Lipidomics Analysis

Integrated pipeline for lipid identification, classification, pathway mapping, and disease association analysis. Distinct from general metabolomics because lipids have unique classification systems (LIPID MAPS), specialized pathways (sphingolipid, eicosanoid, steroid), and disease associations (cardiovascular, neurodegeneration, metabolic syndrome).

Reasoning Strategy

Lipid identification starts with mass spectrometry: the lipid class is determined by the head group fragment mass (e.g., m/z 184 for phosphocholine in positive mode), total chain length and saturation from the precursor exact mass, and individual fatty acid chains from neutral loss or product ion scans. LIPID MAPS classification organizes lipids by chemical structure into 8 categories — knowing the category immediately tells you the likely biological context (sphingolipids → apoptosis/neurodegeneration; glycerophospholipids → membrane remodeling; eicosanoids → inflammation). Structural specificity matters biologically: Cer(d18:1/16:0) and Cer(d18:1/24:1) have different membrane properties and disease associations despite being the same lipid class. Always map changed lipids back to metabolic pathways because lipids are intermediates — an elevated ceramide could mean increased synthesis (CERS activity up), decreased degradation (ASAH1 down), or shunting from sphingomyelin (SMPD1 up).

LOOK UP DON'T GUESS: Do not assume a lipid's LIPID MAPS ID, exact mass, or pathway membership — query LipidMaps_search_by_name first. Do not guess which diseases are associated with a lipid class; retrieve them from HMDB or CTD.

Key principles:

1. LIPID MAPS classification first — use the 8-category system (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, polyketides)
2. Structural specificity matters — chain length, unsaturation, and sn-position affect biological function
3. Connect to pathways — lipids are metabolic intermediates; always map to biosynthesis/degradation pathways
4. Disease context — many lipids are disease biomarkers (sphingolipids in neurodegeneration, oxidized lipids in CVD)
5. Evidence grading — T1: clinical biomarker studies, T2: mechanistic studies, T3: association data, T4: computational prediction