Biology subjects

Groves, T.

Publications and source records attributed to Groves, T..

4 recordsLinked to original sources

bioRxiv2025

Two-photon microscopy of brain endothelial glycocalyx uncovers spatial heterogeneity, vesicular transport, and lectin-binding kinetics in the living brain

The endothelial glycocalyx is a key regulator of cerebrovascular function and remains one of the most difficult structures to study in vivo. Here we uncover new structural and dynamical features of the brain endothelial glycocalyx using in vivo two-photon microscopy. We identified glycocalyx enrichment at endothelial junctions and arteriolar branch points, visualized its Vesicular transport in real-time, and found evidence for its compositional Variations along the arteriovenous axis. Fluorescence recovery after photobleaching revealed two distinct kinetics of wheat germ agglutinin binding, including a previously undescribed one. Finally, super-localization of the glycocalyx estimated glycocalyx thickness as 775{+/-}17 nm and 622{+/-}34 nm before and after enzymatic shedding, reconciling discrepancies between past optical and electron microscopy estimates. Together these findings establish the first miltiscale framework of glycocalyx distribution and heterogeneity, transport, and molecular interaction kinetics in the living brain.

neuroscience↗

bioRxiv2025

Bayesian Independent Component Analysis reconstructs independent modules of gene expression

Transcriptional regulation--the modulation of gene expression in response to environmental stimuli--is fundamental to cellular function. Identifying groups of co-regulated genes helps elucidate gene functions and characterize how an organism has evolved to respond to various stimuli. In previous works, signal processing algorithms have been applied to characterize the transcriptional regulatory modes, known as iModulons, of bacteria. However, these methods do not quantify uncertainty of the results and are difficult to integrate with different sources of information. In this work, we propose a Bayesian model of Independent Component Analysis that addresses these issues by providing a formal structure to quantify the uncertainty of gene activations and membership of co-regulated genes, achieving state-of-the-art alignment with known regulators. Furthermore, we expand this Bayesian model to explain and integrate first multi-strain and then multi-omics data. Author summaryUnderstanding how genes are turned on and off is crucial for deciphering how living organisms respond to their environment. Genes often work together in groups, and identifying these co-regulated groups can reveal their functions and how organisms adapt to changes. Previous methods have used complex mathematical techniques to find these gene groups in bacteria, but they come with limitations: they do not measure how confident we can be in the results and are hard to combine with other types of biological information. In our study, we introduce a new approach using Bayesian statistics to overcome these challenges. This method not only helps us identify groups of co-regulated genes more accurately but also allows us to quantify our confidence in these findings. Additionally, our approach can easily integrate different kinds of data, such as information from various bacterial strains or other biological processes. This makes our method a powerful tool for exploring gene regulation, with potential applications in understanding diseases, developing new treatments and advancing biotechnology.

systems biology↗

bioRxiv2025

Brain precapillary sphincters modulate myogenic tone in adult and aged mice

Brain precapillary sphincters, which are surrounded by contractile pericytes and are located at the junction of penetrating arterioles and first-order capillaries, can increase their diameter by [~]30% in a few seconds during sensory stimulation, allowing for rapid control of capillary blood flow over a wide dynamic range. We hypothesized that these properties could help precapillary sphincters maintain the capillary blood flow and shield the downstream capillaries during surges in blood pressure. To test this, we visualized microvessels in adult and old anaesthetized mice using in vivo two-photon microscopy. We showed that a blood pressure surge disrupts both microvascular myogenic response and neurovascular coupling in both adult and old mice, with old mice exhibiting a more diminished myogenic response. Similarly, laser ablation of contractile pericytes encircling precapillary sphincters disrupted neurovascular coupling and myogenic response. The resistance provided by precapillary sphincters may be increasingly important in old mice, where we found changes in the topology of microvessels, potentially affecting microvascular blood flow. Old mice displayed more tortuous penetrating arterioles, reduced pial collateral arteriolar density and altered capillary densities: reduced in the arterial end and increased in the venous end. Our results illustrate how blood pressure surges affect brain microvascular function, underscore the protective role of precapillary sphincters during cerebrovascular autoregulation in response to blood pressure surges and compare vascular topology in adult and old mice in vivo.

neuroscience↗

bioRxiv2024

Pseudo batch transformation: A novel method to correct for mass removal through sample withdrawal of fed-batch fermentations

SummaryWe present a novel "pseudo batch" transformation algorithm that maps analytical data obtained for fed-batch bioreactor cultivations onto a constant volume batch process, significantly decreasing the complexity of characterizing the fed-batch process. Availability and implementationOur method is implemented in both Excel and Python and is available with tutorials and example data from https://github.com/biosustain/pseudobatch. The Python package is also available on PYPI under the name "pseudobatch". ContactLars Keld Nielsen, e-mail: lars.nielsen@uq.edu.au Supplementary informationA comprehensive explanation of the simulated fed-batch, parameter estimation procedures, and the Bayesian model can be found in supplementary information (S1-S5).

bioinformatics↗