Quantifying molecular colocalization in live cell fluorescence microscopy

Humpert, Fabian; Yahiatène, Idir; Lummer, Martina; Sauer, Markus; Huser, Thomas

doi:10.1109/cleoe-iqec.2013.6801498

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…interactions. [3][4][5][6] There are mainly two FRET methods, intensity-based and°uorescence-lifetime FRET methods. 7 Although intensity-based FRET is more preferable if the measurements of donoracceptor stoichiometry and fast dynamic FRET changes are needed, it requires complicated experimental process and manual image analyses to obtain precise results, which impedes FRET experiment's e±ciency promotion and restrict its application.…”

Section: Introductionmentioning

confidence: 99%

Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

Liu

Luo

2020

J. Innov. Opt. Health Sci.

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Intensity-based quantitative fluorescence resonance energy transfer (FRET) is a technique to measure the distance of molecules in scale of a few nanometers which is far beyond optical diffraction limit. This widely used technique needs complicated experimental process and manual image analyses to obtain precise results, which take a long time and restrict the application of quantitative FRET especially in living cells. In this paper, a simplified and automatic quantitative FRET (saqFRET) method with high efficiency is presented. In saqFRET, photoactivatable acceptor PA-mCherry and optimized excitation wavelength of donor enhanced green fluorescent protein (EGFP) are used to simplify FRET crosstalk elimination. Traditional manual image analyses are time consuming when the dataset is large. The proposed automatic image analyses based on deep learning can analyze 100 samples within 30[Formula: see text]s and demonstrate the same precision as manual image analyses.

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Section: Introductionmentioning

confidence: 99%

Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

Liu

Luo

2020

J. Innov. Opt. Health Sci.

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“…By one-photon fluorescence microscopes, biologists often utilize multiple fluorescence indicators with distinct absorption and emission spectra to label multiple targets within the same specimens. In the applications of multi-color one-photon fluorescence microscopy, observations of various organelles [10], different molecules [11], molecular co-localization [12,13], and even molecular interactions [13] within single cells were achieved in earlierreports. In addition, it was also applied to investigate the mapping of distinct neural circuits in tissues [14] and the tracking of multiple cell populations such as in the studies of stem cell colonies [15], immune cell types [16], cancer cell growth and metastasis [17].…”

Section: Introductionmentioning

confidence: 99%

Simple approach to three-color two-photon microscopy by a fiber-optic wavelength convertor

Huang²,

Liang

et al. 2016

Biomed. Opt. Express

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A simple approach to multi-color two-photon microscopy of the red, green, and blue fluorescent indicators was reported based on an ultra-compact 1.03-μm femtosecond laser and a nonlinear fiber. Inside the nonlinear fiber, the 1.03-μm laser pulses were simultaneously blue-shifted to 0.6~0.8 μm and red-shifted to 1.2~1.4 μm region by the Cherenkov radiation and fiber Raman gain effects. The wavelength-shifted 0.6~0.8 μm and 1.2~1.4 μm radiations were co-propagated with the residual non-converted 1.03-μm pulses inside the same nonlinear fiber to form a fiber-output three-color femtosecond source. The application of the multi-wavelength sources on multi-color two-photon fluorescence microscopy were also demonstrated. Overall, due to simple system configuration, convenient wavelength conversion, easy wavelength tunability within the entire 0.7~1.35 μm bio-penetration window and less requirement for high power and bulky light sources, the simple approach to multi-color two-photon microscopy could be widely applicable as an easily implemented and excellent research tool for future biomedical and possibly even clinical applications.

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Quantifying molecular colocalization in live cell fluorescence microscopy

Cited by 2 publications

References 1 publication

Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

Highly-efficient quantitative fluorescence resonance energy transfer measurements based on deep learning

Simple approach to three-color two-photon microscopy by a fiber-optic wavelength convertor

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