Biology subjects

Ferguson, K.

Publications and source records attributed to Ferguson, K..

12 recordsLinked to original sources

bioRxiv2026

Spatial multi-omics of multiple myeloma uncovers niche-dependent pro-myeloma and immunosuppressive signaling in the bone marrow and extramedullary lesions

Multiple myeloma (MM) is a plasma cell malignancy shaped by dynamic interactions between MM cells and non-malignant cells in the immune microenvironment. To spatially profile the influence of cellular context on MM and immune cell expression, we developed a multimodal framework integrating 10x Genomics Visium HD, 10x Genomics Xenium, and clinically annotated single-cell RNA (scRNA-seq) sequencing datasets. Visium HD enabled unbiased, whole transcriptome, spatial discovery at 16 {micro}m resolution, Xenium provided orthogonal validation at single-cell resolution, and scRNA-seq extended findings by mapping spatial labels and leveraging the greater sequencing depth. We developed a custom framework for cell type annotation within Visium HD spatial bins. Our approach enabled identification of plasma cell-dense niches enriched for non-canonical Wnt signaling, associated with gene expression supporting cell adhesion mediated drug resistance, inferior progression-free survival, and extramedullary lesions. Immune cells within these neighborhoods exhibited suppressed transcriptional states, including increased inhibitory receptor expression such as LAG3. Utilizing the niche-driven transcriptional states in MM and immune cells, we were able to develop a 15-gene signature independently predictive of progression free survival (HR = 2.00, p < 0.0001). Collectively, this study demonstrates the potential of integrated spatial and single-cell transcriptomics to define niche-specific programs supporting MM progression.

cancer biology↗

bioRxiv2025

Using targeted therapy to promote a pro-inflammatory tumour microenvironment and anti-tumour immune response in high grade serous ovarian cancer

BACKGROUNDHigh-grade serous ovarian cancer (HGSOC) is characterized by elevated replication stress and an immunosuppressive microenvironment. A synergistic combination of checkpoint kinase 1 inhibitor (CHK1i) with low-dose hydroxyurea (LDHU) promotes a unique ATR-independent moderate replication stress response with potent anti-tumour effects. The ability of this approach to reprogram the tumour immune microenvironment (TIME) to overcome the immunosuppression and promote an anti-tumour immune response in HGSOC is the focus of this study. METHODSWe investigated the therapeutic potential of CHK1i+LDHU in established HGSOC cell cultures, fresh tumour cell explants from HGSOC patient ascites, and syngeneic mouse models, assessing tumour cell killing, immunogenic cell death, pro-inflammatory cytokine/chemokine expression, and anti-tumour immune responses. RESULTSCHK1i+LDHU effectively killed ovarian cancer cells regardless of chemotherapy resistance, BRCA2 mutation and homologous recombination repair status in vitro. In vivo, treatment significantly reduced tumour burden and ascites accumulation. CHK1i+LDHU enhanced expression of pro-inflammatory cytokines/chemokines and triggered immunogenic cell death in tumour. In syngeneic models, treatment promoted CD8+ cytotoxic T cell-dependent anti-tumour responses and reduced immunosuppressive signalling within the TIME. CONCLUSIONSCHK1i+LDHU is a promising therapy for chemotherapy-resistant HGSOC, combining direct cytotoxic effects with reprogramming the TIME to reduce immunosuppression and activate a CD8+ T cell-dependent anti-tumour response.

cancer biology↗

bioRxiv2024

Brain maps of general cognitive function and spatial correlations with neurobiological cortical profiles

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|{beta}| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing -and derive new -cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

neuroscience↗

bioRxiv2024

Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates

A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e., a single MRI scan per participant. LBA was simply calculated based on the relationship between total brain volume (TBV) and intracranial volume (ICV), using three computationally distinct approaches: the difference (ICV-TBV), ratio (TBV/ICV), and regression-residual method (TBV[~]ICV). LBA derived with all three methods were substantially correlated with well-validated neuroradiological atrophy rating scales (r = 0.37-0.44). Compared with the difference or ratio method, LBA computed with the residual method most strongly captured phenotypic variance associated with cognitive decline (r = 0.36), frailty (r = 0.24), age-moderated brain shrinkage (r = 0.45), and longitudinally-measured atrophic changes (r = 0.36). LBA computed using a difference score was strongly correlated with baseline (i.e., ICV; r = 0.81) and yielded GWAS signal similar to ICV (rg = 0.75). We performed the largest genetic study of LBA to date (N = 43,110), which was highly heritable (h2 SNP GCTA = 41% [95% CI = 38-43%]) and had strong polygenic signal (LDSC h2 = 26%; mean{chi} 2 = 1.23). The strongest association in our genome-wide association study (GWAS) implicated WNT16, a gene previously linked with neurodegenerative diseases such as Alzheimer, and Parkinson disease, and amyotrophic lateral sclerosis. This study is the first side-by-side evaluation of different computational approaches to estimate lifetime brain changes and their measurement characteristics. Careful assessment of methods for LBA computation had important implications for the interpretation of existing phenotypic and genetic results, and showed that relying on the residual method to estimate LBA from a single MRI scan captured brain shrinkage rather than current brain size. This makes this computationally-simple definition of LBA a strong candidate for more powerful analyses, promising accelerated genetic discoveries by maximising the use of available cross-sectional data.

genetics↗

bioRxiv2022

Respiratory strategy at birth initiates distinct lung injury phenotypes in the preterm lamb lung

Bronchopulmonary disease is the chronic manifestation of the acute injury that may accompany ventilation following preterm birth. A lack of clear trial evidence often hampers clinical decision-making during support of the preterm lung at birth. Protein biomarkers have been used to define acute lung injury phenotypes and improve patient selection for specific interventions in adult respiratory distress syndrome. Here we present a mass spectrometry-based approach to profile the protein phenotype associated with three different aeration strategies known to cause different pathophysiological responses when applied at birth to preterm lambs. We were able to identify pathway enrichments specific to both ventilation strategy and lung regions based upon gravity-dependency. Ventilation strategy-specific phenotypes were further delineated by applying partial least square modelling to identify associations between specific proteins and clinical, physiological and morphological outcomes. This work highlights the specificity of lung injury responses to routinely applied birth interventions such as different respiratory support approaches and identified the molecular events associated with each. Furthermore, we demonstrate the capacity to subdivide preterm infants by the direct aetiology and response to lung injury; the first step towards true precision medicine in neonatology.

systems biology↗

bioRxiv2022

Redefining normal breast cell populations using long noncoding RNAs

Single-cell RNAseq has allowed unprecedented insight into gene expression across different cell populations in normal tissue and disease states. However, almost all studies rely on annotated gene sets to capture gene expression levels and sequencing reads that do not align to known genes are discarded. Here, we discover thousands of long noncoding RNAs (lncRNAs) expressed in human mammary epithelial cells and analyze their expression in individual cells of the normal breast. We show that lncRNA expression alone can discriminate between luminal and basal cell types and define subpopulations of both compartments. Clustering cells based on lncRNA expression identified additional basal subpopulations, compared to clustering based on annotated gene expression, suggesting that lncRNAs can provide an additional layer of information to better distinguish breast cell subpopulations. In contrast, these breast-specific lncRNAs poorly distinguish brain cell populations, highlighting the need to annotate tissue-specific lncRNAs prior to expression analyses. We also identified a panel of 100 breast lncRNAs that could discern breast cancer subtypes better than protein-coding markers. Overall, our results suggest that lncRNAs are an unexplored resource for new biomarker and therapeutic target discovery in the normal breast and breast cancer subtypes.

cell biology↗

bioRxiv2022

Exploring Xylem Anatomical Adaptations Associated with Crassulacean Acid Metabolism and Hydraulic Capacitance in Clusia Leaves: Lessons for CAM Bioengineering

* Background and AimsMany succulent species are characterised by the presence of crassulacean acid metabolism (CAM) and/or elevated bulk hydraulic capacitance (CFT). Both CAM and elevated CFT substantially reduce the rate at which water moves through transpiring leaves. However, little is known about how these physiological adaptations are coordinated with leaf vascular architecture and xylem anatomy. * MethodsThe genus Clusia contains species spanning the entire C3-CAM continuum, and also is known to have > 5-fold interspecific variation in CFT. We used this highly diverse genus to explore how interspecific variation in vein density and xylem vessel dimensions are coordinated with CAM and CFT. * Key ResultsWe found that constitutive CAM phenotypes were associated with lower vein length per leaf area (VLA) and vein termini density (VTD), compared to C3 or facultative CAM species. However, when vein densities were standardised by leaf depth, this value was higher in CAM than C3 species, which is likely an adaptation to overcome apoplastic hydraulic resistance in deep chlorenchyma tissue. In contrast, CFT did not correlate with any xylem anatomical trait measured, suggesting CAM has a greater impact on leaf hydraulic physiology than CFT. * ConclusionsOur findings strongly suggest that CAM photosynthesis is coordinated with leaf vein densities. The link between CAM and vascular anatomy will be important to consider when attempting to bioengineer CAM into C3 crops.

plant biology↗

bioRxiv2021

N-glycolylneuraminic acid serum biomarker levels are elevated in breast cancer patients at all stages of disease.

BackgroundNormal human tissues do not express glycans terminating with the sialic acid N-glycolylneuraminic acid (Neu5Gc), yet Neu5Gc-containing glycans have been consistently found in human tumor tissues, cells and secretions and have been proposed as a cancer biomarker. We engineered a Neu5Gc-specific lectin called SubB2M, and previously reported elevated Neu5Gc biomarkers in serum from ovarian cancer patients using a Surface Plasmon Resonance (SPR)-based assay. Here we report an optimized SubB2M SPR-based assay and use this new assay to analyse sera from breast cancer patients for Neu5Gc levels. MethodsTo enhance specificity of our SPR-based assay, we included a non-sialic acid binding version of SubB, SubBA12, to control for any non-specific binding to SubB2M, which improved discrimination of cancer-free controls from early-stage ovarian cancer. We analysed 96 serum samples from breast cancer patients at all stages of disease compared to 22 cancer-free controls using our optimized SubB2M-A12-SPR assay. We also analysed a collection of serum samples collected at 6 monthly intervals from breast cancer patients at high risk for disease recurrence or spread. ResultsAnalysis of sera from breast cancer cases revealed significantly elevated levels of Neu5Gc biomarkers at all stages of breast cancer. We show that Neu5Gc serum biomarker levels can discriminate breast cancer patients from cancer-free individuals with 98.96% sensitivity and 100% specificity. Analysis of serum collected prospectively, post-diagnosis, from breast cancer patients at high risk for disease recurrence showed a trend for a decrease in Neu5Gc levels immediately following treatment for those in remission. ConclusionsNeu5Gc serum biomarkers are a promising new tool for early detection and disease monitoring for breast cancer that may complement current imaging- and biopsy-based approaches.

cancer biology↗

bioRxiv2021

Characterisation of immune cell subsets of tumour infiltrating lymphocytes in brain metastases

The heterogeneity of tumor infiltrating lymphocytes is not well characterized in brain metastasis. To address this, we performed a targeted analysis of immune cell subsets in brain metastasis tissues to test which immunosuppressive routes are involved in brain metastasis. We performed multiplex immunofluorescence (mIF), using commercially available validated antibodies on twenty formalin-fixed paraffin embedded whole sections. We quantitated the subsets of immune cells utilizing a targeted panel of proteins including PanCK, CD8, CD4, VISTA and Iba1, and analyzed an average of 15000 cells per sample. We classified tumours as either high (>30%) or low (<30%) tumour infiltrating lymphocytes (TILs) and found that increased TILs density correlated with survival. We next sought out to phenotype these TILs using mIF. The tumours with low TILs (n=9) had significantly higher expression of the immune-checkpoint molecule VISTA in tumor cells (p<0.01) as well as in their microenvironment (p<0.001). Contrastingly, the brain metastatic tumours with high TILs (n=8) displayed higher levels of activated microglia, as measured by Iba1 expression. Low TILs-tumours displayed CD8+ T-cells that co-express VISTA (p<0.01) significantly more compared to high TILs group, where CD8+ T-cells significantly co-express Iba1 (p<0.05). Interestingly, no definite phenotypes of CD4+ subsets were observed. These results were supported by RNA analysis of a publicly available, independent cohort. In conclusion, our work contributes to a growing understanding of the immune surveillance escape routes active in brain metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/430531v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@11b6cd8org.highwire.dtl.DTLVardef@186ff6dorg.highwire.dtl.DTLVardef@1eb8962org.highwire.dtl.DTLVardef@88bb5c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO Brain metastasis patients with low TILs have high VISTA expression and patients with high TILs have significantly more activated microglia C_FIG

pathology↗

bioRxiv2019

Modeling implicates inhibitory network bistability as an underpinning of seizure initiation

A plethora of recent experimental literature implicates the abrupt, synchronous activation of GABAergic interneurons in driving the sudden change in brain activity that heralds seizure initiation. However, the mechanisms predisposing an inhibitory network toward sudden coherence specifically during ictogenesis remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyper-excitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony due to a brief perturbation. We further show that perturbations driving this transition could reasonably arise in vivo based on models of background excitatory synaptic activity in the cortex. Thus, these results propose a mechanism by which an inhibitory network can transition from incoherent to coherent dynamics in a fashion that may precipitate seizure as a downstream effect. Moreover, this mechanism specifically explains why inhibitory networks containing hyper-excitable interneurons are more vulnerable to this state change, and how such networks can undergo this transition without a permanent change in the drive to the system. This, in turn, potentially explains such networks increased vulnerability to seizure initiated by GABAergic activity.\n\nAuthor summaryFor decades, the study of epilepsy has focused on the hypothesis that over-excitation or dis-inhibition of pyramidal neurons underlies the transition from normal brain activity to seizure. However, a variety of recent experimental findings have implicated a sudden synchronous burst of activity amongst inhibitory interneurons in driving this transition. Given the counter-intuitive nature of these findings and the correspondingly novel hypothesis of seizure generation, the articulation of a feasible mechanism of action underlying this dynamic is of paramount importance for this theorys viability. Here, we use computational techniques, particularly the concept of bistability in the context of dynamical systems, to propose a mechanism for the necessary first step in such a process: the sudden synchronization of a network of inhibitory interneurons. This is the first detailed proposal of a computational mechanism explaining any aspect of this hypothesis of which we are aware. By articulating a mechanism that not only underlies this transition, but does so in a fashion explaining why ictogenic networks might be more prone to this behavior, we provide critical support for this novel hypothesis of seizure generation and potential insight into the larger question of why individuals with epilepsy are particularly vulnerable to seizure.

neuroscience↗

bioRxiv2018

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.\n\nMethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.\n\nResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.\n\nConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.

bioinformatics↗

bioRxiv2017

Combining theory, model and experiment to understand how theta rhythms are generated in thehippocampus

Scientists have observed theta rhythms (3-12 Hz) in the hippocampus for decades, but we do not have a clear understanding of how they are generated. This is largely due to the complex, multi-scale and nonlinear nature of the brain. To obtain insight into mechanisms underlying the generation of theta rhythms, we develop cellular-based network models of the hippocampus based on a whole hippocampus in vitro preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and post-inhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. The particular theta frequency is more controlled by PYR to PV+ cell interactions rather than PV+ to PYR cell ones. We identify two scenarios by which theta rhythms can emerge and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+ cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+ to PYR cells needs to be larger than from PYR to PV+ cells. Our models can serve as a platform on which to build and develop an understanding of in vivo theta generation, and of microcircuit dynamics in the hippocampus.\n\nSignificanceBrain rhythms have been linked to cognition and are disrupted in disease. This makes it essential to understand mechanisms underlying their generation. Theory and mathematical models help provide an understanding and generate hypotheses. Together with experiment they contribute a framework to dissect the cellular contributions to network activity. However, models are inherently biological approximations, and thus the specific experimental and theoretical context upon which they are built will shape their output. If the approximations and contexts are not taken into account, particularly when using previously constructed models, misinterpretations can arise. Here, we use both theory and microcircuit models derived from a specific experimental context to provide insight into cellular-based mechanisms involved in theta rhythm generation in the hippocampus.

neuroscience↗