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

被引:9
|
作者
Martens, Koen J. A. [1 ,2 ]
Turkowyd, Bartosz [1 ,2 ,3 ]
Endesfelder, Ulrike [1 ,2 ,3 ]
机构
[1] Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15213 USA
[2] Rhein Friedrich Wilhelms Univ Bonn, Inst Microbiol & Biotechnol, Bonn, Germany
[3] LOEWE Ctr Synthet Microbiol SYNMIKRO, Max Planck Inst Terr Microbiol, Dept Syst & Synthet Microbiol, Marburg, Germany
来源
基金
美国安德鲁·梅隆基金会;
关键词
SMLM [!text type='Python']Python[!/text] 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; OPTICAL RECONSTRUCTION MICROSCOPY; SAMPLE DRIFT CORRECTION; PARTICLE TRACKING; SUPERRESOLUTION MICROSCOPY; RESOLUTION CRITERION; TRANSITION RATES; CLUSTER-ANALYSIS; DIFFUSION; PRECISION; NANOSCOPY;
D O I
10.3389/fbinf.2021.817254
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:19
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