Anthony Barsic

ATPase-Modulated Stress Granules Contain a Diverse Proteome and Substructure

Stress granules are mRNA-protein granules that form when translation initiation is limited, and they are related to pathological granules in various neurodegenerative diseases. Super-resolution microscopy reveals stable substructures, referred to as cores, within stress granules that can be purified. Proteomic analysis of stress granule cores reveals a dense network of protein-protein interactions and links between stress granules and human diseases and identifies ATP-dependent helicases and protein remodelers as conserved stress granule components. ATP is required for stress granule assembly and dynamics. Moreover, multiple ATP-driven machines affect stress granules differently, with the CCT complex inhibiting stress granule assembly, while the MCM and RVB complexes promote stress granule persistence. Our observations suggest that stress granules contain a stable core structure surrounded by a dynamic shell with assembly, disassembly, and transitions between the core and shell modulated by numerous protein and RNA remodeling complexes.

Three-dimensional super-resolution and localization of dense clusters of single molecules

When a single molecule is detected in a wide-field microscope, the image approximates the point spread function of the system. However, as the distribution of molecules becomes denser and their images begin to overlap, existing solutions to determine the number of molecules present and their precise three-dimensional locations can tolerate little to no overlap. We propose a localization scheme that can identify several overlapping molecule images while maintaining high localization precision. A solution to this problem involving matched optical and digital techniques, as here proposed, can substantially increase the allowable labeling density and accelerate the data collection time of single-molecule localization microscopy by more than one order of magnitude.

Super-resolution of dense nanoscale emitters beyond the diffraction limit using spatial and temporal information

We propose a super-resolution technique for dense clusters of blinking emitters. The method relies on two basic assumptions: the emitters are statistically independent and a model of the imaging system is known. We numerically analyze the performance limits of the method as a function of emitter density and noise level. Numerical simulations show that five closely packed emitters can be resolved and localized to a precision of 17 nm. The experimental resolution of five quantum dots located within a diffraction-limited spot confirms the applicability of this approach. Statistical tests validate the independence of our quantum dots separated by nanoscale distances.

Dictionary Generation for Sparsity-based Three-Dimensional Super-resolution Microscopy

Sparsity-based reconstruction methods enable high-precision localization of overlapping images of single molecules. Appropriate dictionary selection is critical for 3D sparsity methods. Important dictionary selection considerations include element step size, generation method, and normalization.

Drift Correction in Super-resolution Localization Microscopy without Fiducial Markers

The long data acquisition times in localization microscopy necessitate drift correction in order to achieve super-resolution. Typically, a fiduciary marker is used to track sample movement. We demonstrate a fiduciary-free drift correction method based on repeated activation of labeling dyes.

Three-dimensional super-resolution of dense single molecule scenes for localization microscopy

We demonstrate a three-dimensional super-resolution method that can interpret dense scenes of single molecules. By increasing the labeling density, the acquisition time of single molecule localization microscopy can be substantially reduced.

Metrology of 3D nanostructures.

We propose a superresolution technique to resolve dense clusters of blinking emitters. The method relies on two basic assumptions: the emitters are statistically independent, and a model of the imaging system is known. We numerically analyze the performance limits of the method as a function of the emitter density and the noise level. Numerical simulations show that five closely packed emitters can be resolved and localized to a precision of 17nm. The experimental resolution of five quantum dots located within a diffraction limited spot confirms the applicability of this approach.

Superresolution of Dense Quantum Dot Clusters using Independent Component Classification

We propose a superresolution technique to resolve dense clusters of independently blinking emitters. Experiments and numerical simulations demonstrate the ability to superresolve up to five emitters located within an area of a diffraction limited spot.