Ms Adduct Annotation And Recognition

ms-adduct-annotation-and-recognition

Summary

Automated recognition and annotation of MS adducts, neutral losses, and in-source fragments to merge redundant LC-MS features into canonical peak identities. This skill enables deduplication of isotope-labeled and unlabeled peak tables before pairing intermediates in stable isotope tracing metabolomics.

When to use

When processing LC-MS peak tables from isotope tracing experiments where multiple ionization adducts ([M+H]+, [M+Na]+, [M+NH4]+, etc.) and in-source fragments have generated redundant features at different m/z values that represent the same underlying metabolite. Apply this skill as a prerequisite step before pairing 12C/13C features, especially when comparing unlabeled (C12) and labeled (C13) peak tables where adduct patterns may differ between conditions.

When NOT to use

• When your peak table has already been deduplicated or merged by an upstream tool (e.g., XCMS grouping or MS-DIAL adduct clustering) — applying recognition a second time may remove valid features.
• When analyzing non-isotope-labeled metabolomics data where adduct annotation alone (without 12C/13C pairing context) is the goal — use a dedicated adduct annotation tool instead.
• When working with data from instruments or ionization methods where adduct patterns are non-standard or instrument-specific and not covered by the built-in recognition rules.

Inputs

• peak_table_C12.csv (unlabeled peak area table with m/z, retention time, and peak intensities)
• peak_table_C13.csv (labeled peak area table with m/z, retention time, and peak intensities)
• sample_info.csv (sample metadata including group, tracer_group, and sample identifiers)
• file path to project directory containing sample groups and raw data folders

Outputs

• Merged/deduplicated peak table with redundant adducts and neutral losses consolidated
• tracer_pair_result.xlsx (ion pair matches between deduplicated C12 and C13 features)
• Diagnostic PDF plots summarizing pairing results and statistical summaries

How to apply

Enable the is_recognize_adducts parameter (set to TRUE) within the find_intemidates function call. The function accepts the unlabeled peak table (C12.csv), labeled peak table (C13.csv), sample metadata, and analysis path. Internally, the workflow merges redundant adduct variants and neutral-loss derivatives across the peak tables using mass-to-charge (m/z) and retention time (RT) tolerances (mz_tol=10 ppm, rt_tol=0.05 min by default). This preprocessing consolidates multiple ionization states of the same metabolite into single canonical features before downstream differential analysis and 12C/13C pairing. The adduct recognition reduces false negatives in tracer-pair matching by ensuring that isotope pairs are compared at the consolidated feature level, not at the raw peak level where adduct variants could obscure true matches.

Related tools

• IsoPairFinder (Primary workflow orchestrator that embeds adduct recognition as a preprocessing step within find_intemidates function) — https://github.com/DoddLab/IsoPairFinder
• dplyr (Data manipulation and feature merging during adduct consolidation)
• tidyr (Data reshaping and normalization of peak tables before adduct matching)
• readr (Efficient reading of peak table CSV files and sample metadata)
• ggplot2 (Visualization of adduct recognition results and pairing diagnostics in PDF output)

Examples

find_intemidates(peak_table_unlabel = 'peak_table_C12.csv', peak_table_label = 'peak_table_C13.csv', sample_info = 'sample_info.csv', path = './', control_group = c('WT'), case_group = c('MUTANT'), mz_tol = 10, rt_tol = 0.05, is_recognize_adducts = TRUE)

Evaluation signals

• Verify that redundant m/z peaks within mz_tol (±10 ppm) and rt_tol (±0.05 min) windows have been consolidated into single canonical features in the output peak table.
• Check tracer_pair_result.xlsx for higher pairing success rate (more ion pair matches) compared to running find_intemidates with is_recognize_adducts=FALSE.
• Inspect diagnostic PDF plots to confirm that adduct variants (e.g., [M+H]+ and [M+Na]+ of the same compound) are represented by a single merged feature line, not separate traces.
• Validate that the number of unique features in the merged output is fewer than the input (indicating successful consolidation) while retaining the biochemically relevant intermediate identifications.
• Cross-check peak intensity distributions — merged features should show combined or representative intensity values that reflect the original adduct variants.

Limitations

• Adduct recognition rules are built-in and may not cover non-standard or exotic ionization adducts specific to particular instruments or sample matrices.
• The fixed mz_tol (10 ppm) and rt_tol (0.05 min) may be too stringent or too permissive depending on mass spectrometer resolution and chromatographic peak width; manual tuning may be needed for unusual sample types.
• Neutral loss patterns vary by metabolite class — the built-in recognition may misattribute or fail to detect in-source fragments for understudied compound classes.
• No changelog is available in the repository documentation, so the evolution and validation history of adduct recognition rules cannot be independently reviewed.

Evidence

• [readme] The find_intemidates workflow provides an end-to-end workflow serving this objective, including (1) differential analysis; (2) merging of the redundant LC-MS features (adducts, neutral losses, and in-source fragments); (3) pairing 12C/13C features to determine potential intermediates.: "merging of the redundant LC-MS features (adducts, neutral losses, and in-source fragments)"
• [other] Execute find_intemidates function with mz_tol=10 ppm, rt_tol=0.05 min, p_value_cutoff=0.05, fold_change_cutoff=20, adduct recognition enabled, comparing hyuA case group against WT control group across positive and negative polarities.: "mz_tol=10 ppm, rt_tol=0.05 min, p_value_cutoff=0.05, fold_change_cutoff=20, adduct recognition enabled"
• [readme] find_intemidates(peak_table_unlabel = 'peak_table_C12.csv', peak_table_label = 'peak_table_C13.csv', sample_info = 'sample_info.csv', path = '~/Project/00_Uric_Acid_project/Data/20250817_submit_test/', polarity = 'positive', control_group = c("WT"), case_group = c('MUTANT'), mz_tol = 10, rt_tol = 0.05, p_value_cutoff = 0.05, p_adjust = TRUE, fold_change_cutoff = 20, is_recognize_adducts = TRUE): "is_recognize_adducts = TRUE"
• [other] Generate tracer_pair_result.xlsx containing identified ion pair matches between unlabeled and labeled peak tables. Produce diagnostic PDF plots summarizing pairing results and statistical summaries.: "Generate tracer_pair_result.xlsx containing identified ion pair matches between unlabeled and labeled peak tables. Produce diagnostic PDF plots summarizing pairing results"