4d Lcimmsms Feature Extraction

4D LC-IM-MS/MS Feature Extraction

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

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"