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Robert Post, Ansh Johri, Bhaskar Ganti, Paul Sajda, Thomas Ach, Christine A Curcio, Theodore Smith; Simultaneous decomposition of multiple retinal pigment epithelium (RPE) autofluorescence hyperspectral datasets for fluorophor discovery. Invest. Ophthalmol. Vis. Sci. 2014;55(13):263.
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© ARVO (1962-2015); The Authors (2016-present)
Excitation of RPE autofluorescence with different wavelengths produces different but closely related spectral data. We hypothesized that simultaneous decomposition of multiple hyperspectral datasets into major spectral signatures and their spatial distributions with non negative matrix factorization (NMF) could exploit these relationships to recover results superior to factoring any single hypercube.
Pure RPE/BrM flat mounts were separately excited at 436-460nm and 480-510nm and hyperspectral emission data were captured by methods described in detail by Johri and Agarwal abstracts. Standard NMF factors a hypercube A into the product of matrices W and H (Fig 1a), where W is the spectra of the recovered sources and H carries their spatial localizations (abundance images). In our formulation, we always retrieve 4 spectral signatures for RPE and one for BrM. We paired each signal found at 436nm excitation to its corresponding signal at 480nm, and linked the two datasets by requiring that the spatial localizations of the paired signals must be exactly the same, because they come from the same compound. (Fig 1b)
Fig. 2 (a, b) shows the 5 spectra recovered from the fovea of a 34 y/o female donor at 436nm and 480nm with standard NMF. The spectra are clearly paired according to the emission maxima. Fig 2c shows the results when the data are decomposed simultaneously: 10 abundant spectra are clearly paired in shape and location, suggesting single species. Each pair corresponds to one clearly defined abundance image.
Simultaneous decomposition of multiple RPE hyperspectral datasets is superior to standard NMF at breaking down a complex spectrum representing a mixture of fluorophors into its individual spectral signals, hence providing better candidates for biochemical identification.
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