3.8 Article

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Journal

FRONTIERS IN BIOINFORMATICS
Volume 1, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fbinf.2021.817254

Keywords

SMLM Python and MATLAB code; temporal median filtering; SMLM localization and localization merging; drift and chromatic aberration correction; SMLM image formation; single-particle tracking; SMLM clustering; SMLM localization precision and structural image resolution

Funding

  1. This work was supported by the DFG priority program SPP 2141 (En1171/1-1) in the frame of the DFG priority program SPP214 (BT and UE), start-up funds at Carnegie Mellon University (BT, KM, and UE), the NSF AI Institute: Physics of the Future (NSF PHY- 2020 [SPP 2141 (En1171/1-1)]
  2. DFG priority program [SPP214]
  3. DFG
  4. Carnegie Mellon University [PHY- 2020295]
  5. NSF AI Institute: Physics of the Future (NSF)
  6. start-up funds at Bonn University

Ask authors/readers for more resources

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine their position with a resolution below the diffraction limit. While SMLM imaging is growing in popularity, the computational analysis associated with the technique is still a specialized area and often remains a black box for experimental researchers.
Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (similar to 5-40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a black box for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

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