ReviewCurrent topics in membranes2015
Dances with Membranes: Breakthroughs from Super-resolution Imaging.
Review in Current topics in membranes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Approaches to visualize, quantify, and manipulate phosphoinositides in cells.Histochemistry and cell biology · 2026Review
- Dynamics and Patterning of 5-Hydroxytryptamine 2 Subtype Receptors in JC Polyomavirus Entry.Viruses · 2022Article
- G protein-coupled receptor-effector macromolecular membrane assemblies (GEMMAs).Pharmacology & therapeutics · 2022Review
- Quantitative FRET Microscopy Reveals a Crucial Role of Cytoskeleton in Promoting PI(4,5)PInternational journal of molecular sciences · 2021Article
- Triarylmethane Fluorophores Resistant to Oxidative Photobluing.Journal of the American Chemical Society · 2019Article
- Sphingolipids and lipid rafts: Novel concepts and methods of analysis.Chemistry and physics of lipids · 2018Review
- Functional link between plasma membrane spatiotemporal dynamics, cancer biology, and dietary membrane-altering agents.Cancer metastasis reviews · 2018Review
- Small-Molecule Modulation of Lipid-Dependent Cellular Processes against Cancer: Fats on the Gunpoint.BioMed research international · 2018Review
- Super-resolution Analysis of TCR-Dependent Signaling: Single-Molecule Localization Microscopy.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Calcium Sensors as Key Hubs in Plant Responses to Biotic and Abiotic Stresses.Frontiers in plant science · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Biological membrane organization mediates numerous cellular functions and has also been connected with an immense number of human diseases. However, until recently, experimental methodologies have been unable to directly visualize the nanoscale details of biological membranes, particularly in intact living cells. Numerous models explaining membrane organization have been proposed, but testing those models has required indirect methods; the desire to directly image proteins and lipids in living cell membranes is a strong motivation for the advancement of technology. The development of super-resolution microscopy has provided powerful tools for quantification of membrane organization at the level of individual proteins and lipids, and many of these tools are compatible with living cells. Previously inaccessible questions are now being addressed, and the field of membrane biology is developing rapidly. This chapter discusses how the development of super-resolution microscopy has led to fundamental advances in the field of biological membrane organization. We summarize the history and some models explaining how proteins are organized in cell membranes, and give an overview of various super-resolution techniques and methods of quantifying super-resolution data. We discuss the application of super-resolution techniques to membrane biology in general, and also with specific reference to the fields of actin and actin-binding proteins, virus infection, mitochondria, immune cell biology, and phosphoinositide signaling. Finally, we present our hopes and expectations for the future of super-resolution microscopy in the field of membrane biology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.