scholarly journals Conventional fluorescence microscopy below the diffraction limit with simultaneous capture of two fluorophores in DNA origami

2016 ◽  
Author(s):  
Ben J. Glasgow
Keyword(s):  
Fluorescence Microscopy ◽  
Diffraction Limit ◽  
Dna Origami ◽  
Conventional Fluorescence Microscopy

Structured Illumination Fluorescence Microscopy: Diffraction-Limit Breaking Principle and Application in Life Science

2015 ◽  
Vol 52 (1) ◽  
pp. 010003 ◽  
Author(s):  
吴美瑞 Wu Meirui ◽  
杨西斌 Yang Xibin ◽  
熊大曦 Xiong Daxi ◽  
李辉 Li Hui ◽  
武晓东 Wu Xiaodong
Keyword(s):  
Fluorescence Microscopy ◽  
Life Science ◽  
Diffraction Limit ◽  
Structured Illumination

scholarly journals Phase separation at the nanoscale quantified by dcFCCS

2020 ◽  
Vol 117 (44) ◽  
pp. 27124-27131
Author(s):  
Sijia Peng ◽  
Weiping Li ◽  
Yirong Yao ◽  
Wenjing Xing ◽  
Pilong Li ◽  
...  
Keyword(s):  
Phase Separation ◽  
Detection Limit ◽  
Fluorescence Microscopy ◽  
Critical Concentration ◽  
Transition Process ◽  
Correlation Spectroscopy ◽  
Cellular Processes ◽  
Conventional Fluorescence Microscopy ◽  
Micrometer Scale ◽  
Liquid Liquid Phase Separation

Liquid–liquid phase separation, driven by multivalent macromolecular interactions, causes formation of membraneless compartments, which are biomolecular condensates containing concentrated macromolecules. These condensates are essential in diverse cellular processes. Formation and dynamics of micrometer-scale phase-separated condensates are examined routinely. However, limited by commonly used methods which cannot capture small-sized free-diffusing condensates, the transition process from miscible individual molecules to micrometer-scale condensates is mostly unknown. Herein, with a dual-color fluorescence cross-correlation spectroscopy (dcFCCS) method, we captured formation of nanoscale condensates beyond the detection limit of conventional fluorescence microscopy. In addition, dcFCCS is able to quantify size and growth rate of condensates as well as molecular stoichiometry and binding affinity of client molecules within condensates. The critical concentration to form nanoscale condensates, identified by our experimental measurements and Monte Carlo simulations, is at least several fold lower than the detection limit of conventional fluorescence microscopy. Our results emphasize that, in addition to micrometer-scale condensates, nanoscale condensates are likely to play important roles in various cellular processes and dcFCCS is a simple and powerful quantitative tool to examine them in detail.

Two-Point Separation in Far-Field Super-Resolution Fluorescence Microscopy Based on Two-Color Fluorescence Dip Spectroscopy, Part I: Experimental Evaluation

2005 ◽  
Vol 59 (7) ◽  
pp. 868-872 ◽  
Author(s):  
Takeshi Watanabe ◽  
Yoshinori Iketaki ◽  
Takashige Omatsu ◽  
Kimihisa Yamamoto ◽  
Masaaki Fujii
Keyword(s):  
Fluorescence Microscopy ◽  
Phase Modulation ◽  
Laser Light ◽  
Super Resolution ◽  
Separation Distance ◽  
Diffraction Limit ◽  
Far Field ◽  
Fluorescent Beads ◽  
Fluorescent Image ◽  
Point Separation

The two-point resolution of a novel two-color far-field super-resolution fluorescence microscopy was evaluated by measuring fluorescent beads 100 nm in diameter. This microscopy is based on a combination of two-color fluorescence dip spectroscopy and a phase-modulation technique for a laser beam. By simply introducing two-color laser light, the size of the fluorescent image of a bead was shrunk down to a diameter of 250 nm from the diffraction-limited image with a diameter of 360 nm. For two closely adjacent fluorescent beads with a separation distance of 350 nm, the two-color microscope clearly gave separated fluorescence images, while the conventional one-color fluorescence microscope could not resolve them. It has been proved that our technique breaks Rayleigh's diffraction limit.

DNA origami–based standards for quantitative fluorescence microscopy

2014 ◽  
Vol 9 (6) ◽  
pp. 1367-1391 ◽  
Author(s):  
Jürgen J Schmied ◽  
Mario Raab ◽  
Carsten Forthmann ◽  
Enrico Pibiri ◽  
Bettina Wünsch ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Dna Origami ◽  
Quantitative Fluorescence

Fluorescence Microscopy with 6 nm Resolution on DNA Origami

ChemPhysChem ◽  
2014 ◽  
Vol 15 (12) ◽  
pp. 2431-2435 ◽  
Author(s):  
Mario Raab ◽  
Jürgen J. Schmied ◽  
Ija Jusuk ◽  
Carsten Forthmann ◽  
Philip Tinnefeld
Keyword(s):  
Fluorescence Microscopy ◽  
Dna Origami

Localization-Based Super-Resolution Light Microscopy

2011 ◽  
Vol 19 (4) ◽  
pp. 12-16 ◽  
Author(s):  
Kristin A. Gabor ◽  
Mudalige S. Gunewardene ◽  
David Santucci ◽  
Samuel T. Hess
Keyword(s):  
Fluorescence Microscopy ◽  
Light Microscopy ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Temporal Scales ◽  
Flexible Tool ◽  
Length Scales ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Molecular Length

Fluorescence microscopy is an essential and flexible tool for the study of biology, chemistry, and physics. It can provide information on a wide range of spatial and temporal scales. However, since the inception of light microscopy, diffraction has limited the size of the smallest details that could be imaged in any sample using light. Because much of biology occurs on molecular length scales, interest in circumventing the diffraction limit has been high for many years. Recently, several techniques have been introduced that can bend or break the diffraction limit. Localization-based methods introduced in 2006 have reached this goal and are now rapidly growing in popularity.

scholarly journals Application of Convolutional Neural Networks Towards Nuclei Segmentation in Localization-based Super-Resolution Fluorescence Microscopy Images

2021 ◽  
Author(s):  
Christopher Mela ◽  
Yang Liu
Keyword(s):  
Neural Networks ◽  
Fluorescence Microscopy ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Biological Research ◽  
Nuclei Segmentation ◽  
Segmentation Accuracy ◽  
Conventional Fluorescence Microscopy ◽  
Microscopy Images ◽  
Resolution Data

Abstract Background Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures. Results Results indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture. Conclusions We found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community (https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation).

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