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Universal inverse modeling of point spread functions for SMLM localization and microscope characterization.

Sheng LiuJianwei ChenJonas HellgothLucas-Raphael MüllerBoris FerdmanChristian KarrasDafei XiaoKeith A LidkeRainer HeintzmannYoav ShechtmanYiming LiJonas Ries
Published in: Nature methods (2024)
The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data.
Keyphrases
  • single molecule
  • living cells
  • atomic force microscopy
  • electronic health record
  • high resolution
  • deep learning
  • big data
  • machine learning
  • genome wide
  • single cell