Lipidomics Analysis

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

When to Use

• "Identify this lipid species from m/z and retention time"
• "What pathways involve ceramide/sphingomyelin?"
• "Lipid biomarkers for Alzheimer's disease"
• "What diseases are associated with altered sphingolipid metabolism?"
• "Map my lipidomics results to KEGG pathways"
• "Compare lipid profiles between conditions"

Not this skill: For general metabolomics (amino acids, sugars, organic acids), use tooluniverse-metabolomics. For drug ADMET properties, use tooluniverse-admet-prediction.

Core Tools

Workflow

Phase 0: Lipid Identity Resolution
  Name/mass/abbreviation → LIPID MAPS ID → classification
    |
Phase 1: Structural Classification
  LIPID MAPS 8-category system → subclass → molecular species
    |
Phase 2: Pathway Mapping
  KEGG lipid metabolism → biosynthesis/degradation enzymes
    |
Phase 3: Disease Associations
  CTD/DisGeNET/HMDB → lipid-disease links with evidence
    |
Phase 4: Interpretation & Report
  Biological significance → biomarker potential → recommendations

Phase 0: Lipid Identity Resolution

LipidMaps_search_by_name(query="ceramide")  → LMSP ID, exact mass, classification
HMDB_search(compound_name="ceramide")       → HMDB ID, disease links
PubChem_get_CID_by_compound_name(name="ceramide") → CID, SMILES

LIPID MAPS search tips:

• Generic names work well: "ceramide", "sphingomyelin", "phosphatidylcholine"
• Species-level abbreviations like "Cer(d18:1/16:0)" may return 0 results — use the generic class name first, then filter by chain length from results
• For exact mass search: use LipidMaps_search_by_formula with molecular formula (e.g., "C34H67NO3")
• If name search fails, try PubChem: PubChem_get_CID_by_compound_name(name="C16 Ceramide") then cross-reference

Phase 1: Structural Classification

Use LipidMaps_get_compound_by_id to retrieve the LIPID MAPS 8-category classification (FA, GL, GP, SP, ST, PR, SL, PK) for any lipid. The category immediately signals biological context: SP (sphingolipids) → apoptosis/neurodegeneration; GP (glycerophospholipids) → membrane remodeling; FA-derived eicosanoids → inflammation.

Phase 2: Pathway Mapping

Key lipid metabolism pathways in KEGG:

# Map lipids to pathways
kegg_search_pathway(keyword="sphingolipid")  # → hsa00600
KEGG_get_pathway_genes(pathway_id="hsa00600")  # → SMPD1, CERS1, ...

Phase 3: Disease Associations

For each lipid or lipid enzyme, check disease links:

CTD_get_gene_diseases(input_terms="SMPD1")  # sphingomyelinase → Niemann-Pick
DisGeNET_search_gene(gene="SMPD1")  # broader disease associations
HMDB_get_metabolite(compound_name="ceramide")  # metabolite-disease links
PubMed_search_articles(query="ceramide biomarker Alzheimer")  # clinical evidence

Disease context: Ceramide elevation → apoptosis, Alzheimer's, insulin resistance. Sphingomyelin depletion → Niemann-Pick. Oxidized phospholipids → CVD. Altered bile acid ratios → NAFLD, cholestasis. Eicosanoid elevation → inflammation. Always verify via HMDB or CTD rather than relying on memory.

Phase 4: Interpretation & Report

Computational procedure: Lipid class enrichment analysis

# When user provides a list of significantly changed lipids
import pandas as pd
from scipy.stats import fisher_exact

# Input: list of changed lipids with LIPID MAPS categories
changed = pd.DataFrame({
    'lipid': ['Cer(d18:1/16:0)', 'SM(d18:1/16:0)', 'PC(16:0/18:1)', 'LPC(18:0)'],
    'category': ['SP', 'SP', 'GP', 'GP'],
    'direction': ['up', 'down', 'unchanged', 'up'],
    'fold_change': [2.1, 0.5, 1.1, 1.8]
})

# Count changed vs unchanged per category
from collections import Counter
changed_cats = Counter(changed[changed['direction'] != 'unchanged']['category'])
total_cats = Counter(changed['category'])

# Report
print("Lipid class enrichment:")
for cat in total_cats:
    n_changed = changed_cats.get(cat, 0)
    n_total = total_cats[cat]
    print(f"  {cat}: {n_changed}/{n_total} changed")

# Interpretation
if changed_cats.get('SP', 0) / max(total_cats.get('SP', 1), 1) > 0.5:
    print("→ Sphingolipid metabolism is significantly altered")
    print("  Consider: apoptosis, neurodegeneration, insulin resistance")

Report structure:

1. Lipid Identity — LIPID MAPS classification, structural features
2. Pathway Context — which metabolic pathways are affected
3. Disease Associations — evidence-graded disease links
4. Biomarker Assessment — clinical utility of identified lipid changes
5. Mechanistic Model — how lipid changes connect to disease biology
6. Recommendations — validation experiments, clinical follow-up

Limitations

• No raw MS data processing — this skill interprets identified lipids, not raw spectra. Use LipidSearch, MS-DIAL, or LipiDex for identification first.
• LIPID MAPS coverage — some rare or novel lipid species may not be in the database
• Quantitative thresholds — fold-change cutoffs are context-dependent; the skill provides frameworks, not universal thresholds
• Species-specific — most disease data is human; rat/mouse lipid metabolism can differ significantly