Stimulated emission depletion (STED) nanoscopy has overcome a serious diffraction barrier on the optical resolution and facilitated new discoveries on detailed nanostructures in cell biology. Traditional fluorescence probes employed in the super-resolution imaging approach include organic dyes and fluorescent proteins. However, some limitations of these probes, such as photobleaching, short emission wavelengths, and high saturation intensity, still hamper the promotion of optical resolution and bio-applications. Recently, lanthanide luminescent probes with unique optical properties of non-photobleaching and sharp emissions have been applied in super-resolution imaging. In this mini-review, we will introduce several different mechanisms for lanthanide ions to achieve super-resolution imaging based on an STED-like setup. Then, several lanthanide ions used in super-resolution imaging will be described in detail and discussed. Last but not least, we will emphasize the future challenges and outlooks in hope of advancing the next-generation lanthanide fluorescent probes for super-resolution optical imaging.
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http://dx.doi.org/10.3389/fbioe.2021.692075 | DOI Listing |
Protein Sci
February 2025
IRR Chemistry Hub, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK.
Super-resolution microscopy has revolutionized biological imaging, enabling the visualization of structures at the nanometer length scale. Its application in live cells, however, has remained challenging. To address this, we adapted LIVE-PAINT, an approach we established in yeast, for application in live mammalian cells.
View Article and Find Full Text PDFMicromachines (Basel)
December 2024
State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an 710054, China.
Inspired by metasurfaces' control over light fields, this study created a liquid microlens coated with a layer of Au@TiO, Core-Shell nanospheres. Utilizing the surface plasmon resonance (SPR) effect of Au@TiO, Core-Shell nanospheres, and the formation of photonic nanojets (PNJs), this study aimed to extend the imaging system's cutoff frequency, improve microlens focusing, enhance the capture capability of evanescent waves, and utilize nanospheres to improve the conversion of evanescent waves into propagating waves, thus boosting the liquid microlens's super-resolution capabilities. The finite difference time domain (FDTD) method analyzed the impact of parameters including nanosphere size, microlens sample contact width, and droplet's initial contact angle on super-resolution imaging.
View Article and Find Full Text PDFAdv Healthc Mater
January 2025
Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zürich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
Efficient drug delivery remains a significant challenge in modern medicine and pharmaceutical research. Micrometer-scale robots have recently emerged as a promising solution to enhance the precision of drug administration through remotely controlled navigation within microvascular networks. Real-time tracking is crucial for accurate guidance and confirmation of target arrival.
View Article and Find Full Text PDFMembranes (Basel)
January 2025
Department of Chemistry, RCSI, University of Medicine and Health Sciences, 123 St Stephen's Green, D02 YN77 Dublin, Ireland.
The endoplasmic reticulum and the internal nuclear compartments are intrinsically connected through the nuclear membrane, pores and lamina. High resolution imaging of each of these cellular features concurrently remains a significant challenge. To that end we have developed a new molecular nuclear membrane-endoplasmic reticulum (NM-ER) staining fluorophore with emission maxima at 650 nm.
View Article and Find Full Text PDFImaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
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