Authors:

Heiner A. Quintero

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About Paper:

Single-Molecule Localization Microscopy (SMLM) is a super-resolution imaging technique that achieves nanometer resolution by localizing individual molecules. This process is based on the principle that within a certain time frame, only a portion of fluorescent molecules emit light and are sufficiently distanced to enable accurate position determination with a precision far exceeding traditional optical resolution limits. One critical step in SMLM is segmentation: cropping sub-regions from camera frames containing emission pattern of single molecules. Conventional methods necessitate an empirical threshold to separate sub-regions containing a single molecule and ones that do not -an error prone step leading to sub-regions without molecule, regions containing multiple molecules, or missing regions containing a single molecule. To this end, we developed an innovative method that eliminates the need for manual threshold input and reduces the error rates. This novel approach centers on identifying the maxima of the similarity map-the normalized correlation between the raw image and a Point Spread Function (PSF) pattern. Implementing this refined segmentation method in SMLM reconstruction not only minimizes the artifacts in the reconstructed super-resolution image but also enhances the overall computational efficiency of image reconstruction and speed. Importantly, our approach negates the need for users, often cell biologists, to enter potentially confusing parameters through trial and error. We expect that our novel single molecule segmentation algorithm provides a user-friendly and robust tool for high- accuracy SMLM image reconstruction and analysis.

Source:

Purdue University / 2023

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Co-authors:

Heiner A. Quintero