Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images

Abstract

A new global analysis algorithm to analyse (hyper-) spectral images is presented. It is based on the phasor representation that has been demonstrated to be very powerful for the analysis of lifetime imaging data. In spectral phasor analysis the fluorescence spectrum of each pixel in the image is Fourier transformed. Next, the real and imaginary components of the first harmonic of the transform are employed as X and Y coordinates in a scatter (spectral phasor) plot. Importantly, the spectral phasor representation allows for rapid (real time) semi-blind spectral unmixing of up to three components in the image. This is demonstrated on slides with fixed cells containing three fluorescent labels. In addition the method is used to analyse autofluorescence of cells in a fresh grass blade. It is shown that the spectral phasor approach is compatible with spectral imaging data recorded with a low number of spectral channels.

Characterizing fibrosis in UUO mice model using multiparametric analysis of phasor distribution from FLIM images

“…Depending on the tissue architecture and the origin of fluorescence in that particular pixel, different tissues can have differential distribution of the phasor points and the idea in this paper is to use these divergent distributions of phasor points which can be exploited to distinguish diseased from normal tissue and follow the extent of disease progression with time. The phasor histogram and the FLIM images have been analyzed in different ways [16,[19][20][21]. In the method described in this paper, this distribution of phasor points is split into multiple levels based on the height of peak of the phasor distribution.…”

The Laurdan Spectral Phasor Method to Explore Membrane Micro-heterogeneity and Lipid Domains in Live Cells

“…The phasor approach provides a global analysis of entire images and does not require a priori knowledge of the number or spectral shape of the components in the sample. Fereidouni et al recently applied the phasor approach to spectral imaging data as a simple graphical method for spectral un-mixing [40]. Here we use the spectral phasor approach to distinguish Laurdan interactions with different environments in a pixel.…”

Live-Cell TIRF Imaging of Molecular Assembly and Plasma Membrane Topography

“…Another advantage is that if there are only two (or a few) discrete spectra that are typical of a type of membrane domain, then it will be possible to distinguish the combination of domains containing regions with characteristic spectra because the phasor components add linearly for two (or more) species. [7][8][9] Given a spectrum measured at each pixel indicated by I(λ), according to Fereidouni et al, 10 we define the following two quantities that we interpret as two coordinates in a Cartesian plot. The symbol n indicate the "harmonic order" and we use generally either a value of 1 (first harmonic) or 2 (for the second harmonic) for n. A typical spectrum for Laurdan in the laser scanning microscope Zeiss 710NLO is shown in Figure 13.4, using two-photon excitation at 790 nm.…”