Abstract
Three-dimensional single molecule localization microscopy relies on the fitting of the individual molecules with a point spread function (PSF) model. The reconstructed images often show local squeezing or expansion in z. A common cause is depth-induced aberrations in conjunction with an imperfect PSF model calibrated from beads on a coverslip, resulting in a mismatch between measured PSF and real PSF. Here, we developed a strategy for accurate z-localization in which we use the imperfect PSF model for fitting, determine the fitting errors and correct for them in a post-processing step. We present an open-source software tool and a simple experimental calibration procedure that allow retrieving accurate z-positions in any PSF engineering approach or fitting modality, even at large imaging depths.
Highlights
A mismatch between the calibrated point spread function (PSF) model and the real PSF within the biological specimen often leads to a distortion of the reconstructed super-resolution images
All the beads were fitted with a PSF model that was calibrated on the coverslip
If the real PSF model is close to the PSF model calibrated on the coverslip, the fitted z-position would correspond to the objective position
Summary
A mismatch between the calibrated PSF model and the real PSF within the biological specimen often leads to a distortion of the reconstructed super-resolution images To compensate for these aberration-induced localization errors, several techniques have been proposed, either by active aberration correction with adaptive optics [8,9] or realistic PSF estimation by numerical computation and experimental PSF measurements. Each fluorescent molecule gives a random sampling of the zposition This approach works well for astigmatism-based 3D methods in combination with a Gaussian PSF model, because the calibration curve can be determined based on the PSF width in the x and y direction, respectively. The correction for z-positions can be determined at any depth by interpolating between different beads and is applied to an SMLM data set in a postprocessing step
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