Can single molecule localization microscopy detect nanoclusters in T cells?

Heterogeneity of Signaling Complex Nanostructure in T Cells Activated Via the T Cell Antigen Receptor

Activation of the T cell antigen receptor (TCR) is a key step in initiating the adaptive immune response. Single-molecule localization techniques have been used to investigate the arrangement of proteins within the signaling complexes formed around activated TCRs, but a clear picture of nanoscale organization in stimulated T cells has not emerged. Here, we have improved the examination of T cell nanostructure by visualizing individual molecules of six different proteins in a single sample of activated Jurkat T cells using the multiplexed antibody-size limited direct stochastic optical reconstruction microscopy (madSTORM) technique. We formally define irregularly shaped regions of interest, compare areas where signaling complexes are concentrated with other areas, and improve the statistical analyses of the locations of molecules. We show that nanoscale organization of proteins is mainly confined to the areas with dense concentrations of TCR-based signaling complexes. However, randomly distributed molecules are also found in some areas containing concentrated signaling complexes. These results are consistent with the view that the proteins within signaling complexes are connected by numerous weak interactions, leading to flexible, dynamic, and mutable structures which produce large variations in the nanostructure found in activated T cells.

Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane

Localization and tracking of individual receptors by single-molecule imaging opens unique possibilities to unravel the assembly and dynamics of signaling complexes in the plasma membrane. We present a comprehensive workflow for imaging and analyzing receptor diffusion and interaction in live cells at single molecule level with up to four colors. Two engineered, monomeric GFP variants, which are orthogonally recognized by anti-GFP nanobodies, are employed for efficient and selective labeling of target proteins in the plasma membrane with photostable fluorescence dyes. This labeling technique enables us to quantitatively resolve the stoichiometry and dynamics of the interferon-γ (IFNγ) receptor signaling complex in the plasma membrane of living cells by multicolor single-molecule imaging. Based on versatile spatial and spatiotemporal correlation analyses, we identify ligand-induced receptor homo- and heterodimerization. Multicolor single-molecule co-tracking and quantitative single-molecule Förster resonance energy transfer moreover reveals transient assembly of IFNγ receptor heterotetramers and confirms its structural architecture.

K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised K-nearest neighbour vectors of a spatially random point pattern. Here, we use KNA to define a novel visualisation of individual images, to compare within and between groups of images and to investigate the preferential patterns of phosphorylation. This methodology is also highly flexible and can be used to augment existing clustering methods by providing clustering diagnostics as well as revealing substructure within microclusters. In summary, we have presented a highly flexible analysis tool that presents new conceptual possibilities in the analysis of SMLM images.

An update on molecular counting in fluorescence microscopy

Quantitative assessment of protein complexes, such as receptor clusters in the context of cellular signalling, has become a pressing objective in cell biology. The advancements in the field of single molecule fluorescence microscopy have led to different approaches for counting protein copy numbers in various cellular structures. This has resulted in an increasing interest in robust calibration protocols addressing photophysical properties of fluorescent labels and the effect of labelling efficiencies. Here, we want to give an update on recent methods for protein counting with a focus on novel calibration protocols. In this context, we discuss different types of calibration samples and identify some of the challenges arising in molecular counting experiments. Some recently published applications offer potential approaches to tackle these challenges.

The Nanoscopic Organization of Synapse Structures: A Common Basis for Cell Communication

Synapse structures, including neuronal and immunological synapses, can be seen as the plasma membrane contact sites between two individual cells where information is transmitted from one cell to the other. The distance between the two plasma membranes is only a few tens of nanometers, but these areas are densely populated with functionally different proteins, including adhesion proteins, receptors, and transporters. The narrow space between the two plasma membranes has been a barrier for resolving the synaptic architecture due to the diffraction limit in conventional microscopy (~250 nm). Various advanced super-resolution microscopy techniques, such as stimulated emission depletion (STED), structured illumination microscopy (SIM), and single-molecule localization microscopy (SMLM), bypass the diffraction limit and provide a sub-diffraction-limit resolving power, ranging from 10 to 100 nm. The studies using super-resolution microscopy have revealed unprecedented details of the nanoscopic organization and dynamics of synaptic molecules. In general, most synaptic proteins appear to be heterogeneously distributed and form nanodomains at the membranes. These nanodomains are dynamic functional units, playing important roles in mediating signal transmission through synapses. Herein, we discuss our current knowledge on the super-resolution nanoscopic architecture of synapses and their functional implications, with a particular focus on the neuronal synapses and immune synapses.

A cross-nearest neighbor/Monte Carlo algorithm for single-molecule localization microscopy defines interactions between p53, Mdm2, and MEG3

The functions of long noncoding (lnc)RNAs, such as MEG3, are defined by their interactions with other RNAs and proteins. These interactions, in turn, are shaped by their subcellular localization and temporal context. Therefore, it is important to be able to analyze the relationships of lncRNAs while preserving cellular architecture. The ability of MEG3 to suppress cell proliferation led to its recognition as a tumor suppressor. MEG3 has been proposed to activate p53 by disrupting the interaction of p53 with mouse double minute 2 homolog (Mdm2). To test this mechanism in the native cellular context, we employed two-color direct stochastic optical reconstruction microscopy, a single-molecule localization microscopy technique, to detect and quantify the localizations of p53, Mdm2, and MEG3 in U2OS cells. We developed a new cross-nearest neighbor/Monte Carlo algorithm to quantify the association of these molecules. Proof of concept for our method was obtained by examining the association between FKBP1A and mTOR, MEG3 and p53, and Mdm2 and p53. In contrast to previous models, our data support a model in which MEG3 modulates p53 independently of the interaction with Mdm2.

Single-Molecule Super-Resolution Imaging of T-Cell Plasma Membrane CD4 Redistribution upon HIV-1 Binding

The first step of cellular entry for the human immunodeficiency virus type-1 (HIV-1) occurs through the binding of its envelope protein (Env) with the plasma membrane receptor CD4 and co-receptor CCR5 or CXCR4 on susceptible cells, primarily CD4+ T cells and macrophages. Although there is considerable knowledge of the molecular interactions between Env and host cell receptors that lead to successful fusion, the precise way in which HIV-1 receptors redistribute to sites of virus binding at the nanoscale remains unknown. Here, we quantitatively examine changes in the nanoscale organisation of CD4 on the surface of CD4+ T cells following HIV-1 binding. Using single-molecule super-resolution imaging, we show that CD4 molecules are distributed mostly as either individual molecules or small clusters of up to 4 molecules. Following virus binding, we observe a local 3-to-10-fold increase in cluster diameter and molecule number for virus-associated CD4 clusters. Moreover, a similar but smaller magnitude reorganisation of CD4 was also observed with recombinant gp120. For one of the first times, our results quantify the nanoscale CD4 reorganisation triggered by HIV-1 on host CD4+ T cells. Our quantitative approach provides a robust methodology for characterising the nanoscale organisation of plasma membrane receptors in general with the potential to link spatial organisation to function.

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, single-molecule 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.

Two- and three-color STORM analysis reveals higher-order assembly of leukotriene synthetic complexes on the nuclear envelope of murine neutrophils

Over the last several years it has become clear that higher order assemblies on membranes, exemplified by signalosomes, are a paradigm for the regulation of many membrane signaling processes. We have recently combined two-color direct stochastic optical reconstruction microscopy (dSTORM) with the (Clus-DoC) algorithm that combines cluster detection and colocalization analysis to observe the organization of 5-lipoxygenase (5-LO) and 5-lipoxygenase-activating protein (FLAP) into higher order assemblies on the nuclear envelope of mast cells; these assemblies were linked to leukotriene (LT) C4 production. In this study we investigated whether higher order assemblies of 5-LO and FLAP included cytosolic phospholipase A2 (cPLA2) and were linked to LTB4 production in murine neutrophils. Using two- and three-color dSTORM supported by fluorescence lifetime imaging microscopy we identified higher order assemblies containing 40 molecules (median) (IQR: 23, 87) of 5-LO, and 53 molecules (62, 156) of FLAP monomer. 98 (18, 154) molecules of cPLA2 were clustered with 5-LO, and 77 (33, 114) molecules of cPLA2 were associated with FLAP. These assemblies were tightly linked to LTB4 formation. The activation-dependent close associations of cPLA2, FLAP, and 5-LO in higher order assemblies on the nuclear envelope support a model in which arachidonic acid is generated by cPLA2 in apposition to FLAP, facilitating its transfer to 5-LO to initiate LT synthesis.

Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering

Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further permits the quantitation relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately estimate these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular aggregates of different stoichiometry from segmented images. The distribution of proxies for the number of molecules in a cluster, such as the number of localizations or the fluorescence intensity, is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and thus determine the optimum number of mixture components and their weights. We test the performance of the algorithm on in silico data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling an accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmic single motors and higher-order structures.