A systemic workflow for profiling metabolome and lipidome in tissue.

Simple metabolome and lipidome sample preparation procedures involving two successive extractions using small pieces of tissue, and a subsequent metabolite identification (MetID) strategy were developed. The sample preparation can significantly circumvent incomplete analysis due to insufficient amounts of tissue as a result of splitting into several aliquots for multiple measurements, with advantages over the similar previously reported methods in metabolite coverage, extraction efficiency, method robustness and friendly experimental operation. A MetID strategy, based on the integration of MS information mining (including adduct ions, in-source CID, MS information from both ESI (+) and ESI (-), characteristic fragmentation ions (CFIs), constant neutral losses (CNLs) and multimers) and in silico MS simulation, was demonstrated. A large number of adduct ions (83 features), in-source CID (123 features), ESI (+/-) ionization (20 features), CFIs& CNLs (more than 120 features) and multimers (17 features) were mined by manually or in silico recognition/filtering, which provide the most suspicious structures for subsequent in silico MS simulation. The unknown features presented the same score distribution as the known (83 features) features with scores ≥25% (geomean score: 52%) and with satisfactory match for the main ions of interest. The MS/MS noise and fragment ions of coeluted quasi-molecular ions of interest are the main reason for the low score in the simulation. Manual check/evaluation is always suggested for the simulation with a score less than 50%. This strategy presents satisfactory performance with 2.5 times more metabolites structurally characterized compared with that of the traditional method based on accurate-mass-based MS and MS/MS library matching. This strategy would be useful for potentially identifying metabolites without available MS/MS information in the library.