The Neutrophil.

Neutrophils are immune cells with unusual biological features that furnish potent antimicrobial properties. These cells phagocytose and subsequently kill prokaryotic and eukaryotic organisms very efficiently. Importantly, it is not only their ability to attack microbes within a constrained intracellular compartment that endows neutrophils with antimicrobial function.

Machine learning for cluster analysis of localization microscopy data.

Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in their ability to process large-scale data sets, to deal effectively with sample heterogeneity, or require subjective user-defined analysis parameters. Here, we develop a supervised machine-learning approach to cluster analysis which is fast and accurate.

Gasdermin D plays a vital role in the generation of neutrophil extracellular traps.

The death of a cell is an inevitable part of its biology. During homeostasis, most cells die through apoptosis. If homeostasis is disturbed, cell death can switch to proinflammatory forms of death, such as necroptosis, pyroptosis, or NETosis.

Quantitative fibre analysis of single-molecule localization microscopy data.

Single molecule localization microscopy (SMLM) methods produce data in the form of a spatial point pattern (SPP) of all localized emitters. Whilst numerous tools exist to quantify molecular clustering in SPP data, the analysis of fibrous structures has remained understudied. Taking the SMLM localization coordinates as input, we present an algorithm capable of tracing fibrous structures in data generated by SMLM.

A switch-variant model integrates the functions of an autoimmune variant of the phosphatase PTPN22.

The R620W polymorphism in protein tyrosine phosphatase nonreceptor type 22 (PTPN22) predisposes carriers to several autoimmune diseases. Two papers in and on this human disease-associated variant lead us to propose a new "switch-of-function" model.

Membrane lipid order of sub-synaptic T cell vesicles correlates with their dynamics and function.

During an immune response, T cells survey antigen presenting cells for antigenic peptides via the formation of an interface known as an immunological synapse. Among the complex and dynamic biophysical phenomena occurring at this interface is the trafficking of sub-synaptic vesicles carrying a variety of proximal signalling molecules. Here, we show that rather than being a homogeneous population, these vesicles display a diversity of membrane lipid order profiles, as measured using the environmentally sensitive dye di-4-ANEPPDHQ and multi-spectral TIRF microscopy.

3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse.

Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell's nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D.

Live-Cell Super-resolution Reveals F-Actin and Plasma Membrane Dynamics at the T Cell Synapse.

The cortical actin cytoskeleton has been shown to be critical for the reorganization and heterogeneity of plasma membrane components of many cells, including T cells. Building on previous studies at the T cell immunological synapse, we quantitatively assess the structure and dynamics of this meshwork using live-cell superresolution fluorescence microscopy and spatio-temporal image correlation spectroscopy. We show for the first time, to our knowledge, that not only does the dense actin cortex flow in a retrograde fashion toward the synapse center, but the plasma membrane itself shows similar behavior.

A Bayesian cluster analysis method for single-molecule localization microscopy data.

Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position.

Superresolution imaging of the cytoplasmic phosphatase PTPN22 links integrin-mediated T cell adhesion with autoimmunity.

Integrins are heterodimeric transmembrane proteins that play a fundamental role in the migration of leukocytes to sites of infection or injury. We found that protein tyrosine phosphatase nonreceptor type 22 (PTPN22) inhibits signaling by the integrin lymphocyte function-associated antigen-1 (LFA-1) in effector T cells. PTPN22 colocalized with its substrates at the leading edge of cells migrating on surfaces coated with the LFA-1 ligand intercellular adhesion molecule-1 (ICAM-1).

Bayesian cluster identification in single-molecule localization microscopy data.

Single-molecule localization-based super-resolution microscopy techniques such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. Although many algorithms exist for the identification and localization of molecules from raw image data, methods for analyzing the resulting point patterns for properties such as clustering have remained relatively under-studied. Here we present a model-based Bayesian approach to evaluate molecular cluster assignment proposals, generated in this study by analysis based on Ripley's K function.

Why is PTPN22 a good candidate susceptibility gene for autoimmune disease?

The PTPN22 locus is one of the strongest risk factors outside of the major histocompatability complex that associates with autoimmune diseases. PTPN22 encodes lymphoid protein tyrosine phosphatase (Lyp) which is expressed exclusively in immune cells. A single base change in the coding region of this gene resulting in an arginine to tryptophan amino acid substitution within a polyproline binding motif associates with type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosis, Hashimotos thyroiditis, Graves disease, Addison's disease, Myasthenia Gravis, vitiligo, systemic sclerosis juvenile idiopathic arthritis and psoriatic arthritis.