Isotope Mass Offset Enumeration

isotope-mass-offset-enumeration

Summary

Detects isotopic variants (e.g., C13 substitutions) in mass spectrometry feature tables by enumerating expected m/z offsets and matching them against observed peaks within specified mass, drift time, and retention time tolerances. This approach improves feature annotation confidence by explicitly grouping monoisotopes with their heavier isotopologues.

When to use

Apply this skill when you have a detected feature table (m/z, drift_time, retention_time, intensity) from LC-IMS-MS or similar multidimensional MS data and want to identify and annotate isotopic families (e.g., singly-charged C13 patterns). Use it before downstream analysis such as compound identification or quantification to avoid treating monoisotopes and isotopologues as independent features.

When NOT to use

• Input is already an annotated or pre-processed feature table with isotopic assignments.
• Data is from a single-dimension MS instrument (e.g., ESI-MS without IMS or LC separation) where drift time and retention time filtering would be unavailable or uninformative.
• Expected isotopic mass delta is unknown or highly variable across the sample (e.g., complex mixed-charge-state data without explicit charge-state constraints).

Inputs

• Feature table (HDF5 or mzML format) containing detected peaks with m/z, drift_time, retention_time, intensity columns
• Isotopic mass delta (e.g., 1.003355 Da for C13)
• Maximum number of isotopic substitutions (e.g., 5)
• Charge state constraint (e.g., z=+1 for singly-charged features)
• m/z tolerance (ppm), drift time tolerance, retention time tolerance

Outputs

• Annotated feature table with isotope group identifiers
• Isotopic cluster membership assignments (monoisotope label and isotopogue count)
• Filtered feature list retaining only isotopic signatures ≥3 members

How to apply

Load detected features from HDF5 or mzML using deimos.load() with columns m/z, drift_time, retention_time, and intensity. For each feature, calculate expected m/z values for isotopologues by adding multiples of the isotopic mass delta (1.003355 Da per C13 substitution, up to a maximum number of substitutions, typically 5). Search for matching peaks within the specified m/z tolerance window (typically constrained by expected delta and refined to ≤50 ppm m/z error). Apply secondary filters on drift time and retention time alignment to confirm isotopic membership. Retain only isotopic signatures with at least 3 members (monoisotope plus ≥2 isotopologues) to minimize false positives. Assign isotope group identifiers and export the annotated feature table using deimos.save().

Related tools

• DEIMoS (Core Python API and CLI tool for executing isotope detection via m/z offset enumeration; provides deimos.load(), deimos.save(), and isotope filtering functions) — https://github.com/pnnl/deimos
• Python (Programming language in which DEIMoS is implemented and in which the workflow (load, calculate m/z offsets, search, filter) is scripted)
• numpy (Numerical operations for vectorized m/z offset calculations and tolerance-based matching)
• ProteoWizard msconvert (Utility to convert raw MS data into mzML format before loading into DEIMoS)

Examples

import deimos; ms1 = deimos.load('example_data_peaks.h5', key='ms1', columns=['mz', 'drift_time', 'retention_time', 'intensity']); isotopes = deimos.isotope_detect(ms1, mz_delta=1.003355, max_isotopes=5, max_charge=1, mz_tol=50); deimos.save(isotopes, 'annotated_isotopes.h5', key='ms1')

Evaluation signals

• Monoisotope and isotopogue peaks align within specified m/z tolerance (≤50 ppm error reported).
• Isotopic clusters contain ≥3 members (monoisotope plus ≥2 isotopologues); clusters with <3 members are filtered out.
• Drift time and retention time of matched isotopologues are within defined tolerance windows of the monoisotope, indicating co-elution and co-mobility.
• Intensity ratios among isotopologues follow expected natural abundance patterns (e.g., C13 peak ~1% of monoisotope for singly-charged species).
• Output feature table contains new isotope group identifier columns and can be validated by spot-checking a sample of annotated clusters in visualization (e.g., matplotlib plots of m/z vs. drift_time colored by group).

Limitations

• Requires explicit specification of isotopic mass delta and maximum substitution count; does not auto-detect novel isotopic patterns (e.g., uncommon heavy isotopes like N15, S34).
• Performance degrades on highly congested m/z regions where many overlapping peaks compete for isotopic assignment; tolerance windows must be chosen carefully to balance sensitivity and specificity.
• Assumes features are within a single charge state; mixed-charge data must be pre-separated or processed in multiple passes with different charge-state constraints.
• No explicit handling of modified isotopes (e.g., deuteration) without manual recalibration of mass delta.
• Clustering algorithm depends on sequential peak matching; order of feature processing and overlap handling in congested regions may influence final isotope group assignments.

Evidence

• [other] DEIMoS detects C13 isotopologues by specifying an m/z delta of 1.003355 Da, a maximum of 5 isotopic substitutions, and maximum charge of 1, with initial constraint by m/z tolerance from expected delta, followed by drift time and retention time tolerances, and final downselection by maximum m/z error of 50 ppm.: "DEIMoS detects C13 isotopologues by specifying an m/z delta of 1.003355 Da, a maximum of 5 isotopic substitutions, and maximum charge of 1, with initial constraint by m/z tolerance from expected"
• [intro] Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution: "Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution"
• [results] A good first screening is to only consider those isotopic signatures with at least 3 members: "A good first screening is to only consider those isotopic signatures with at least 3 members"
• [readme] DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation: "DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation"
• [readme] algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra: "algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity"