Biology subjects

Griffie, J.

Publications and source records attributed to Griffie, J..

2 recordsLinked to original sources

bioRxiv2019

Differential nanoscale organisation of LFA-1 modulates T cell migration

Effector T-cells rely on integrins to drive adhesion and migration to facilitate their immune function. Heterodimeric transmembrane integrin LFA-1 (L{beta}2) regulates adhesion and migration through linkage of the extracellular matrix with the intracellular actin treadmill machinery. We quantitated the velocity and direction of F-actin flow in migrating T-cells alongside single molecule localisation of transmembrane and intracellular LFA-1. Our results show that retrograde actin flow positively correlated and immobile actin negatively correlated with T-cell velocity. Plasma membrane localised LFA-1 forms unique nano-clustering patterns in the leading edge, compared to the mid-focal zone, in migrating T-cells. Deleting the cytosolic phosphatase PTPN22, a negative regulator of integrin signaling, increased T-cell velocity, and leading-edge cluster co-localisation of pY397 FAK, pY416 Src family kinases and LFA-1. These data suggest that differential nanoclustering patterns of LFA-1 in migrating T-cells can instruct intracellular signalling linked with the actin treadmill. Our data presents a paradigm where T cells modulate the nanoscale organisation of adhesion and signalling molecules to fine tune their migration speed. This has implications for the regulation of immune and inflammatory responses.

immunology↗

bioRxiv2018

Machine-learning for cluster analysis of localization microscopy data.

Quantifying the clustering of points within single-molecule localization microscopy data is useful to understanding the spatial relationships of the molecules in the underlying sample. The conversion of point pattern data into a meaningful description of clustering is difficult, especially for biologically derived data, as the definitions of clustering are often subjective or simplistic. Many existing computational approaches are also limited in their ability to process large-scale data-sets or to deal effectively with inhomogeneities in clustering. Here we have developed a supervised machine-learning approach to cluster analysis which is fast and accurate. Trained on a variety of simulated clustered data, the network can then classify all points from a typical localization microscopy data-set (several million points from the entire field of view) as being either clustered or not-clustered, with the potential to include additional classifiers to describe different types of clusters. Clustered points can then be further refined into like-clusters for the measurement of cluster area, shape, and point-density. We demonstrate the performance on simulated data and experimental data of the kinase Csk and the adaptor PAG in both naive and pre-stimulated primary human T cell synapses.

cell biology↗