--- name: 4d-lcimmsms-feature-extraction description: Use when you have raw LC-IM-MS/MS data files from sterol lipid analysis and need to identify unsaturated sterol isomers by matching experimental collision cross section values against a quantum chemistry calculation-assisted CCS prediction database. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3632 edam_topics: - http://edamontology.org/topic_3172 - http://edamontology.org/topic_0769 - http://edamontology.org/topic_3520 tools: - Python - Jupyter Notebook - RDKit - scikit-learn techniques: - LC-MS - ion-mobility-MS derived_from: - doi: 10.1002/anie.202507483 title: NA evidence_spans: - collection of Python scripts - All functions are implemented in jupyter notebook claims: [] provenance: collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2 assembled_by: scripts/collect_metabolomics_collection.py sources: - build: coll_na_cq doi: 10.1002/anie.202507483 title: NA dedup_kept_from: coll_na_cq schema_version: 0.2.0 attribution: generator: AgenticScienceBuilder original_doi: 10.1002/anie.202507483 all_source_dois: - 10.1002/anie.202507483 zenodo_doi: 10.5281/zenodo.20794027 curators: [] promoter: Louis-Félix Nothias sponsor: CNRS & Université Côte d'Azur --- # 4D LC-IM-MS/MS Feature Extraction ## Summary Extract four-dimensional features (retention time, m/z, drift time, intensity) from raw LC-IM-MS/MS data to enable collision cross section matching and sterol isomer identification. This skill is essential for converting raw instrument output into annotated molecular features with stereochemical and structural metadata. ## When to use Apply this skill when you have raw LC-IM-MS/MS data files from sterol lipid analysis and need to identify unsaturated sterol isomers by matching experimental collision cross section values against a quantum chemistry calculation-assisted CCS prediction database. The skill is specifically designed for N-Me derived unsaturated sterol lipids with known double-bond positions. ## When NOT to use - Input is already a processed feature table or identified metabolite table (skip directly to quantification or statistical analysis) - Data come from saturated sterols or non-N-Me lipid classes (the RDKit fragmentation model is validated only for sterols with C=C bonds) - Raw data lack drift time dimension (IM-MS/MS orthogonal separation is required for CCS calculation and isomer resolution) ## Inputs - Raw LC-IM-MS/MS data files (instrument binary or vendor format) - Quantum chemistry calculation-assisted CCS prediction database for N-Me derived unsaturated sterols - MS/MS fragmentation pattern reference library (calculated from RDKit-based fragmentation rules) - Sample metadata (tissue type, biological replicate identifier) ## Outputs - Annotated 4D feature table with retention time, m/z, drift time, and intensity - Sterol identification records with double-bond position and stereochemistry assignments - Confidence scores per feature (CCS tolerance match quality, MS/MS spectral similarity) - Tissue-specific quantitative sterol abundance data - Unidentified feature table (features not matching CCS or spectral criteria) ## How to apply Load raw LC-IM-MS/MS data and apply 4D feature extraction using Python to extract retention time, m/z, drift time, and intensity dimensions. Match detected m/z values against a quantum chemistry calculation-assisted CCS prediction database of N-Me derived unsaturated sterol lipids. Filter candidate identifications by CCS tolerance and MS/MS fragmentation pattern matching against calculated N-Me fragmentation patterns. Annotate matched features with sterol structure (including double-bond position and stereochemistry) and assign confidence scores based on CCS match quality and spectral similarity. Generate output records linking each identified sterol feature to tissue-specific quantitative intensity data. ## Related tools - **Python** (Language for implementing 4D feature extraction, CCS matching, and annotation logic) - **Jupyter Notebook** (Interactive environment for executing feature extraction workflows and data inspection) — https://github.com/Chen-micslab/QCCAssisted4DSterol - **RDKit** (Cheminformatics toolkit for recognizing double-bond positions and generating N-Me MS/MS fragmentation patterns) - **scikit-learn** (Machine learning library used for CCS prediction model (SVR with LASSO feature selection) that supports candidate filtering) ## Evaluation signals - All raw LC-IM-MS/MS data points are assigned to a 4D feature (retention time, m/z, drift time, intensity) or explicitly flagged as noise/artifact - Extracted CCS values from drift time fall within expected range (typically 100–500 Ų for sterol lipids) and show monotonic increase with m/z within isomer series - MS/MS fragmentation patterns of matched features contain expected N-Me cleavage ions (diagnostic fragment m/z values) with intensity ratios consistent with structure - Confidence scores are inversely correlated with CCS prediction database match error and MS/MS spectral cosine similarity; features with score > 0.7 are manually spot-checked against reference standards when available - Annotated sterols show tissue-specific distribution patterns (e.g., cholesterol predominance in brain tissue, phytosterol enrichment in plant-derived samples) consistent with prior literature ## Limitations - The RDKit fragmentation model is validated only for sterol lipids with C=C bonds; saturated sterols or other lipid classes are not reliably fragmented - CCS prediction accuracy depends on the size and chemical diversity of the training dataset; out-of-domain lipid structures may have poor CCS predictions - Isomers with identical m/z and very similar CCS values (e.g., positional double-bond isomers differing by <2% CCS) may not be fully resolved and require manual spectral inspection - Feature extraction requires tuning of retention time, m/z, drift time, and intensity thresholds; suboptimal threshold selection leads to missed low-abundance features or false positives - Tissue-specific quantification assumes equal ionization efficiency across sterol isomers; differential ionization of regioisomers may introduce systematic bias ## Evidence - [other] Load raw LC-IM-MS/MS data and apply 4D feature extraction (retention time, m/z, drift time, intensity) using Python.: "Load raw LC-IM-MS/MS data and apply 4D feature extraction (retention time, m/z, drift time, intensity) using Python." - [other] Match detected m/z values against a quantum chemistry calculation-assisted CCS prediction database of N-Me derived unsaturated sterol lipids.: "Match detected m/z values against a quantum chemistry calculation-assisted CCS prediction database of N-Me derived unsaturated sterol lipids." - [other] Filter candidate identifications by CCS tolerance and MS/MS fragmentation pattern matching.: "Filter candidate identifications by CCS tolerance and MS/MS fragmentation pattern matching." - [other] Annotate matched features with sterol structure (including double-bond position and stereochemistry) and assign confidence scores based on CCS and spectral match quality.: "Annotate matched features with sterol structure (including double-bond position and stereochemistry) and assign confidence scores based on CCS and spectral match quality." - [readme] The script recognises double bond positions and generates MS/MS based on N-Me fragmentation patterns.: "The script recognises double bond positions and generates MS/MS based on N-Me fragmentation patterns." - [readme] The CCS prediction process in the paper is implemented using the scikit-learn API. It employs LASSO for feature selection and uses cross-validation to select the best hyperparameters for the SVR model.: "The CCS prediction process in the paper is implemented using the scikit-learn API. It employs LASSO for feature selection and uses cross-validation to select the best hyperparameters for the SVR" - [readme] All functions are implemented in jupyter notebook: "All functions are implemented in jupyter notebook"