Data processing. LC-MS datasets, defined as the data obtained from a single LC-MS analysis, were processed using the PRISM Data Analysis system [56] , a series of software tools freely available at http://ncrr.pnl.gov/software/ and developed in-house. The first step involved deisotoping of the raw MS data to give the monoisotopic mass, charge state, and intensity of the major peaks in each mass spectrum using Decon2LS [16] . The data were next examined in a 2-D fashion using MultiAlign to identify groups of mass spectral peaks that were observed in sequential spectra using an algorithm [57] that computes a Euclidean distance in n-dimensional space for combinations of peaks. Each group, generally ascribed to one detected species and referred to as a ''feature'', has a median monoisotopic mass, central normalized elution time (NET), and abundance estimate computed by summing the intensities of the MS peaks that comprise the entire LC-MS feature. LC-MS features were then chromatographically aligned across all datasets using the LCMSWARP algorithm [58] in MultiAlign, and the lipid identities of detected features were initially determined by comparing their measured monoisotopic masses and NETs to calculated monoisotopic masses and observed NETs for lipids in an AMT tag database [59] within search tolerances of 63 ppm and 60.03 NET for monoisotopic mass and elution time, respectively. Subsequent identifications of lipid features that did not match to entries in the AMT tag database were made by searching their molecular weights against entries in the Lipid MAPS database followed by manual confirmation based on MS/ MS spectra.