Burn, G. L. | Search bioRxiv

bioRxiv2025

Nanoscale Mapping Reveals Periodic Organization of Neutrophil Extracellular Trap Proteins

Neutrophils are essential cells of the innate immune system that release Neutrophil Extracellular Traps (NETs) via NETosis. This specialized cell death pathway results in the extrusion of decondensed chromatin into the extracellular space. NET formation participates in antimicrobial defense and coagulation but dysregulated NETosis can contribute to neoplasticity, coagulopathy, and autoimmune disease. Here, we present a workflow combining optimized sample preparation, super-resolution imaging and quantitative bioimage analysis to investigate the nanoscale organization of NETs. Our newly developed analysis tool, NanoNET, facilitated the automated identification of DNA filaments and the analyses of NET binding proteins along those filaments through auto- and cross-correlation. We identify specific NET proteins, including neutrophil elastase (NE) and proteinase 3 (PR3), that are bound along DNA strands in a periodic fashion that mirrored nucleosomes. Cross-correlation of NE and PR3 with nucleosomes revealed that these proteins were highly colocalized suggesting that they dock onto or around nucleosomes. The presented workflow and analysis tools represent a significant methodological advance for studying protein distributions along NETs and along filamentous structures in general. Understanding how NET filaments are organized is a critical step toward elucidating both their formation and function.

cell biology↗

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↗