Biology subjects

Ward, E.

Publications and source records attributed to Ward, E..

12 recordsLinked to original sources

bioRxiv2026

MitoDecode: quantitative phenotypic decoding of mitochondrial stress states from fluorescence images

The quantitative measurement of mitochondrial stress from fluorescence images of cells remains a challenging task. Existing methods rely on mitochondrial segmentation, manually predefined features, and discrete phenotype severity classes, which are prone to error and bias. Here, we present MitoDecode, a deep learning network that automatically assesses type and severity of mitochondrial stress from fluorescence microscopy images. MitoDecode eliminates the need for mitochondrial segmentation, is parameter-free, and operates across both diffraction-limited and super-resolution optical imaging modalities. It assigns a phenotypic probability profile for each individual cell, indicating both type and severity of phenotypic stress. The readout thus enables comparisons between different stressors and provides clues for underlying mechanisms. Furthermore, the method captures phenotypic shifts continuously across treatment doses, revealing subtle variations that would otherwise be missed. We demonstrate MitoDecode's adaptability across diverse cell types, imaging modalities, and stress conditions, and show that it can reliably detect mitochondrial phenotypes, even when stressors target non-mitochondrial pathways. We validate the reliability of the model with biochemical assays, showcase its utility for phenotypic stress profiling of drug treatments, and envisage its application for early-stage disease detection, drug discovery, and mechanistic lead generation.

bioengineering↗

bioRxiv2026

Correcting trend bias from preferential sampling in fishery-dependent indices

Fishery-dependent indices of abundance are often used to inform stock assessments, but fishing effort is generally not randomly distributed. Fishers concentrate sampling in locations expected to be profitable, leading to a form of preferential sampling that can bias model-based indices of abundance. We show that when the strength of preferential sampling is constant over time, it biases only the magnitude of an index, which is absorbed by estimated catchability parameters in most stock assessments. A more interesting case arises when the strength of preferential sampling changes over time; in these cases the index trend can be biased in a way that conventional catchability estimation cannot correct. Using simulations structured on long running trawl survey data from the west coast of the USA for shortspine thornyhead (Sebastolobus alascanus), we evaluate two structurally different methods for correcting time-varying trend bias: a joint spatiotemporal model of the density and sampling processes, and a discrete-choice model of fisher location choice. Our simulations include two mechanisms generating preferential biases: effort tracking local density, and effort responding to a spatially structured, but unobserved anomaly that also inflates catch. We show that the ability of the estimation methods to correct bias depends on the mechanism responsible for concentrating effort; while the joint model is robust to both data-generating mechanisms, the discrete choice model is only able to correct biases when the mechanism is anomaly-driven. As a real-world case study, we apply these corrections to simulated preferentially sampled data and include indices in the shortspine thornyhead stock assessment model. We found that uncorrected time-varying preferential sampling positively biased estimates of a major recruitment event and, to a lesser extent, unfished recruitment. This bias propagated through to bias in overfishing limits that grew as the population was projected forward. While bias-corrected indices still produced some positive biases in overfishing limits, these were substantially reduced compared to the naive approach. As the mechanism of preferential sampling is rarely known in advance, our results suggest that the joint model represents a robust default for correcting fishery-dependent indices of abundance.

ecology↗

bioRxiv2026

Deciphering the microbial contributors to methane cycling in coastal wetlands

Although wetlands are increasingly recognized as important contributors to the global methane budget, the microorganisms and processes involved methane cycling are poorly characterized, particularly in coastal brackish and saline systems. Here, we investigated microbial and geochemical factors contributing to methane dynamics in three coastal wetlands with different salinities, dominant vegetation types, and soil chemical characteristics. These included a freshwater flotant marsh, a cypress swamp, and a mesohaline salt marsh. Specifically, we paired methane porewater concentrations, surface fluxes, geochemistry, and 16S rRNA gene sequencing to address how microbial community composition links to porewater concentrations and its potential effects on emissions. We found that porewater methane concentrations across sites were the highest in the swamp, followed by the salt marsh and the flotant marsh, and were explained by methanogen richness and abundance. While methane-cycling microbial communities were significantly structured by salinity, two microbial taxa (Methanosaeta and Methanomicrobiaceae) were present across all sites. Hydrogenotrophs were the most abundant methanogen functional group, with Methanomicrobiaceae and Methanobacterium discriminant among wetlands. In contrast, methanotroph functional types varied among wetlands. Type I dominated the freshwater flotant marsh, while the anaerobic methanotrophic archaea the saltwater marsh. These findings contribute to an enhanced understanding of the microbiological contributions to methane emissions from coastal wetlands. Scientific Significance Statement TopicThis study provides critical insights into the microbial and geochemical controls on methane emissions across coastal wetlands along a salinity gradient. Challenging the prevailing paradigm, methane porewater concentrations did not inversely correlate with salinity, as the swamp site with intermediate salinity exhibited the highest concentrations. Methanogen richness and abundance emerged as strong predictors of methane concentrations, while methanotroph richness had no predictive value. Two core methanogens, Methanosaeta and Methanomicrobiaceae, were consistently present across all wetland types. The findings highlight potential role of the water column as a biological methane filter, especially in saline environments. This study significantly advances the understanding of methane cycling in coastal wetlands by decoupling methane emissions from salinity gradients and emphasizing the role of microbial communities and local environmental factors. These insights are essential for refining biogeochemical models to forecast greenhouse gas emissions under sea-level rise and saltwater intrusion scenarios. Scientific Significance Statement OutletThis work integrates microbial ecology, geochemistry, and ecosystem structure to address interdisciplinary questions relevant to the limnological community. Here, we reveal how microbial community composition, rather than salinity alone, predicts methane emissions, offering a fresh perspective into carbon cycling in estuarine and coastal environments. The findings also provide critical insights for improving greenhouse gas emission models to predict climate change feedback from coastal areas.

microbiology↗

bioRxiv2026

α-Synuclein Facilitates Spontaneous Dopamine Release in a Calcium- and Phosphorylation-Dependent Manner

-Synuclein (aSyn) is central to Parkinsons disease pathogenesis, yet its native physiological role at the presynapse remain poorly defined. Here, super-resolution imaging in dopaminergic neurons reveals that endogenous aSyn localises within nanometres of L-type voltage-gated calcium channels (LTCC), with closer proximity under both spontaneous neuronal activity and stimulated conditions compared to when extracellular calcium is chelated. Blocking Ca2+/calmodulin-dependent kinase II (CaMKII) reduces aSyn clustering at LTCC under spontaneous activity, suggesting that calcium entry and downstream calcium-dependent kinase activity contribute to aSyn localisation. Moreover, quantitative single-molecule analyses indicate that calcium increases the abundance of both total and serine129 phosphorylated (pS129) aSyn in synaptosomes under spontaneous conditions, and NMR analysis reveals that both calcium and S129 phosphorylation increase the binding affinity of aSyn to synaptic vesicles. Functional assays further demonstrate that LTCC blockade elevates intracellular DA levels exclusively in the presence of aSyn under spontaneous but not stimulated conditions. Finally, biochemical fractionation and multi-colour single-molecule imaging reveal that aSyn preferentially associates with small vesicles that are not obligately coupled to full-fusion associated recycling pools. These results suggest that aSyn acts as a calcium- and phosphorylation-regulated modulator of spontaneous DA release through pathways that are largely independent of full-fusion recycling mechanisms, and that pS129 aSyn is not solely a pathological marker but may also reflects physiological regulation. Together, these insights provide a framework for understanding how therapeutic strategies targeting aSyn may impact its normal synaptic functions.

neuroscience↗

bioRxiv2025

'surveyjoin': A Standardized Database of Fisheries Bottom Trawl Surveys in the Northeast Pacific Ocean

Fisheries management faces challenges due to political, spatial, and ecological complexities, which are further exacerbated by variation or shifts in species distributions. Effective management depends on the ability to integrate fisheries data across political and geographic boundaries. However, such efforts may be hindered by inconsistent data formats, limited data sharing, methodological differences in sampling, and regional governance differences. To address these issues, we introduce the surveyjoin R package, which combines and provides public access to bottom trawl survey data collected by NOAA Fisheries and Fisheries and Oceans Canada in the Northeast Pacific Ocean. This initial database integrates over 3.3 million observations from 14 bottom trawl surveys spanning Alaska, British Columbia, Washington, Oregon, and California from the 1980s to present. This effort standardizes variables such as catch-per-unit-effort (CPUE), haul data, and in-situ measurements of bottom temperature. We demonstrate the utility of this database through three case studies. Our first case study develops a coastwide biomass index for Pacific hake (Merluccius productus) using geostatistical index standardization, comparing results to independent acoustic survey estimates. The second case study examines changes in the spatial distribution of groundfish species across marine heatwave and non-heatwave years, highlighting species-specific and community-level responses to warming events. Our third example applies spatially varying coefficient models to assess sablefish (Anoplopoma fimbria) biomass trends, identifying regional variability in increases in occurrence and biomass. Together, these case studies demonstrate how the surveyjoin R package and database may improve species and ecosystem assessments by providing insights into population trends across geopolitical boundaries. This database and package represent an important step toward offering a scalable framework that can be extended to include additional data types, surveys, and species. By fostering collaboration, transparency, and data-driven decision-making, surveyjoin supports international efforts to sustainably manage shared marine resources under dynamic environmental conditions.

ecology↗

bioRxiv2025

An Enhanced Mountain Climbing Search Algorithm to Enable Fast and Accurate Autofocusing in High Resolution Fluorescence Microscopy

Accurate and efficient autofocusing is essential for the automation of fluorescence microscopy, but background noise and shallow depth of field at high magnifications make autofocusing particularly challenging. Here, we present a fast and accurate autofocus algorithm to address these challenges. It is highly effective for high-magnification imaging, while performing equally well for low-magnification imaging tasks. The method is based on the mountain climbing search algorithm and yields improvements on autofocusing precision of up to 200-fold over current methods, whilst offering competitive speed and greatly extended search ranges. Our approach is broadly applicable: it demonstrated good stability and reproducibility across magnifications ranging from 20X to 100X, excels in both live cell imaging and high-resolution fixed sample imaging, and it is compatible with various microscopy techniques without the need for fiducial markers or hardware modifications on existing microscopes. To maximise its accessibility, we constructed a user-friendly interface compatible with the widely used Micromanager software. It generalises well across various imaging modalities and hardware platforms, making it particularly suitable for use in high-resolution screening of candidate drugs.

cell biology↗

bioRxiv2024

MDRepo - an open environment for data warehousing and knowledge discovery from molecular dynamics simulations

BackgroundMolecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. This enables advances in drug discovery and the design of therapeutic interventions. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. A needIdeally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. A solutionHere, we introduce MDRepo, a robust infrastructure that supports a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyberinfrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

biochemistry↗

bioRxiv2024

Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use graphene microelectrode arrays (G-MEAs) to address these challenges, as they are compatible with high spatial resolution imaging across various scales as well as high temporal resolution electrophysiological recordings. Furthermore, alongside G-MEAs, we deploy an easy-to-implement machine learning-based algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML)-based spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables the monitoring of the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC) and show that synaptic boutons, compared to untreated controls, significantly increase in size, which leads to a loss in neuronal signalling capacity.

neuroscience↗

bioRxiv2023

Molecular mechanisms of liposome interactions with bacterial envelopes

Although fusogenic liposomes offer a promising approach for the delivery of antibiotic payloads across the cell envelope of Gram-negative bacteria, there is still limited understanding of the individual nanocarrier interactions with the bacterial target. Using super-resolution microscopy, we characterize the interaction dynamics of positively charged fusogenic liposomes with Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtilis) bacteria. The liposomes merge with the outer membrane (OM) of Gram-negative bacteria, while attachment or lipid internalization is observed in Gram-positive cells. Employing total internal reflection fluorescence microscopy, we demonstrated liposome fusion with model supported lipid bilayers. For whole E. coli cells, however, we observed heterogeneous membrane integrations, primarily involving liposome attachment and hemifusion events. With increasing lipopolysaccharide length the likelihood of full-fusion events was reduced. The integration of artificial lipids into the OM of Gram-negative cells led to membrane destabilization, resulting in decreased bacterial vitality, membrane detachment, and improved co-delivery of Vancomycin--an effective antibiotic against Gram-positive cells. These findings provide significant insights into the interactions of individual nanocarriers with bacterial envelopes at the single-cell level, uncovering effects that would be missed in bulk measurements. This highlights the importance of conducting single-particle and single-cell investigations to assess the performance of next-generation drug delivery platforms.

biophysics↗

bioRxiv2022

Intracellular Aβ42 aggregation leads to cellular thermogenesis

The aggregation of A{beta}42 is a hallmark of Alzheimers disease. It is still not known what the biochemical changes are inside a cell which will eventually lead to A{beta}42 aggregation. Thermogenesis has been associated with cellular stress, the latter of which may promote aggregation. We perform intracellular thermometry measurements using fluorescent polymeric thermometers (FPTs) to show that A{beta}42 aggregation in live cells leads to an increase in cell-averaged temperatures. This rise in temperature is mitigated upon treatment with an aggregation inhibitor of A{beta}42 and is independent of mitochondrial damage that can otherwise lead to thermogenesis. With this, we present a diagnostic assay which could be used to screen small-molecule inhibitors to amyloid proteins in physiologically relevant settings. To interpret our experimental observations and motivate the development of future models, we perform classical molecular dynamics of model A{beta} peptides to examine the factors that hinder thermal disspation. We observe that this is controlled by the presence of ions in its surrounding environment, the morphology of the amyloid peptides and the extent of its hydrogen-bonding interactions with water. We show that aggregation and heat retention by A{beta} peptides are favoured under intracellular-mimicking ionic conditions, which could potentially promote thermogenesis. The latter will, in turn, trigger further nucleation events that accelerate disease progression.

biophysics↗

bioRxiv2021

Label-free characterisation of amyloids and alpha-Synuclein polymorphs by exploiting their intrinsic fluorescence property

Conventional in vitro aggregation assays often involve tagging with extrinsic fluorophores which can interfere with aggregation. We propose the use of intrinsic amyloid fluorescence lifetime probed using two-photon excitation and represented by model-free phasor plots, as a label-free assay to characterise amyloid structure. Intrinsic amyloid fluorescence arises from structured packing of {beta}-sheets in amyloids and is independent of aromatic-based fluorescence. We show that different amyloids (i.e., -Synuclein (S), {beta}-Lactoglobulin and TasA) and different polymorphic populations of S (induced by aggregation in salt-free and salt buffers mimicking the intra-/extracellular environments) can be differentiated by their unique fluorescence lifetimes. Moreover, we observe that disaggregation of pre-formed fibrils of S and {beta}LG leads to increased fluorescence lifetimes, distinct to those of their fibrillar counterpart. Our assay presents a medium-throughput method for rapid classification of amyloids and their polymorphs (the latter of which recent studies have shown lead to different disease pathology), and for testing small molecule inhibitory compounds.

molecular biology↗

bioRxiv2020

Combining light sheet microscopy and expansion microscopy for fast 3D imaging of virus-infected cells with super-resolution

Expansion microscopy is a sample preparation technique that enables the optical imaging of biological specimens at super-resolution owing to their physical magnification, which is achieved through water-absorbing polymers. The technique uses readily available chemicals and does not require sophisticated equipment, thus offering super-resolution to laboratories that are not microscopy-specialised. Here we present a protocol combining sample expansion with light sheet microscopy to generate high-contrast, high-resolution 3D reconstructions of whole virus-infected cells. The results are superior to those achievable with comparable imaging modalities and reveal details of the infection cycle that are not discernible before expansion. An image resolution of approximately 95 nm could be achieved in samples labelled in 3 colours. We clearly resolve the concentration of viral nucleoprotein on the surface of vesicular structures within the cell and their positioning relative to cellular organelles. We provide detailed guidance and a video protocol for the optimal application of the method and demonstrate its potential to study virus-host cell interactions.

microbiology↗