Biology subjects

Sweeney, P. W.

Publications and source records attributed to Sweeney, P. W..

11 recordsLinked to original sources

bioRxiv2025

Phylogenomic analyses of the diverse desert-alpine plant lineage Cistantheae

Deserts and alpine habitats, though ecologically distinct, share similar environmental stressors such as drought and high radiation. Various plant lineages traverse both biomes, which is often associated with transitions in life history strategy, where annuality is more often associated with drier desert habitats and perenniality more common in higher elevations. One such lineage is Cistantheae (Montiaceae), a morphologically diverse herbaceous clade in western North and South America. We aimed to infer a robust phylogeny of the clade as a foundation for taxonomic and comparative work. We used double-digest RADSeq to generate reduced-representation genomic data from over 160 samples representing 48 putative species in Cistantheae. Maximum likelihood and coalescent-based phylogenetic methods were utilized to infer evolutionary relationships across the full clade and within major subclades. We tested for signatures of admixture and introgressive gene flow, and reconstructed ancestral life history and climate niche to identify patterns of correlated evolution. We inferred a well-resolved phylogeny of Cistantheae, providing strong support for relationships among subclades within Cistantheae. While many species relationships were clarified, we also found evidence of rampant gene flow and incomplete lineage sorting, particularly within the annual Cistanthe clade from the Atacama Desert. Life history is evolutionarily labile across the clade, and was strongly correlated with temperature/ precipitation-related bioclimatic variables: annuals tend to occur in hotter, drier environments, while perennials in cooler, wetter. Elevational range was also evolutionarily labile, with several species occupying broad elevational gradients. We present the first densely-sampled, phylogenomic analysis of Cistantheae, providing key insights into species relationships in the clade. Repeated transitions in life history and climate niche, alongside wide elevational ranges, suggest that many Cistantheae species may be preadapted to both arid and montane habitats. This phylogeny will underpin further comparative, taxonomic, and phylogenomic studies in this ecologically important lineage.

evolutionary biology↗

bioRxiv2025

Unravelling the in vivo traits of vasculogenic mimicry

Vasculogenic mimicry (VM) describes the ability of cancer cells to acquire endothelial properties and form vessel-like channels that facilitate tumour blood supply. While the molecular drivers of VM have been well-explored in cell cultures and biopsies, an in vivo description remains elusive. Here, we used graph theory to define VM biomarkers and elucidate the spatiotemporal dynamics of VM using in vitro and in vivo breast cancer models with and without anti-angiogenic treatment. Optical microscopy was used to assess pseudo-vascular networks in vitro while photoacoustic imaging across scales was applied in vivo to identify and locate haemoglobin contrast-derived blood vessel morphology and functionality. VM was associated with greater oxygenation heterogeneity and poorer anti-angiogenic response, reflected as stable meshed networks in vitro and blood-containing circular structures in vivo. We demonstrate for the first time a multi-scale approach bridging the in vitro-in vivo translational gap to assess anti-vascular treatment resistance and the therapeutic potential in vasculogenic mimicry-rich tumours, exploring novel avenues in preclinical drug screening and systemic drug delivery.

cancer biology↗

bioRxiv2025

Early Radiation Therapy Response Assessment using Multi-scale Photoacoustic Imaging

There is a critical unmet clinical need to identify biomarkers that predict and detect radiotherapy response in cancer. Using the unique capabilities of multi-scale photoacoustic imaging (PAI) for depicting tumour oxygenation and vasculature in vivo, we identified surrogate biomarkers of radiation response in two human breast cancer models (MCF7 and MDA-MB-231), comparing hypofractionated delivery with an ablative single dose scheme. Ex vivo immunohistochemistry results underpinned findings from mesoscopic and multispectral tomographic PAI, performed 24h pre-RT, 24h post-RT, and at endpoint. A denser and more mature vasculature of the MCF7 xenografts afforded an improved response to both RT schemes compared to MDA-MB-231, in terms of overall tumour oxygenation, tumour volume and proliferation. Increased intratumoural blood oxygen saturation and oxygen diffusion pre-RT were associated with improved outcomes and decreased proliferation, expected given the oxygen-enhancement effect in RT. In vivo PAI revealed the differential effect between ablative courses of RT in both models, with the ablative scheme altering the tumour vasculature as early as 24h post-RT, and pruning the looping vessels and total blood volume at endpoint in the more radiosensitive MCF-7 xenografts. An increase in blood oxygen saturation at endpoint was observed only in the MCF7 xenografts treated with hypofractionated RT, confirming the reduced oxygen consumption of damaged tumour cells, indicative of response. Thus, we showed that PAI could capture early RT response and inform on radioresistance, thus demonstrating promise of PAI as an in vivo and future clinical tool to monitor the tumour vascular response to RT.

cancer biology↗

bioRxiv2023

Segmentation of 3D blood vessel networks using unsupervised deep learning

Mesoscopic photoacoustic imaging (PAI) enables label-free visualisation of vascular networks in tissue at high contrast and resolution. The segmentation of vascular networks from 3D PAI data and interpretation of their meaning in the context of physiological and pathological processes is a crucial but time consuming and error-prone task. Deep learning holds potential to solve these problems, but current supervised analysis frameworks require human-annotated ground-truth labels. Here, we overcome the need for ground-truth labels by introducing an unsupervised image-to-image translation deep learning model called the vessel segmentation generative adversarial network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D biomedical images. With a variety of in silico, in vitro and in vivo data, including patient-derived breast cancer xenograft models, we show that VAN-GAN facilitates accurate and unbiased segmentation of 3D vascular networks from PAI data volumes. By leveraging synthetic data to reduce the reliance on manual labelling, VAN-GAN lowers the barrier to entry for high-quality blood vessel segmentation to benefit users in the life sciences applying PAI to studies of vascular structure and function.

biophysics↗

bioRxiv2022

A three-dimensional, discrete-continuum model of blood flow in microvascular networks

We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.

biophysics↗

bioRxiv2022

Performance evaluation of mesoscopic photoacoustic imaging

Photoacoustic mesoscopy visualises vascular architecture and associated tissue structures at high resolution at up to 3 mm depth. The technique has shown promise in both preclinical and clinical imaging studies, with possible applications in oncology and dermatology, however, the accuracy and precision of photoacoustic mesoscopy has not been well established. Here, we present a performance evaluation of a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models we demonstrate high system precision, with acceptable coefficients of variation (COV) between repeated scans (short term (1h): COV=1.2%; long term (25 days): COV=9.6%), from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varying in vivo user experience (experienced: COV=15.9%, unexperienced: COV=20.2%). While our findings support the robustness of the technique, they also underscore the general challenges of limited field-of-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users to correctly interpret biologically-relevant information.

bioengineering↗

bioRxiv2021

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

bioengineering↗

bioRxiv2020

Multi-fluorescence high-resolution episcopic microscopy (MF-HREM) for three dimensional imaging of adult murine organs

Three-dimensional microscopy of large biological samples (>0.5 cm3) is transforming biological research. Many existing techniques require trade-offs between image resolution, sample size and method complexity. A simple robust instrument with the potential to perform large volume 3D imaging currently exists in the form of the Optical HREM, however the development of the instrument to date is limited to single fluorescent wavelength imaging with non-specific eosin staining. This work presents developments to realize the potential of the HREM to become Multi-fluorescent High Resolution Episcopic Microscopy (MF-HREM). MF-HREM is a serial-sectioning and block-facing wide-field fluorescence imaging technique, which does not require tissue clearing or optical sectioning. Multiple developments are detailed in sample preparation and image post-processing to enable multiple specific stains in large samples, and show how these enable segmentation and quantification of the data. The application of MF-HREM is demonstrated in a variety of biological contexts: 3D imaging of whole tumor vascular networks and tumor cell invasion in xenograft tumors up to 7.5 mm3 at resolutions of 2.75 m, quantification of glomeruli volume in the adult mouse kidney, and quantification of vascular networks and white matter track orientation in adult mouse brain.

biophysics↗

bioRxiv2019

Joint phylogenetic estimation of geographic movements and biome shifts during the global diversification of Viburnum

Phylogeny, fossils, biogeography, and biome occupancy provide evidence that reflects the singular evolutionary history of a clade. Despite the connections that bind them together, these lines of evidence are most often studied separately, by first inferring a fossil-dated molecular phylogeny, then mapping on ancestral ranges and biomes inferred from extant species. Here we jointly model the evolution of biogeographic ranges, biome affinities, and molecular sequences, incorporating fossils to estimate a dated phylogeny for all of the 163 extant species of the woody plant clade Viburnum (Adoxaceae) that we currently recognize. Our analyses indicate that while the major Viburnum lineages evolved in the Eocene, the majority of extant species originated since the Miocene. Viburnum radiated first in Asia, in warm, broad-leaved evergreen (lucidophyllous) forests. Within Asia we infer several early shifts into more tropical forests, and multiple shifts into forests that experience prolonged freezing. From Asia we infer two early movements into the New World. These two lineages probably first occupied warm temperate forests and adapted later to spreading cold climates. One of these lineages (Porphyrotinus) occupied cloud forests and moved south through the mountains of the Neotropics. Several other movements into North America took place more recently, facilitated by prior adaptations to freezing in the Old World. We also infer four disjunctions between Asia and Europe: the Tinus lineage is the oldest and probably occupied warm forests when it spread, while the other three were more recent and in cold-adapted lineages. These results variously contradict published accounts, especially the view that Viburnum radiated initially in cold forests and, accordingly, maintained vessel elements with scalariform perforations. We explored how the location and biome assignments of fossils affected our inference of ancestral areas and biome states. Our results are sensitive to, but not entirely dependent upon, the inclusion of fossil biome data. We argue that it will be critical to take advantage of all available lines of evidence to decipher events in the distant past, and the joint estimation approach developed here provides cautious hope even when fossil evidence is limited.

evolutionary biology↗

bioRxiv2019

Modelling the transport of fluid through heterogeneous, whole tumours in silico

It is critically important to understand and predict fluid transport within both physiological and pathological tissues in order to develop effective treatment strategies. Recent advances in high-resolution optical imaging allow the acquisition of whole tumour vascular networks which can be used to parameterise computational models to predict the fluid dynamics at all length scales across the tissue. This enables hypothesis testing around the role of the tumour microenvironment in determining transport characteristics, which would otherwise be unavailable using traditional experiments.\n\nIn this study, we present a novel computational framework for the efficient simulation of vascular blood flow and interstitial fluid transport based on complete three-dimensional, whole tumour vasculature obtained using high-resolution optical imaging. This framework comprises a Poiseuille flow model which simulates vascular blood flow within the vessel network, coupled via point sources of flux to a porous medium model describing interstitial fluid transport. We develop a computational algorithm for prescription of network boundary conditions and validation of tissue-scale fluid transport against measured in vivo perfusion data acquired using biomedical imaging tools. We present simulations of the model on orthoptic murine glioma and human colorectal carcinoma xenograft data (GL261 and LS147T, respectively), and perform sensitivity analysis on key unknown parameters relating to the tissue microenvironment, to understand their impact in predicting vascular and interstitial flow. Finally, we simulate radially varying vascular normalisation in a LS147T tumour and hypothesise that uniform normalisation is required to lower tumour interstitial fluid pressure.\n\nOur computational framework permits predictions of whole tumour fluid dynamics which incorporate the inherent architectural heterogeneities appearing at the micron-scale, and outputs three-dimensional spatial maps detailing these flow properties from micro to macro length scales. This provides vital information on the tumour microenvironment which could enable the design and delivery of future anti-cancer therapies.\n\nAuthor summaryThe structure of tumours varies widely, with dense and chaotically-formed networks of blood vessels that differ between each individual tumour and even between different regions of the same tumour. This atypical environment can inhibit the delivery of anti-cancer therapies. Computational tools are urgently required which incorporate micron-scale tumour biomechanics to predict tissue-scale fluid dynamics, and consequently the efficacy of cancer therapies.\n\nWe have developed a computational framework which integrates the complex tumour vascular architecture to predict fluid transport across all lengths scales in whole tumours. This enables computationally efficient hypothesis testing of cancer therapies which manipulate the tumour microenvironment in order to improve drug delivery to tumours.

cancer biology↗

bioRxiv2017

Widespread sampling biases in herbaria revealed from large-scale digitization

O_LINon-random collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras offer a timely opportunity to assess commonalities and differences in herbarium sampling biases.\nC_LIO_LIWe determined spatial, temporal, trait, phylogenetic, and collector biases in [~]5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England (NE).\nC_LIO_LIWe identified numerous shared and unique biases among these regions. Shared biases included specimens i) collected close to roads and herbaria; ii) collected more frequently during spring; iii) of threatened species collected less frequently; and iv) of close relatives collected in similar numbers. Regional differences included i) over-representation of graminoids in SA and AU and of annuals in AU; and ii) peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by a few individuals. These mega-collectors, and their associated preferences and idiosyncrasies, may have shaped patterns of collection bias via founder effects.\nC_LIO_LIStudies using herbarium collections should account for sampling biases and future collecting efforts should avoid compounding these biases.\nC_LI

plant biology↗