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Spectral Unmixing

A common issue in fluorescence microscopy is channel cross-talk when fluorophore emission spectra overlap, which can cause issues for downstream analysis. The number of fluorophores one can use in fluorescence microscopy is also limited by the number of detectors available. 

To solve the problem of spectral bleeding and expand the capabilities of fluorescence microscopy, we developed a novel method "LUMoS" by using k-means clustering to separate mixed channels. We also integrated this tool into Imaris and Fiji for easy applications. You can find more detailed information about LUMoS in this paper: McRae TD, Oleksyn D, Miller J, Gao Y-R (2019) Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning. PLoS ONE 14(12): e0225410. https://doi.org/10.1371/journal.pone.0225410

 

****If you use this program or algorithm in a publication please cite and read the following reference:
McRae TD, Oleksyn D, Miller J, Gao Y-R (2019) Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning. PLoS ONE 14(12): e0225410. https://doi.org/10.1371/journal.pone.0225410​ . Download the publication from PLoS ONE