Quantitative data analysis in single-molecule localization microscopy

• Verfasst von: Wu, Yu-Le [VerfasserIn]
• Verfasst von: Tschanz, Aline [VerfasserIn]
• Verfasst von: Krupnik, Leonard [VerfasserIn]
• Verfasst von: Ries, Jonas [VerfasserIn]
• Titel: Quantitative data analysis in single-molecule localization microscopy
• Verf.angabe: Yu-Le Wu, Aline Tschanz, Leonard Krupnik, and Jonas Ries
• E-Jahr: 2020
• Jahr: 20 August 2020
• Umfang: 15 S.
• Fussnoten: Gesehen am 09.12.2020
• Titel Quelle: Enthalten in: Trends in cell biology
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1991
• Jahr Quelle: 2020
• Band/Heft Quelle: 30(2020), 11, Seite 837-851
• ISSN Quelle: 1879-3088
• DOI: doi:10.1016/j.tcb.2020.07.005
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: coordinate-based data
• Sach-SW: nuclear pore complex
• Sach-SW: quantitative analysis
• Sach-SW: single-molecule localization microscopy
• Sach-SW: super-resolution microscopy
• K10plus-PPN: 1742334059

Abstract

Super-resolution microscopy, and specifically single-molecule localization microscopy (SMLM), is becoming a transformative technology for cell biology, as it allows the study of cellular structures with nanometer resolution. Here, we review a wide range of data analyses approaches for SMLM that extract quantitative information about the distribution, size, shape, spatial organization, and stoichiometry of macromolecular complexes to guide biological interpretation. We present a case study using the nuclear pore complex as an example that highlights the power of combining complementary approaches by identifying its symmetry, ringlike structure, and protein copy number. In face of recent technical and computational advances, this review serves as a guideline for selecting appropriate analysis tools and controls to exploit the potential of SMLM for a wide range of biological questions.