Abstract
Fluorescence microscopy using single molecule imaging and localization (PALM, STORM, and similar approaches) has quickly been adopted as a convenient method for obtaining multicolor, 3D superresolution images of biological samples. Using an approach based on extensive Monte Carlo simulations, we examined the performance of various noise reducing filters required for the detection of candidate molecules. We determined a suitable noise reduction method and derived an optimal, nonlinear threshold which minimizes detection errors introduced by conventional algorithms. We also present a new technique for visualization of single molecule localization microscopy data based on adaptively jittered 2D histograms. We have used our new methods to image both Atto565-phalloidin labeled actin in fibroblast cells, and mCitrine-erbB3 expressed in A431 cells. The enhanced methods developed here were crucial in processing the data we obtained from these samples, as the overall signal to noise ratio was quite low.
Highlights
Low SNRs in SMLM data can result in significant numbers of missed molecules, and worse, an unacceptably high rate of false positive detections
Using an approach based on extensive Monte Carlo simulations, we examined the performance of various noise reducing filters required for the detection of candidate molecules
We determined the best noise reduction method among those tested, and derived an optimal, nonlinear threshold which minimizes detection errors introduced by conventional algorithms
Summary
Low SNRs in SMLM data can result in significant numbers of missed molecules, and worse, an unacceptably high rate of false positive detections. ABSTRACT Fluorescence microscopy using single molecule imaging and localization (PALM, STORM, and similar approaches) has quickly been adopted as a convenient method for obtaining multicolor, 3D superresolution images of biological samples.
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