2013
DOI: 10.1007/978-1-60761-847-8_5
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Clearing Up the Signal: Spectral Imaging and Linear Unmixing in Fluorescence Microscopy

Abstract: The ongoing progress in fluorescence labeling and in microscope instrumentation allows the generation and the imaging of complex biological samples that contain increasing numbers of fluorophores. For the correct quantitative analysis of datasets with multiple fluorescence channels, it is essential that the signals of the different fluorophores are reliably separated. Due to the width of fluorescence spectra, this cannot always be achieved using the fluorescence filters in the microscope. In such cases spectra… Show more

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Cited by 96 publications
(90 citation statements)
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“…There are many sophisticated tools for spectral unmixing, which include the use of optimization algorithms; however, we focus on a simple mathematical approach to spectral unmixing that is adequate for many applications. A more exhaustive introduction and dissertation on spectral unmixing may be found in [362366]. …”
Section: Image Renderingmentioning
confidence: 99%
“…There are many sophisticated tools for spectral unmixing, which include the use of optimization algorithms; however, we focus on a simple mathematical approach to spectral unmixing that is adequate for many applications. A more exhaustive introduction and dissertation on spectral unmixing may be found in [362366]. …”
Section: Image Renderingmentioning
confidence: 99%
“…Multicolor intravital TPM can benefit from the use of spectral chips, composed of an array of PMTs and a prism or diffraction grating to separate the fluorescence photons (Im et al 2010; Shi et al 2012, 2015; Zimmermann et al 2014). Spectral segmentation of the wavelengths arising for the different fluorophores will be thereby both more accurate and easier to achieve.…”
Section: Introductionmentioning
confidence: 99%
“…, spectral deconvolution), the contribution of each of the fluorophores used in the experiment in each of the NDD must be measured. Then, using dedicated algorithms based on maximum-likelihood unmixing (Davis and Shen 2007), each image from each NDD can be spectrally unmixed and then analyzed (Thaler and Vogel 2006; Brenner et al 2013; Zimmermann et al 2014). As an example of the crucial importance of spectral unmixing, Ducros et al have used it to detect small spectral variations of odor-evoked FRET transients up to 250 μm in the olfactory bulb of living mice (Ducros et al 2009).…”
Section: Introductionmentioning
confidence: 99%
“…Imaging using conventional bandpass based microscopy is common, where spectral separation is limited to four to five colors that span the ultraviolet (UV) to near-infrared (NIR) spectra. Unique fluorophore detection in a single sample can be improved using spectral imaging and linear unmixing based fluorescence microscopy to simultaneously identify overlapping emission spectra, increasing multicolor imaging capability 1,2 . Fluorescence linear unmixing studies have demonstrated up to five color imaging with overlapping subcellular entities using two excitation wavelengths and emission distributed over 150 nm of spectral space 3 .…”
Section: Introductionmentioning
confidence: 99%