Complementarity of PALM and SOFI for super-resolution live cell imaging of focal adhesions

Live cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenging task for super-resolution microscopy. We have addressed this important issue by combining photo-activated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed cell focal adhesion images, we investigated the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework was used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualized the dynamics of focal adhesions, and revealed local mean velocities around 190 nm per minute. The complementarity of PALM and SOFI was assessed in detail with a methodology that integrates a quantitative resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as the fluorophore density and the photo-activation and photo-switching rates

Combining PALM and SOFI for quantitative imaging of focal adhesions in living cells

Focal adhesions are complicated assemblies of hundreds of proteins that allow cells to sense their extracellular matrix and adhere to it. Although most focal adhesion proteins have been identified, their spatial organization in living cells remains challenging to observe. Photo-activated localization microscopy (PALM) is an interesting technique for this purpose, especially since it allows estimation of molecular parameters such as the number of fluorophores. However, focal adhesions are dynamic entities, requiring a temporal resolution below one minute, which is difficult to achieve with PALM. In order to address this problem, we merged PALM with super-resolution optical fluctuation imaging (SOFI) by applying both techniques to the same data. Since SOFI tolerates an overlap of single molecule images, it can improve the temporal resolution compared to PALM. Moreover, an adaptation called balanced SOFI (bSOFI) allows estimation of molecular parameters, such as the fluorophore density. We therefore performed simulations in order to assess PALM and SOFI for quantitative imaging of dynamic structures. We demonstrated the potential of our PALM–SOFI concept as a quantitative imaging framework by investigating moving focal adhesions in living cells

Investigating Focal Adhesion Substructures by Localization Microscopy

Cells rely on focal adhesions (FAs) to carry out a variety of important tasks, including motion, environmental sensing, and adhesion to the extracellular matrix. Although attaining a fundamental characterization of FAs is a compelling goal, their extensive complexity and small size, which can be below the diffraction limit, have hindered a full understanding. In this study we have used single-molecule localization microscopy (SMLM) to investigate integrin β3 and paxillin in rat embryonic fibroblasts growing on two different extracellular matrix-representing substrates (i.e., fibronectin-coated substrates and specifically biofunctionalized nanopatterned substrates). To quantify the substructure of FAs, we developed a clustering method based on expectation maximization of a Gaussian mixture that accounts for localization uncertainty and background. Analysis of our SMLM data indicates that the structures within FAs, characterized as a Gaussian mixture, typically have areas between 0.01 and 1 μm2 , contain 10–100 localizations, and can exhibit substantial eccentricity. Our approach based on SMLM opens new avenues for studying structural and functional biology of molecular assemblies that display substantial varieties in size, shape, and density

High-Resolution Correlative Microscopy: Bridging the Gap between Single Molecule Localization Microscopy and Atomic Force Microscopy

Nanoscale characterization of living samples has become essential for modern biology. Atomic force microscopy (AFM) creates topological images of fragile biological structures from biomolecules to living cells in aqueous environments. However, correlating nanoscale structure to biological function of specific proteins can be challenging. To this end we have built and characterized a correlated single molecule localization microscope (SMLM)/AFM that allows localizing specific, labeled proteins within high-resolution AFM images in a biologically relevant context. Using direct stochastic optical reconstruction microscopy (dSTORM)/AFM, we directly correlate and quantify the density of localizations with the 3D topography using both imaging modalities along (F-)actin cytoskeletal filaments. In addition, using photo activated light microscopy (PALM)/AFM, we provide correlative images of bacterial cells in aqueous conditions. Moreover, we report the first correlated AFM/PALM imaging of live mammalian cells. The complementary information provided by the two techniques opens a new dimension for structural and functional nanoscale biology

Label-free identification of protein aggregates using deep learning

Abstract Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington’s disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min-1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.status: publishe