Malignant transformation of normal epithelial cells involves extensive epigenetic reprogramming that alters the structure of chromatin within the cell nuclei. We have shown that Fourier phase associated with nanoscale nuclear architecture mapping (nanoNAM) of epithelial cells based on Fourier-domain optical coherence tomography (FD-OCT) can capture the resulting changes in nuclear architecture via nanoscale-sensitive joint characterization of coherence-gated mean alteration in nuclear refractive index and its associated heterogeneity. Here, using computational modeling and stochastic optical reconstruction microscopy (STORM) based super-resolution imaging, we demonstrate the underlying structural changes of chromatin remodeling during malignant transformation in epithelial cells measured by depth-resolved nanoNAM.
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.
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.
Optical Aberrations are a major challenge in imaging biological samples. In particular, in single molecule localization (SML) microscopy techniques (STORM, PALM, etc.) a high Strehl ratio point spread function (PSF) is necessary to achieve sub-diffraction resolution. Distortions in the PSF shape directly reduce the resolution of SML microscopy. The system aberrations caused by the imperfections in the optics and instruments can be compensated using Adaptive Optics (AO) techniques prior to imaging. However, aberrations caused by the biological sample, both static and dynamic, have to be dealt with in real time. A challenge for wavefront correction in SML microscopy is a robust optimization approach in the presence of noise because of the naturally high fluctuations in photon emission from single molecules. Here we demonstrate particle swarm optimization for real time correction of the wavefront using an intensity independent metric. We show that the particle swarm algorithm converges faster than the genetic algorithm for bright fluorophores.
Although Single Molecule Localization (SML) techniques have pushed the resolution of fluorescence microscopy beyond the diffraction limit, the accuracy of SML has been limited by the brightness of the fluorophores. The introduction of Quantum Dots (QD) for SML promises to overcome this barrier, and the QD Blueing technique provides a novel approach to SML microscopy. QDs have a higher quantum yield and absorption cross-section, making them brighter, thereby providing a higher accuracy of localization. The blueing technique is also faster and more quantitative than other SML techniques such as dSTORM. The initial bleaching step required by dSTORM is not necessary and each QD is imaged only once as its emission spectrum moves through the blueing window in contrast to dSTORM where the same molecule might be imaged multiple times. Single color QD Blueing has been demonstrated. However in biological imaging, multi-color imaging is essential for understanding the samples under study. Here we introduce two color superresolution microscopy using QD Blueing on biological samples. We demonstrate simultaneous imaging of microtubules and mitochondria in HepG2 cells with a localization accuracy of 40nm. We further show how QD Blueing can be optimized through the control of the sample mounting medium.