Spectroscopic single-molecule localization microscopy (sSMLM) combines super-resolution microscopy and spectroscopy. Its single molecule sensitivity and high spectral precision have made it uniquely valuable for several applications, including multicolor imaging, chemical characterization, polarity sensing, and multiplexed single particle tracking. However, widespread adoption is hampered by a lack of standardization in optical implementation, calibration techniques, and image processing. We demonstrate our lab’s efforts to develop tools that simplify adoption and optimize photon efficiency, including protocols for calibration techniques, a user-friendly imageJ plugin for image processing, and a fabricated monolithic beam splitter and prism designed to fit into a microscope body with minimal optical alignment.
This study explores uncertainties in fluorescence labeling, a complication in Single-Molecule Localization Microscopy (SMLM) image interpretation. We examine variability caused by antibody and fluorophore attachment, orientation, and photobleaching, focusing on protein tagging and indirect immunofluorescence, techniques known for their specificity but prone to introducing variable label densities. We use a Monte Carlo (MC) model to simulate SMLM images, providing a 'ground truth' for comparison. This model also investigates the balance between labeling size and density, considering the possibility of single fluorophore attachment in protein tagging and multiple fluorophores in indirect immunofluorescence. We propose methods to quantify the effects of labeling strategies on image quality and accuracy, considering parameters such as labeling linker length and fluorophore photoswitching. Our work enhances the accuracy of SMLM image interpretation and guides the selection of labeling strategies, advancing super-resolution microscopy.
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