Calorimetry-Derived Composition Vectors to Resolve Component Raman Spectra in Phospholipid Phase Transitions

Jay P. Kitt, David A. Bryce, Joel M. Harris

Multidimensional least squares analysis is a well-established technique for resolving component vibrational spectra from mixed samples or systems. Component resolution of temperature-dependent vibrational spectra is challenging, however, due to the lack of a suitable model for the variation in sample composition with temperature. In this work, analysis of temperature-dependent Raman spectra of lipid membranes is accomplished by using “concentration” vectors independently derived from enthalpy changes determined by differential scanning calorimetry. Specifically, the lipid–bilayer phase transitions of DMPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine) are investigated through Raman spectra acquired from individual, optically trapped vesicles in suspension as a function of temperature. Heat capacity profiles of the same vesicle suspension are measured using differential scanning calorimetry and numerically integrated to generate enthalpy change curves of each phase transition, which are in turn used to construct composition vectors. Multidimensional least squares analysis optimized for a fit to these composition vectors allows resolution of the component spectra corresponding to gel, ripple, and liquid–crystalline phases of the DMPC. The quality of fit of the calorimetry-derived results is confirmed by unstructured residual differences between the data and the model, and a composition variation predicted by the resolved spectra that matches the calorimetry results. This approach to analysis of temperature-dependent spectral data could be readily applied in other areas of materials characterization, where one is seeking to learn about structural changes that occur through temperature-dependent phase transitions.

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