Adduct Mass Matching And Clustering

Adduct-Mass Matching and Clustering

Summary

This skill groups LC-MS features that share a common neutral mass by calculating hypothetical neutral masses from common electrospray ionisation adducts and matching them within a mass tolerance window. It is essential for linking multiply-charged or adducted ions of the same molecule into structural clusters.

When to use

Apply this skill after identifying statistically significant LC-MS features (e.g. via MB-VIP permutation testing) when you need to consolidate redundant measurements of the same metabolite arising from different ionisation adducts (e.g. [M+H]+, [M+Na]+, [M−H]−). Use it as part of structural clustering when the input feature table contains m/z and retention time columns and you suspect multiple features represent isotopologues or adducts of the same compound.

When NOT to use

• Input features are already annotated to specific chemical standards or databases — annotation takes precedence over de novo mass matching.
• LC-MS data were collected in atmospheric pressure chemical ionisation (APCI) or other non-ESI modes where common ESI adduct patterns do not apply.
• Features come from targeted (scheduled) LC-MS methods where ion identities are pre-specified by retention time and m/z windows.

Inputs

• Feature table with columns: m/z, retention time (RT), assay identifier
• Mass tolerance threshold (ppm)
• List of common electrospray ionisation adducts and their mass shifts

Outputs

• Adduct cluster assignments (feature→cluster_id mapping)
• Hypothetical neutral mass values for each cluster
• Merged structural clusters (combining adduct and isotopologue groups)

How to apply

First, define a mass tolerance threshold (typically 15 ppm) appropriate to your instrument's mass accuracy. For each feature's observed m/z value, calculate hypothetical neutral masses by reversing common electrospray ionisation adduct mass shifts (e.g. subtract 1.00728 Da for [M+H]+, subtract 22.98922 Da for [M+Na]+, add 1.00728 Da for [M−H]−). Group features whose calculated neutral masses match within the ppm tolerance into adduct clusters. Merge these adduct clusters with any overlapping isotopologue clusters (identified separately by 1.00335 Da mass-difference searches) to form final structural clusters. This approach assumes accurate m/z calibration and benefits from prior retention time windowing to reduce false-positive mass matches across chemically unrelated compounds.

Related tools

• MamsiStructSearch (Implements adduct mass matching by calculating hypothetical neutral masses from ESI adducts and clustering features with matching neutral masses) — https://github.com/kopeckylukas/py-mamsi
• pandas (Data manipulation and feature table handling)
• scipy (Mass tolerance calculations and clustering operations)
• scikit-learn (Optional hierarchical clustering for correlation-based cluster refinement)

Examples

struct = MamsiStructSearch(rt_win=5, ppm=15)
struct.load_lcms(selected_features)
struct.get_structural_clusters()

Evaluation signals

• All features within an adduct cluster should have calculated neutral masses agreeing within the specified ppm tolerance (e.g. ±15 ppm)
• Features assigned to the same adduct cluster should have retention times within the defined RT window (default 5 seconds) to avoid spurious cross-compound matches
• Each structural cluster should contain at most one feature per assay unless the assay genuinely measures the same metabolite in different ionisation modes
• Merged adduct + isotopologue clusters should show expected mass differences (1.00335 Da for isotopologues; adduct-specific differences for [M+H]+/[M+Na]+/etc.)
• Cross-assay cluster links identified using [M+H]+/[M−H]− as references should have matching neutral masses across positive and negative mode assays

Limitations

• Requires accurate m/z calibration; systematic mass drift can prevent correct matching, especially at tight ppm tolerances (< 10 ppm).
• Cannot distinguish between structural isomers or isobars with identical m/z and retention time; the method assumes one metabolite per neutral mass per RT window.
• Performance depends on prior retention time windowing and isotopologue/adduct discovery steps; overlapping clusters from different metabolites can cause false merges if RT windows are too broad.
• Limited to common ESI adducts; unusual or instrument-specific adducts (e.g. [M+K]+, [M+NH4]+) may not be included in the default adduct list.
• The framework was tested on metabolomics phenotyping data; applicability to other LC-MS data types (e.g. proteomics, lipidomics with non-standard ionisation) is not guaranteed.

Evidence

• [readme] This is followed by a search for common adduct signatures. This is achieved by calculating hypothetical neutral masses based on common adducts in electrospray ionisation.: "This is followed by a search for common adduct signatures. This is achieved by calculating hypothetical neutral masses based on common adducts in electrospray ionisation."
• [readme] If hypothetical neutral masses match for two or more features within a pre-defined tolerance (15 ppm) then these features are grouped together.: "If hypothetical neutral masses match for two or more features within a pre-defined tolerance (15 ppm) then these features are grouped together."
• [readme] Overlapping adduct clusters and isotopologue clusters are then merged to form structural clusters.: "Overlapping adduct clusters and isotopologue clusters are then merged to form structural clusters."
• [methods] Search for adduct signatures by calculating hypothetical neutral masses from common ESI adducts and matching within ppm tolerance; group features with matching neutral masses.: "Search for adduct signatures by calculating hypothetical neutral masses from common ESI adducts and matching within ppm tolerance; group features with matching neutral masses."
• [methods] Define retention time tolerance window (rt_win, default 5 seconds) and mass tolerance (ppm, default 15 ppm) for structural matching.: "Define retention time tolerance window (rt_win, default 5 seconds) and mass tolerance (ppm, default 15 ppm) for structural matching."
• [readme] Further, we search cross-assay clusters using [M+H]+/[M-H]- as link references.: "Further, we search cross-assay clusters using [M+H]+/[M-H]- as link references."