--- name: tof-ms-signal-enhancement-low-abundance description: Use when your raw TOF-MS data (Agilent MassHunter .d format) exhibits jagged, artifact-prone peaks in low-abundance ions that compromise peak quality assessment or when you need to improve signal-to-noise before ion mobility demultiplexing or peak deconvolution. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3630 edam_topics: - http://edamontology.org/topic_0121 - http://edamontology.org/topic_3520 tools: - PNNL PreProcessor - Agilent MassHunter - .NET Framework 4.7.2 or later - Microsoft Visual C++ Runtime x64 techniques: - LC-MS - ion-mobility-MS derived_from: - doi: 10.1021/jasms.4c00220 title: PNNL PreProcessor - doi: 10.1021/acs.jproteome.1c00425 title: '' evidence_spans: - we have developed this user-friendly tool for Agilent MassHunter (.d) and UIMF mass spectrometry data files - we have developed this user-friendly tool for Agilent MassHunter (.d) and UIMF mass spectrometry data files (MS-files) from drift tube (DT) and structure for lossless ion manipulations (SLIM) IM-MS - Agilent MassHunter (.d) and UIMF mass spectrometry data files (MS-files) - Agilent MassHunter (.d) and UIMF mass spectrometry data files claims: [] provenance: collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2 assembled_by: scripts/collect_metabolomics_collection.py sources: - build: coll_pnnl_preprocessor_cq doi: 10.1021/jasms.4c00220 title: PNNL PreProcessor dedup_kept_from: coll_pnnl_preprocessor_cq schema_version: 0.2.0 attribution: generator: AgenticScienceBuilder original_doi: 10.1021/jasms.4c00220 all_source_dois: - 10.1021/jasms.4c00220 - 10.1021/acs.jproteome.1c00425 zenodo_doi: 10.5281/zenodo.20794027 curators: [] promoter: Louis-Félix Nothias sponsor: CNRS & Université Côte d'Azur --- # TOF-MS Signal Enhancement for Low-Abundance Ions ## Summary Multidimensional smoothing is a preprocessing operation that removes noise artifacts from jagged peaks common in low-abundance ion signals while preserving real signal integrity in time-of-flight mass spectrometry data. Applied within the PNNL PreProcessor workflow, it enhances signal quality across drift time, retention time, and spectral dimensions before downstream analysis. ## When to use Apply this skill when your raw TOF-MS data (Agilent MassHunter .d format) exhibits jagged, artifact-prone peaks in low-abundance ions that compromise peak quality assessment or when you need to improve signal-to-noise before ion mobility demultiplexing or peak deconvolution. Use it as an early preprocessing step in IM-MS omics workflows where structural isomer differentiation depends on clean, resolved peaks. ## When NOT to use - Input data is already denoised or has been processed by other smoothing algorithms (risk of over-smoothing and signal loss) - High-abundance ions or well-resolved peaks dominate the dataset (smoothing provides minimal benefit and may degrade signal fidelity) - Raw data is in formats other than Agilent MassHunter .d or UIMF; PNNL PreProcessor does not support other instrument vendor formats ## Inputs - Raw TOF-MS data file in Agilent MassHunter .d format - Optionally: retention time range filter specification - Optionally: reference peak quality baseline for comparison ## Outputs - Preprocessed MS-file in native Agilent MassHunter .d format with enhanced signal quality - Preprocessing log documenting artifact removal and smoothing parameters applied - Peak quality validation report (user-generated from visual inspection or metrics comparison) ## How to apply Load your raw TOF-MS data file (.d format from Agilent MassHunter) into PNNL PreProcessor and select the multidimensional smoothing operation from the preprocessing utilities menu. The algorithm reduces variations simultaneously across abundance, elution (retention time), and spectral feature dimensions while preserving signal integrity. Execute the smoothing pass to generate output in native Agilent MassHunter format (.d), then verify artifact removal by inspecting the output log and validating peak quality metrics against the input reference. The smoothing step is typically applied after data compression and interpolation but before saturation repair and spike removal. ## Related tools - **PNNL PreProcessor** (Primary software platform that implements multidimensional smoothing and integrates it within the full preprocessing workflow (data compression, demultiplexing, saturation repair, spike removal)) — https://github.com/PNNL-Comp-Mass-Spec/PNNL-PreProcessor - **Agilent MassHunter** (Instrument data acquisition and format provider; PNNL PreProcessor reads and writes native .d file format for interoperability) - **.NET Framework 4.7.2 or later** (Runtime dependency required to execute PNNL PreProcessor) - **Microsoft Visual C++ Runtime x64** (Compiled library dependency for PNNL PreProcessor binary execution) ## Evaluation signals - Output peak shapes in low-abundance ions transition from jagged/noisy to smooth, with clear, resolvable apex and base - Output log confirms smoothing operation completed without errors and reports number of scans/frames processed - Peak quality metrics (e.g., peak height, signal-to-noise ratio, area) improve in smoothed output relative to raw input for low-abundance species - Retention time and ion mobility alignment are preserved; no artificial peak shifting or coalescence of adjacent signals - Real signals are not suppressed: high-abundance ions and known metabolite peaks retain or improve their intensity and resolution ## Limitations - Saturation repair software (applied after smoothing) may produce incorrect results for ions with highly convoluted elution/mobility profiles caused by interferences, limiting the utility of the full preprocessing chain in complex samples with severe coelution - Smoothing is a multidimensional operation tuned for IM-MS data; effectiveness on conventional LC-MS without ion mobility dimension is not characterized in the article - No changelog provided in repository; version tracking and reproducibility of smoothing parameters across software releases are not documented ## Evidence - [methods] Smoothing removes artifacts in jagged peaks, which are common in low-abundance ions. Real signals are enhanced.: "Smoothing removes artifacts in jagged peaks, which are common in low-abundance ions. Real signals are enhanced." - [other] Apply multidimensional smoothing algorithm to reduce variations in abundance, elution, and spectral features while preserving signal integrity.: "Apply multidimensional smoothing algorithm to reduce variations in abundance, elution, and spectral features while preserving signal integrity." - [intro] PNNL-PreProcessor provides a software tool with various algorithms and utilities including data compression and interpolation, ion mobility demultiplexing, multidimensional smoothing, noise filtering by low intensity threshold and spike removal, saturation repair and metadata export: "data compression and interpolation, ion mobility demultiplexing, multidimensional smoothing, noise filtering by low intensity threshold and spike removal, saturation repair and metadata export" - [other] Load raw TOF-MS data file (.d format from Agilent MassHunter) into PNNL PreProcessor and select the multidimensional smoothing operation.: "Load raw TOF-MS data file (.d format from Agilent MassHunter) into PNNL PreProcessor. Select the multidimensional smoothing operation from the preprocessing utilities menu." - [methods] The saturation repair software may produce incorrect results for ions with highly convoluted elution/mobility profiles caused by interferences: "the saturation repair software may produce incorrect results for ions with highly convoluted elution/mobility profiles caused by interferences" - [readme] Ion mobility-mass spectrometry (IM-MS) provides an increasingly popular platform for analyzing complex samples due to its separation power and ability to differentiate structural isomers, which are difficult to resolve using conventional LC-MS systems.: "Ion mobility-mass spectrometry (IM-MS) provides an increasingly popular platform for analyzing complex samples due to its separation power and ability to differentiate structural isomers, which are"