Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis
This work provides a label-free method for analyzing Raman hyperspectral data in biological imaging, though it is incremental as it adapts an existing technique to a specific domain.
The authors tackled the problem of unmixing Raman microscopic images of single human cells by applying Independent Component Analysis (ICA), which successfully reconstructed false color maps showing nuclear constituents, subcellular organelles, and mitochondrial distribution without requiring extensive preprocessing.
Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a raster pattern on a 0.5um grid. Unlike previously used unsupervised machine learning techniques such as principal component analysis, ICA is based on non-Gaussianity and statistical independence of data which is the case for mixture Raman spectra. Hence, ICA is a great candidate for assembling pseudo-colour maps from the spectral hypercube of Raman spectra. Our experimental results revealed that ICA is capable of reconstructing false colour maps of Raman hyperspectral data of human cells, showing the nuclear region constituents as well as subcellular organelle in the cytoplasm and distribution of mitochondria in the perinuclear region. Minimum preprocessing requirements and label-free nature of the ICA method make it a great unmixed method for extraction of endmembers in Raman hyperspectral maps of living cells.