Spectroscopic analysis beyond the diffraction limit

Spectral Phasor Applied to Spectrally-Resolved Single Molecule Localization Microscopy

Spectrally-resolved single-molecule localization microscopy (srSMLM) has emerged as a powerful tool for exploring the spectral properties of single emitters in localization microscopy. By simultaneously capturing the spatial positions and spectroscopic signatures of individual fluorescent molecules, srSMLM opens up the possibility of investigating an additional dimension in super-resolution imaging. However, appropriate and dedicated tools are required to fully capitalize on the spectral dimension. Here, we propose the application of the spectral phasor analysis as an effective method for summarizing and analyzing the spectral information obtained from srSMLM experiments. The spectral phasor condenses the complete spectrum of a single emitter into a two-dimensional space, preserving key spectral characteristics for single-molecule spectral exploration. We demonstrate the effectiveness of spectral phasor in efficiently classifying single Nile Red fluorescence emissions from largely overlapping cyanine fluorescence signals in dual-color PAINT experiments. Additionally, we employed spectral phasor with srSMLM to reveal subtle alterations occurring in the membrane of Gram-positive Enterococcus hirae in response to gramicidin exposure, a membrane-perturbing antibiotic treatment. Spectral phasor provides a robust, model-free analytic tool for the detailed analysis of the spectral component of srSMLM, enhancing the capabilities of multi-color spectrally-resolved single-molecule imaging.

Multimodal single-molecule microscopy with continuously controlled spectral resolution

Color is a fundamental contrast mechanism in fluorescence microscopy, providing the basis for numerous imaging and spectroscopy techniques. Building on spectral imaging schemes that encode color into a fixed spatial intensity distribution, here, we introduce continuously controlled spectral-resolution (CoCoS) microscopy, which allows the spectral resolution of the system to be adjusted in real-time. By optimizing the spectral resolution for each experiment, we achieve maximal sensitivity and throughput, allowing for single-frame acquisition of multiple color channels with single-molecule sensitivity and 140-fold larger fields of view compared with previous super-resolution spectral imaging techniques. Here, we demonstrate the utility of CoCoS in three experimental formats, single-molecule spectroscopy, single-molecule Förster resonance energy transfer, and multicolor single-particle tracking in live neurons, using a range of samples and 12 distinct fluorescent markers. A simple add-on allows CoCoS to be integrated into existing fluorescence microscopes, rendering spectral imaging accessible to the wider scientific community.

Accelerating multicolor spectroscopic single-molecule localization microscopy using deep learning

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously provides spatial localization and spectral information of individual single-molecules emission, offering multicolor super-resolution imaging of multiple molecules in a single sample with the nanoscopic resolution. However, this technique is limited by the requirements of acquiring a large number of frames to reconstruct a super-resolution image. In addition, multicolor sSMLM imaging suffers from spectral cross-talk while using multiple dyes with relatively broad spectral bands that produce cross-color contamination. Here, we present a computational strategy to accelerate multicolor sSMLM imaging. Our method uses deep convolution neural networks to reconstruct high-density multicolor super-resolution images from low-density, contaminated multicolor images rendered using sSMLM datasets with much fewer frames, without compromising spatial resolution. High-quality, super-resolution images are reconstructed using up to 8-fold fewer frames than usually needed. Thus, our technique generates multicolor super-resolution images within a much shorter time, without any changes in the existing sSMLM hardware system. Two-color and three-color sSMLM experimental results demonstrate superior reconstructions of tubulin/mitochondria, peroxisome/mitochondria, and tubulin/mitochondria/peroxisome in fixed COS-7 and U2-OS cells with a significant reduction in acquisition time.

Super-Resolution Imaging of Self-Assembled Nanocarriers Using Quantitative Spectroscopic Analysis for Cluster Extraction

Self-assembled nanocarriers have inspired a range of applications for bioimaging, diagnostics, and drug delivery. The noninvasive visualization and characterization of these nanocarriers are important to understand their structure to function relationship. However, the quantitative visualization of nanocarriers in the sample's native environment remains challenging with the use of existing technologies. Single-molecule localization microscopy (SMLM) has the potential to provide both high-resolution visualization and quantitative analysis of nanocarriers in their native environment. However, nonspecific binding of fluorescent probes used in SMLM can introduce artifacts, which imposes challenges in the quantitative analysis of SMLM images. We showed the feasibility of using spectroscopic point accumulation for imaging in nanoscale topography (sPAINT) to visualize self-assembled polymersomes (PS) with molecular specificity. Furthermore, we analyzed the unique spectral signatures of Nile Red (NR) molecules bound to the PS to reject artifacts from nonspecific NR bindings. We further developed quantitative spectroscopic analysis for cluster extraction (qSPACE) to increase the localization density by 4-fold compared to sPAINT; thus, reducing variations in PS size measurements to less than 5%. Finally, using qSPACE, we quantitatively imaged PS at various concentrations in aqueous solutions with ∼20 nm localization precision and 97% reduction in sample misidentification relative to conventional SMLM.