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. Trained on a variety of simulated clustered data, the neural network can classify millions of points from a typical single-molecule localization microscopy data set, with the potential to include additional classifiers to describe different subtypes of clusters. The output can be further refined for the measurement of cluster area, shape, and point-density. We demonstrate this approach on simulated data and experimental data of the kinase Csk and the adaptor PAG in primary human T cell immunological synapses.

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. Based upon a density parameter tracing routine, the algorithm outputs several fibre descriptors, such as number of fibres, length of fibres, area of enclosed regions and locations and angles of fibre branch points. The method is validated in a variety of simulated conditions and experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) technique. For this, the nanoscale architecture of F-actin at the T cell immunological synapse in both untreated and pharmacologically treated cells, designed to perturb actin structure, was analysed.

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 Science Immunology and Science Signaling 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. Using live-cell imaging, vesicle tracking and a variety of small molecule drugs to manipulate components of the actin and tubulin cytoskeleton, we show that the membrane lipid order of these vesicles correlate with their dynamics. Furthermore, we show that the key proximal signalling molecule Linker for Activation of T cells (LAT) is enriched in specific vesicle populations as defined by their higher membrane order. These results imply that vesicle lipid order may represent a novel regulatory mechanism for the sorting and trafficking of signalling molecules at the immunological synapse, and, potentially, other cellular structures.

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. Furthermore, using two-color, live-cell superresolution cross-correlation spectroscopy, we demonstrate that the two flows are correlated and, in addition, we show that coupling may extend to the outer leaflet of the plasma membrane by examining the flow of GPI-anchored proteins. Finally, we demonstrate that the actin flow is correlated with a third component, α-actinin, which upon CRISPR knockout led to reduced plasma membrane flow directionality despite increased actin flow velocity. We hypothesize that this apparent cytoskeletal-membrane coupling could provide a mechanism for driving the observed retrograde flow of signaling molecules such as the TCR, Lck, ZAP70, LAT, and SLP76.

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. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.

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). Knockout or knockdown of PTPN22 or expression of the autoimmune disease-associated PTPN22-R620W variant resulted in the enhanced phosphorylation of signaling molecules downstream of integrins. Superresolution imaging revealed that PTPN22-R620 (wild-type PTPN22) was present as large clusters in unstimulated T cells and that these disaggregated upon stimulation of LFA-1, enabling increased association of PTPN22 with its binding partners at the leading edge. The failure of PTPN22-R620W molecules to be retained at the leading edge led to increased LFA-1 clustering and integrin-mediated cell adhesion. Our data define a previously uncharacterized mechanism for fine-tuning integrin signaling in T cells, as well as a paradigm of autoimmunity in humans in which disease susceptibility is underpinned by inherited phosphatase mutations that perturb integrin function.

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. The method takes full account of the individual localization precisions calculated for each emitter. We validate the approach using simulated data, as well as experimental data on the clustering behavior of CD3ζ, a subunit of the CD3 T cell receptor complex, in resting and activated primary human T cells.

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. Here, we review the current understanding of the PTPN22 locus from a genetic, geographical, biochemical and functional perspective.