Despite significant progress, high-speed live-cell super-resolution studies remain limited to specialized optical setups, generally requiring intense phototoxic illumination. Here, we describe a new analytical approach, super-resolution radial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin. In the most challenging data sets for super-resolution, such as those obtained in low-illumination live-cell imaging with GFP, we show that SRRF is generally capable of achieving resolutions better than 150 nm. Meanwhile, for data sets similar to those obtained in PALM or STORM imaging, SRRF achieves resolutions approaching those of standard single-molecule localization analysis. The broad applicability of SRRF and its performance at low signal-to-noise ratios allows super-resolution using modern widefield, confocal or TIRF microscopes with illumination orders of magnitude lower than methods such as PALM, STORM or STED. We demonstrate this by super-resolution live-cell imaging over timescales ranging from minutes to hours.

Abstract:Integrins are transmembrane heterodimers that play a fundamental role in the migration of leukocytes to sites of infection or injury. Here, we provide evidence that the protein tyrosine phosphatase PTPN22 is a novel regulator of LFA-1 signaling in effector T-cells.PTPN22 co-localizes with its substrates at the leading edge of cells migrating on ICAM-1. Gene targeting, or expression of the autoimmune disease-associated PTPN22-R620W variant, results in hyper-phosphorylation of integrin signaling intermediates. Super-resolution imaging reveals that in the steady state PTPN22-R620 exists in large clusters that disaggregate upon LFA-1 stimulation, permitting increased association with its binding partners at the membrane. Failure to retain PTPN22-R620W molecules at the membrane leads to increased LFA-1 clustering and integrin-mediated cell adhesion. Our data define a novel mechanism for fine-tuning integrin signaling in T-cells, and a new paradigm of autoimmunity in man in which disease susceptibility is underpinned by inherited perturbations of integrin function.One Sentence Summary: PTPN22 is a negative regulator of integrin signaling and loss-offunction mutants increase cell adhesion.3

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.

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

Plasmodium knowlesi, a zoonotic parasite causing severe-to-lethal malaria disease in humans, has only recently been adapted to continuous culture with human red blood cells (RBCs). In comparison with the most virulent human malaria, Plasmodium falciparum, there are, however, few cellular tools available to study its biology, in particular direct investigation of RBC invasion by blood-stage P. knowlesi merozoites. This leaves our current understanding of biological differences across pathogenic Plasmodium spp. incomplete. Here, we report a robust method for isolating viable and invasive P. knowlesi merozoites to high purity and yield. Using this approach, we present detailed comparative dissection of merozoite invasion (using a variety of microscopy platforms) and direct assessment of kinetic differences between knowlesi and falciparum merozoites. We go on to assess the inhibitory potential of molecules targeting discrete steps of invasion in either species via a quantitative invasion inhibition assay, identifying a class of polysulfonate polymer able to efficiently inhibit invasion in both, providing a foundation for pan-Plasmodium merozoite inhibitor development. Given the close evolutionary relationship between P. knowlesi and P. vivax, the second leading cause of malaria-related morbidity, this study paves the way for inter-specific dissection of invasion by all three major pathogenic malaria species.