Evaluating the Sulfo-Phospho-Vanillin Assay for Total Lipid Sample Normalization as Compared to Gravimetric and...

Sample normalization is essential for lipid quantitation. While normalization methods involving pre- or postgravimetric measurements, counting, or protein prequantitation exist, a single, common sample normalization method is not widely accepted. Previously, we proposed and evaluated the sulfo-phospho-vanillin assay (SPVA), a total lipid quantitation reaction, for prequantifying total lipids for LC-MS/MS sample normalization purposes. This current investigation furthers our evaluation of the SPVA as a sample normalization method in untargeted lipidomic LC-MS/MS by comparing SPVA total lipid prequantitation to protein prequantitation and gravimetric measurements. This study was applied to a wide selection of matrices, including Escherichia coli, plasma, brain, heart, and kidney. Resulting relative lipid concentrations showed smaller bioreplica variation when a prequantitation method was applied, either measuring total lipid or total protein; however, several relative lipid abundances showed inverse concentration relationships when normalizing with total protein compared to total lipid (from SPVA measurements) or gravimetric sample normalization. Further investigation using lipid extracts linearly spiked with pure, non-native lipid showed that gravimetric and protein normalization nonlinearly overproduced significant lipids, while linear increases in significant lipid features were observed from SPVA normalization. Lipid extracts linearly spiked with pure, non-native protein yielded fewer significant lipid features using SPVA normalization, and this was unchanged as protein was added; however, both gravimetric and protein normalization continued to yield large numbers of significant lipid features. Together, these results suggest neither gravimetric nor protein sample normalization appropriately normalizes quantitative lipidomic experiments and greatly overgenerates statistically significant lipid features for biomarker investigation. SPVA normalization more accurately adjusts for lipid changes in bioreplicate samples, leading to fewer but more biologically relevant statistically significant lipid features.