Linear Spectral Unmixing
Linear Spectral Unmixing - Related Videos
08:47
Spectral Confocal Imaging of Fluorescently tagged Nicotinic Receptors in Knock-in Mice with Chronic Nicotine Administration
0 Views •
Cited by 12 •
2012
We have developed a novel technique of quantifying nicotinic acetylcholine receptor changes within subcellular regions of specific subtypes of CNS neurons to better understand the mechanisms of nicotine addiction by using a combination of approaches including fluorescent protein tagging of the receptor using the knock-in approach and spectral confocal imaging.
10:28
High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
0 Views •
2025
Spectral Iterative Bleaching Extends Multiplexity (IBEX) builds upon the base IBEX technique by adding heparin blocking to minimize nonspecific binding and leveraging spectral detection with computational unmixing to suppress autofluorescence. This approach accelerates image acquisition while reducing sources of background, enabling robust multi-round, high-parameter spatial proteomic analyses.
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
0 Views •
Cited by 30 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
06:35
Linearization of the Bradford Protein Assay
0 Views •
Cited by 292 •
2010
The accuracy and sensitivity of protein determination by the rapid and convenient Bradford assay is compromised by intrinsic nonlinearity. We show a simple linearization procedure that greatly increases the accuracy, improves the sensitivity of the assay about 10-fold, and significantly reduces interference by detergents.
14:26
In vivo Quantification of G Protein Coupled Receptor Interactions using Spectrally Resolved Two-photon Microscopy
0 Views •
Cited by 6 •
2011
By employing a spectrally resolved two-photon microscopy imaging system, pixel-level maps of Förster Resonance Energy Transfer (FRET) efficiencies are obtained for cells expressing membrane receptors hypothesized to form homo-oligomeric complexes. From the FRET efficiency maps, we are able to estimate stoichiometric information about the oligomer complex under study.
FAQs
Endmembers serve as reference spectral signatures for the sources expected in a measurement. The method compares each pixel spectrum with these references and estimates an abundance coefficient for each one. Those coefficients indicate how strongly each source contributes to that pixel, allowing the resulting component maps to represent source-specific distributions rather than only mixed intensity.
Least-squares fitting estimates coefficients that best account for the measured spectrum using the reference spectra. Adding nonnegative constraints restricts those coefficients to zero or positive values. This distinction matters when coefficients are interpreted as source abundances, because the constraint prevents negative contributions from appearing in the estimated composition and can make the result easier to interpret.
When fluorescent labels or activity-related signals have overlapping emission spectra, intensity alone cannot reliably show which source produced a measured signal. Estimating separate coefficients assigns portions of the mixed spectrum to the relevant references. The resulting source-specific maps can reduce spectral cross-talk, helping researchers localize molecular signals or distinguish cellular structures in multiplexed recordings.
An analysis begins with spectra measured from microscopy pixels and a set of reference spectra, or endmembers. The method fits each measured spectrum as a combination of those references, calculates the corresponding abundance coefficients, and organizes them into component maps. Researchers can then inspect the maps to interpret where each molecular, structural, or activity-related signal contributes.
In neuroscience, the approach is useful when fluorescent labels, cellular structures, and activity-related signals occupy overlapping spectral ranges. It can separate their estimated contributions within multiplexed recordings, making it easier to localize molecular signals and analyze the spatial distribution of components. This supports visualization and quantitative analysis when physical separation alone is insufficient for interpretation.
Each component map should be read as an estimate of a source's contribution across the measured image, based on its abundance coefficients and reference spectrum. These maps do not represent a physically isolated signal by themselves; they provide computationally separated views of overlapping measurements. Their value lies in improving visualization and quantitative interpretation of multiplexed neural recordings.