I will present our recent development of high-throughput super-resolution microscopy for robust imaging and reconstruction of super-resolution images on a widely used type of clinical samples – formalin-fixed, paraffin-embedded (FFPE) tissue, referred to as PathSTORM. Its application to visualize disrupted higher-order chromatin folding in early carcinogenesis will also be discussed.
Super-resolution localization microscopy is a powerful tool to visualize molecular structures at a nanoscale resolution. High-density emitter localization combined with a large field of view and fast imaging frame rate is an effective strategy to achieve a high throughput. But the complex algorithms used to precisely localize the overlapping molecules in dense emitter scenarios limits their usage to mostly small image size. Here we present a computationally simple non-iterative method for high-density emitter localization to enable online image processing that remains robust even for low signals and heterogeneous background. Through numerical simulation and biological experiments, we demonstrate that our approach improves the computation speed by two orders of magnitude on CPU and three orders of magnitude upon GPU acceleration to realize online image processing, without compromising localization accuracy for various image characteristics.
High-density localization of multiple fluorescent emitters is a common strategy to improve the temporal
resolution of super-resolution localization microscopy. In recent years, various high-density localization
algorithms have been developed. Despite their rigorous mathematical model and the subsequent
improvement in image resolution, they still suffer from high computing complexity and the resulting
extremely low computation speed, thus limiting the application to either small dataset or expensive
computer clusters. It is still impractical as a routine tool for a large dataset. With the recent advance of
high-throughput localization microscopy with sCMOS cameras that can produce a huge amount of data
in a short period of time, fast processing now becomes even more important. Here, we present a simple
algebraic algorithm based on our previously developed method, gradient fitting, for fast and precise
high-density localization of multiple overlapping fluorescent emitters. Through numerical simulation and
biological experiments, we showed that our algorithm can yield comparable localization precision and
recall rate as DAOSTORM in various densities and signal levels, but with much simpler computation
complexity. After being implemented on a GPU device (NVidia GTX1080) for parallel computing, it can
run over three orders of magnitude faster than DAOSTORM implemented on a high-end workstation.
Therefore, our method presents a possibility for online reconstruction of high-speed super-resolution
imaging with high-density fluorescent emitters.
Astigmatism imaging is widely used to encode the 3D position of fluorophore in single-particle tracking and super-resolution localization microscopy. Here, we present a fast and precise localization algorithm based on gradient fitting to decode the 3D subpixel position of the fluorophore. This algorithm determines the center of the emitter by finding the position with the best-fit gradient direction distribution to the measured point spread function (PSF), and can retrieve the 3D subpixel position of the emitter in a single iteration. Through numerical simulation and experiments with mammalian cells, we demonstrate that our algorithm yields comparable localization precision to the traditional iterative Gaussian function fitting (GF) based method, while exhibits over two orders-of-magnitude faster execution speed. Our algorithm is a promising online reconstruction method for 3D super-resolution microscopy.