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

Koen J A Martens; Bartosz Turkowyd; Ulrike Endesfelder

doi:10.3389/fbinf.2021.817254

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

Front Bioinform. 2022 Feb 1:1:817254. doi: 10.3389/fbinf.2021.817254. eCollection 2021.

Authors

Koen J A Martens^{1

2}, Bartosz Turkowyd^{1

2

3}, Ulrike Endesfelder^{1

2

3}

Affiliations

¹ Department of Physics, Carnegie Mellon University, Pittsburgh, PA, United States.
² Institute for Microbiology and Biotechnology, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
³ Department of Systems and Synthetic Microbiology, Max Planck Institute for Terrestrial Microbiology, LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany.

Abstract

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 (∼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.

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