Discriminating isogenic cancer cells and identifying altered unsaturated fatty acid content as associated with metastasis status, using k-means clustering and partial least squares-discriminant analysis of Raman maps.

Raman spectroscopy is a label-free, real-time diagnostic tool that shows great promise in identifying cell differences. We have evaluated the discriminatory power of Raman spectroscopy using a unique model system consisting of two isogenic cancer cell lines derived from the MDA-MB-435 cell line. The two cell lines are equally tumorigenic in mice, but while M-4A4 gives rise to metastasis, NM-2C5 only disseminates single cells that remain dormant in distant organs. Previous comparative proteomic and transcriptomic analyses of the two cell lines have shown that they differ only in the expression level of a few proteins and genes. Raman maps were recorded of single cells after fixation and drying using 785 nm laser excitation. K-means clustering reduced the amount of data from each cell and improved the signal-to-noise ratio of cluster-averaged spectra. Spectra representing the nucleus were discarded as they showed much smaller differences between the two cell lines compared to cytoplasm spectra. Partial least squares-discriminant analysis (PLS-DA) was applied to distinguish the two cell lines. A cross-validated PLS-DA resulted in 92% correctly classified samples. Spectral differences were assigned to a higher unsaturated fatty acid content in the metastatic vs nonmetastatic cell line. Our study demonstrates the unique ability of Raman spectroscopy to distinguish minute differences at the subcellular level and yield new biological information. Our study is the first to demonstrate the association between polyunsaturated fatty acid content and metastatic ability in this unique cell model system and is in agreement with previous studies on this topic.