Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.

Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.

Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern-a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.

Phospholipid tail asymmetry allows cellular adaptation to anoxic environments.

Membrane biophysical properties are critical to cell fitness and depend on unsaturated phospholipid acyl tails. These can only be produced in aerobic environments since eukaryotic desaturases require molecular oxygen. This raises the question of how cells maintain bilayer properties in anoxic environments. Using advanced microscopy, molecular dynamics simulations, and lipidomics by mass spectrometry we demonstrated the existence of an alternative pathway to regulate membrane fluidity that exploits phospholipid acyl tail length asymmetry, replacing unsaturated species in the membrane lipidome. We show that the fission yeast, Schizosaccharomyces japonicus, which can grow in aerobic and anaerobic conditions, is capable of utilizing this strategy, whereas its sister species, the well-known model organism Schizosaccharomyces pombe, cannot. The incorporation of asymmetric-tailed phospholipids might be a general adaptation to hypoxic environmental niches.

Validation of agent-based models of surface receptor oligomerisation.

Receptor dimerisation and higher order oligomerisation regulates signalling by a wide variety of transmembrane receptors. We discuss how agent-based modelling (ABM) combined with advanced microscopy and structural studies can provide new insights into the regulation of clustering, including spatial considerations, revealing novel targets for therapeutic intervention.

Plasma membrane preassociation drives ß-arrestin coupling to receptors and activation.

ß-arrestin plays a key role in G protein-coupled receptor (GPCR) signaling and desensitization. Despite recent structural advances, the mechanisms that govern receptor-ß-arrestin interactions at the plasma membrane of living cells remain elusive. Here, we combine single-molecule microscopy with molecular dynamics simulations to dissect the complex sequence of events involved in ß-arrestin interactions with both receptors and the lipid bilayer. Unexpectedly, our results reveal that ß-arrestin spontaneously inserts into the lipid bilayer and transiently interacts with receptors via lateral diffusion on the plasma membrane. Moreover, they indicate that, following receptor interaction, the plasma membrane stabilizes ß-arrestin in a longer-lived, membrane-bound state, allowing it to diffuse to clathrin-coated pits separately from the activating receptor. These results expand our current understanding of ß-arrestin function at the plasma membrane, revealing a critical role for ß-arrestin preassociation with the lipid bilayer in facilitating its interactions with receptors and subsequent activation.

A framework for evaluating the performance of SMLM cluster analysis algorithms.

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.

Revealing the tissue-level complexity of endogenous glucagon-like peptide-1 receptor expression and signaling.

The glucagon-like peptide-1 receptor (GLP1R) is a class B G protein-coupled receptor (GPCR) involved in glucose homeostasis and food intake. GLP1R agonists (GLP1RA) are widely used in the treatment of diabetes and obesity, yet visualizing the endogenous localization, organization and dynamics of a GPCR has so far remained out of reach. In the present study, we generate mice harboring an enzyme self-label genome-edited into the endogenous Glp1r locus. We also rationally design and test various fluorescent dyes, spanning cyan to far-red wavelengths, for labeling performance in tissue. By combining these technologies, we show that endogenous GLP1R can be specifically and sensitively detected in primary tissue using multiple colors. Longitudinal analysis of GLP1R dynamics reveals heterogeneous recruitment of neighboring cell subpopulations into signaling and trafficking, with differences observed between GLP1RA classes and dual agonists. At the nanoscopic level, GLP1Rs are found to possess higher organization, undergoing GLP1RA-dependent membrane diffusion. Together, these results show the utility of enzyme self-labels for visualization and interrogation of endogenous proteins, and provide insight into the biology of a class B GPCR in primary cells and tissue.

Topology-based fluorescence image analysis for automated cell identification and segmentation.

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins.

The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects.

Correction of multiple-blinking artifacts in photoactivated localization microscopy.

Photoactivated localization microscopy (PALM) produces an array of localization coordinates by means of photoactivatable fluorescent proteins. However, observations are subject to fluorophore multiple blinking and each protein is included in the dataset an unknown number of times at different positions, due to localization error. This causes artificial clustering to be observed in the data. We present a 'model-based correction' (MBC) workflow using calibration-free estimation of blinking dynamics and model-based clustering to produce a corrected set of localization coordinates representing the true underlying fluorophore locations with enhanced localization precision, outperforming the state of the art. The corrected data can be reliably tested for spatial randomness or analyzed by other clustering approaches, and descriptors such as the absolute number of fluorophores per cluster are now quantifiable, which we validate with simulated data and experimental data with known ground truth. Using MBC, we confirm that the adapter protein, the linker for activation of T cells, is clustered at the T cell immunological synapse.

The T cell receptor displays lateral signal propagation involving non-engaged receptors.

T cells are highly sensitive to low levels of antigen, but how this sensitivity is achieved is currently unknown. Here, we imaged proximal TCR-CD3 signal propagation with single molecule localization microscopy (SMLM) in T cells activated with nanoscale clusters of TCR stimuli. We observed the formation of large TCR-CD3 clusters that exceeded the area of the ligand clusters, and required multivalent interactions facilitated by TCR-CD3 phosphorylation for assembly. Within these clustered TCR-CD3 domains, TCR-CD3 signaling spread laterally for ∼500 nm, far beyond the activating site, via non-engaged receptors. Local receptor density determined the functional cooperativity between engaged and non-engaged receptors, but lateral signal propagation was not influenced by the genetic deletion of ZAP70. Taken together, our data demonstrates that clustered ligands induced the clustering of non-ligated TCR-CD3 into domains that cooperatively facilitate lateral signal propagation.

Quantitative Measurements of Membrane Lipid Order in Yeast and Fungi.

Membrane lateral heterogeneity, historically referred to as the lipid raft hypothesis, has been extensively investigated through physiochemical experiments on model membranes. Currently, the basic principles are well understood; however, the physiological relevance of these structures in living organisms is still not clear. Thus, studying membrane organization in vivo is extremely important and elucidates the role of such structures in various membrane-associated processes. This is particularly true when a whole single-celled organism can be studied rather than an isolated cell. The ordered and disordered membrane phases are characterized by the degree of acyl chain packing in the lipid bilayer. Polar water molecules can penetrate into the low-density lipid packing of the disordered phase, but are more excluded from the tightly packed ordered phase. Here, polarity-sensitive probes, embedded in the lipid bilayer, are used to report on membrane organization and to quantitate this parameter via 2-channel fluorescence microscopy. Coupling genetic approaches, which are easily accessible in yeast model organisms, with the imaging approach described here provides a great opportunity to investigate how membrane heterogeneity impacts physiology.

Super-Resolution Imaging Approaches for Quantifying F-Actin in Immune Cells.

Immune cells comprise a diverse set of cells that undergo a complex array of biological processes that must be tightly regulated. A key component of cellular machinery that achieves this is the cytoskeleton. Therefore, imaging and quantitatively describing the architecture and dynamics of the cytoskeleton is an important research goal. Optical microscopy is well suited to this task. Here, we review the latest in the state-of-the-art methodology for labeling the cytoskeleton, fluorescence microscopy hardware suitable for such imaging and quantitative statistical analysis software applicable to describing cytoskeletal structures. We also highlight ongoing challenges and areas for future development.

LXR directly regulates glycosphingolipid synthesis and affects human CD4+ T cell function.

The liver X receptor (LXR) is a key transcriptional regulator of cholesterol, fatty acid, and phospholipid metabolism. Dynamic remodeling of immunometabolic pathways, including lipid metabolism, is a crucial step in T cell activation. Here, we explored the role of LXR-regulated metabolic processes in primary human CD4+ T cells and their role in controlling plasma membrane lipids (glycosphingolipids and cholesterol), which strongly influence T cell immune signaling and function. Crucially, we identified the glycosphingolipid biosynthesis enzyme glucosylceramide synthase as a direct transcriptional LXR target. LXR activation by agonist GW3965 or endogenous oxysterol ligands significantly altered the glycosphingolipid:cholesterol balance in the plasma membrane by increasing glycosphingolipid levels and reducing cholesterol. Consequently, LXR activation lowered plasma membrane lipid order (stability), and an LXR antagonist could block this effect. LXR stimulation also reduced lipid order at the immune synapse and accelerated activation of proximal T cell signaling molecules. Ultimately, LXR activation dampened proinflammatory T cell function. Finally, compared with responder T cells, regulatory T cells had a distinct pattern of LXR target gene expression corresponding to reduced lipid order. This suggests LXR-driven lipid metabolism could contribute to functional specialization of these T cell subsets. Overall, we report a mode of action for LXR in T cells involving the regulation of glycosphingolipid and cholesterol metabolism and demonstrate its relevance in modulating T cell function.

Asymmetric glycerophospholipids impart distinctive biophysical properties to lipid bilayers.

Phospholipids are a diverse group of biomolecules consisting of a hydrophilic headgroup and two hydrophobic acyl tails. The nature of the head and length and saturation of the acyl tails are important for defining the biophysical properties of lipid bilayers. It has recently been shown that the membranes of certain yeast species contain high levels of unusual asymmetric phospholipids consisting of one long and one medium-chain acyl moiety, a configuration not common in mammalian cells or other well-studied model yeast species. This raises the possibility that structurally asymmetric glycerophospholipids impart distinctive biophysical properties to the yeast membranes. Previously, it has been shown that lipids with asymmetric length tails form a mixed interdigitated gel phase and exhibit unusual endotherm behavior upon heating and cooling. Here, however, we address physiologically relevant temperature conditions and, using atomistic molecular dynamics simulations and environmentally sensitive fluorescent membrane probes, characterize key biophysical parameters (such as lipid packing, diffusion coefficient, membrane thickness, and area per lipid) in membranes composed of both length-asymmetric glycerophospholipids and ergosterol. Interestingly, we show that saturated but asymmetric glycerophospholipids maintain membrane lipid order across a wide range of temperatures. We also show that these asymmetric lipids can substiture of unsaturated symmetric lipids in the phase behaviour of ternary lipid bilayers. This may allow cells to maintain membrane fluidity, even in environments that lack oxygen, which is required for the synthesis of unsaturated lipids and sterols.

The Role of Protein and Lipid Clustering in Lymphocyte Activation.

Lymphocytes must strike a delicate balance between activating in response to signals from potentially pathogenic organisms and avoiding activation from stimuli emanating from the body's own cells. For cells, such as T or B cells, maximizing the efficiency and fidelity, whilst minimizing the crosstalk, of complex signaling pathways is crucial. One way of achieving this control is by carefully orchestrating the spatiotemporal organization of signaling molecules, thereby regulating the rates of protein-protein interactions. This is particularly true at the plasma membrane where proximal signaling events take place and the phenomenon of protein microclustering has been extensively observed and characterized. This review will focus on what is known about the heterogeneous distribution of proteins and lipids at the cell surface, illustrating how such distributions can influence signaling in health and disease. We particularly focus on nanoscale molecular organization, which has recently become accessible for study through advances in microscope technology and analysis methodology.

Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM).

MOTIVATION: Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. RESULTS: Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. AVAILABILITY AND IMPLEMENTATION: Software and data available at https://github.com/lp1611/mol_count_dstorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Three-dimensional total-internal reflection fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules.

Single-molecule localization microscopy enables far-field imaging with lateral resolution in the range of 10 to 20 nanometres, exploiting the fact that the centre position of a single-molecule's image can be determined with much higher accuracy than the size of that image itself. However, attaining the same level of resolution in the axial (third) dimension remains challenging. Here, we present Supercritical Illumination Microscopy Photometric z-Localization with Enhanced Resolution (SIMPLER), a photometric method to decode the axial position of single molecules in a total internal reflection fluorescence microscope. SIMPLER requires no hardware modification whatsoever to a conventional total internal reflection fluorescence microscope and complements any 2D single-molecule localization microscopy method to deliver 3D images with nearly isotropic nanometric resolution. Performance examples include SIMPLER-direct stochastic optical reconstruction microscopy images of the nuclear pore complex with sub-20 nm axial localization precision and visualization of microtubule cross-sections through SIMPLER-DNA points accumulation for imaging in nanoscale topography with sub-10 nm axial localization precision.

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.