Biology subjects

Russell, C.

Publications and source records attributed to Russell, C..

17 recordsLinked to original sources

bioRxiv2026

Tunable Bessel beam two-photon fluorescence microscopy for high-speed volumetric imaging of brain dynamics

High-speed volumetric imaging of the brain is essential for linking diverse cellular events to tissue-level functions. However, the brains structural and dynamic heterogeneity--spanning microns to millimeters and milliseconds to hours--requires imaging techniques with tunable spatiotemporal resolution, flexible 3D sampling, and compatibility with targeted perturbations. Here, we present tunable Bessel beam two-photon fluorescence microscopy (tBessel-TPFM), a compact, low-cost, and versatile platform for intravital brain imaging across millimeter scale with subcellular resolution. tBessel-TPFM transforms slow 3D volume scans into fast 2D frame scans via an axially elongated Bessel focus, achieving acquisition rates [~]100-fold faster and reduced motion artifacts compared with conventional TPFM. Exploiting its full tunability of the Bessel focus, we applied tBessel-TPFM for quantitative mapping of cerebral blood flow and neurovascular coupling in normal and ischemic stroke mice. Unlike existing Bessel focus generation methods, the axial center of tBessel-TPFM remains fixed at the objective focal plane during profile tuning. Leveraging this advantage, we integrated tBessel-TPFM with simultaneous 3D targeted optogenetic stimulation for volumetric neuronal connectivity mapping. We also tracked microglial process dynamics following single-cell laser ablation, revealing diverse neuroimmune responses across spatial and temporal scales. By combining high speed, deep penetration, tunable sampling, and multimodal perturbation, tBessel-TPFM empowers a broad spectrum of neurobiological investigations--from vascular physiology and functional connectivity to neuroimmune interactions.

bioengineering↗

bioRxiv2025

Whole-organism screening in a zebrafish model of CLN2 disease identifies pregnenolone as a modulator of lysosomal functions with anti-epileptic properties

Lysosomal storage disorders (LSDs), a group of inherited genetic diseases, are often associated with early-onset neurodegeneration and refractory epileptic seizures. In CLN2 disease, an LSD caused by recessively inherited dysfunction of lysosomal serine protease Tripeptidyl Peptidase 1 (TPP1), lysosomes are functionally impaired through a characteristic accumulation of subcellular materials. Here, we develop and apply a whole-organism screening workflow in tpp1-/- zebrafish to identify small molecules that suppress epileptic seizures - a hallmark of the human disease - in this model. Among 640 US Food and Drug Administration-approved drugs, pregnenolone, an endogenous precursor for steroid biosynthesis, efficiently suppresses seizures and cell death in tpp1-/- zebrafish. Using a semi-automated high-content workflow, we further show that pregnenolone normalizes lysosomal architecture in tpp1-/- zebrafish. Pregnenolone stimulates steroid hormone biosynthesis and related gene expression, which is dysregulated in tpp1-/- zebrafish. Taken together, tpp1-/- zebrafish are a suitable model to study CLN2 disease, in which we have identified pregnenolone as a candidate with therapeutic properties.

pharmacology and toxicology↗

bioRxiv2025

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA produced stronger effect sizes and revealed findings in brain regions that traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

neuroscience↗

bioRxiv2025

OPTRACE: Optical Imaging Guided Transplantation and Tracking of Cells in the Mouse Brain

Intracerebral cell transplantation holds great promise for treating stroke and other neurological disorders, but progress is hindered by challenges in precise cell delivery and limited methods for monitoring post-engraftment dynamics. To address these limitations, we introduce OPTRACE (OPtical imaging-guided Transplantation and tRAcking of CElls), a two-step framework that integrates precision delivery with longitudinal in vivo tracking to advance cell therapy. During transplantation, OPTRACE enables real-time visualization using cost-effective translucent glass micropipettes, guided by predictive modeling to optimize delivery parameters. A novel pulse-elevation injection technique further enhances the precision of superficial cortical engraftment. Following transplantation, multicolor labeling combined with two-photon fluorescence microscopy permits longitudinal single-cell tracking, revealing host microglial responses, and altered neuronal calcium signaling at the graft interface. By unifying high-resolution delivery and tracking strategies, OPTRACE provides a powerful platform for elucidating engraftment biology and accelerating the development of effective cell-based interventions for neurological diseases.

bioengineering↗

bioRxiv2024

Impacts of Disinfection Methods in a Granular Activated Carbon (GAC) Treatment System on Disinfected Drinking Water Toxicity and Antibiotic Resistance Induction Potential

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/608195v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@abf38dorg.highwire.dtl.DTLVardef@102d02dorg.highwire.dtl.DTLVardef@1b14e5dorg.highwire.dtl.DTLVardef@18fd10d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphic AbstractC_FLOATNO C_FIG Granular activated carbon (GAC) treatment followed by chlorination (GAC/Cl2) and chlorination followed by chloramination (Cl2/NH2Cl) are two methods utilized by drinking water treatment facilities to mitigate the formation of disinfection byproducts (DBPs) in treated water. However, the effectiveness of these methods in reducing the overall toxicity of drinking water, driven by DBPs, remains largely unknown. In this study, we evaluate the total toxicity of water samples from a pilot-scale GAC system with post-chlorination (GAC/Cl2), and occasionally pre-chlorination upstream of GAC (Cl2/GAC/Cl2), compared to water treated by chlorination followed by chloramination (Cl2/NH2Cl). The research was conducted at various bromide and iodide levels and across three GAC bed volumes. To assess DNA stress and oxidative stress in water extracts, we employed the yeast toxicogenomic assay and human cell RT-qPCR assay, along with the DBP analysis from our previous study. Our results indicated that under environmental halogen conditions, GAC/Cl2 typically reduces both genotoxicity and oxidative stress in treated water more effectively than Cl2/NH2Cl and Cl2 treatment. However, Cl2/GAC/Cl2 does not consistently lower toxicity compared to GAC/Cl2. Notably, under high halogen conditions, Cl2/GAC/Cl2 fails to reduce genotoxicity and oxidative stress compared to samples without GAC treatment. Correlation analysis suggested that iodinated DBPs (I-DBPs) and nitrogenous DBPs (N-DBPs) were particularly associated with increased DNA stress and oxidative stress, indicating these classes of DBPs as significant contributors to the observed toxicity. While neither of these two categories of DBPs are regulated by the EPA, it appears that unregulated and unidentified DBPs significantly contribute to the genotoxicity and oxidative stress in drinking water. This research highlights the complex dynamics of water treatment processes and underscores the critical impact of unregulated DBPs on water toxicity.

pharmacology and toxicology↗

bioRxiv2024

Evolution of Omicron lineage towards increased fitness in the upper respiratory tract in the absence of severe lung pathology

The emergence of the Omicron lineage represented a major genetic drift in SARS-CoV-2 evolution. This was associated with phenotypic changes including evasion of pre-existing immunity and decreased disease severity. Continuous evolution within the Omicron lineage raised concerns of potential increased transmissibility and/or disease severity. To address this, we evaluated the fitness and pathogenesis of contemporary Omicron variants XBB.1.5, XBB.1.16, EG.5.1, and JN.1 in the upper (URT) and lower respiratory tract (LRT). We compared in vivo infection in Syrian hamsters with infection in primary human nasal and lung epithelium cells and assessed differences in transmissibility, antigenicity, and innate immune activation. Omicron variants replicated efficiently in the URT but displayed limited pathology in the lungs compared to previous variants and failed to replicate in human lung organoids. JN.1 was attenuated in both URT and LRT compared to other Omicron variants and failed to transmit in the hamster model. Our data demonstrate that Omicron lineage evolution has favored increased fitness in the URT.

microbiology↗

bioRxiv2023

Immune experience at time of circulation and time since virus circulation are predictors of HAI titre

Nearly everyone is infected with seasonal influenza viruses multiple times over the course of their lives due to the antigenic evolution of the virus to escape immunity induced by prior infections and vaccinations. Because antibody escape is usually partial, the antibody response to new infections depends on prior responses which wane and are boosted throughout life. We used serum antibody binding measurements of 864 individuals against a range of historical A/H3N2 viruses collected in previous studies in combination with statistical models to investigate how metrics of age and strain heterogeneities affect haemagglutination inhibition titres. We refine prior modelling of antigenic seniority by characterising immune experience as years of life since a (sub)types emergence at time of strain circulation, rather than age at time of circulation. Using Bayesian statistical modeling, we show that this variable, combined with time since circulation of the variant and an individuals age at circulation, yields the best model fit. Based on the best-fit model, we devised a novel parametric model, and use it to demonstrate and estimate how effects of antigenic seniority, individual age, strain age and strain effects act in concert to shape an individuals HAI titre.

immunology↗

bioRxiv2023

Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

The influenza A (IAV) RNA polymerase is an essential driver of IAV evolution. Mutations that the polymerase introduces into viral genome segments during replication are the ultimate source of genetic variation, including within the three subunits of the IAV polymerase (PB2, PB1, and PA). Evolutionary analysis of the IAV polymerase is complicated, because changes in mutation rate, replication speed, and drug resistance involve epistatic interactions among its subunits. In order to study the evolution of the human seasonal H3N2 polymerase since the 1968 pandemic, we identified pairwise evolutionary relationships among [~]7000 H3N2 polymerase sequences using mutual information (MI), which measures the information gained about the identity of one residue when a second residue is known. To account for uneven sampling of viral sequences over time, we developed a weighted MI metric (wMI) and demonstrate that wMI outperforms raw MI through simulations using a well-sampled SARS-CoV-2 dataset. We then constructed wMI networks of the H3N2 polymerase to extend the inherently pairwise wMI statistic to encompass relationships among larger groups of residues. We included HA in the wMI network to distinguish between functional wMI relationships within the polymerase and those potentially due to hitchhiking on antigenic changes in HA. The wMI networks reveal coevolutionary relationships among residues with roles in replication and encapsidation. Inclusion of HA highlighted polymerase-only subgraphs containing residues with roles in the enzymatic functions of the polymerase and host adaptability. This work provides insight into the factors that drive and constrain the rapid evolution of influenza viruses.

evolutionary biology↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

bioRxiv2022

Simulating structurally variable Nuclear Pore Complexes for Microscopy

MotivationThe Nuclear Pore Complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and one of localization microscopys most important reference standards: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudo-atomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single Molecule Localization Microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However the true picture of NPC variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from true variability of the underlying structure. ResultsWe introduce CIR4MICS ("ceramics", Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that creates artificial datasets of structurally variable synthetic NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. We provide an open-source simulation pipeline, as well as reference datasets of simulated human NPCs. Accompanying ground truth annotations allow to test the capabilities of image analysis software and facilitate a side-by-side comparison with real data. We demonstrate this by synthetically replicating a geometric analysis of real NPC radii and reveal that a wide range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to benchmark and develop image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. AvailabilityCode: https://github.com/uhlmanngroup/cir4mics. Simulated data is available at BioStudies (Accession number S-BSST1058). Contacttheiss@ebi.ac.uk Supplementary informationSupplementary data are available at

bioinformatics↗

bioRxiv2021

A Process-Based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens and Culex restuans mosquito populations. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like the West Nile virus.

ecology↗

bioRxiv2021

Modeling autism-associated SHANK3 deficiency using human cortico-striatal organoids generated from single neural rosettes

Our understanding of the human brain is limited by the lack of experimental models to mechanistically probe the properties of brain cells at different developmental stages under normal and pathological conditions. We developed a new method for generating human cortico-striatal organoids from stem cell-derived single neural rosettes (SNRs) and used it to investigate cortico-striatal development and deficits caused by the deficiency of an autism- and intellectual disability-associated gene SHANK3. We show that SNR-derived organoids consist of different cortico-striatal cells, including pallial and subpallial progenitors, primary cortical and striatal neurons, interneurons, as well as macroglial and mural cells. We also demonstrate that neurons in SNR-derived organoids are predictably organized, functionally mature, and capable of establishing functional neural networks. Interestingly, we found that the cellular and electrophysiological deficits in SHANK3-deficient SNR-derived organoids are dependent on the level of SHANK3 expression and that organoids with complete hemizygous SHANK3 deletion have disrupted expression of several clustered protocadherins and multiple primate-specific zinc-finger genes. Together, this study describes a new method for using SNRs to generate organoids, provides new insights into the cell lineages associated with human cortico-striatal development, and identifies specific molecular pathways disrupted by hemizygous SHANK3 deletion, which is the most common genetic abnormality detected in patients with 22q13 deletion syndrome.

neuroscience↗

bioRxiv2020

Genotypic diversity and dynamic nomenclature of Parechovirus A

Human parechoviruses (PeV-A) can cause severe sepsis and neurological syndromes in neonates and children and are currently classified into 19 genotypes based on genetic divergence in the VP1 gene. However, the genotyping system has notable limitations including an arbitrary distance threshold and reliance on insufficiently robust phylogenetic reconstruction approaches leading to inconsistent genotype definitions. In order to improve the genotyping system, we investigated the molecular epidemiology of human parechoviruses, including the evolutionary history of the different PeV-A lineages as far as is possible. We found that PeV-A lineages suffer from severe substitution saturation in the VP1 gene which limit the inference of deep evolutionary timescales among the extant PeV-A and suggest that the degree of evolutionary divergence among current PeV-A lineages has been substantially underestimated, further confounding the current genotyping system. We propose an alternative nomenclature system based on robust, amino-acid level phylogenetic reconstruction and clustering with the PhyCLIP algorithm which delineates highly divergent currently designated genotypes more informatively. We also describe a dynamic nomenclature framework that combines PhyCLIPs progressive clustering with phylogenetic placement for genotype assignment.

evolutionary biology↗

bioRxiv2019

An Open-Hardware sample mounting solution for inverted light-sheet microscopes with large detection objective lenses

Implementations of light-sheet microscopes are often incompatible with standard methods of sample mounting. Light-sheet microscopy uses orthogonal illumination and detection to create a thin sheet of light which does not illuminate the sample outside of the depth of field of the detection axis. Typically, this configuration involves a pair of orthogonal objectives which constrains the positioning of a length of coverslips or microscopes in range of the detection objective. We present an open-hardware (1, 2) sample mounting system for light-sheet microscopes using large detection objectives.

cell biology↗

bioRxiv2018

Inferring putative transmission clusters with Phydelity

Current phylogenetic clustering approaches for identifying pathogen transmission clusters are limited by their dependency on arbitrarily-defined genetic distance thresholds for within-cluster divergence. Incomplete knowledge of a pathogens underlying dynamics often reduces the choice of distance threshold to an exploratory, ad-hoc exercise that is difficult to standardise across studies. Phydelity is a new tool for the identification of transmission clusters in pathogen phylogenies. It identifies groups of sequences that are more closely-related than the ensemble distribution of the phylogeny under a statistically-principled and phylogeny-informed framework, without the introduction of arbitrary distance thresholds. Relative to other distance threshold-based and model-based methods, Phydelity outputs clusters with higher purity and lower probability of misclassification in simulated phylogenies. Applying Phydelity to empirical datasets of hepatitis B and C virus infections showed that Phydelity identified clusters with better correspondence to individuals that are more likely to be linked by transmission events relative to other widely-used non-parametric phylogenetic clustering methods without the need for parameter calibration. Phydelity is generalisable to any pathogen and can be used to identify putative direct transmission events. Phydelity is freely available at https://github.com/alvinxhan/Phydelity.

bioinformatics↗

bioRxiv2018

Phylogenetic Clustering by Linear Integer Programming (PhyCLIP)

Sub-species nomenclature systems of pathogens are increasingly based on sequence data. The use of phylogenetics to identify and differentiate between clusters of genetically similar pathogens is particularly prevalent in virology from the nomenclature of human papillomaviruses to highly pathogenic avian influenza (HPAI) H5Nx viruses. These nomenclature systems rely on absolute genetic distance thresholds to define the maximum genetic divergence tolerated between viruses designated as closely related. However, the phylogenetic clustering methods used in these nomenclature systems are limited by the arbitrariness of setting intra- and inter-cluster diversity thresholds. The lack of a consensus ground truth to define well-delineated, meaningful phylogenetic subpopulations amplifies the difficulties in identifying an informative distance threshold. Consequently, phylogenetic clustering often becomes an exploratory, ad-hoc exercise.\n\nPhylogenetic Clustering by Linear Integer Programming (PhyCLIP) was developed to provide a statistically-principled phylogenetic clustering framework that negates the need for an arbitrarily-defined distance threshold. Using the pairwise patristic distance distributions of an input phylogeny, PhyCLIP parameterises the intra- and inter-cluster divergence limits as statistical bounds in an integer linear programming model which is subsequently optimised to cluster as many sequences as possible. When applied to the haemagglutinin phylogeny of HPAI H5Nx viruses, PhyCLIP was not only able to recapitulate the current WHO/OIE/FAO H5 nomenclature system but also further delineated informative higher resolution clusters that capture geographically-distinct subpopulations of viruses. PhyCLIP is pathogen-agnostic and can be generalised to a wide variety of research questions concerning the identification of biologically informative clusters in pathogen phylogenies. PhyCLIP is freely available at http://github.com/alvinxhan/PhyCLIP.

bioinformatics↗

bioRxiv2017

Active Trachoma Cases In The Solomon Islands Have Varied Polymicrobial Community Structures But Do Not Associate With Individual Non-Chlamydial Pathogens Of The Eye

BackgroundSeveral non-chlamydial microbial pathogens are associated with clinical signs of active trachoma in trachoma-endemic communities with a low prevalence of ocular Chlamydia trachomatis (Ct) infection. In the Solomon Islands, the prevalence of Ct among children is low despite the prevalence of active trachoma being moderate. We therefore set out to investigate whether active trachoma was associated with a common non-chlamydial infection or with a dominant polymicrobial community dysbiosis in the Solomon Islands.\n\nMethodsWe studied DNA from conjunctival swabs collected from 257 Solomon Islanders with active trachoma and matched controls. Droplet digital PCR was used to test for pathogens suspected to be able to induce follicular conjunctivitis. Polymicrobial community diversity and composition were studied by sequencing of hypervariable regions of the 16S ribosomal ribonucleic acid gene in a subset of 54 cases and 53 controls.\n\nResultsAlthough Ct was associated with active trachoma, the number of infections was low (cases: 3.9%, controls: 0.4%). Estimated prevalence (cases, controls) of each non-chlamydial infection was as follows: S. aureus (1.9%, 1.9%), Adenoviridae (1.2%, 1.2%), coagulase-negative Staphylococcus (5.8%, 4.3%), H. influenzae (7.4%, 11.7%), M. catarrhalis (2.3%, 4.7%) and S. pneumoniae (7.0%, 6.2%). There was no statistically significant association between clinical signs of trachoma and presence or load of any of the non-Ct infections that were assayed. Inter-individual variations in the conjunctival microbiome were characterised by differences in the levels of Corynebacterium, Proprionibacterium, Helicobacter and Paracoccus, but diversity and relative abundance of these specific genera did not differ significantly between cases and controls.\n\nDiscussionIt is unlikely that the prevalent trachoma-like follicular conjunctivitis in the Solomon Islands has a dominant bacterial aetiology. Before implementing community-wide azithromycin distribution for trachoma, policy makers should consider that clinical signs of trachoma can be observed in the absence of any detectable azithromycin-susceptible organism.

microbiology↗