High Resolution Mass Spectral Data Processing

high-resolution-mass-spectral-data-processing

Summary

Automated extraction of isotopologue abundances and quantification metrics (mass error, peak area, intensity) from high-resolution Orbitrap or GC-CI-MS data by converting vendor formats to centroided mzML, loading a formulaTable of target compounds, and calling the autoQ function with isotope-pattern matching via enviPat. This skill is essential when stable-isotope labeling experiments require precise measurement of M+0, M+1, M+2 isotopic forms across multiple samples.

When to use

Apply this skill when you have high-resolution mass-spectrometry data (Orbitrap, GC-CI-MS) from stable-isotope labeling experiments and need to quantify isotopologue abundances for a defined list of target compounds. Use it specifically when your input is vendor-format raw data that must be converted to mzML, you have retention time and monoisotopic m/z values for each compound, and you want automated extraction of abundance, ppm mass error, peak area (area), and maximum intensity (maxo) for each isotopic form per sample condition.

When NOT to use

• Input data is already in profile format and you require centroided mzML; use Proteowizard MSconvert first.
• You have low-resolution data; switch to profile-format mzML instead of centroiding (autoQ can process both but requires different preprocessing).
• Your target compound list lacks precise retention time and monoisotopic m/z values; the formulaTable columns are mandatory and missing data will cause autoQ to fail.
• You need to detect unknown or unanticipated isotopologues; autoQ is designed for a predefined formulaTable and will not discover novel compounds.

Inputs

• Vendor-format raw mass-spectrometry files (Orbitrap .raw, GC-CI-MS proprietary formats)
• formulaTable data frame (CSV) with columns: CompoundName, mz, RT, Formula, NumAtoms
• enviPat isotopes object for isotope-pattern database
• Centroided mzML files (post-Proteowizard MSconvert conversion)

Outputs

• Quantification table with columns: CompoundName, m/z, abundance, Isotopologue (M+0, M+1, M+2), ppm mass error, maxo (maximum intensity), area (peak area)
• Quality-control plots (rawPlot, meanRawPlot) showing peak shape and intensity across scans and samples

How to apply

First, prepare a formulaTable data frame with columns CompoundName, mz (monoisotopic ion), RT (retention time in seconds), Formula (must match derivatized formula for high-resolution), and NumAtoms, loaded via read.csv. Next, convert your raw mass-spectrometry data from vendor format to centroided mzML using Proteowizard MSconvert (essential for high-resolution; use peakPicking=True). Load the enviPat isotopes object for isotope-pattern calculation. Call the autoQ function with parameters: labelatom='13C' (or other stable isotope), mzerror or maxppm (mass tolerance, typically a few ppm), RTwin (retention-time window around RT), minscans, SNR (signal-to-noise ratio threshold), and minwidth/maxwidth (peak width constraints in scans). The function will return a table with CompoundName, m/z, abundance, Isotopologue designation (M+0, M+1, M+2), ppm mass error, and paired maxo and area values. Inspect results using rawPlot and meanRawPlot for quality control to detect moving peaks, noise, or saturation before reporting quantification.

Related tools

• isoSCAN (Core R package that implements the autoQ function for automated isotopologue extraction and quantification from mzML files) — https://github.com/jcapelladesto/isoSCAN
• Proteowizard MSconvert (Converts vendor-format raw data to centroided mzML format required for autoQ processing of high-resolution data)
• mzR (Underlying R package used by isoSCAN to read and parse mzML/mzXML files)
• enviPat (Provides isotope-pattern calculation and database to distinguish targeted compounds from interfering ions in high-resolution spectra)
• R (Runtime environment and scripting language for executing autoQ, metBarPlot, rawPlot, and meanRawPlot functions)

Examples

library(isoSCAN); formulaTable <- read.csv('targets.csv'); autoQ(formulaTable=formulaTable, files='sample.mzML', labelatom='13C', maxppm=5, RTwin=30, SNR=3, minscans=5, minwidth=10, maxwidth=100)

Evaluation signals

• Output table contains no NA or NaN values for CompoundName, m/z, abundance, or ppm columns for detected isotopologues; missing values indicate autoQ failed to match a compound or peak.
• Ppm mass error for detected peaks falls within the tolerance specified in maxppm parameter (typically 1–5 ppm for high-resolution Orbitrap); values outside this range suggest misidentification or miscalibration.
• M+0, M+1, M+2 abundance values sum to 100% (or close to it) for each compound per sample; large deviations suggest isotope-pattern mismatch or peak overlap.
• rawPlot and meanRawPlot inspection shows clean, symmetric peak shapes within the minwidth/maxwidth range; jagged, split, or extremely broad peaks indicate SNR, retention-time window, or formula-mismatch problems.
• Replicate samples show consistent area and maxo values (low coefficient of variation across replicates); high variance suggests process variation, calibration drift, or suboptimal SNR threshold.

Limitations

• formulaTable Formula column must match the derivatized form of the compound for high-resolution data; incorrect or non-derivatized formulas will cause peak-pattern mismatches and missed detections.
• autoQ requires accurate retention-time windows (RTwin parameter); peaks with poor or shifting retention times may fall outside the window and be missed.
• Mass accuracy and resolution of the Orbitrap or GC-CI-MS instrument directly affect sensitivity to isotope patterns; low-resolution or poorly calibrated instruments may fail to resolve M+0, M+1, M+2 isotopologues.
• No changelog is available for the isoSCAN package; updates and bug fixes may not be documented, making reproducibility across versions potentially problematic.
• autoQ is designed for a predefined formulaTable and cannot discover novel or unlabeled compounds; all target compounds must be specified in advance.

Evidence

• [other] The autoQ function returns for high-resolution Orbitrap data a table with columns for CompoundName, m.z, abundance, Isotopologue designation (M+0, M+1, M+2), ppm mass error, and paired maxo and area values for each isotopic form per sample condition.: "The autoQ function returns for high-resolution Orbitrap data a table with columns for CompoundName, m.z, abundance, Isotopologue designation (M+0, M+1, M+2), ppm mass error, and paired maxo and area"
• [intro] In the case of High-resolution, please use centroiding (e.g. peakPicking= True in MSconvert): "In the case of High-resolution, please use centroiding (e.g. peakPicking= True in MSconvert)"
• [intro] Before starting with file processing, we need to load the targeted compounds as a formulaTable data frame. This can be done either with read.table or read.csv functions.: "load the targeted compounds as a formulaTable data frame. This can be done either with read.table or read.csv functions"
• [intro] formulaTable must contain the following column names in no specific order: * CompoundName * mz * RT * Formula * NumAtoms: "formulaTable must contain the following column names: CompoundName, mz, RT, Formula, NumAtoms"
• [intro] This parameters refer to peak width and number of scans recorded, together with signal-to-noise ratio and mass error.: "autoQ uses parameters: minscans, SNR (signal-to-noise ratio), mzerror or maxppm (mass error), RTwin (retention time window), maxwidth and minwidth (peak width parameters)"
• [intro] rawPlot and meanRawPlot functions should be used for quality control purposes. They are useful to check for moving peaks, noisy spots or saturated peaks.: "rawPlot and meanRawPlot functions should be used for quality control purposes. They are useful to check for moving peaks, noisy spots or saturated peaks"
• [intro] The first step is file format transformation, isoSCAN uses mzR package in order to read MS files. Therefore, you will have to transform the raw data from vendor format into mz(X)ML format: "transform the raw data from vendor format into mz(X)ML format using Proteowizard MSconvert or similar tools"
• [intro] Higher-resolution helps to differ targeted compounds from other ions, though the complexity of the data isoSCAN makes use of enviPat package: "Higher-resolution helps to differ targeted compounds from other ions, though the complexity of the data isoSCAN makes use of enviPat package"