimilation pathways were engineered and tested: a synthetic xylulose monophosphate (XuMP), a hybrid methanol dehydrogenase-XuMP, and a bacterial ribulose monophosphate (RuMP) pathway, with the latter identified as the most effective at assimilating methanol. Additionally, 13C-methanol tracer analysis uncovered a native capacity for methanol assimilation in S. cerevisiae, which was optimized using Adaptive Laboratory Evolution. Three independent lineages selected in liquid methanol-yeast extract medium evolved premature stop codons in YGR067C, which encodes an uncharacterised protein that has a predicted DNA-binding domain with homology to the ADR1 transcriptional regulator. Adr1p regulates genes involved in ethanol metabolism and peroxisomal proliferation, suggesting YGR067C has a related function. When one of the evolved YGR067C mutations was reverse engineered into the parental CEN.PK113-5D strain, there were up to 5-fold increases in 13C-labelling of intracellular metabolites from 13C-labelled methanol when 0.1 % yeast extract was a co-substrate, and a 44 % increase in final biomass. Transcriptomics and proteomics revealed that the reconstructed YGR067C mutation results in down-regulation of genes in the TCA cycle, glyoxylate cycle, and gluconeogenesis, which would normally be up-regulated during growth on a non-fermentable carbon source. Combining the synthetic RuMP and XuMP pathways with the reconstructed Ygr067cp truncation led to further improvements in growth. These results identify a latent methylotrophic metabolism in S. cerevisiae and pave the way for further development of native and synthetic one-carbon assimilation pathways in this model eukaryote.

synthetic biology↗

bioRxiv2019

Site-directed M2 proton channel inhibitors enable synergistic combination therapy for rimantadine-resistant pandemic influenza

Pandemic influenza A virus (IAV) remains a significant threat to global health. Preparedness relies primarily upon a single class of neuraminidase (NA) targeted antivirals, against which resistance is steadily growing. The M2 proton channel is an alternative clinically proven antiviral target, yet a near-ubiquitous S31N polymorphism in M2 evokes resistance to licensed adamantane drugs. Hence, inhibitors capable of targeting N31 containing M2 (M2-N31) are highly desirable.\n\nRational in silico design and in vitro screens delineated compounds favouring either lumenal or peripheral M2 binding, yielding effective M2-N31 inhibitors in both cases. Hits included adamantanes as well as novel compounds, with some showing low micromolar potency versus pandemic \"swine\" H1N1 influenza (Eng195) in culture. Interestingly, a published adamantane-based M2-N31 inhibitor rapidly selected a resistant V27A polymorphism (M2-A27/N31), whereas this was not the case for non-adamantane compounds. Nevertheless, combinations of adamantanes and novel compounds achieved synergistic antiviral effects, and the latter synergised with the neuraminidase inhibitor (NAi), Zanamivir. Thus, site-directed drug combinations show potential to rejuvenate M2 as an antiviral target whilst reducing the risk of drug resistance.

microbiology↗

bioRxiv2019

A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets

Unsupervised feature selection, or gene filtering, is a common preprocessing step to reduce the dimensionality of single-cell RNA sequencing (scRNAseq) data sets. Existing gene filters operate on scRNAseq datasets in isolation from other datasets. When jointly analyzing multiple datasets, however, there is a need for gene filters that are tailored to comparative analysis. In this work, we present a method for ranking the relevance of genes for comparing trajectory datasets. Our method is unsupervised, i.e., the cell metadata are not assumed to be known. Using the top-ranking genes significantly improves performance compared to methods not tailored to comparative analysis. We demonstrate the effectiveness of our algorithm on previously published datasets from studies on preimplantation embryo development, neurogenesis and cardiogenesis.

bioinformatics↗