Multi-color Molecular Visualization of Signaling Proteins Reveals How C-Terminal Src Kinase Nanoclusters Regulate T Cell Receptor Activation

Elucidating the mechanisms that controlled T cell activation requires visualization of the spatial organization of multiple proteins on the submicron scale. Here, we use stoichiometrically accurate, multiplexed, singlemolecule super-resolution microscopy (DNA-PAINT) to image the nanoscale spatial architecture of the primary inhibitor of the T cell signaling pathway, Csk, and two binding partners implicated in its membrane association, PAG and TRAF3. Combined with a newly developed co-clustering analysis framework, we find that Csk forms nanoscale clusters proximal to the plasma membrane that are lost post-stimulation and are re-recruited at later time points. Unexpectedly, these clusters do not co-localize with PAG at the membrane but instead provide a ready pool of monomers to downregulate signaling. By generating CRISPR-Cas9 knockout T cells, our data also identify that a major risk factor for autoimmune diseases, the protein tyrosine phosphatase non-receptor type 22 (PTPN22) locus, is essential for Csk nanocluster re-recruitment and for maintenance of the synaptic PAG population

Fluorescent d-Amino Acids for Super-resolution Microscopy of the Bacterial Cell Wall.

Fluorescent d-amino acids (FDAAs) have previously been developed to enable in situ highlighting of locations of bacterial cell wall growth. Most bacterial cells lie at the edge of the diffraction limit of visible light; thus, resolving the precise details of peptidoglycan (PG) biosynthesis requires super-resolution microscopy after probe incorporation. Single molecule localization microscopy (SMLM) has stringent requirements on the fluorophore photophysical properties and therefore has remained challenging in this context. Here, we report the synthesis and characterization of new FDAAs compatible with one-step labeling and SMLM imaging. We demonstrate the incorporation of our probes and their utility for visualizing PG at the nanoscale in Gram-negative, Gram-positive, and mycobacteria species. This improved FDAA toolkit will endow researchers with a nanoscale perspective on the spatial distribution of PG biosynthesis for a broad range of bacterial species

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

Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data

Abstract Motivation Unlike conventional microscopy which produces pixelated images, SMLM produces data in the form of a list of localization coordinates—a spatial point pattern (SPP). Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular clustering within, for example, the plasma membrane. While SMLM cluster analysis is now well developed, techniques for analyzing fibrous structures remain poorly explored. Results Here, we demonstrate a statistical methodology, based on Ripley’s K-function to quantitatively assess fibrous structures in 2D SMLM datasets. Using simulated data, we present the underlying theory to describe fiber spatial arrangements and show how these descriptions can be quantitatively derived from pointillist datasets. We also demonstrate the techniques on experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) approach to SMLM, in the context of the fibrous actin meshwork at the T cell immunological synapse, whose structure has been shown to be important for T cell activation. Availability and Implementation Freely available on the web at https://github.com/RubyPeters/Angular-Ripleys-K. Implemented in MatLab. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec