Caveolae and scaffold detection from single molecule localization microscopy data using deep learning

SuperResNET - single-molecule network analysis detects changes to clathrin structure induced by small-molecule inhibitors

SuperResNET is a network analysis pipeline for the analysis of point cloud data generated by single-molecule localization microscopy (SMLM). Here, we applied SuperResNET network analysis of SMLM direct stochastic optical reconstruction microscopy (dSTORM) data to determine how the clathrin endocytosis inhibitors pitstop 2, dynasore and latrunculin A (LatA) alter the morphology of clathrin-coated pits. SuperResNET analysis of HeLa and Cos7 cells identified three classes of clathrin structures: small oligomers (class I), pits and vesicles (class II), and larger clusters corresponding to fused pits or clathrin plaques (class III). Pitstop 2 and dynasore treatment induced distinct homogeneous populations of class II structures in HeLa cells, suggesting that they arrest endocytosis at different stages. Inhibition of endocytosis was not via actin depolymerization, as the actin-depolymerizing agent LatA induced large, heterogeneous clathrin structures. Ternary analysis of SuperResNET shape features presented a distinct more planar profile for blobs from pitstop 2-treated cells, which aligned with clathrin pits identified with high-resolution minimal photon fluxes (MINFLUX) microscopy, whereas control structures resembled MINFLUX clathrin vesicles. SuperResNET analysis therefore showed that pitstop 2 arrests clathrin pit maturation at early stages of pit formation, representing an approach to detect the effect of small molecules on target structures in situ in the cell from SMLM datasets.

AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network's internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Caveolin-1 in Müller Glia Exists as Heat-Resistant, High Molecular Weight Complexes

Caveolin-1 (Cav1), the core structural and scaffolding protein of caveolae membrane domains, is highly expressed in many retinal cells and is associated with ocular diseases. Cav1 regulates innate immune responses and is implicated in neuroinflammatory and neuroprotective signaling in the retina. We have shown that Cav1 expression in Müller glia accounts for over 70% of all retinal Cav1 expression. However, the proteins interacting with Cav1 in Müller glia are not established. Here, we show that immortalized MIO-M1 Müller glia, like endogenous Müller glia, highly express Cav1. Surprisingly, we found that Cav1 in MIO-M1 cells exists as heat-resistant, high molecular weight complexes that are stable after immunoprecipitation (IP). Mass spectrometric analysis of high molecular weight Cav1 complexes after Cav1 IP revealed an interactome network of intermediate filament, desmosomes, and actin-, and microtubule-based cytoskeleton. These results suggest Cav1 domains in Müller glia act as a scaffolding nexus for the cytoskeleton.

Caveolar and non-Caveolar Caveolin-1 in ocular homeostasis and disease

Caveolae, specialized plasma membrane invaginations present in most cell types, play important roles in multiple cellular processes including cell signaling, lipid uptake and metabolism, endocytosis and mechanotransduction. They are found in almost all cell types but most abundant in endothelial cells, adipocytes and fibroblasts. Caveolin-1 (Cav1), the signature structural protein of caveolae was the first protein associated with caveolae, and in association with Cavin1/PTRF is required for caveolae formation. Genetic ablation of either Cav1 or Cavin1/PTRF downregulates expression of the other resulting in loss of caveolae. Studies using Cav1-deficient mouse models have implicated caveolae with human diseases such as cardiomyopathies, lipodystrophies, diabetes and muscular dystrophies. While caveolins and caveolae are extensively studied in extra-ocular settings, their contributions to ocular function and disease pathogenesis are just beginning to be appreciated. Several putative caveolin/caveolae functions are relevant to the eye and Cav1 is highly expressed in retinal vascular and choroidal endothelium, Müller glia, the retinal pigment epithelium (RPE), and the Schlemm's canal endothelium and trabecular meshwork cells. Variants at the CAV1/2 gene locus are associated with risk of primary open angle glaucoma and the high risk HTRA1 variant for age-related macular degeneration is thought to exert its effect through regulation of Cav1 expression. Caveolins also play important roles in modulating retinal neuroinflammation and blood retinal barrier permeability. In this article, we describe the current state of caveolin/caveolae research in the context of ocular function and pathophysiology. Finally, we discuss new evidence showing that retinal Cav1 exists and functions outside caveolae.

Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data

Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.

Single molecule network analysis identifies structural changes to caveolae and scaffolds due to mutation of the caveolin-1 scaffolding domain

Caveolin-1 (CAV1), the caveolae coat protein, also associates with non-caveolar scaffold domains. Single molecule localization microscopy (SMLM) network analysis distinguishes caveolae and three scaffold domains, hemispherical S2 scaffolds and smaller S1B and S1A scaffolds. The caveolin scaffolding domain (CSD) is a highly conserved hydrophobic region that mediates interaction of CAV1 with multiple effector molecules. F92A/V94A mutation disrupts CSD function, however the structural impact of CSD mutation on caveolae or scaffolds remains unknown. Here, SMLM network analysis quantitatively shows that expression of the CAV1 CSD F92A/V94A mutant in CRISPR/Cas CAV1 knockout MDA-MB-231 breast cancer cells reduces the size and volume and enhances the elongation of caveolae and scaffold domains, with more pronounced effects on S2 and S1B scaffolds. Convex hull analysis of the outer surface of the CAV1 point clouds confirms the size reduction of CSD mutant CAV1 blobs and shows that CSD mutation reduces volume variation amongst S2 and S1B CAV1 blobs at increasing shrink values, that may reflect retraction of the CAV1 N-terminus towards the membrane, potentially preventing accessibility of the CSD. Detection of point mutation-induced changes to CAV1 domains highlights the utility of SMLM network analysis for mesoscale structural analysis of oligomers in their native environment.

Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.

Tyrosine phosphorylation of tumor cell caveolin-1: impact on cancer progression

Caveolin-1 (CAV1) has long been implicated in cancer progression, and while widely accepted as an oncogenic protein, CAV1 also has tumor suppressor activity. CAV1 was first identified in an early study as the primary substrate of Src kinase, a potent oncoprotein, where its phosphorylation correlated with cellular transformation. Indeed, CAV1 phosphorylation on tyrosine-14 (Y14; pCAV1) has been associated with several cancer-associated processes such as focal adhesion dynamics, tumor cell migration and invasion, growth suppression, cancer cell metabolism, and mechanical and oxidative stress. Despite this, a clear understanding of the role of Y14-phosphorylated pCAV1 in cancer progression has not been thoroughly established. Here, we provide an overview of the role of Src-dependent phosphorylation of tumor cell CAV1 in cancer progression, focusing on pCAV1 in tumor cell migration, focal adhesion signaling and metabolism, and in the cancer cell response to stress pathways characteristic of the tumor microenvironment. We also discuss a model for Y14 phosphorylation regulation of CAV1 effector protein interactions via the caveolin scaffolding domain.

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

Caveolin-1 in oncogenic metabolic symbiosis

Metabolic phenotypes of cancer cells are heterogeneous and flexible as a tumor mass is a hurriedly evolving system capable of constant adaptation to oxygen and nutrient availability. The exact type of cancer metabolism arises from the combined effects of factors intrinsic to the cancer cells and factors proposed by the tumor microenvironment. As a result, a condition termed oncogenic metabolic symbiosis in which components of the tumor microenvironment (TME) promote tumor growth often occurs. Understanding how oncogenic metabolic symbiosis emerges and evolves is crucial for perceiving tumorigenesis. The process by which tumor cells reprogram their TME involves many mechanisms, including changes in intercellular communication, alterations in metabolic phenotypes of TME cells, and rearrangement of the extracellular matrix. It is possible that one molecule with a pleiotropic effect such as Caveolin-1 may affect many of these pathways. Here, we discuss the significance of Caveolin-1 in establishing metabolic symbiosis in TME.

The importance of caveolins and caveolae to dermatology: Lessons from the caves and beyond

Caveolae are flask-shaped invaginations of the cell membrane rich in cholesterol and sphingomyelin, with caveolin proteins acting as their primary structural components that allow compartmentalization and orchestration of various signalling molecules. In this review, we discuss how pleiotropic functions of caveolin-1 (Cav1) and its intricate roles in numerous cellular functions including lipid trafficking, signalling, cell migration and proliferation, as well as cellular senescence, infection and inflammation, are integral for normal development and functioning of skin and its appendages. We then examine how disruption of the homeostatic levels of Cav1 can lead to development of various cutaneous pathophysiologies including skin cancers, cutaneous fibroses, psoriasis, alopecia, age-related changes in skin and aberrant wound healing and propose how levels of Cav1 may have theragnostic value in skin physiology/pathophysiology.