Biology subjects

Styles, I. B.

Publications and source records attributed to Styles, I. B..

5 recordsLinked to original sources

bioRxiv2024

Expansion microscopy allows quantitative characterisation of structural organisation of platelet aggregates

1Current microscopy approaches applied to platelet aggregates in both haemostatic and thrombotic settings indicate their structure has important implications in efficient haemostasis and in clinical treatment of thrombosis. However, current fluorescence microscopy approaches are not amenable to volumetric imaging of platelet aggregate structures. This is largely due to the small size of individual platelets and the tight packing of platelets within aggregates, resulting in optical opacity. Here we demonstrate that expansion microscopy, applied to platelet aggregates, can reveal multi-scale information about the structure of platelet aggregates. We produced volumetric images at nanoscale resolution of >700 platelet aggregates under normal and perturbed conditions, stained for cytoskeletal and membrane components. We demonstrate our custom analysis workflow provides quantitative description of platelet numbers, volumes and morphology within entire platelet aggregates. Additionally, we quantitatively describe subcellular organisation of F-actin. By comparing these measurements following treatment with the actin inhibitors, cytochalasin D and latrunculin A, we can robustly detect structural disruptions in platelet aggregates. Together these data provide a workflow to qualitatively and quantitatively describe the architecture of platelet aggregates at a range of scales (whole aggregates down to sub-cellular features within individual platelets).

cell biology↗

bioRxiv2021

Individual cells traffic the Vasopressin 2 Receptor to their cell surface with different success

G protein coupled receptors (GPCRs) translate the actions of hormones into intracellular signalling events. Mutations in GPCRs can prevent their correct expression and trafficking to the cell surface and cause disease. We use single cell measurements in HEK293 cells to show that the balance between endoplasmic reticulum (ER) and cell surface localisation of the Vasopressin 2 receptor (V2R) varies significantly from cell to cell. We find that mutations in the V2R affect the proportion of cells able to send this GPCR to the cell surface but do not prevent all cells in the population from correctly trafficking the mutant receptors. These findings reveal that the ability of cells to correctly traffic V2R to the cell surface depends not only on the expressed V2R mutant but also on the individual cell environment. Significance statementMissense mutations in the Vasopressin 2 Receptor (V2R) cause Nephrogenic Diabetes Insipidus. Some of these mutations prevent correct expression and trafficking of V2R to the cell surface resulting in a loss-of-function. We show -using single cell measurements-that the balance between endoplasmic reticulum and cell surface localisation of the V2R varies significantly from cell to cell, independent from its expression level. Mutations affect the proportion of cells able to send V2R to the cell surface but do not prevent all cells in the population from correctly trafficking the mutant receptors. Hence, the ability of cells to correctly traffic V2R to the cell surface depends not only on expressed V2R mutant but also on the cell environment.

molecular biology↗

bioRxiv2021

Prediction of ligand-receptor pharmacological activities using a combined docking and machine learning approach

G protein coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands which bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesised that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known {beta}2AR ligands to multiple {beta}2AR structures, generating ca 75,000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure-activity relationship that identifies small changes to the ligands that invert their activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.

bioinformatics↗

bioRxiv2020

An adaptable analysis workflow for characterization of platelet spreading and morphology

The assessment of platelet spreading through light microscopy, and the subsequent quantification of parameters such as surface area and circularity, is a key assay for many platelet biologists. Here we present an analysis workflow which robustly segments individual platelets to facilitate the analysis of large numbers of cells while minimising user bias. Image segmentation is performed by interactive learning and touching platelets are separated with an efficient semi-automated protocol. We also use machine learning methods to robustly automate the classification of platelets into different subtypes. These adaptable and reproducible workflows are made freely available and are implemented using the open source software KNIME and ilastik.

bioinformatics↗

bioRxiv2018

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

The study of complex molecular organisation and nano-structure by localization based microscopy is limited by the available analysis tools. We present a segmentation protocol which, through the application of persistence based clustering, is capable of probing densely packed structures which vary in scale. An increase in segmentation performance over state-of-the-art methods is demonstrated. Moreover we employ persistent homology to move beyond clustering, and quantify the topological structure within data. This provides new information about the preserved shapes formed by molecular architecture. Our methods are flexible and we demonstrate this by applying them to receptor clustering in platelets, nuclear pore components and endocytic proteins. Both 2D and 3D implementations are provided within RSMLM, an R package for pointillist based analysis and batch processing of localization microscopy data.

bioinformatics↗