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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