4.5 Review

Super-Resolution Microscopy for Structural Cell Biology

期刊

ANNUAL REVIEW OF BIOPHYSICS
卷 51, 期 -, 页码 301-326

出版社

ANNUAL REVIEWS
DOI: 10.1146/annurev-biophys-102521-112912

关键词

-

向作者/读者索取更多资源

This review introduces super-resolution microscopy techniques, focusing on SMLM and MINFLUX, and summarizes recent technical developments. It also outlines key experimental conditions and analysis methods, highlighting the application of SMLM in studying the structures of biologically relevant molecular machines.
Super-resolution microscopy techniques, and specifically single-molecule localization microscopy (SMLM), are approaching nanometer resolution inside cells and thus have great potential to complement structural biology techniques such as electron microscopy for structural cell biology. In this review, we introduce the different flavors of super-resolution microscopy, with a special emphasis on SMLM and MINFLUX (minimal photon flux). We summarize recent technical developments that pushed these localization-based techniques to structural scales and review the experimental conditions that are key to obtaining data of the highest quality. Furthermore, we give an overview of different analysis methods and highlight studies that used SMLM to gain structural insights into biologically relevant molecular machines. Ultimately, we give our perspective on what is needed to push the resolution of these techniques even further and to apply them to investigating dynamic structural rearrangements in living cells.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Multidisciplinary Sciences

A tessellation-based colocalization analysis approach for single-molecule localization microscopy

Florian Levet, Guillaume Julien, Remi Galland, Corey Butler, Anne Beghin, Anael Chazeau, Philipp Hoess, Jonas Ries, Gregory Giannone, Jean-Baptiste Sibarita

NATURE COMMUNICATIONS (2019)

Article Biochemical Research Methods

Nuclear pores as versatile reference standards for quantitative superresolution microscopy

Jervis Vermal Thevathasan, Maurice Kahnwald, Konstanty Cieslinski, Philipp Hoess, Sudheer Kumar Peneti, Manuel Reitberger, Daniel Heid, Krishna Chaitanya Kasuba, Sarah Janice Hoerner, Yiming Li, Yu-Le Wu, Markus Mund, Ulf Matti, Pedro Matos Pereira, Ricardo Henriques, Bianca Nijmeijer, Moritz Kueblbeck, Vilma Jimenez Sabinina, Jan Ellenberg, Jonas Ries

NATURE METHODS (2019)

Article Biochemical Research Methods

Production of Phytochromes by High-Cell-Density E. coli Fermentation

Maximilian Hoerner, Karl Gerhard, Pavel Salavei, Philipp Hoess, Daniel Haerrer, Johannes Kaiser, Jeffrey J. Tabor, Wilfried Weber

ACS SYNTHETIC BIOLOGY (2019)

Correction Biochemical Research Methods

Nuclear pores as versatile reference standards for quantitative superresolution microscopy (vol 16, pg 1045, 2019)

Jervis Vermal Thevathasan, Maurice Kahnwald, Konstanty Cieslinski, Philipp Hoess, Sudheer Kumar Peneti, Manuel Reitberger, Daniel Heid, Krishna Chaitanya Kasuba, Sarah Janice Hoerner, Yiming Li, Yu-Le Wu, Markus Mund, Ulf Matti, Pedro Matos Pereira, Ricardo Henriques, Bianca Nijmeijer, Moritz Kueblbeck, Vilma Jimenez Sabinina, Jan Ellenberg, Jonas Ries

NATURE METHODS (2019)

Article Multidisciplinary Sciences

Photoactivation of silicon rhodamines via a light-induced protonation

Michelle S. Frei, Philipp Hoess, Marko Lampe, Bianca Nijmeijer, Moritz Kueblbeck, Jan Ellenberg, Hubert Wadepohl, Jonas Ries, Stefan Pitsch, Luc Reymond, Kai Johnsson

NATURE COMMUNICATIONS (2019)

Article Biochemical Research Methods

MINFLUX nanoscopy delivers 3D multicolor nanometer resolution in cells

Klaus C. Gwosch, Jasmin K. Pape, Francisco Balzarotti, Philipp Hoess, Jan Ellenberg, Jonas Ries, Stefan W. Hell

NATURE METHODS (2020)

Article Cell Biology

Identification of novel synaptonemal complex components in C. elegans

Matthew E. Hurlock, Ivana Cavka, Lisa E. Kursel, Jocelyn Haversat, Matthew Wooten, Zehra Nizami, Rashi Turniansky, Philipp Hoess, Jonas Ries, Joseph G. Gall, Ofer Rog, Simone Koehler, Yumi Kim

JOURNAL OF CELL BIOLOGY (2020)

Article Biochemical Research Methods

Deep learning enables fast and dense single-molecule localization with high accuracy

Artur Speiser, Lucas-Raphael Mueller, Philipp Hoess, Ulf Matti, Christopher J. Obara, Wesley R. Legant, Anna Kreshuk, Jakob H. Macke, Jonas Ries, Srinivas C. Turaga

Summary: DECODE is a computational tool that uses deep learning to localize single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. It outperforms available methods and enables fast live-cell SMLM of dynamic processes.

NATURE METHODS (2021)

Correction Biochemical Research Methods

Deep learning enables fast and dense single-molecule localization with high accuracy (vol 18, pg 1082, 2021)

Artur Speiser, Lucas-Raphael Muller, Philipp Hoess, Ulf Matti, Christopher J. Obara, Wesley R. Legant, Anna Kreshuk, Jakob H. Macke, Jonas Ries, Srinivas C. Turaga

NATURE METHODS (2021)

Article Materials Science, Biomaterials

Benchmarking of Cph1 Mutants and DrBphP for Light-Responsive Phytochrome-Based Hydrogels with Reversibly Adjustable Mechanical Properties

Ramona Emig, Philipp Hoess, Hanyang Cai, Peter Kohl, Remi Peyronnet, Wilfried Weber, Maximilian Hoerner

Summary: In this study, bacterial phytochromes Cph1 and DrBphP were engineered and characterized for their switching properties to synthesize biohybrid hydrogels with increased light-responsive stiffness modulations. The R472A mutant of Cph1 was found to improve the dynamic range of storage modulus in the hydrogels, but showed a different light-response for the loss modulus compared to the original Cph1-based hydrogel. These findings highlight the importance of matrix viscoelasticity on cellular mechanotransduction.

ADVANCED BIOLOGY (2022)

Article Biochemical Research Methods

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries

Summary: Quantitative data analysis is crucial for extracting biological insights from the coordinates of single fluorophores in a single-molecule localization microscopy (SMLM) workflow. LocMoFit, an open-source framework, is introduced to fit an arbitrary model to localization coordinates, extract meaningful parameters, and select suitable models. It enables the analysis of complex, heterogeneous, and dynamic structures, and has been demonstrated in assembling multi-protein distribution maps, calculating single-particle averages, and performing time-resolved reconstruction of dynamic processes from static snapshots. Extensive simulation and visualization routines validate the robustness of LocMoFit, and tutorials are provided to enhance users' extraction of information from SMLM data.

NATURE METHODS (2023)

Letter Biochemical Research Methods

Reply to: Assessment of 3D MINFLUX data for quantitative structural biology in cells

Klaus C. Gwosch, Francisco Balzarotti, Jasmin K. Pape, Philipp Hoess, Jan Ellenberg, Jonas Ries, Ulf Matti, Roman Schmidt, Steffen J. Sahl, Stefan W. Hell

NATURE METHODS (2023)

Article Multidisciplinary Sciences

Direct observation of motor protein stepping in living cells using MINFLUX

Takahiro Deguchi, Malina K. Iwanski, Eva-Maria Schentarra, Christopher Heidebrecht, Lisa Schmidt, Jennifer Heck, Tobias Weihs, Sebastian Schnorrenberg, Philipp Hoess, Sheng Liu, Veronika Chevyreva, Kyung-Min Noh, Lukas C. Kapitein, Jonas Ries

Summary: We developed a live-cell tracking method with nanometer spatial and millisecond temporal resolution using MINFLUX super-resolution technique. This method allowed us to resolve the precise stepping motion of motor protein kinesin-1 on microtubules in living cells. Tracking of motors on fixed cell microtubules also revealed the architecture of the microtubule cytoskeleton with protofilament resolution.

SCIENCE (2023)

Article Biochemical Research Methods

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries

Summary: This paper presents an open-source framework called LocMoFit for fitting arbitrary models to localization coordinates. The framework has significant potential for analyzing complex, heterogeneous, and dynamic structures, and can be used for assembling protein distribution maps, calculating single-particle averages, and performing time-resolved analysis of highly dynamic processes.

NATURE METHODS (2023)

暂无数据