[ { "$schema": "../../schemas/strategy.schema.json", "slug": "bioactivity-based-molecular-networking", "name": "Bioactivity-Based Molecular Networking", "summary": "A transferable natural-product discovery strategy that overlays quantitative bioassay results onto LC-MS feature abundance and MS/MS molecular networks to prioritize molecular families and individual features associated with biological activity.", "description": "Bioactivity-Based Molecular Networking adds an orthogonal biological evidence layer to molecular networking. Fractionated samples are profiled by LC-MS/MS and tested in a quantitative bioassay. Feature abundances across fractions are correlated with bioactivity, producing a bioactivity score that is mapped onto the molecular network. This allows active molecular families and candidate analogues to be prioritized before isolation while preserving molecular-network annotation and dereplication context.", "publication": { "title": "Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation", "doi": "10.1021/acs.jnatprod.7b00737", "url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.7b00737", "citation": "Nothias L-F, Nothias-Esposito M, da Silva R, et al. Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation. Journal of Natural Products 81, 758–767 (2018).", "journal": "Journal of Natural Products", "year": 2018 }, "purpose": { "primary": "natural_product_discovery", "secondary": [ "phenotype_association", "molecular_family_discovery", "dereplication" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "chromatography", "data_acquisition", "preprocessing", "molecular_networking", "statistics", "biological_validation", "data_sharing" ], "workflow_steps": [ { "title": "Generate fractions and quantitative bioassay data", "description": "Fractionate the bioactive extract or sample set and measure a quantitative biological response for the same fractions used for metabolomics." }, { "title": "Acquire untargeted LC-MS/MS profiles", "description": "Profile the fractions by LC-MS/MS so that feature abundance across fractions and fragmentation spectra are available." }, { "title": "Detect and quantify features", "description": "Process the LC-MS/MS data to generate aligned feature intensities across the fraction series.", "tool_slugs": [ "mzmine", "openms" ] }, { "title": "Calculate feature–bioactivity associations", "description": "Correlate feature abundance across fractions with the quantitative bioassay response to calculate a bioactivity score for each feature.", "external_tools": [ { "name": "Bioactive Molecular Networks Jupyter workflow", "url": "https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks" } ] }, { "title": "Build and annotate the molecular network", "description": "Generate a molecular network from MS/MS similarity and attach the bioactivity scores as node attributes so active molecular families can be recognized in chemical context.", "tool_slugs": [ "gnps" ] }, { "title": "Prioritize and validate active chemistry", "description": "Select high-scoring features or molecular families for isolation, orthogonal structural work, and direct activity confirmation." } ], "tools": [ { "slug": "mzmine", "role": "One supported route for aligned LC-MS/MS feature detection and quantification." }, { "slug": "openms", "role": "Alternative supported preprocessing route used by the original workflow through Optimus/OpenMS." }, { "slug": "gnps", "role": "MS/MS molecular networking and library-based dereplication." }, { "name": "Bioactive Molecular Networks Jupyter workflow", "url": "https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks", "role": "Calculates and maps feature–bioactivity association scores." }, { "role": "Visualization of molecular networks with bioactivity attributes.", "name": "Cytoscape", "url": "https://cytoscape.org/" } ], "resources": { "code_available": true, "code_url": "https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks", "method_available": true, "method_url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.7b00737" }, "scope": { "designed_for": [ "Bioassay-guided natural-product discovery where the same fractions or samples have quantitative LC-MS feature data and biological activity measurements.", "Prioritizing active analogues within molecular families before purification." ], "requirements": [ "Reproducible quantitative bioassay values matched to metabolomics samples.", "Sufficient variation in both feature abundance and bioactivity across the sample or fraction series.", "The supplied implementation uses MZmine 2 or Optimus exports and an R script or R-kernel Jupyter notebook; check current export compatibility." ], "considerations": [ "Correlation between a feature and bioactivity is prioritization evidence, not proof that the feature causes the phenotype.", "Coeluting or covarying metabolites can receive similar activity scores and require follow-up isolation or orthogonal validation." ] }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included as an Analytical Strategy because the method adds quantitative bioassay information to an existing molecular-networking workflow and remains transferable across natural-product sources and assays. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [ "reverse-metabolomics" ], "ask_keywords": [ "bioassay guided fractionation", "bioactivity guided fractionation", "connect bioactivity to metabolites", "antimicrobial activity and metabolomics", "phenotype associated metabolites", "prioritize active natural products", "correlate fractions with activity" ], "implementations": [ { "title": "Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus", "doi": "10.3390/md18060311", "url": "https://doi.org/10.3390/md18060311", "citation": "Buedenbender L, Astone FA, Tasdemir D. Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus. Marine Drugs 18, 311 (2020).", "journal": "Marine Drugs", "year": 2020, "relationship": "application", "summary": "Applied bioactive molecular networking to fractionated Fucus vesiculosus extracts, combining antimicrobial activity against MRSA with UPLC-QToF-MS/MS feature abundances, MZmine preprocessing, GNPS feature-based molecular networking, Pearson correlation scoring and Cytoscape visualization.", "biological_contexts": [ "marine" ], "sample_types": [ "other" ], "organisms": [ "Fucus vesiculosus" ], "tool_slugs": [ "mzmine", "gnps", "cytoscape" ] }, { "title": "Bioactivity-Based Molecular Networking-Guided Isolation of Epicolidines A-C from the Endophytic Fungus Epicoccum sp. 1-042", "doi": "10.1021/acs.jnatprod.4c00214", "url": "https://doi.org/10.1021/acs.jnatprod.4c00214", "citation": "Chang S, Li Y, Huang X, et al. Bioactivity-Based Molecular Networking-Guided Isolation of Epicolidines A-C from the Endophytic Fungus Epicoccum sp. 1-042. Journal of Natural Products 87, 1582-1590 (2024).", "journal": "Journal of Natural Products", "year": 2024, "relationship": "application", "summary": "Used bioactivity-based molecular networking-guided fractionation in an endophytic fungal natural-product study to prioritize and isolate antibacterial polycyclic tetramic acids.", "biological_contexts": [ "fungal" ], "sample_types": [ "microbial_culture" ], "organisms": [ "Epicoccum sp. 1-042" ] } ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "chemical-proportionality-network-transformations", "name": "Chemical proportionality for transformation prioritization", "aliases": [ "ChemProp strategy" ], "summary": "Combine molecular-network edges with abundance changes across ordered samples to prioritize possible chemical transformations.", "publication": { "title": "Chemical Proportionality within Molecular Networks", "doi": "10.1021/acs.analchem.1c01520", "year": 2021, "journal": "Analytical Chemistry", "citation": "Petras D, et al. Chemical Proportionality within Molecular Networks. Analytical Chemistry 93, 12833-12839 (2021)." }, "purpose": { "primary": "biotransformation" }, "platforms": [ "lc_ms" ], "components": [ "molecular_networking", "metadata_analysis", "statistics" ], "tools": [ { "slug": "chemprop", "role": "Score abundance changes between connected features." } ], "workflow_steps": [ { "title": "Prepare matched network and abundance data", "description": "Connect quantified features to fragmentation-based network edges and ordered sample metadata." }, { "title": "Score proportional changes", "description": "Apply the appropriate ChemProp scoring mode to connected feature pairs.", "tool_slugs": [ "chemprop" ] }, { "title": "Investigate candidate transformations", "description": "Inspect abundance trends, mass differences and spectra before follow-up validation." } ], "scope": { "designed_for": [ "Prioritizing possible transformations among network-connected features." ], "requirements": [ "MS/MS network edges linked to quantified features.", "Consistent sample identifiers and an ordered temporal or spatial variable.", "Choose the scoring mode for the available sampling design." ], "considerations": [ "Direction and proportionality are hypotheses, not proof of a reaction or its mechanism.", "Co-variation, normalization and missing signals affect interpretation.", "This does not estimate metabolic flux." ] }, "ask_keywords": [ "chemical proportionality", "prioritize biotransformations", "ordered abundance changes", "drug metabolite transformation", "time resolved molecular network" ], "related_strategies": [ "gnps-feature-based-molecular-networking" ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/acs.analchem.1c01520" }, "implementations": [ { "title": "A Functional Metabolomics Framework to Track Microbiome Drug Metabolism", "doi": "10.64898/2026.01.30.702925", "year": 2026, "journal": "bioRxiv (preprint; not peer reviewed)", "citation": "Pakkir Shah AK, et al. bioRxiv (2026), preprint. DOI: 10.64898/2026.01.30.702925.", "relationship": "extension", "summary": "ChemProp2 extends scoring to time-series correlations with a decoy-based empirical FDR procedure. Demonstrated with a synthetic gut community and drugs; microbial association does not establish a producing organism.", "tool_slugs": [ "chemprop", "gnps-feature-based-molecular-networking" ] } ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary ChemProp paper anchors the transferable relationship. ChemProp2 is an extension, not a separate drug-specific Strategy. Primary DOI/title/year and preprint status verified; workflows not executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-29", "updated_at": "2026-09-29", "last_verified": "2026-09-29", "last_editorial_review": "2026-09-29" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "chemical-similarity-set-enrichment", "name": "Chemical-similarity metabolite-set enrichment", "summary": "Group identified metabolites by chemical similarity and test which groups change between study conditions.", "description": "ChemRICH combines chemical ontology and structural similarity with set-level statistics, without requiring membership in predefined biochemical pathways.", "publication": { "title": "Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets", "doi": "10.1038/s41598-017-15231-w", "year": 2017, "journal": "Scientific Reports" }, "purpose": { "primary": "phenotype_association", "secondary": [] }, "platforms": [ "lc_ms", "gc_ms" ], "analysis_types": [ "metabolite_profiling", "lipidomics" ], "components": [ "statistics", "metadata_analysis" ], "ask_keywords": [ "ChemRICH", "chemical class enrichment", "chemical similarity", "metabolite sets", "identified metabolites", "SMILES", "lipid classes" ], "workflow_steps": [ { "title": "Prepare identified metabolites and statistics", "description": "Provide structures, identifiers and per-metabolite comparison statistics." }, { "title": "Construct chemical sets", "description": "ChemRICH combines MeSH annotations and Tanimoto similarity into non-overlapping groups.", "external_tools": [ { "name": "ChemRICH", "url": "https://chemrich.idsl.me/" } ] }, { "title": "Evaluate and interpret enrichment", "description": "Use the published Kolmogorov-Smirnov set test and inspect directions of change.", "external_tools": [ { "name": "ChemRICH", "url": "https://chemrich.idsl.me/" } ] } ], "tools": [ { "name": "ChemRICH", "url": "https://chemrich.idsl.me/", "role": "Implementation introduced by the primary paper; linked service is its current maintained entry point." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1038/s41598-017-15231-w" }, "scope": { "designed_for": [ "Chemical-class interpretation of identified MS metabolomics measurements." ], "requirements": [ "Chemical structures/identifiers and valid comparison statistics." ], "considerations": [ "Does not infer identities for unknown m/z features. Chemical-set enrichment is not proof of pathway activation or flux." ] }, "related_strategies": [ "feature-level-pathway-inference" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary method reviewed using open-access full text. Distinct from mummichog: known structures and chemical sets rather than ambiguous feature-to-pathway mapping. No new Tool entry or software execution claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-20", "updated_at": "2026-09-20", "last_editorial_review": "2026-09-20" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "chemoselective-reactivity-resolved-metabolomics", "name": "Chemoselective reactivity-resolved metabolomics", "summary": "A functional-group-resolved metabolomics strategy that uses orthogonal chemoselective probes, covalent capture and diagnostic reporter chemistry to enrich and organize otherwise chemically heterogeneous metabolite subsets before LC-MS.", "description": "The modular reactivity-encoding platform (MREP) uses distinct alkyne-tagged probes for carboxyl, amine, carbonyl and thiol functionalities. Tagged metabolites are captured through click chemistry on a common resin, matrix is removed, and released derivatives share reporter-ion behavior. The strategy converts intrinsic chemical reactivity into an experimental annotation/enrichment dimension.", "publication": { "title": "Chemoselective Metabolomics via a Modular Reactivity-Encoding Platform", "doi": "10.1002/advs.76599", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13379249/", "citation": "Tao X, Zhang C-M, Yang R-J, et al. Chemoselective Metabolomics via a Modular Reactivity-Encoding Platform. Advanced Science (2026).", "journal": "Advanced Science", "year": 2026 }, "purpose": { "primary": "chemical_reactivity", "secondary": [ "metabolite_annotation", "molecular_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": {}, "components": [ "sample_preparation", "chromatography", "data_acquisition", "annotation", "biological_validation" ], "workflow_steps": [ { "title": "Select a functional-group-specific probe", "description": "Choose a chemoselective probe for the metabolite reactivity class of interest, such as carboxyl, amine, carbonyl or thiol groups." }, { "title": "Encode metabolites by covalent derivatization", "description": "React the sample under conditions designed to selectively label the targeted functional group." }, { "title": "Capture and clean up the encoded submetabolome", "description": "Use click chemistry and solid-phase capture to enrich tagged metabolites while reducing matrix background." }, { "title": "Release and analyze derivatives by LC-MS/MS", "description": "Profile enriched derivatives, using shared reporter behavior and functional-group information to aid classification and annotation." }, { "title": "Reconstruct reactivity-resolved metabolome layers", "description": "Compare the parallel functional-group-defined measurements to expand detectable chemical space and interpret metabolic changes." } ], "tools": [], "resources": { "method_available": true, "method_url": "https://doi.org/10.1002/advs.76599" }, "scope": { "designed_for": [ "Expanding metabolite coverage through functional-group-selective enrichment and reporter chemistry.", "Separating complex metabolomes into chemically interpretable reactivity classes." ], "requirements": [ "Validated chemoselective reagents and controls for reaction selectivity.", "Careful assessment of derivatization yield, matrix effects and quantitative behavior." ], "considerations": [ "Probe selectivity and conversion efficiency are chemistry-dependent.", "Derivatization changes retention, ionization and fragmentation and therefore requires method-specific references.", "The source demonstrates mouse serum and liver; transfer to other organisms and matrices requires method validation." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because chemoselective reactivity is an orthogonal experimental evidence/enrichment layer, not simply a different LC-MS software workflow. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "serum", "tissue" ], "biological_contexts": [ "animal" ], "organisms": [ "Mus musculus" ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "credentialing-untargeted-features", "name": "Credentialing untargeted metabolomics features", "summary": "A method-development strategy that uses mixtures of natural-abundance and uniformly 13C-labeled biological extracts to distinguish genuine biological features from artifacts and benchmark untargeted metabolomics workflows.", "description": "Credentialing creates an experimental ground truth for whether untargeted LC-MS features arise from biological metabolites. Natural-abundance and uniformly 13C-labeled material are mixed at defined ratios; expected isotope spacing and intensity relationships are used to label credible biological features. The number and quality of credentialed features can then be used to optimize extraction, chromatography, acquisition or feature-processing parameters.", "publication": { "title": "Credentialing Features: A Platform to Benchmark and Optimize Untargeted Metabolomic Methods", "doi": "10.1021/ac503092d", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4188275/", "citation": "Mahieu NG, Huang X, Chen Y-J, Patti GJ. Credentialing Features: A Platform to Benchmark and Optimize Untargeted Metabolomic Methods. Analytical Chemistry 86, 9583–9589 (2014).", "journal": "Analytical Chemistry", "year": 2014 }, "purpose": { "primary": "stable_isotope_tracing", "secondary": [] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "stable_isotope_tracing" ], "acquisition": {}, "components": [ "sample_preparation", "data_acquisition", "preprocessing", "quality_control", "statistics" ], "workflow_steps": [ { "title": "Generate matched unlabeled and uniformly 13C-labeled biological material", "description": "Grow a model biological system in natural-abundance and uniformly 13C-enriched media." }, { "title": "Prepare defined isotope mixtures", "description": "Mix labeled and unlabeled extracts at known ratios so true biological features have predictable isotope spacing and intensity relationships." }, { "title": "Run the candidate untargeted workflow", "description": "Process the mixtures using the extraction, chromatography, instrument and feature-detection workflow to be benchmarked." }, { "title": "Credential biological features", "description": "Identify feature pairs that satisfy the expected mass shifts, retention-time agreement and isotope-intensity ratios." }, { "title": "Optimize the workflow", "description": "Compare the number and behavior of credentialed features rather than total feature count when tuning experimental or computational parameters." } ], "tools": [], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/ac503092d" }, "scope": { "designed_for": [ "Benchmarking and optimizing untargeted metabolomics pipelines using experimentally defined biological features." ], "requirements": [ "A biological model that can be grown with uniform stable-isotope labeling.", "Accurate-mass data capable of resolving the expected isotope relationships." ], "considerations": [ "Optimization in a model system does not guarantee identical performance in every biological matrix.", "The strategy distinguishes biological origin from artifact but does not identify metabolite structures.", "The source establishes credentialing using labeled and unlabeled E. coli extracts; adaptation to other matrices or platforms requires validation." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because isotope credentialing supplies an experimental truth layer for method optimization rather than merely applying an analysis pipeline. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "microbial_culture" ], "biological_contexts": [ "microbial", "broadly_applicable" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "dadia-hybrid-acquisition", "name": "DDA-assisted DIA metabolomics", "aliases": [ "DaDIA" ], "summary": "A hybrid untargeted acquisition strategy that analyzes individual samples by DIA for broad feature and MS/MS coverage while using pooled quality-control samples by DDA to obtain cleaner fragmentation spectra for annotation.", "description": "DaDIA deliberately separates the strengths of two acquisition modes across the study design. Biological samples are acquired using data-independent acquisition to maximize feature and fragmentation coverage, while pooled QC samples are acquired using data-dependent acquisition to generate higher-quality, less multiplexed MS/MS spectra. Computational integration then associates the complementary DDA and DIA evidence for metabolite annotation.", "publication": { "title": "DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data", "doi": "10.1021/acs.analchem.0c05022", "url": "https://doi.org/10.1021/acs.analchem.0c05022", "citation": "Guo J, Shen S, Xing S, Huan T. DaDIA Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data. Analytical Chemistry 93, 2669-2677 (2021).", "journal": "Analytical Chemistry", "year": 2021 }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "molecular_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "dia" ], "ion_mobility": "unspecified" }, "components": [ "sample_preparation", "data_acquisition", "quality_control", "preprocessing", "annotation" ], "workflow_steps": [ { "title": "Prepare representative pooled QC material", "description": "Pool study material so the QC contains a broad representation of metabolites observed across the sample set." }, { "title": "Acquire individual study samples by DIA", "description": "Use DIA on individual biological samples to maximize feature detection and fragmentation coverage." }, { "title": "Acquire pooled QC samples by DDA", "description": "Use DDA on pooled QC material to obtain cleaner precursor-specific fragmentation spectra." }, { "title": "Integrate DDA and DIA information", "description": "Associate feature-level quantitative data and DIA coverage with higher-quality DDA spectra from the pooled QC.", "external_tools": [ { "name": "DaDIA.R", "url": "https://github.com/HuanLab/DaDIA" } ] }, { "title": "Annotate and interpret the combined dataset", "description": "Use integrated evidence for metabolite annotation and downstream statistical interpretation." } ], "tools": [ { "name": "DaDIA.R", "url": "https://github.com/HuanLab/DaDIA", "role": "Computational integration and annotation of the hybrid DDA/DIA dataset." } ], "resources": { "code_available": true, "code_url": "https://github.com/HuanLab/DaDIA", "method_available": true, "method_url": "https://doi.org/10.1021/acs.analchem.0c05022" }, "scope": { "designed_for": [ "Untargeted LC-MS studies seeking broad DIA coverage without sacrificing access to cleaner DDA spectra.", "Studies already using pooled QC samples and able to dedicate additional injections to complementary acquisition." ], "requirements": [ "A representative pooled QC sample.", "Instrument support for both DIA and DDA acquisition." ], "considerations": [ "The workflow increases acquisition and computational complexity relative to one acquisition mode.", "Spectra obtained from pooled QC material must be mapped appropriately back to features in individual samples." ] }, "ask_keywords": [ "combine dda and dia", "hybrid dda dia metabolomics", "improve msms coverage and quality", "pooled qc dda individual samples dia", "dadia" ], "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because the publication introduces a transferable hybrid acquisition design rather than merely comparing existing acquisition modes." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_verified": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "epoxidation-lipid-double-bond-localization", "name": "Epoxidation-assisted lipid double-bond localization", "summary": "Convert unsaturated fatty-acid double bonds to epoxides and use diagnostic tandem-MS fragments to distinguish positional isomers.", "publication": { "title": "Identification and Quantitation of C=C Location Isomers of Unsaturated Fatty Acids by Epoxidation Reaction and Tandem Mass Spectrometry", "doi": "10.1021/acs.analchem.7b01870", "year": 2017, "journal": "Analytical Chemistry", "citation": "Zhao Y, Zhao H, Zhao X, et al. Analytical Chemistry 89, 10270-10278 (2017)." }, "purpose": { "primary": "structure_elucidation", "secondary": [ "chemical_reactivity" ] }, "platforms": [ "direct_infusion_ms" ], "analysis_types": [ "lipidomics" ], "components": [ "sample_preparation", "data_acquisition", "annotation" ], "tools": [], "workflow_steps": [ { "title": "Epoxidize unsaturated fatty acids", "description": "Use the published low-temperature-plasma reaction to modify double bonds." }, { "title": "Fragment epoxide products", "description": "Acquire tandem mass spectra of the derivatized analytes." }, { "title": "Assign double-bond positions", "description": "Interpret diagnostic cleavage ions and compare appropriate positional-isomer standards." } ], "scope": { "designed_for": [ "Unsaturated fatty-acid double-bond positional isomers." ], "requirements": [ "Suitable epoxidation apparatus and validated reaction conditions.", "Tandem-MS measurements of reaction products and relevant standards." ], "considerations": [ "Conventional unmodified lipid spectra cannot be retrospectively treated as epoxidation data.", "Reaction yield and competing products affect interpretation.", "Double-bond position does not by itself establish cis/trans geometry or full stereochemistry." ] }, "ask_keywords": [ "epoxidation lipid double bond localization", "epoxide diagnostic fragments", "fatty acid positional isomers", "low temperature plasma epoxidation" ], "related_strategies": [ "paterno-buchi-lipid-double-bond-localization", "ozid-structural-lipidomics" ], "implementations": [ { "title": "Locating Carbon-Carbon Double Bonds in Unsaturated Phospholipids by Epoxidation Reaction and Tandem Mass Spectrometry", "doi": "10.1021/acs.analchem.8b02021", "year": 2018, "relationship": "extension", "summary": "Extends the epoxidation/CID approach to phospholipid classes in positive and negative ion modes." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/acs.analchem.7b01870" }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Anchored to the 2017 fatty-acid method, not the later 2018 phospholipid extension. Distinct reaction chemistry from Paterno-Buchi and OzID. No software implementation assumed; experiment not executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-29", "updated_at": "2026-09-29", "last_verified": "2026-09-29", "last_editorial_review": "2026-09-29" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "exposomics-suspect-screening", "name": "Suspect screening of exposome-related xenobiotics", "summary": "An exposomics strategy that interrogates untargeted UHPLC-HRMS/MS data with curated lists of plausible xenobiotics and metabolites, then applies chromatographic, spectral, and error-control evidence to prioritize candidate exposures.", "description": "Suspect screening occupies the space between targeted analysis and fully non-target screening. Rather than requiring authentic standards for every analyte or searching every detected feature without prior hypotheses, the workflow begins with curated suspect lists and tests whether those candidates are supported by accurate mass, chromatographic peak quality, fragmentation evidence, and matrix-aware filtering. The 2024 workflow evaluated this approach across multiple human biofluids and explicitly measured false-positive and false-negative behavior.", "publication": { "title": "Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids", "doi": "10.1016/j.chemosphere.2024.141221", "url": "https://doi.org/10.1016/j.chemosphere.2024.141221", "citation": "Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Chemosphere 351, 141221 (2024).", "journal": "Chemosphere", "year": 2024 }, "purpose": { "primary": "exposomics", "secondary": [ "metabolite_annotation", "unknown_identification" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "exposomics", "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "data_acquisition", "preprocessing", "spectral_search", "annotation", "metadata_analysis", "biological_validation" ], "workflow_steps": [ { "title": "Build suspect lists", "description": "Assemble plausible parent xenobiotics and phase-II metabolites relevant to the exposure question and sample matrix." }, { "title": "Acquire UHPLC-HRMS/MS data", "description": "Analyze biofluids using high-resolution MS with fragmentation information suitable for candidate assessment." }, { "title": "Filter chromatographic features for peak quality", "description": "Apply automated peak-quality criteria to reduce poor chromatographic candidates before manual review." }, { "title": "Compare candidates with suspect and endogenous lists", "description": "Evaluate expected xenobiotic candidates while accounting for endogenous molecules that may create annotation interference." }, { "title": "Evaluate structural evidence", "description": "Combine accurate mass, isotope/adduct behavior, fragmentation evidence, and available reference information to annotate candidates." }, { "title": "Estimate identification errors", "description": "Assess false positives and false negatives using standards or spiked samples where available." } ], "tools": [ { "name": "Compound Discoverer", "role": "Software used in the published implementation for peak-quality assessment and suspect-screening processing." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1016/j.chemosphere.2024.141221" }, "scope": { "designed_for": [ "Screening broad but hypothesis-informed sets of environmental or exogenous chemicals in human biofluids.", "Exposomics studies where fully targeted assays would be too narrow but unconstrained non-target identification is impractical." ], "requirements": [ "Curated suspect lists appropriate to the study question.", "High-resolution MS data and enough spectral/chromatographic evidence to evaluate candidates." ], "considerations": [ "Suspect-list composition determines which exposure space is visible.", "Matrix interferences and endogenous molecules can create both false positives and false negatives." ] }, "ask_keywords": [ "suspect screening exposomics", "find xenobiotics in untargeted metabolomics", "screen environmental exposures", "phase ii metabolites suspect list", "human biofluid exposomics" ], "sample_types": [ "urine", "saliva", "other_biofluid" ], "biological_contexts": [ "human" ], "organisms": [ "Homo sapiens" ], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because the publication develops and evaluates a transferable suspect-screening workflow with explicit error analysis across multiple biofluids." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_verified": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "feature-level-pathway-inference", "name": "Feature-level pathway inference before metabolite identification", "summary": "Infer pathway and metabolic-network hypotheses from significant untargeted MS features before assigning every metabolite identity.", "description": "The mummichog method connects ambiguous mass-to-metabolite matches to metabolic-network structure. Enrichment is evaluated against the measured feature background, allowing collective evidence to prioritize biological hypotheses for experimental follow-up.", "publication": { "title": "Predicting Network Activity from High Throughput Metabolomics", "doi": "10.1371/journal.pcbi.1003123", "year": 2013, "journal": "PLOS Computational Biology", "citation": "Li S, Park Y, Duraisingham S, et al. Predicting Network Activity from High Throughput Metabolomics. PLOS Computational Biology 9, e1003123 (2013)." }, "purpose": { "primary": "phenotype_association", "secondary": [ "metabolite_annotation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "components": [ "statistics", "annotation", "biological_validation" ], "ask_keywords": [ "pathway enrichment", "unknown metabolites", "unidentified features", "biological interpretation", "mummichog", "metabolic pathways" ], "workflow_steps": [ { "title": "Define significant and background features", "description": "Prepare significant feature masses and the full measured feature list from the comparison." }, { "title": "Map candidate metabolites to a network", "description": "Use mass accuracy, ionization assumptions and a suitable metabolic model to retain plausible matches.", "tool_slugs": [ "mummichog" ] }, { "title": "Evaluate network and pathway enrichment", "description": "Compare observed enrichment with permutations from the feature background.", "tool_slugs": [ "mummichog" ] }, { "title": "Validate prioritized hypotheses", "description": "Follow up selected metabolites and pathways with independent measurements." } ], "tools": [ { "slug": "mummichog", "role": "Software implementation introduced with the defining paper." } ], "resources": { "method_available": true, "method_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003123" }, "scope": { "designed_for": [ "Functional interpretation when many significant features remain unidentified." ], "requirements": [ "Feature statistics, measured background and a relevant metabolic model." ], "considerations": [ "Enrichment is a hypothesis, not confirmation of individual identities or a measurement of flux.", "Background selection and ambiguous ion assignments influence results." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary-paper review: 2013 method introduction, not its immune-cell application as a separate Strategy. The existing mummichog Tool remains the implementation record. No software execution tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "gnps-feature-based-molecular-networking", "name": "Feature-Based Molecular Networking (FBMN)", "summary": "A strategy that combines aligned LC-MS feature abundances with MS/MS spectral similarity to explore quantitative molecular families while retaining chromatographic feature identity.", "description": "Feature-Based Molecular Networking connects an aligned chromatographic feature table to a network of representative fragmentation spectra. Unlike spectrum-centric networking, it retains feature-level abundance and can keep chromatographically separated isomers distinct. The Strategy is the analytical design; the GNPS2 FBMN workflow is one software implementation, used with compatible upstream feature-processing tools.", "publication": { "title": "Feature-based molecular networking in the GNPS analysis environment", "doi": "10.1038/s41592-020-0933-6", "url": "https://www.nature.com/articles/s41592-020-0933-6", "citation": "Nothias L-F, Petras D, Schmid R, et al. Feature-based molecular networking in the GNPS analysis environment. Nature Methods 17, 905–908 (2020).", "journal": "Nature Methods", "year": 2020 }, "purpose": { "primary": "molecular_family_discovery", "secondary": [ "metabolite_annotation", "molecular_discovery", "natural_product_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling", "lipidomics" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "dia" ], "ion_mobility": "not_required" }, "components": [ "preprocessing", "quality_control", "annotation", "spectral_search", "molecular_networking", "data_sharing" ], "workflow_steps": [ { "title": "Process LC-MS/MS data into aligned features", "description": "Perform feature detection, chromatogram construction/resolution, alignment, filtering, and MS/MS association in a compatible processing tool.", "tool_slugs": [ "mzmine", "ms-dial", "xcms", "openms" ] }, { "title": "Export FBMN inputs", "description": "Export a feature quantification table and representative MS/MS spectra using the selected implementation's supported format. Match feature identifiers between files and supply sample metadata when comparing groups.", "tool_slugs": [ "mzmine", "ms-dial", "xcms", "openms" ] }, { "title": "Run the FBMN workflow in GNPS2", "description": "Upload or select the feature table and spectral summary, configure tolerances and networking filters, and launch Feature-Based Molecular Networking.", "tool_slugs": [ "gnps-feature-based-molecular-networking" ] }, { "title": "Overlay spectral-library annotations", "description": "Search compatible nodes against GNPS spectral libraries and inspect molecular families containing annotated and unannotated features.", "tool_slugs": [ "gnps-feature-based-molecular-networking" ] }, { "title": "Interpret quantitative molecular families", "description": "Use feature abundances, metadata, retention time, ion mobility where available, and spectral relationships to compare molecular families across samples or conditions.", "tool_slugs": [ "cytoscape" ] } ], "tools": [ { "slug": "gnps-feature-based-molecular-networking", "role": "GNPS2 implementation of feature-based networking and spectral-library matching." }, { "slug": "mzmine", "role": "Compatible upstream feature processing and FBMN export." }, { "slug": "ms-dial", "role": "Compatible upstream feature processing and FBMN export." }, { "slug": "xcms", "role": "Compatible upstream feature processing and FBMN export." }, { "slug": "openms", "role": "Compatible upstream feature processing and FBMN export." }, { "slug": "cytoscape", "role": "Inspect and style exported networks while retaining edge-type and feature metadata." } ], "resources": { "code_available": true, "code_url": "https://github.com/CCMS-UCSD/GNPS_Workflows/tree/master/feature-based-molecular-networking", "method_available": true, "method_url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/fbmn/" }, "scope": { "designed_for": [ "Untargeted LC-MS/MS studies where chromatographic feature detection and quantitative feature tables are important for molecular-network interpretation.", "Resolving isomeric or chromatographically separated features that would otherwise be combined in purely spectrum-centric networking." ], "requirements": [ "An aligned feature quantification table and representative MS/MS spectra exported from a compatible preprocessing workflow.", "Consistent feature identifiers across the quantification table and spectral export; raw files alone are not ready-to-use FBMN inputs." ], "considerations": [ "The quality of the molecular network depends on upstream feature detection, alignment, MS/MS assignment, and preprocessing choices.", "FBMN does not remove the need for independent structural validation; library matches and network propagation represent different levels of annotation evidence." ] }, "related_strategies": [ "gnps-ion-identity-molecular-networking", "bioactivity-based-molecular-networking" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Classified as a transferable analytical Strategy; linked Tool entries describe software implementations. Reviewed against the original method publication and GNPS documentation." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-21", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-21" }, "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "aliases": [ "FBMN" ], "ask_keywords": [ "DIA MSE molecular networking", "DIA IntOpenStream" ], "implementations": [ { "title": "Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data", "doi": "10.1093/bib/bbae013", "year": 2024, "journal": "Briefings in Bioinformatics", "relationship": "adaptation", "summary": "DIA-IntOpenStream adapts FBMN to MSE/DIA data. Waters2mzML supplies mzML; KNIME assembles custom structure databases; MS-DIAL assigns DIA fragments to precursors and exports FBMN inputs for GNPS and Cytoscape. MZmine 3 is used for a parallel MS1 annotation branch, not DIA fragment deconvolution in this study. Reliable precursor-fragment assignment is required; custom mass matches and network neighbors are not confirmed structures. The demonstrated conversion uses Windows and Waters data; other vendor compatibility is not established here.", "tool_slugs": [ "ms-dial", "mzmine", "gnps", "cytoscape" ], "external_tools": [ { "name": "Waters2mzML", "url": "https://github.com/AnP311/Waters2mzML" }, { "name": "Published KNIME database workflow", "url": "https://hub.knime.com/-/spaces/-/~8bZEbbknV8tVptea/current-state/" } ] } ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "gnps-ion-identity-molecular-networking", "name": "Ion Identity Molecular Networking (IIMN)", "summary": "A strategy extending feature-based molecular networking with MS1-derived ion relationships, linking candidate ion species of the same molecule alongside MS/MS spectral-similarity edges.", "description": "Ion Identity Molecular Networking adds a second evidence layer to an FBMN network: relationships between candidate adducts, multimers and other ion species supported by chromatographic behavior and mass differences. MS1 ion-identity edges and MS/MS similarity edges have different meanings and should be interpreted separately. The published GNPS implementation can also collapse ion groups for visualization. These groupings are hypotheses about ion origin, not proof of a molecular structure.", "publication": { "title": "Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment", "doi": "10.1038/s41467-021-23953-9", "url": "https://www.nature.com/articles/s41467-021-23953-9", "citation": "Schmid R, Petras D, Nothias L-F, et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nature Communications 12, 3832 (2021).", "journal": "Nature Communications", "year": 2021 }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "molecular_family_discovery", "unknown_identification", "molecular_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "not_required" }, "components": [ "preprocessing", "annotation", "molecular_networking", "spectral_search", "data_sharing" ], "workflow_steps": [ { "title": "Detect and align LC-MS/MS features", "description": "Generate an aligned feature table with representative MS/MS spectra using a compatible preprocessing tool.", "tool_slugs": [ "mzmine" ] }, { "title": "Annotate ion identities", "description": "In a documented ion-identity workflow, group chromatographic features and annotate plausible ion species using retention behavior, peak-shape correlation and expected mass relationships.", "tool_slugs": [ "mzmine" ] }, { "title": "Export FBMN and ion-identity edges", "description": "Export the quantification table, representative MS/MS spectra and supplementary ion-identity pairs with matching feature identifiers. Follow the specific GNPS and preprocessing version documentation.", "tool_slugs": [ "mzmine" ] }, { "title": "Build the ion identity molecular network", "description": "Run FBMN in GNPS with supplementary ion-identity relationships so that MS1 ion relationships and MS/MS spectral-similarity relationships are represented together.", "tool_slugs": [ "gnps" ] }, { "title": "Inspect compound-level ion groups and molecular families", "description": "Distinguish ion-identity edges from spectral-similarity edges and review collapsed groups against the uncollapsed network. Check ambiguous assignments before propagating annotations.", "tool_slugs": [ "cytoscape" ] } ], "tools": [ { "slug": "gnps", "role": "Published GNPS FBMN/IIMN implementation; consult the linked IIMN documentation for the supported workflow." }, { "slug": "mzmine", "role": "Documented route for feature processing, ion-identity annotation and supplementary-pairs export." }, { "slug": "cytoscape", "role": "Inspect and style exported networks while retaining edge-type and feature metadata." } ], "resources": { "code_available": true, "code_url": "https://github.com/CCMS-UCSD/GNPS_Workflows", "method_available": true, "method_url": "https://ccms-ucsd.github.io/GNPSDocumentation/fbmn-iin/" }, "scope": { "designed_for": [ "LC-MS/MS datasets in which multiple ion species from the same molecule create redundant nodes or obscure molecular-family interpretation.", "Combining chromatographic ion relationships with fragmentation-spectral relationships." ], "requirements": [ "Feature-level chromatographic information, MS/MS spectra, and an ion-identity/supplementary-pairs export from a compatible preprocessing workflow." ], "considerations": [ "Ion-identity assignments rely on chromatographic correlation and expected mass relationships and should be reviewed, particularly for crowded or poorly resolved chromatographic regions.", "The original paper also describes MS-DIAL and XCMS with CAMERA export routes. Their version-specific scripts and formats must be checked before substituting them for the MZmine route shown here.", "Features without MS/MS can participate through ion-identity edges when supported by the implementation; they do not thereby acquire an observed fragmentation spectrum.", "Ion identity networking is not ion mobility networking and does not require ion mobility acquisition." ] }, "related_strategies": [ "gnps-feature-based-molecular-networking", "bioactivity-based-molecular-networking" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Classified as a transferable analytical Strategy; linked Tool entries describe software implementations. Reviewed against the original method publication and GNPS documentation." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "aliases": [ "IIMN", "Ion identity networking" ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "idbac-colony-maldi-msms-dereplication", "name": "Colony-based MALDI-MS/MS dereplication with IDBac and GNPS2", "summary": "A miniaturized microbial natural-product strategy that adds MALDI tandem mass spectrometry and GNPS2 molecular networking to IDBac so metabolite families can be preliminarily annotated directly from colonies before fermentation scale-up and extraction.", "description": "This strategy extends colony-level IDBac screening beyond MALDI-MS1 chemical fingerprints. Protein and specialized-metabolite MS1 data are first used to understand isolate identity and metabolite-production potential. Selected precursor ions are then fragmented by MALDI-MS/MS, and the resulting spectra are passed to GNPS2 for molecular networking and library searching. The layered workflow adds preliminary structural context early enough to influence which strains are advanced to scale-up.", "publication": { "title": "MALDI Tandem Mass Spectrometry for Colony-Based Dereplication of Natural Products", "doi": "10.1021/acs.jnatprod.6c00765", "url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.6c00765", "citation": "Shepherd RA, Gad LY, Strobel M, et al. MALDI Tandem Mass Spectrometry for Colony-Based Dereplication of Natural Products. Journal of Natural Products (2026).", "journal": "Journal of Natural Products", "year": 2026 }, "purpose": { "primary": "natural_product_discovery", "secondary": [ "dereplication", "molecular_family_discovery", "metabolite_annotation" ] }, "platforms": [ "maldi_ms" ], "analysis_types": [ "metabolite_profiling", "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "data_acquisition", "preprocessing", "spectral_search", "molecular_networking", "annotation", "biological_validation" ], "workflow_steps": [ { "title": "Profile colonies by MALDI-MS1", "description": "Acquire protein-region and specialized-metabolite spectra directly from microbial colonies and inspect the collection in IDBac.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Select small-molecule precursors for fragmentation", "description": "Use the colony-level specialized-metabolite data to choose precursor ions for MALDI tandem-MS acquisition while filtering matrix/background signals.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Acquire MALDI-MS/MS spectra", "description": "Fragment selected precursor ions directly from colony-derived samples to generate tandem spectra before fermentation scale-up." }, { "title": "Run downstream GNPS2 molecular networking", "description": "Submit compatible MALDI-MS/MS spectra to GNPS2 for molecular networking and spectral-library searching.", "tool_slugs": [ "gnps" ] }, { "title": "Prioritize strains and chemistry", "description": "Combine colony identity, metabolite-production patterns, molecular-family context, and preliminary annotations to select strains and compounds for scale-up and orthogonal structural validation." } ], "tools": [ { "role": "Colony-level bacterial grouping, specialized-metabolite profiling, and precursor-selection context.", "name": "IDBac", "url": "https://idbac.org/" }, { "slug": "gnps", "role": "Downstream MS/MS molecular networking and spectral-library searching." } ], "resources": { "method_available": true, "method_url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.6c00765" }, "scope": { "designed_for": [ "Early dereplication and strain prioritization in microbial natural-product discovery before large-scale fermentation and extraction.", "Linking colony-level metabolite production to MS/MS molecular families and tentative annotations." ], "requirements": [ "MALDI instrumentation capable of the required MS1 and tandem-MS acquisition workflow.", "Cultured microbial colonies and compatible IDBac/GNPS2 data preparation." ], "considerations": [ "Colony-level production can differ from larger-scale fermentation, so prioritized chemistry should be reconfirmed after scale-up.", "Spectral-library matches and molecular-network relationships provide dereplication evidence but do not replace full structure elucidation." ] }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included as an Analytical Strategy because it layers MALDI-MS/MS and GNPS2 structural context onto IDBac colony screening to move dereplication and prioritization earlier in the discovery pipeline. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "microbial_culture" ], "biological_contexts": [ "microbial" ], "organisms": [], "related_strategies": [ "idbac-microbial-library-dereplication" ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "idbac-microbial-library-dereplication", "name": "Taxonomy- and metabolite-aware microbial library prioritization with IDBac", "summary": "A microbial natural-product discovery strategy that combines MALDI protein fingerprinting with specialized-metabolite overlap to reduce taxonomic and chemical redundancy when constructing or downsizing isolate libraries.", "description": "This strategy uses two complementary MALDI information spaces before scale-up. Protein-region spectra group bacterial isolates by taxonomic or strain-level similarity, while lower-mass specialized-metabolite spectra reveal chemical overlap within those groups. Isolates can then be selected to preserve phylogenetic breadth while minimizing redundant natural-product profiles, shifting strain prioritization from morphology or taxonomy alone to combined biological and chemical evidence.", "publication": { "title": "Minimizing Taxonomic and Natural Product Redundancy in Microbial Libraries Using MALDI-TOF MS and the Bioinformatics Pipeline IDBac", "doi": "10.1021/acs.jnatprod.9b00168", "url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.9b00168", "citation": "Costa MS, Clark CM, Ómarsdóttir S, Sanchez LM, Murphy BT. Minimizing Taxonomic and Natural Product Redundancy in Microbial Libraries Using MALDI-TOF MS and the Bioinformatics Pipeline IDBac. Journal of Natural Products 82, 2167–2173 (2019).", "journal": "Journal of Natural Products", "year": 2019 }, "purpose": { "primary": "natural_product_discovery", "secondary": [ "phenotype_association" ] }, "platforms": [ "maldi_ms" ], "analysis_types": [ "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "full_scan" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "data_acquisition", "preprocessing", "metadata_analysis", "statistics", "biological_validation" ], "workflow_steps": [ { "title": "Acquire MALDI protein fingerprints from individual isolates", "description": "Analyze colony material in the higher mass range to generate replicate protein fingerprints suitable for similarity grouping.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Group taxonomically similar isolates", "description": "Use protein-profile clustering to organize the collection into operational groups of closely related isolates.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Acquire specialized-metabolite profiles", "description": "Analyze the lower mass region from the same isolate collection to capture differences in specialized-metabolite production.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Compare chemical overlap within taxonomic groups", "description": "Use metabolite association patterns to identify isolates that add distinct chemistry rather than duplicating existing profiles.", "tool_slugs": [], "external_tools": [ { "name": "IDBac", "url": "https://idbac.org/" } ] }, { "title": "Select a reduced but diverse library", "description": "Prioritize representatives that retain taxonomic breadth and specialized-metabolite diversity for fermentation, extraction, and downstream discovery." } ], "tools": [ { "role": "Protein-profile grouping, specialized-metabolite comparison, and isolate prioritization.", "name": "IDBac", "url": "https://idbac.org/" } ], "resources": { "method_available": true, "method_url": "https://pubs.acs.org/doi/10.1021/acs.jnatprod.9b00168" }, "scope": { "designed_for": [ "Building or reducing bacterial isolate libraries for natural-product discovery.", "Prioritizing isolates before scale-up using both relatedness and chemical phenotype." ], "requirements": [ "Cultured bacterial isolates and reproducible MALDI protein and small-molecule acquisitions." ], "considerations": [ "Culture conditions strongly influence specialized-metabolite profiles, so observed chemical redundancy is condition-dependent.", "Protein spectral similarity is an operational grouping method and does not replace sequence-based taxonomy when definitive identification is required." ] }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included as an Analytical Strategy because the publication converts IDBac measurements into a transferable decision framework for constructing chemically and taxonomically diverse microbial libraries. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "microbial_culture" ], "biological_contexts": [ "microbial" ], "organisms": [], "related_strategies": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "isotope-coded-dansylation-metabolomics", "name": "Isotope-coded dansylation for comparative metabolomics", "summary": "Pair light and heavy chemical labels to quantify amine- and phenol-containing metabolites by LC-MS.", "description": "Differential dansylation couples selective chemical tagging with coeluting isotope pairs. Sample-to-reference signal ratios support comparative quantification; labeled standards enable calibrated measurements.", "publication": { "title": "Differential 12C-/13C-Isotope Dansylation Labeling and Fast Liquid Chromatography/Mass Spectrometry for Absolute and Relative Quantification of the Metabolome", "doi": "10.1021/ac900166a", "year": 2009, "journal": "Analytical Chemistry", "citation": "Guo K, Li L. Differential 12C-/13C-Isotope Dansylation Labeling and Fast Liquid Chromatography/Mass Spectrometry for Absolute and Relative Quantification of the Metabolome. Analytical Chemistry 81, 3919–3932 (2009)." }, "purpose": { "primary": "chemical_reactivity", "secondary": [] }, "platforms": [ "lc_ms" ], "analysis_types": [ "metabolite_profiling" ], "components": [ "sample_preparation", "chromatography", "data_acquisition", "statistics" ], "ask_keywords": [ "dansylation", "chemical isotope labeling", "amine", "phenol", "relative quantification", "light heavy reference" ], "workflow_steps": [ { "title": "Label samples and reference", "description": "React compatible metabolites with light or heavy dansyl reagents." }, { "title": "Mix labeled material", "description": "Combine labeled sample with reference material or calibrated standards." }, { "title": "Separate and measure isotope pairs", "description": "Analyze coeluting pairs by LC-MS." }, { "title": "Calculate comparative abundances", "description": "Use pair ratios and appropriate calibration for the intended measurement." } ], "tools": [], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/ac900166a" }, "scope": { "designed_for": [ "Comparative profiling of chemically reactive amines and phenols." ], "requirements": [ "Compatible derivatization chemistry, labeled reference material and LC-MS." ], "considerations": [ "Chemical isotope tags do not measure biological isotope incorporation or metabolic flux.", "Coverage is restricted by reactive functional groups; isotope pairs alone do not confirm structure." ] }, "implementations": [ { "title": "Qualitative Metabolome Analysis of Human Cerebrospinal Fluid by 13C-/12C-Isotope Dansylation Labeling Combined with Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry", "doi": "10.1007/s13361-010-0033-4", "year": 2011, "journal": "Journal of the American Society for Mass Spectrometry", "relationship": "application", "summary": "Application of isotope dansylation to cerebrospinal-fluid profiling; the sample matrix does not define a new Strategy.", "tool_slugs": [], "citation": "Guo K, Bamforth F, Li L. Qualitative Metabolome Analysis of Human Cerebrospinal Fluid by 13C-/12C-Isotope Dansylation Labeling Combined with Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Journal of the American Society for Mass Spectrometry (2011)." } ], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "The defining 2009 method establishes chemical labeling and isotope-ratio quantification. The later CSF paper is an application. No original catalogue software dependency established; no experimental execution claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "iterative-exclusion-list-acquisition", "name": "Iterative exclusion-list MS/MS acquisition", "aliases": [ "IE-Omics acquisition workflow" ], "summary": "Use precursors fragmented in earlier injections to guide subsequent DDA runs toward additional ions.", "description": "The IE-Omics workflow automates exclusion-list preparation between repeat injections. This record uses its 2017 metabolomics methods paper as a reproducible implementation; iterative exclusion has earlier proteomics origins. The defining relationship is measured fragmentation history informing the next acquisition, rather than precomputing survey-based schedules.", "publication": { "title": "Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation", "doi": "10.1007/s13361-017-1608-0", "url": "https://doi.org/10.1007/s13361-017-1608-0", "citation": "Koelmel JP, Kroeger NM, et al. Journal of the American Society for Mass Spectrometry 28, 908-917 (2017).", "journal": "Journal of the American Society for Mass Spectrometry", "year": 2017 }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "molecular_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "unspecified" }, "components": [ "data_acquisition", "preprocessing", "annotation" ], "workflow_steps": [ { "title": "Acquire the initial DDA run", "description": "Begin with top-N fragmentation of a representative sample." }, { "title": "Convert acquired spectra", "description": "Export the measured run to MS2 format with msconvert.", "tool_slugs": [ "msconvert" ] }, { "title": "Generate the next exclusion list", "description": "IE-Omics groups fragmented precursors by mass and retention time and can flag recurrent background ions.", "external_tools": [ { "name": "IE-Omics", "url": "https://github.com/GarrettLab-UF/IE-Omics" } ] }, { "title": "Import the list and repeat acquisition", "description": "Load the CSV into the instrument method before the next injection; the reported workflow is not real-time control." }, { "title": "Process and annotate spectra", "description": "The study uses MZmine 2 feature detection with LipidMatch, alongside LipidSearch annotation.", "tool_slugs": [ "mzmine" ], "external_tools": [ { "name": "LipidMatch", "url": "https://secim.ufl.edu/secim-tools/lipidmatch/" }, { "name": "LipidSearch" } ] } ], "tools": [ { "slug": "msconvert", "role": "Converts the acquired run to MS2 format." }, { "slug": "mzmine", "role": "MZmine 2 supplies features for the published LipidMatch analysis." }, { "name": "IE-Omics", "url": "https://github.com/GarrettLab-UF/IE-Omics", "role": "Builds exclusion lists for successive acquisitions." }, { "name": "LipidMatch", "url": "https://secim.ufl.edu/secim-tools/lipidmatch/", "role": "Lipid annotation in the demonstrated workflow." }, { "name": "LipidSearch", "role": "Additional lipid annotation in the study." } ], "resources": { "code_available": true, "code_url": "https://github.com/GarrettLab-UF/IE-Omics", "method_available": true, "method_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5408749/" }, "scope": { "designed_for": [ "Extending fragmentation coverage when repeated LC-MS/MS injections are feasible." ], "requirements": [ "Sufficient sample and instrument time for repeated injections.", "Compatible instrument exclusion-list import; the documented implementation targets Thermo instruments." ], "considerations": [ "Check mass and retention-time tolerances and chromatographic reproducibility with the MS facility.", "Additional spectra do not guarantee identification or equal gains across sample types.", "This is not a replacement for quantitative study design or quality control." ] }, "ask_keywords": [ "iterative exclusion list", "fragment additional precursors", "previously fragmented ions", "repeated DDA injections", "IE Omics" ], "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [ "iterative-optimized-ms2-acquisition" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary indexed methods, official SECIM documentation and author code repository reviewed on 2026-09-21. DOI/title/year checked against Crossref. The 2017 paper formalizes this automated metabolomics workflow, not the invention of iterative exclusion. No instrument or software execution validation performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-21", "updated_at": "2026-09-21", "last_verified": "2026-09-21", "last_editorial_review": "2026-09-21" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "iterative-optimized-ms2-acquisition", "name": "Iterative optimized MS/MS acquisition", "aliases": [ "IODA" ], "summary": "Plan successive MS/MS acquisitions from an MS1 survey by optimizing precursor schedules across mass and retention time.", "description": "MS2Planner uses topological sorting on a feature graph to maximize scheduled coverage. Features selected for one path are excluded from the next planning iteration. This is computational scheduling of repeat acquisitions, rather than a requirement to measure fragmentation quality after every run.", "publication": { "title": "MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition", "doi": "10.1093/bioinformatics/btab279", "url": "https://doi.org/10.1093/bioinformatics/btab279", "citation": "Zuo Z, Cao L, et al. MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition. Bioinformatics 37(Suppl 1), i231-i236 (2021).", "journal": "Bioinformatics", "year": 2021 }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "molecular_discovery", "unknown_identification" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "targeted_msms" ], "ion_mobility": "unspecified" }, "components": [ "data_acquisition", "preprocessing", "annotation" ], "workflow_steps": [ { "title": "Acquire an MS1 survey", "description": "The published IODA experiment begins with a full-scan MS1 measurement." }, { "title": "Prepare features for scheduling", "description": "Provide precursor masses, retention times and signals for the acquisition model." }, { "title": "Optimize successive acquisition paths", "description": "Use topological sorting; exclude scheduled features before computing the next path.", "tool_slugs": [ "ms2planner" ] }, { "title": "Export instrument inclusion lists", "description": "The study provides companion scripts for inclusion-list generation.", "external_tools": [ { "name": "IODA_MS", "url": "https://github.com/lfnothias/IODA_MS" } ] }, { "title": "Acquire and assess scheduled spectra", "description": "The paper tests inclusion-prioritized DDA and targeted-MS2 variants, with and without dynamic exclusion. Review coverage and spectral quality for the chosen mode." } ], "tools": [ { "slug": "ms2planner", "role": "Computes optimized paths over survey features for successive acquisitions." }, { "name": "IODA_MS", "url": "https://github.com/lfnothias/IODA_MS", "role": "Companion inclusion-list generation scripts reported by the primary paper." } ], "resources": { "code_available": true, "code_url": "https://github.com/mohimanilab/MS2Planner", "method_available": true, "method_url": "https://doi.org/10.1093/bioinformatics/btab279" }, "scope": { "designed_for": [ "Increasing MS/MS coverage in untargeted LC-MS experiments when repeat injections are practical.", "Building richer fragmentation coverage for annotation and discovery from representative or pooled samples." ], "requirements": [ "Full-scan survey features and instrument support for the selected inclusion-list acquisition mode.", "Sufficient sample for repeated injections and chromatography consistent with the planned retention-time windows." ], "considerations": [ "Planning assumptions and acquisition timing must fit the instrument and chromatography.", "This is not equivalent to a simple exclusion list updated after each measured DDA run; no universal coverage gain is promised." ] }, "ask_keywords": [ "get more msms spectra", "improve fragmentation coverage", "iterative dda", "exclusion inclusion list metabolomics", "pooled sample deeper msms coverage", "topological sorting", "schedule precursor ions" ], "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary full text re-reviewed through Europe PMC XML on 2026-09-20. Corrected author citation, MS1 initialization, computational iteration and companion export scripts. No software execution or instrument validation performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-20", "last_verified": "2026-09-19", "last_editorial_review": "2026-09-20" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "kinetic-flux-profiling", "name": "Kinetic flux profiling from isotope-labeling time courses", "summary": "Combine isotope-label incorporation kinetics with metabolite pool sizes to estimate cellular fluxes under an explicit kinetic model.", "description": "Kinetic flux profiling follows replacement of unlabeled metabolites after switching a nutrient source to its isotope-labeled equivalent. The original demonstration used nitrogen assimilation in cultured E. coli; the transferable contribution is the link between labeling dynamics, pool sizes and flux.", "publication": { "title": "Kinetic flux profiling of nitrogen assimilation in Escherichia coli", "doi": "10.1038/nchembio816", "year": 2006, "journal": "Nature Chemical Biology", "citation": "Yuan et al. Kinetic flux profiling of nitrogen assimilation in Escherichia coli. Nature Chemical Biology (2006)." }, "purpose": { "primary": "flux_analysis", "secondary": [ "stable_isotope_tracing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "stable_isotope_tracing", "fluxomics" ], "components": [ "sample_preparation", "data_acquisition", "statistics" ], "ask_keywords": [ "kinetic flux profiling", "KFP", "isotope labeling", "metabolic flux", "turnover", "time course", "pool size" ], "workflow_steps": [ { "title": "Switch nutrient isotope labeling", "description": "Change the isotopic nutrient source while preserving the intended culture conditions." }, { "title": "Sample the labeling time course", "description": "Quench and extract replicate samples at times that resolve metabolite turnover." }, { "title": "Measure labeling and pool sizes", "description": "Use chromatography-MS to quantify unlabeled fractions and intracellular metabolite concentrations." }, { "title": "Fit the specified kinetic model", "description": "Estimate turnover and flux using equations appropriate to precursor labeling and pathway structure." } ], "tools": [], "resources": { "method_available": true, "method_url": "https://doi.org/10.1038/nprot.2008.131" }, "scope": { "designed_for": [ "Flux estimation in cultured cells with experimentally resolved labeling kinetics." ], "requirements": [ "Rapid nutrient switching, timed quenching, isotope-resolved measurements and pool-size quantification." ], "considerations": [ "Basic interpretation assumes metabolic steady state; dynamic variants require additional modeling.", "Reliable quantification requires resolving turnover relative to upstream precursor labeling.", "A single endpoint or labeling half-time without pool-size and model context does not establish absolute flux." ] }, "implementations": [ { "title": "Kinetic flux profiling for quantitation of cellular metabolic fluxes", "doi": "10.1038/nprot.2008.131", "year": 2008, "journal": "Nature Protocols", "relationship": "protocol", "summary": "Expanded experimental and quantitative procedure, including culture handling and kinetic interpretation.", "tool_slugs": [], "citation": "Yuan et al. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nature Protocols (2008)." } ], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "The 2006 introduction defines the method; the 2008 procedure supplies implementation detail. Distinct from isotope-assisted networking and credentialing. No original catalogue software dependency was established; do not invent one. No experiment or software execution performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "maldi-fish", "name": "Correlative metabolite and microbial imaging with MALDI-FISH", "aliases": [ "(MA)LDI-FISH", "MALDI-FISH", "metaFISH" ], "summary": "Combine mass spectrometry imaging with taxon-specific fluorescence labeling to associate spatial chemistry with microbial identity.", "publication": { "title": "Linking metabolite production to taxonomic identity in environmental samples by (MA)LDI-FISH", "doi": "10.1038/ismej.2015.122", "year": 2016, "journal": "The ISME Journal", "citation": "Kaltenpoth M, Strupat K, Svatos A. The ISME Journal 10, 527-531 (2016); first published online 14 July 2015." }, "purpose": { "primary": "spatial_metabolomics", "secondary": [ "host_microbe_interaction" ] }, "platforms": [ "imaging_ms" ], "components": [ "sample_preparation", "data_acquisition", "metadata_analysis" ], "tools": [], "workflow_steps": [ { "title": "Preserve spatial correspondence", "description": "Prepare material compatible with both fluorescence labeling and MSI." }, { "title": "Measure chemistry and microbial identity", "description": "Acquire ion images and taxon-specific FISH fluorescence using the sequence specified by the selected published method." }, { "title": "Register and compare images", "description": "Align the two measurements to assess which chemical distributions coincide with labeled microbes." } ], "scope": { "designed_for": [ "Spatial association of metabolites with microbes in heterogeneous samples." ], "requirements": [ "MS imaging and taxon-specific FISH microscopy from corresponding material.", "Validated preparation, imaging order and spatial registration." ], "considerations": [ "Colocalization alone does not establish biosynthesis or metabolite exchange.", "Resolution depends on both modalities; the original method should not be described as universally single-cell." ] }, "ask_keywords": [ "maldi fish", "metafish", "link metabolites to microbial identity", "spatial chemistry and bacterial fluorescence" ], "related_strategies": [ "spacem-single-cell-metabolomics" ], "implementations": [ { "title": "Spatial metabolomics of in situ host–microbe interactions at the micrometre scale", "doi": "10.1038/s41564-019-0664-6", "year": 2020, "relationship": "extension", "summary": "metaFISH integrates fluorescence and metabolite images on the same tissue section at micrometre scale." }, { "title": "Visualization of metabolites and microbes at high spatial resolution using MALDI mass spectrometry imaging and in situ fluorescence labeling", "doi": "10.1038/s41596-023-00864-1", "year": 2023, "relationship": "protocol", "summary": "Detailed metaFISH preparation, acquisition and image-integration instructions." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1038/s41596-023-00864-1" }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary identity and scope checked against the original paper and later metaFISH papers. Distinct from generic cell segmentation in SpaceM. No unverified software assigned." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-28", "updated_at": "2026-09-28", "last_verified": "2026-09-28", "last_editorial_review": "2026-09-28" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "mass2motif-substructure-discovery", "name": "Shared-substructure discovery with Mass2Motifs", "summary": "Discover recurring fragment and neutral-loss patterns across MS/MS spectra to propose shared substructures.", "description": "MS2LDA links spectra through learned Mass2Motifs, including molecules without high overall spectral similarity.", "publication": { "title": "Topic modeling for untargeted substructure exploration in metabolomics", "doi": "10.1073/pnas.1608041113", "year": 2016, "journal": "Proceedings of the National Academy of Sciences" }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "molecular_family_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "components": [ "preprocessing", "annotation", "spectral_search" ], "ask_keywords": [ "MS2LDA", "Mass2Motifs", "shared substructures", "fragment patterns", "neutral losses", "motif discovery", "unknown spectra" ], "workflow_steps": [ { "title": "Prepare precursor-linked spectra", "description": "The original workflow uses XCMS, MzMatch and RMassBank to prepare fragment and loss features.", "tool_slugs": [ "xcms" ] }, { "title": "Learn recurring motifs", "description": "Apply topic modeling to the fragment/loss matrix.", "tool_slugs": [ "ms2lda" ] }, { "title": "Interpret and inspect assignments", "description": "Compare motif patterns with reference spectra and chemical knowledge; examine associated precursors.", "tool_slugs": [ "ms2lda" ] } ], "tools": [ { "slug": "xcms", "role": "Original preprocessing." }, { "name": "MzMatch", "role": "Original peak preprocessing." }, { "name": "RMassBank", "role": "Original MS1-MS2 pairing." }, { "slug": "ms2lda", "role": "Motif inference and exploration." } ], "resources": { "method_available": true, "method_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5137707/" }, "scope": { "designed_for": [ "Substructure hypotheses from collections of MS/MS spectra." ], "requirements": [ "Precursor-linked fragmentation spectra and consistent fragment/loss representation." ], "considerations": [ "A motif need not identify a unique substructure or complete molecule. Expert interpretation remains necessary." ] }, "implementations": [ { "title": "Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics", "doi": "10.1021/acs.analchem.7b01391", "year": 2017, "journal": "Analytical Chemistry", "relationship": "extension", "summary": "MS2LDA+ learns shared motifs across samples while retaining sample-specific prevalence for comparison.", "tool_slugs": [ "ms2lda" ] } ], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "2016 primary method; 2017 multisample extension retained here. Original software roles preserved; modern alternatives are not asserted as original dependencies. No software execution tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-20", "updated_at": "2026-09-20", "last_editorial_review": "2026-09-20" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "metabologenomics", "name": "Metabologenomics", "summary": "A natural-product discovery strategy that correlates metabolite occurrence across a strain collection with biosynthetic gene-cluster or gene-cluster-family occurrence to connect chemistry with its likely genetic origin.", "description": "Metabologenomics treats a microbial strain collection as a panel of naturally varying experiments. Genome mining defines BGCs or GCFs, metabolomics defines spectra or molecular families, and co-occurrence across strains is scored to prioritize plausible BGC–metabolite links. The strategy can reveal both known and previously uncharacterized natural-product families without requiring a prior genetic hypothesis.", "publication": { "title": "Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer", "doi": "10.1021/acscentsci.5b00331", "url": "https://pubs.acs.org/doi/10.1021/acscentsci.5b00331", "citation": "Goering AW, McClure RA, Doroghazi JR, et al. Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer. ACS Central Science (2016).", "journal": "ACS Central Science", "year": 2016 }, "purpose": { "primary": "natural_product_discovery", "secondary": [ "molecular_family_discovery", "unknown_identification" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "multiomics" ], "acquisition": {}, "components": [ "data_acquisition", "preprocessing", "molecular_networking", "metadata_analysis", "statistics", "biological_validation" ], "workflow_steps": [ { "title": "Profile a genetically diverse strain collection", "description": "Generate comparable LC-MS/MS data across many sequenced isolates or conditions so metabolite occurrence can be compared across the collection." }, { "title": "Mine genomes for biosynthetic gene clusters", "description": "Identify BGCs and cluster related BGCs into gene-cluster families using genome-mining tools.", "external_tools": [ { "name": "antiSMASH", "url": "https://antismash.secondarymetabolites.org/" }, { "name": "BiG-SCAPE", "url": "https://bigscape-corason.secondarymetabolites.org/" } ] }, { "title": "Organize metabolite features or molecular families", "description": "Use molecular networking or related spectral grouping to define recurring chemical features across strains.", "tool_slugs": [ "gnps" ] }, { "title": "Score BGC–metabolite co-occurrence", "description": "Correlate the presence/abundance of metabolite features or molecular families with BGC/GCF occurrence across strains.", "tool_slugs": [], "external_tools": [ { "name": "NPLinker", "url": "https://github.com/NPLinker/nplinker" } ] }, { "title": "Prioritize and validate links", "description": "Use genetics, targeted cultivation, isolation, MS/NMR or heterologous expression to validate high-priority BGC–metabolite associations." } ], "tools": [ { "slug": "gnps", "role": "Molecular-family organization and metabolomics comparison." }, { "role": "Modern framework for scoring and exploring BGC–metabolite links.", "name": "NPLinker", "url": "https://github.com/NPLinker/nplinker" }, { "role": "Reference BGC/product knowledge for interpretation.", "name": "MIBiG", "url": "https://mibig.secondarymetabolites.org/" } ], "resources": { "method_available": true, "method_url": "https://pubs.acs.org/doi/10.1021/acscentsci.5b00331" }, "scope": { "designed_for": [ "Linking specialized-metabolite chemistry to candidate biosynthetic gene clusters across strain collections.", "Prioritizing biosynthetic hypotheses before direct genetic validation." ], "requirements": [ "A sufficiently diverse, sequenced strain collection with comparable metabolomics data.", "Consistent identifiers linking each genome/isolate to its metabolomics samples." ], "considerations": [ "Co-occurrence supports association, not causation; population structure and phylogeny can confound links.", "Silent BGCs and condition-dependent metabolite production create false negatives.", "The linked contemporary resources illustrate ways to implement the strategy; they are not all software used in the original publication." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because genome–metabolome co-occurrence is a transferable evidence layer that adds biosynthetic linkage beyond routine molecular networking. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "microbial_culture" ], "biological_contexts": [ "microbial" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "microbe-metabolite-conditional-associations", "name": "Conditional microbe–metabolite association modeling", "aliases": [ "mmvec strategy", "microbe-metabolite vectors" ], "summary": "Model paired microbial and MS metabolomics measurements to prioritize conditional co-occurrence relationships between taxa and molecular features.", "publication": { "title": "Learning representations of microbe–metabolite interactions", "doi": "10.1038/s41592-019-0616-3", "year": 2019, "journal": "Nature Methods", "citation": "Morton JT, Aksenov AA, Nothias LF, et al. Nature Methods 16, 1306-1314 (2019)." }, "purpose": { "primary": "host_microbe_interaction", "secondary": [ "phenotype_association" ] }, "platforms": [ "lc_ms" ], "components": [ "metadata_analysis", "statistics" ], "tools": [ { "name": "mmvec", "url": "https://github.com/biocore/mmvec", "role": "Estimate conditional microbe–metabolite co-occurrences from paired tables." } ], "workflow_steps": [ { "title": "Match the two measurement tables", "description": "Align microbial and metabolite features using shared sample identifiers; inspect filtering, sparsity and study design." }, { "title": "Fit and evaluate the conditional model", "description": "Use mmvec with appropriate tuning and held-out evaluation; inspect training diagnostics.", "external_tools": [ { "name": "mmvec", "url": "https://github.com/biocore/mmvec" } ] }, { "title": "Prioritize and validate associations", "description": "Inspect conditional rankings and ordinations, then design independent tests for biological hypotheses." } ], "scope": { "designed_for": [ "Associations across paired microbiome and MS metabolomics samples." ], "requirements": [ "Matched microbial feature and metabolite abundance tables with sufficient shared samples.", "Recorded preprocessing and a validation design appropriate to the study." ], "considerations": [ "Conditional co-occurrence does not establish metabolite production, consumption or causation.", "A low conditional score is not automatically a negative correlation.", "Compare against suitable compositional-data baselines; universal superiority over linear methods is not established. See the 2021 discussion (10.1038/s41592-020-01006-1) and reply (10.1038/s41592-020-01007-0).", "Small studies and sparse data require careful tuning and uncertainty assessment." ] }, "ask_keywords": [ "paired microbiome metabolomics", "microbe metabolite co occurrence", "conditional microbial metabolite associations", "mmvec", "connect microbial taxa to metabolite profiles" ], "related_strategies": [ "metabologenomics" ], "resources": { "code_available": true, "code_url": "https://github.com/biocore/mmvec", "method_available": true, "method_url": "https://doi.org/10.1038/s41592-019-0616-3" }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary method and official implementation reviewed, including the 2021 methodological discussion and reply. Distinct from BGC-to-metabolite matching and spatial colocalization. Inclusion does not endorse causal inference or benchmark superiority." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-29", "updated_at": "2026-09-29", "last_verified": "2026-09-29", "last_editorial_review": "2026-09-29" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "microfluidic-bioactivity-fractionation", "name": "Microfluidic fractionation for compound-resolved bioactivity", "summary": "Pair rapid chromatographic fraction collection with LC-MS/MS and luminescent bioreporters to connect chemical signals with antibacterial responses.", "publication": { "title": "High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics", "doi": "10.1021/acs.analchem.5c04612", "year": 2025, "journal": "Analytical Chemistry", "citation": "Geibel C, et al. Analytical Chemistry 97, 24093-24104 (2025)." }, "purpose": { "primary": "natural_product_discovery", "secondary": [ "phenotype_association" ] }, "platforms": [ "lc_ms" ], "components": [ "chromatography", "data_acquisition", "biological_validation" ], "tools": [], "workflow_steps": [ { "title": "Collect fractions during LC-MS/MS", "description": "Spot eluent onto a paper microfluidic device at approximately one fraction per second while acquiring spectra." }, { "title": "Measure reporter responses", "description": "Expose fractions to luminescent bioreporters and image the response." }, { "title": "Align activity with chemistry", "description": "Match reporter signals to chromatographic profiles to prioritize active compounds." } ], "scope": { "designed_for": [ "Antibacterial activity screening of extracts or compound mixtures." ], "requirements": [ "Custom rapid fractionation hardware and compatible paper devices.", "LC-MS/MS, luminescent bioreporters and response imaging.", "Reliable correspondence between fraction positions and retention times." ], "considerations": [ "Reporter responses are assay-specific; confirm prioritized activity with follow-up experiments.", "Coelution can complicate assigning activity to one compound." ] }, "related_strategies": [ "bioactivity-based-molecular-networking" ], "ask_keywords": [ "microfluidic bioactivity fractionation", "compound resolved bioactivity", "luminescent bioreporter", "connect chromatography to antibacterial activity" ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/acs.analchem.5c04612" }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Distinct measurement integration, not a new biological application. Primary title, DOI, year and abstract verified. No software dependencies inferred from author overlap." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-28", "updated_at": "2026-09-28", "last_verified": "2026-09-28", "last_editorial_review": "2026-09-28" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "microscopy-msi-predictive-image-fusion", "name": "Predictive fusion of microscopy and mass spectrometry imaging", "aliases": [ "IMS-microscopy image fusion" ], "summary": "Model spatial relationships between microscopy features and measured ion intensities, then use validated relationships to predict molecular distributions at finer spatial resolution or in unmeasured tissue regions.", "description": "This Strategy pairs registered microscopy with mass spectrometry imaging and fits a separate partial least squares relationship for each ion image. It goes beyond image overlays: optical features become predictors of molecular signals. The 2015 methodology formalizes this relationship for molecular tissue mapping, including lipid and small-molecule applications. Predicted distributions remain model outputs, not additional MS measurements or evidence of molecular identification.", "publication": { "title": "Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping", "doi": "10.1038/nmeth.3296", "url": "https://doi.org/10.1038/nmeth.3296", "citation": "Van de Plas R, Yang J, Spraggins J, Caprioli RM. Nature Methods 12, 366-372 (2015).", "journal": "Nature Methods", "year": 2015 }, "purpose": { "primary": "spatial_metabolomics", "secondary": [] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "full_scan" ], "ion_mobility": "not_required" }, "components": [ "data_acquisition", "preprocessing", "metadata_analysis", "statistics" ], "workflow_steps": [ { "title": "Acquire paired molecular and optical images", "description": "Measure MALDI ion images and microscopy of stained tissue from the same or an adjacent section. Preserve tissue coordinates and acquisition conditions." }, { "title": "Register images and derive paired features", "description": "Align tissue locations across modalities and derive microscopy descriptors such as hue, texture and entropy corresponding to the measured MS locations." }, { "title": "Fit ion-specific cross-modal models", "description": "Use partial least squares regression to model each ion intensity distribution from microscopy-derived predictors. Relationships and predictive performance differ between ions." }, { "title": "Evaluate reconstruction and spatial reliability", "description": "Examine reconstruction scores, absolute residual maps and bootstrap confidence-interval images for each ion. Do not use fusion to predict ions for which microscopy provides inadequate support." }, { "title": "Predict supported molecular distributions", "description": "Apply supported models to finer microscopy pixels or tissue regions with microscopy but no MS measurement. Label these outputs as predictions and retain the measured ion maps for comparison." }, { "title": "Interpret with measurement-based checks", "description": "Assess registration, sampling differences and model transfer before biological interpretation. Where feasible compare predictions with independent MS measurements; absence of optical support does not establish that an MS feature is noise." } ], "tools": [ { "name": "Molecular Image Fusion prototype", "url": "https://fusion.vueinnovations.com/", "role": "Published command-line implementation of IMS-microscopy predictive fusion; obtain the prototype and review its licensing and runtime requirements through the official distribution site." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1038/nmeth.3296" }, "scope": { "designed_for": [ "Linking lipid or small-molecule tissue distributions with optical morphology.", "Predictive sharpening and spatial extrapolation when cross-modal relationships are supported." ], "requirements": [ "Spatially registered microscopy and MSI measurements with corresponding tissue structures.", "Ion-specific modeling and assessment of reconstruction, residuals and uncertainty." ], "considerations": [ "Higher-resolution predictions do not increase the spatial resolution of the original MS measurements.", "Some ion distributions have no usable microscopy relationship and should not be predicted.", "Adjacent-section differences, registration errors and changed staining conditions can invalidate relationships.", "Extrapolation to unmeasured regions requires scrutiny of model transfer and overfitting.", "The original software is distributed through a licensing portal; open-source availability and execution have not been established." ] }, "ask_keywords": [ "microscopy mass spectrometry image fusion", "predict ion images from histology", "spatial lipid image sharpening", "MSI microscopy regression", "predict molecular distributions in tissue" ], "sample_types": [ "tissue" ], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [ "spacem-single-cell-metabolomics" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary methodology and DOI metadata checked on 2026-09-23. Models cross-modal molecular distributions rather than assigning measured signals to individual cells; distinct from SpaceM. No software execution or independent performance validation is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23", "last_editorial_review": "2026-09-23" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "molecular-networking-dereplication", "name": "Molecular networking as a dereplication strategy", "summary": "A natural-product dereplication strategy that organizes MS/MS spectra by fragmentation similarity and uses reference spectra, standards, or characterized organisms to recognize known molecules and related analogues before isolation.", "description": "This strategy uses spectral molecular networking as an early dereplication layer in natural-product discovery. Experimental MS/MS spectra from complex extracts are organized according to spectral similarity and compared with spectra from known standards, synthetic compounds, public libraries, or well-characterized organisms. Known chemistry can therefore seed molecular families and highlight related analogues that may not be present in reference databases, allowing novelty and redundancy to be assessed before lengthy purification.", "publication": { "title": "Molecular Networking as a Dereplication Strategy", "doi": "10.1021/np400413s", "url": "https://doi.org/10.1021/np400413s", "citation": "Yang JY, Sanchez LM, Rath CM, et al. Molecular Networking as a Dereplication Strategy. Journal of Natural Products 76, 1686-1699 (2013).", "journal": "Journal of Natural Products", "year": 2013 }, "purpose": { "primary": "dereplication", "secondary": [ "molecular_family_discovery", "metabolite_annotation", "natural_product_discovery" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda" ], "ion_mobility": "not_required" }, "components": [ "data_acquisition", "spectral_search", "molecular_networking", "annotation", "biological_validation" ], "workflow_steps": [ { "title": "Acquire MS/MS spectra from complex extracts", "description": "Generate tandem mass spectra from the natural-product mixture using a compatible ionization and acquisition workflow." }, { "title": "Add reference spectra", "description": "Include spectra from known standards, synthetic compounds, spectral libraries, or well-characterized organisms to provide dereplication anchors." }, { "title": "Build a spectral molecular network", "description": "Organize spectra according to fragmentation similarity so structurally related molecules cluster into molecular families.", "tool_slugs": [ "gnps" ] }, { "title": "Dereplicate known chemistry and related analogues", "description": "Use reference-connected molecular families to recognize known compounds and prioritize related features that may represent analogues rather than rediscoveries." }, { "title": "Prioritize chemistry for follow-up", "description": "Advance molecular families that are poorly represented by known references or that contain features of specific biological or chemical interest." } ], "tools": [ { "slug": "gnps", "role": "Contemporary implementation environment for MS/MS molecular networking and library searching." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/np400413s" }, "scope": { "designed_for": [ "Early-stage dereplication of natural-product extracts before time-intensive isolation.", "Recognizing related analogues around known reference compounds through MS/MS similarity." ], "requirements": [ "Reproducible MS/MS spectra from the sample set.", "Suitable reference spectra or standards improve dereplication power." ], "considerations": [ "Spectral similarity supports structural relatedness but is not complete structural confirmation.", "Network structure depends on fragmentation quality, spectral preprocessing, and similarity thresholds." ] }, "ask_keywords": [ "dereplicate natural products", "avoid rediscovering known compounds", "molecular networking dereplication", "identify known compounds and analogues", "prioritize novel molecular families" ], "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [ "gnps-feature-based-molecular-networking", "bioactivity-based-molecular-networking" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because the publication explicitly introduced molecular networking as a transferable dereplication strategy rather than an application-only use of networking." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_verified": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "ms2decide", "name": "MS2DECIDE", "summary": "Tandem mass spectrum to decision (MS2DECIDE) leverages decision theory and expert knowledge to aggregate the outputs of three widely used annotation tools (GNPS, Sirius, and ISDB-LOTUS) and compute a recommendation for targeting natural products with regard to their potential novelty.", "publication": { "doi": "10.1002/cmtd.202400088", "url": "https://doi.org/10.1002/cmtd.202400088", "title": "MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization", "journal": "Chemistry–Methods", "year": 2025, "citation": "Mejri, Yassine et al. MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization. Chemistry–Methods (2025). DOI: 10.1002/cmtd.202400088." }, "purpose": { "primary": "natural_product_discovery", "secondary": [] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "sample_types": [], "biological_contexts": [], "organisms": [], "components": [ "annotation", "spectral_search" ], "workflow_steps": [ { "title": "Prepare aligned inputs", "description": "Provide a quantitative CSV table, MGF spectra and compatible SIRIUS annotation results with matching feature identifiers." }, { "title": "Collect complementary annotations", "description": "Obtain GNPS spectral matches and ISDB-LOTUS results alongside SIRIUS candidates.", "tool_slugs": [ "gnps", "sirius" ] }, { "title": "Combine evidence and prioritize", "description": "Use the documented K-estimation method to combine annotation evidence and export ranked results for review.", "tool_slugs": [ "ms2decide" ] }, { "title": "Review candidates", "description": "Inspect spectra, annotation disagreements and experimental context before selecting follow-up experiments." } ], "tools": [ { "slug": "ms2decide", "role": "Implements K-estimation and result export." }, { "slug": "gnps" }, { "slug": "sirius" }, { "name": "ISDB-LOTUS" } ], "resources": { "code_available": true, "code_url": "https://github.com/MejriY/MS2DECIDE", "method_available": true, "method_url": "https://github.com/MejriY/MS2DECIDE#usage-guide-k_estimation" }, "scope": { "designed_for": [ "Prioritizing natural-product features using multiple annotation sources." ], "requirements": [ "Compatible feature tables, MS/MS spectra and annotation files; Python and access to the services used by the chosen implementation." ], "considerations": [ "Scores depend on annotation coverage and the decision model; they are not experimental identification or proof of novelty.", "Follow the repository documentation for supported SIRIUS and GNPS versions." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/24", "author_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389", "notes": "Original submission by Morgane Mauduit in issue #24; recovered after the title prevented automation. Method details checked against the linked implementation documentation. Software execution and author verification were not performed." }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-14", "updated_at": "2026-10-05" }, "description": "MS2DECIDE combines annotations from GNPS, SIRIUS and ISDB-LOTUS using an expert-informed decision model. It supports prioritization of natural-product features for follow-up rather than establishing chemical novelty or confirming structures." }, { "$schema": "../../schemas/strategy.schema.json", "slug": "msi-guided-molecular-networking", "name": "Mass spectrometry imaging–guided molecular networking", "summary": "A multimodal natural-product strategy that uses spatial localization from mass spectrometry imaging to prioritize metabolites for LC-MS/MS molecular networking and structural interpretation.", "description": "Mass spectrometry imaging–guided molecular networking connects where a metabolite occurs with how its fragmentation spectrum relates to other molecules. Spatially resolved MSI features are used to identify chemically informative regions or ions, while LC-MS/MS molecular networking supplies fragmentation-based family context and annotation. The combination can focus structural analysis on spatially localized natural products rather than treating imaging and molecular networking as independent readouts.", "publication": { "title": "Mass spectrometry imaging guided molecular networking to expedite discovery and structural analysis of agarwood natural products", "doi": "10.1016/j.aca.2019.05.070", "url": "https://doi.org/10.1016/j.aca.2019.05.070", "citation": "Kuo TH, Huang HC, Hsu CC. Mass spectrometry imaging guided molecular networking to expedite discovery and structural analysis of agarwood natural products. Analytica Chimica Acta 1080, 95–103 (2019).", "journal": "Analytica Chimica Acta", "year": 2019 }, "purpose": { "primary": "spatial_metabolomics", "secondary": [ "natural_product_discovery", "structure_elucidation", "molecular_family_discovery" ] }, "platforms": [ "imaging_ms", "lc_ms" ], "analysis_types": [ "spatial_metabolomics", "untargeted" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "data_acquisition", "molecular_networking", "annotation", "biological_validation" ], "workflow_steps": [ { "title": "Acquire spatial metabolite maps", "description": "Use MALDI or another suitable imaging-MS method to map metabolite ions across the tissue or material of interest." }, { "title": "Prioritize spatially informative features", "description": "Select ions or chemical regions whose localization suggests biological or anatomical relevance." }, { "title": "Acquire complementary LC-MS/MS data", "description": "Generate fragmentation-rich LC-MS/MS data from matched or relevant material so spatially prioritized chemistry can be interpreted structurally." }, { "title": "Build molecular networks", "description": "Organize related MS/MS spectra into molecular families and connect the spatially prioritized features to family-level structural context.", "tool_slugs": [ "gnps" ] }, { "title": "Validate spatial and structural assignments", "description": "Confirm prioritized annotations and localization with orthogonal standards, fragmentation interpretation, or additional imaging experiments where possible." } ], "tools": [ { "slug": "gnps", "role": "Molecular-network analysis of structurally related tandem mass spectra." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1016/j.aca.2019.05.070" }, "scope": { "designed_for": [ "Natural-product or specialized-metabolite discovery where spatial localization can guide which chemistry deserves structural follow-up.", "Multimodal studies combining imaging MS with fragmentation-rich LC-MS/MS." ], "requirements": [ "Spatially resolved MSI data and complementary MS/MS data that can be related to the same chemistry." ], "considerations": [ "Matching an MSI ion to an LC-MS/MS feature requires careful mass, adduct, and sample-context reasoning.", "Spatial colocalization and spectral family membership do not substitute for complete structural confirmation." ] }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included as an Analytical Strategy because spatial localization is used as a transferable evidence layer to guide molecular-network-based discovery and structural prioritization. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "plant_tissue", "tissue" ], "biological_contexts": [ "plant", "broadly_applicable" ], "organisms": [], "related_strategies": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "msi-imc-single-cell-immunometabolism", "name": "Same-section MSI and imaging mass cytometry integration", "summary": "Combine tissue metabolite images with antibody-based cellular phenotypes from imaging mass cytometry.", "description": "This method links spatial metabolite signals to immunophenotyped cells on the same tissue section, adding a molecular cell-identity layer beyond morphology.", "publication": { "title": "Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling", "doi": "10.1038/s41592-024-02392-6", "year": 2024, "journal": "Nature Methods", "citation": "Nunes et al. Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling. Nature Methods (2024)." }, "purpose": { "primary": "single_cell_metabolomics", "secondary": [ "spatial_metabolomics" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics", "lipidomics" ], "components": [ "sample_preparation", "data_acquisition", "metadata_analysis", "statistics" ], "ask_keywords": [ "immunophenotype", "immune cells", "imaging mass cytometry", "IMC", "single cell tissue", "spatial immunometabolism" ], "workflow_steps": [ { "title": "Acquire MALDI images", "description": "Measure metabolites before antibody staining." }, { "title": "Acquire immunophenotypes", "description": "Remove matrix, stain with metal-tagged antibodies and perform IMC." }, { "title": "Register images and segment cells", "description": "Align modalities and derive cellular boundaries and phenotypes." }, { "title": "Assign metabolite signals", "description": "Aggregate overlapping image signals into relative per-cell profiles." } ], "tools": [ { "name": "MALDI-MSI-IMC analysis scripts", "url": "https://github.com/deMirandaLab/MALDI-MSI-IMC", "role": "Published registration and integration implementation." } ], "resources": { "code_available": true, "code_url": "https://github.com/deMirandaLab/MALDI-MSI-IMC", "method_available": true, "method_url": "https://doi.org/10.1038/s41592-024-02392-6" }, "scope": { "designed_for": [ "Spatial comparison of immunophenotyped cell populations." ], "requirements": [ "Compatible same-section MSI and IMC measurements." ], "considerations": [ "MSI pixels can overlap multiple cells, mixing assigned signals.", "Cellular attribution depends on registration and segmentation." ] }, "related_strategies": [ "spacem-single-cell-metabolomics" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Same-section antibody phenotyping distinguishes this from SpaceM. Colorectal tissue is the demonstration, not a separate Strategy. Primary method checked; scripts not executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "native-ms-metal-binding", "name": "Native MS metabolomics for metal-binding compounds", "aliases": [ "Native spray metabolomics" ], "summary": "A transferable LC-MS strategy that combines post-column pH adjustment and metal infusion with ion identity molecular networking to discover small molecules that bind metals in complex biological samples.", "description": "This strategy adapts native electrospray concepts to metabolomics so that protonated and metal-bound forms of the same chromatographic analyte can be detected together. Post-column pH adjustment and infusion of a selected metal are coupled to feature processing and ion identity molecular networking, allowing candidate metal-binding molecules to be prioritized from characteristic mass offsets and shared chromatographic behavior.", "publication": { "title": "Native mass spectrometry-based metabolomics identifies metal-binding compounds", "doi": "10.1038/s41557-021-00803-1", "url": "https://www.nature.com/articles/s41557-021-00803-1", "citation": "Aron AT, Petras D, Schmid R, et al. Native mass spectrometry-based metabolomics identifies metal-binding compounds. Nature Chemistry 14, 100–109 (2022).", "journal": "Nature Chemistry", "year": 2022 }, "purpose": { "primary": "metal_binding", "secondary": [ "native_metabolomics", "natural_product_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "native_esi" ], "ion_mobility": "not_required" }, "components": [ "chromatography", "post_column_modification", "data_acquisition", "preprocessing", "annotation", "molecular_networking", "data_sharing" ], "workflow_steps": [ { "title": "Separate metabolites by LC", "description": "Analyze the extract using an LC-MS method suitable for retaining native-like metal-binding interactions after chromatographic separation." }, { "title": "Adjust post-column pH", "description": "Infuse a volatile base or buffer after the LC column to establish conditions compatible with observing metal complexes during electrospray." }, { "title": "Infuse the metal of interest", "description": "Introduce a metal salt post-column so protonated and metal-bound forms of candidate ligands can be observed with matched retention behavior." }, { "title": "Process features and ion identities", "description": "Detect and align chromatographic features and group coeluting ion species with correlated peak shapes and expected mass relationships.", "tool_slugs": [ "mzmine" ] }, { "title": "Build an ion identity molecular network", "description": "Use ion identity molecular networking to connect ion species and spectral relationships, highlighting candidate metal-binding compounds.", "tool_slugs": [ "mzmine", "gnps" ], "external_tools": [ { "name": "Cytoscape", "url": "https://cytoscape.org/" } ] }, { "title": "Prioritize and validate candidate binders", "description": "Interpret expected metal-adduct mass offsets, inspect spectra and chromatograms, and validate prioritized compounds with standards or orthogonal evidence where available." } ], "tools": [ { "slug": "mzmine", "role": "Feature detection, ion grouping, and ion identity networking preparation." }, { "slug": "gnps", "role": "Ion identity molecular networking and MS/MS-based network analysis." }, { "slug": "massive", "role": "Public deposition and reuse of the study mass-spectrometry datasets." }, { "name": "Cytoscape", "url": "https://cytoscape.org/", "role": "Optional network visualization." } ], "resources": { "raw_data_available": true, "raw_data_url": "https://massive.ucsd.edu/", "processed_data_available": true, "processed_data_url": "https://www.nature.com/articles/s41557-021-00803-1", "code_available": true, "code_url": "https://mzmine.github.io/iin_fbmn", "method_available": true, "method_url": "https://www.nature.com/articles/s41557-021-00803-1" }, "scope": { "designed_for": [ "Discovering metal-binding small molecules in complex extracts using standard LC-MS hardware with post-column modification.", "Comparing protonated and metal-bound ion species through chromatographic correlation and defined mass offsets." ], "requirements": [ "LC-MS system with a post-column setup that permits pH adjustment and metal-salt infusion.", "Appropriate blanks and controls to distinguish biologically relevant complexes from background or source-derived adducts." ], "considerations": [ "Metal identity, pH, salt concentration and electrospray conditions can alter complex formation and should be optimized and reported.", "The network highlights candidate binding behavior; structural identity and biological relevance require additional evidence." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-08-24", "last_verified": "2026-08-21", "last_editorial_review": "2026-08-21" }, "sample_types": [ "microbial_culture", "water", "synthetic_standards" ], "biological_contexts": [ "microbial", "fungal", "marine", "environmental", "synthetic_reference" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "ozid-structural-lipidomics", "name": "Ozone-induced dissociation for structural lipidomics", "summary": "A structural-lipidomics strategy that uses ozone-induced dissociation to generate diagnostic fragments for carbon–carbon double-bond positions that conventional CID-based lipidomics often cannot resolve.", "description": "Ozone-induced dissociation (OzID) adds gas-phase chemical reactivity to lipid analysis. Reactions of mass-selected unsaturated lipid ions produce diagnostic fragments that locate carbon–carbon double bonds. The method complements collision-induced fragmentation. Later implementations can combine separation and automated interpretation, but their acquisition and software requirements are implementation-specific.", "publication": { "title": "Ozone-Induced Dissociation: Elucidation of Double Bond Position within Mass-Selected Lipid Ions", "doi": "10.1021/ac7017684", "url": "https://doi.org/10.1021/ac7017684", "citation": "Thomas MC, Mitchell TW, Harman DG, Deeley JM, Nealon JR, Blanksby SJ. Ozone-Induced Dissociation: Elucidation of Double Bond Position within Mass-Selected Lipid Ions. Analytical Chemistry 80, 303–311 (2008).", "journal": "Analytical Chemistry", "year": 2008 }, "purpose": { "primary": "structure_elucidation", "secondary": [ "metabolite_annotation" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "lipidomics", "targeted" ], "acquisition": {}, "components": [ "data_acquisition", "annotation", "biological_validation" ], "workflow_steps": [ { "title": "Acquire lipids under OzID-compatible conditions", "description": "Introduce ozone into a compatible MS workflow so unsaturated lipid ions undergo diagnostic gas-phase oxidation." }, { "title": "Collect diagnostic OzID fragments", "description": "Acquire fragment pairs that encode the location of carbon–carbon double bonds." }, { "title": "Resolve and score double-bond assignments", "description": "Interpret diagnostic fragment pairs; a later tool such as LipidOz can automate this for its supported OzID–ion mobility MS acquisition format.", "tool_slugs": [], "external_tools": [ { "name": "LipidOz", "url": "https://github.com/PNNL-m-q/lipidoz" } ] }, { "title": "Validate structural isomers", "description": "Confirm critical assignments with standards, retention/ion-mobility behavior or orthogonal structural evidence when available." } ], "tools": [ { "role": "Later automated interpretation for compatible OzID–ion mobility MS data; not part of the original 2008 implementation.", "name": "LipidOz", "url": "https://github.com/PNNL-m-q/lipidoz" } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1021/ac7017684" }, "scope": { "designed_for": [ "Resolving lipid double-bond positional isomers beyond conventional sum-composition or acyl-chain annotation." ], "requirements": [ "Instrumentation or reaction setup capable of ozone-induced dissociation.", "Appropriate acquisition methods and candidate lipid context." ], "considerations": [ "OzID adds structural specificity but does not automatically resolve every lipid stereochemical ambiguity.", "Instrumentation and reaction implementation are platform-dependent.", "The primary source introduces OzID. LipidOz is a later software implementation; OzFAD and other variants are not treated as interchangeable methods here." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because gas-phase ozone chemistry adds orthogonal structural information not provided by routine CID lipidomics. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "paterno-buchi-lipid-double-bond-localization", "name": "Paterno-Buchi photochemistry for lipid double-bond localization", "summary": "Couple lipid photochemical derivatization to tandem MS to locate carbon-carbon double bonds.", "description": "The defining approach converts unsaturated lipids into oxetane reaction products. Collision-induced fragmentation of those products yields diagnostic ions that report double-bond position.", "publication": { "title": "Pinpointing Double Bonds in Lipids by Paternò-Büchi Reactions and Mass Spectrometry", "doi": "10.1002/anie.201310699", "year": 2014, "journal": "Angewandte Chemie International Edition", "citation": "Ma X, Xia Y. Pinpointing Double Bonds in Lipids by Paternò-Büchi Reactions and Mass Spectrometry. Angewandte Chemie International Edition 53, 2592–2596 (2014)." }, "purpose": { "primary": "structure_elucidation", "secondary": [ "chemical_reactivity" ] }, "platforms": [ "direct_infusion_ms" ], "analysis_types": [ "lipidomics" ], "components": [ "sample_preparation", "data_acquisition", "annotation" ], "ask_keywords": [ "Paterno Buchi", "double bond position", "lipid isomers", "photochemical derivatization", "unsaturated lipids" ], "workflow_steps": [ { "title": "Photochemically modify lipid double bonds", "description": "The original implementation irradiates a nanoelectrospray plume containing lipids and acetone with UV light." }, { "title": "Fragment reaction products", "description": "Acquire tandem mass spectra of the derivatized lipids using collision activation." }, { "title": "Interpret diagnostic fragments", "description": "Assign candidate double-bond locations from the oxetane-cleavage products." } ], "tools": [], "resources": { "method_available": true, "method_url": "https://doi.org/10.1002/anie.201310699" }, "scope": { "designed_for": [ "Locating double bonds in unsaturated lipids." ], "requirements": [ "A compatible UV photoreaction and tandem-MS setup." ], "considerations": [ "This is photochemical derivatization, distinct from gas-phase ozone-induced dissociation.", "Do not assume that software written for OzID interprets these spectra." ] }, "related_strategies": [ "ozid-structural-lipidomics" ], "implementations": [ { "title": "Identification and quantitation of lipid C=C location isomers: A shotgun lipidomics approach enabled by photochemical reaction", "doi": "10.1073/pnas.1523356113", "year": 2016, "journal": "Proceedings of the National Academy of Sciences", "relationship": "extension", "summary": "Extends the photochemical method to identification and quantitation in complex lipid mixtures. Tissue comparisons are use cases, not additional Strategies.", "tool_slugs": [] } ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary source introduces the analytical chemistry-to-fragmentation relationship. The 2016 extension is retained as an implementation. No catalogue software was identified as part of the original workflow; no substitute is invented." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "qc-rlsc-signal-drift-correction", "name": "QC-based robust LOESS signal-drift correction", "summary": "Use repeated biological QC injections to estimate and correct feature-specific signal drift along acquisition order.", "description": "QC-RLSC fits local curves to QC intensities and interpolates correction across study injections. It links experimental QC placement with computational drift estimation and subsequent quality assessment.", "publication": { "title": "Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry", "doi": "10.1038/nprot.2011.335", "year": 2011, "journal": "Nature Protocols", "citation": "Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 6, 1060–1083 (2011)." }, "purpose": { "primary": "analytical_quality_control", "secondary": [] }, "platforms": [ "lc_ms", "gc_ms" ], "analysis_types": [ "untargeted" ], "components": [ "data_acquisition", "preprocessing", "quality_control", "statistics" ], "ask_keywords": [ "QC-RLSC", "pooled QC", "LOESS", "signal drift", "injection order", "batch correction", "normalization", "quality control" ], "workflow_steps": [ { "title": "Interleave representative QC injections", "description": "Retain injection order and batch labels with study and QC measurements." }, { "title": "Prepare aligned feature intensities", "description": "The documented LC-MS implementation uses XCMS for preprocessing.", "tool_slugs": [ "xcms" ] }, { "title": "Fit QC correction curves", "description": "Cross-validate feature-wise LOESS smoothing and interpolate across injections." }, { "title": "Correct and assess reliability", "description": "Normalize responses and inspect QC repeatability before combining batches." } ], "tools": [ { "slug": "xcms", "role": "LC-MS preprocessing in the defining workflow; not asserted to implement the correction itself." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1038/nprot.2011.335" }, "scope": { "designed_for": [ "Analytical drift correction when representative QC injections span the run." ], "requirements": [ "Repeated QC observations with usable feature signals and recorded injection order." ], "considerations": [ "Sparse or unrepresentative QCs cannot reliably characterize every feature.", "Check overfitting and corrected QC performance; correction does not fix experimental confounding." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "The 2011 primary procedure materially formalizes QC-RLSC. Scoped to its correction relationship, not every step in serum/plasma profiling. QC filtering is a companion step, not an extra card. No software execution performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "reference-pool-cohort-extraction", "name": "Reference-pool discovery with cohort-wide signal extraction", "summary": "Discover features in pooled reference samples, then extract their signals across a large LC-MS cohort using curated mass and retention-time targets.", "description": "This workflow separates discovery from cohort-wide quantification. Reference pools establish the target list; individual samples supply the quantitative measurements. Both annotated metabolites and retained unknown features can be measured. The method is not defined by the longevity cohort used to demonstrate it, and it does not require every feature to be identified.", "publication": { "title": "An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies", "doi": "10.1021/acs.analchem.2c01270", "url": "https://doi.org/10.1021/acs.analchem.2c01270", "citation": "Stancliffe E, Schwaiger-Haber M, et al. Analytical Chemistry 94, 17370-17378 (2022).", "journal": "Analytical Chemistry", "year": 2022 }, "purpose": { "primary": "phenotype_association", "secondary": [ "metabolite_annotation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "lipidomics" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "unspecified" }, "components": [ "preprocessing", "quality_control", "annotation" ], "workflow_steps": [ { "title": "Prepare representative reference pools", "description": "Include relevant cohort subsets and acquire pooled discovery data, blanks and consistent QC samples." }, { "title": "Build the polar feature list", "description": "XCMS detects pooled features; CAMERA supports adduct selection. The study supplements these with AcquireX results.", "tool_slugs": [ "xcms", "camera" ], "external_tools": [ { "name": "AcquireX" } ] }, { "title": "Curate reference targets", "description": "Review artifacts and annotation evidence. The published polar branch uses DecoID; the lipid branch uses Lipid Annotator. Retain suitable unknown features too.", "external_tools": [ { "name": "DecoID" }, { "name": "Lipid Annotator" } ] }, { "title": "Extract cohort peak areas", "description": "Use Skyline batch by batch with curated masses and retention-time boundaries; inspect integration and adjust for retention shifts.", "tool_slugs": [ "skyline" ] }, { "title": "Evaluate correction and export measurements", "description": "Compare batch-correction choices against common QC samples before downstream biological analysis.", "external_tools": [ { "name": "Published processing notebooks", "url": "https://github.com/pattilab/metabolomics_workflow" } ] } ], "tools": [ { "slug": "xcms", "role": "Detects features in pooled polar-metabolite data." }, { "slug": "camera", "role": "Supports adduct selection during reference-list preparation." }, { "slug": "skyline", "role": "Extracts and reviews cohort-wide peak areas from the curated target list." }, { "name": "AcquireX", "role": "Supplies additional pooled-sample features in the published polar branch." }, { "name": "DecoID", "role": "Supports spectral annotation of polar reference features." }, { "name": "Lipid Annotator", "role": "Generates the published lipid reference list." }, { "name": "Published processing notebooks", "url": "https://github.com/pattilab/metabolomics_workflow", "role": "Reference processing, peak-boundary generation and batch-correction resources." } ], "resources": { "code_available": true, "code_url": "https://github.com/pattilab/metabolomics_workflow", "method_available": true, "method_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11018270/" }, "scope": { "designed_for": [ "Large LC-MS cohorts where pooled discovery can provide a reusable reference list." ], "requirements": [ "Representative pools, blanks, shared QC material and individual-sample raw data.", "Expert curation of reference peaks and integration boundaries." ], "considerations": [ "Rare or low-abundance compounds can be diluted below detection in pools and omitted from the target list.", "Targeted signal extraction from untargeted data is not a validated absolute-concentration assay.", "Check batch correction for preservation of biological differences; no normalization method is universally best.", "The code is documented here, not executed or benchmarked by Navigator." ] }, "ask_keywords": [ "thousands of samples", "large cohort processing", "pooled reference discovery", "cohort wide peak extraction", "Skyline reference targets" ], "related_strategies": [ "qc-rlsc-signal-drift-correction" ], "sample_types": [], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Primary methods and author code repository reviewed on 2026-09-21. DOI/title/year verified against Crossref. Two queue rows consolidated into this one workflow; no longevity-specific Strategy added." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-21", "updated_at": "2026-09-21", "last_verified": "2026-09-21", "last_editorial_review": "2026-09-21" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "retention-order-joint-annotation", "name": "Retention-order-assisted joint molecular annotation", "summary": "Combine relative LC elution order with MS/MS candidate scores to annotate multiple features jointly.", "description": "The 2018 method integrates learned pairwise retention preferences with spectral evidence rather than filtering each feature independently by absolute retention time.", "publication": { "title": "Liquid-chromatography retention order prediction for metabolite identification", "doi": "10.1093/bioinformatics/bty590", "year": 2018, "journal": "Bioinformatics" }, "purpose": { "primary": "metabolite_annotation", "secondary": [] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "components": [ "chromatography", "annotation", "statistics" ], "ask_keywords": [ "retention order", "elution order", "joint annotation", "LC-MS2Struct", "retention time", "candidate ranking" ], "workflow_steps": [ { "title": "Prepare candidate lists", "description": "Associate measured retention times with spectra and candidate structures." }, { "title": "Score spectral candidates", "description": "The original study uses IOKR scores.", "external_tools": [ { "name": "IOKR" } ] }, { "title": "Learn and integrate retention preferences", "description": "Combine RankSVM order predictions and spectral scores using the published dynamic-programming procedure." }, { "title": "Review ranked assignments", "description": "Inspect candidates and seek independent confirmation." } ], "tools": [ { "name": "IOKR", "role": "Original spectral candidate scorer." }, { "name": "Retention-order prediction implementation", "url": "https://version.aalto.fi/gitlab/bache1/retention_order_prediction.git", "role": "Code location reported by the 2018 paper; not execution-tested." } ], "resources": { "method_available": true, "method_url": "https://doi.org/10.1093/bioinformatics/bty590" }, "scope": { "designed_for": [ "Joint interpretation of LC-MS/MS features." ], "requirements": [ "Candidate structures, spectral scores, measured retention order and suitable training data." ], "considerations": [ "Candidate coverage and chromatographic transferability matter. Rankings do not confirm identities." ] }, "implementations": [ { "title": "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data", "doi": "10.1038/s42256-022-00577-2", "year": 2022, "journal": "Nature Machine Intelligence", "relationship": "extension", "summary": "LC-MS2Struct learns structured integration of spectral scores and retention order, including stereochemistry-aware fingerprints. This replaces the original integration model rather than reproducing its algorithm.", "tool_slugs": [ "lc-ms2struct" ] }, { "title": "ROASMI: accelerating small molecule identification by repurposing retention data", "doi": "10.1186/s13321-025-00968-8", "year": 2025, "journal": "Journal of Cheminformatics", "relationship": "extension", "summary": "Uses learned molecular representations and a neural ranking model to predict retention order within a chromatographic application domain, then integrates this evidence with MS/MS scores. This is a later implementation, not the original 2018 workflow.", "tool_slugs": [ "roasmi" ] } ], "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Origin anchored to the 2018 joint retention-order/MS2 method, not the later LC-MS2Struct tool. Primary methods and DOI metadata reviewed; software not executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-20", "updated_at": "2026-10-05", "last_editorial_review": "2026-09-20" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "reverse-metabolomics", "name": "Reverse metabolomics", "summary": "A discovery strategy that starts from a candidate chemical structure or reference MS/MS spectrum, searches public metabolomics repositories for matching spectra, and uses sample metadata to reveal biological or phenotypic associations.", "description": "Reverse metabolomics reverses the usual untargeted workflow. Instead of starting with an unknown feature and attempting to infer its structure, the researcher begins with a candidate structure and an experimental MS/MS spectrum, searches public metabolomics data for matching spectra, and then uses repository metadata to ask where the molecule occurs. The resulting organism, biospecimen, phenotype or disease associations can guide biological validation.", "publication": { "title": "Reverse metabolomics for the discovery of chemical structures from humans", "doi": "10.1038/s41586-023-06906-8", "url": "https://www.nature.com/articles/s41586-023-06906-8", "citation": "Gentry EC, Collins SL, Panitchpakdi M, et al. Reverse metabolomics for the discovery of chemical structures from humans. Nature 626, 419–426 (2024).", "journal": "Nature", "year": 2024 }, "purpose": { "primary": "reverse_metabolomics", "secondary": [ "phenotype_association", "molecular_discovery", "metabolite_annotation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "flow_injection" ], "ion_mobility": "not_required" }, "components": [ "data_acquisition", "spectral_search", "repository_mining", "metadata_analysis", "biological_validation", "data_sharing" ], "workflow_steps": [ { "title": "Generate or obtain a reference MS/MS spectrum", "description": "Acquire tandem mass spectra for a candidate chemical structure, for example from a synthesized standard, authenticated standard, or suitable reference source.", "tool_slugs": [ "msms-chooser" ] }, { "title": "Select a representative reference spectrum", "description": "When standards are analyzed in mixtures, automatically select the MS/MS scan associated with the expected precursor and review the spectrum before repository searching.", "tool_slugs": [ "msms-chooser" ] }, { "title": "Search public MS/MS data", "description": "Use the reference spectrum as a query against public metabolomics datasets to identify files and datasets containing related spectra.", "tool_slugs": [ "masst", "gnps", "massive" ] }, { "title": "Contextualize matches with sample metadata", "description": "Summarize organism, biospecimen, phenotype, disease state and other standardized metadata associated with matching public files.", "tool_slugs": [ "redu" ] }, { "title": "Prioritize biological associations", "description": "Use the distribution of matched spectra across public datasets to generate hypotheses about where the molecule occurs and which phenotypes or sample types it may be associated with." }, { "title": "Validate prioritized associations", "description": "Test the proposed association in independent cohorts, targeted measurements, culture systems, or functional experiments appropriate to the biological question." } ], "tools": [ { "slug": "msms-chooser", "role": "Selection of candidate reference MS/MS spectra from data acquired on standards." }, { "slug": "masst", "role": "Repository-scale MS/MS spectrum searching." }, { "slug": "redu", "role": "Metadata-based contextualization of public mass-spectrometry matches." }, { "slug": "gnps", "role": "Web infrastructure for spectral workflows and public-data analysis." }, { "slug": "massive", "role": "Public repository containing searchable metabolomics datasets." } ], "resources": { "raw_data_available": true, "raw_data_url": "https://massive.ucsd.edu/", "processed_data_available": true, "processed_data_url": "https://www.nature.com/articles/s41586-023-06906-8", "code_available": true, "code_url": "https://github.com/emgentry/Synthesis-based-reverse-metabolomics", "method_available": true, "method_url": "https://www.nature.com/articles/s41586-023-06906-8" }, "scope": { "designed_for": [ "Asking where a known or newly synthesized chemical structure occurs across existing untargeted MS/MS datasets.", "Using repository-scale metadata to generate hypotheses linking a molecule to organisms, sample types, disease states or other phenotypes." ], "requirements": [ "A suitable reference MS/MS spectrum for the candidate molecule.", "Public datasets with matching MS/MS signals and sufficient metadata to interpret biological context." ], "considerations": [ "A repository spectral match is evidence of spectral similarity and occurrence, not by itself a complete structural confirmation.", "Repository composition and metadata coverage can bias which biological contexts are visible." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-08-24", "last_verified": "2026-08-21", "last_editorial_review": "2026-08-21" }, "sample_types": [ "feces", "tissue", "microbial_culture", "synthetic_standards", "other" ], "biological_contexts": [ "human", "animal", "microbial", "host_microbe", "synthetic_reference", "broadly_applicable" ], "organisms": [ "Homo sapiens" ] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "spacem-single-cell-metabolomics", "name": "Spatially registered single-cell metabolomics", "aliases": [ "SpaceM" ], "summary": "A single-cell metabolomics strategy that registers microscopy-derived cell boundaries and morphologic information with MALDI imaging mass spectrometry so spatial ion signals can be assigned to individual cells.", "description": "SpaceM combines light microscopy, image segmentation, MALDI mass spectrometry imaging, and spatial registration to convert tissue- or culture-level ion images into per-cell metabolic profiles. The key methodological step is aligning optical cell information with MSI ablation coordinates so metabolite signals can be associated with individual cells while retaining morpho-spatial context.", "publication": { "title": "SpaceM reveals metabolic states of single cells", "doi": "10.1038/s41592-021-01198-0", "url": "https://doi.org/10.1038/s41592-021-01198-0", "citation": "Rappez L, Stadler M, Triana S, et al. SpaceM reveals metabolic states of single cells. Nature Methods 18, 799-805 (2021).", "journal": "Nature Methods", "year": 2021 }, "purpose": { "primary": "single_cell_metabolomics", "secondary": [ "spatial_metabolomics", "phenotype_association" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics", "metabolite_profiling" ], "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "full_scan" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "data_acquisition", "preprocessing", "metadata_analysis", "statistics", "biological_validation" ], "workflow_steps": [ { "title": "Acquire microscopy of the same cells", "description": "Record optical images that preserve cell morphology and spatial context before MALDI analysis." }, { "title": "Segment individual cells", "description": "Derive cell boundaries and image-derived features from the optical data." }, { "title": "Acquire MALDI mass spectrometry imaging", "description": "Collect spatially resolved metabolite signals across the same cellular field." }, { "title": "Register optical and MSI coordinate systems", "description": "Align cell masks with MALDI ablation locations so mass-spectral signals can be assigned to individual cells." }, { "title": "Construct per-cell metabolic profiles", "description": "Aggregate registered ion signals into a cell-by-metabolite matrix while retaining morphologic and spatial information." }, { "title": "Identify metabolic cell states", "description": "Analyze single-cell metabolic profiles to distinguish heterogeneous cell states and relate them to morphology or experimental conditions." } ], "tools": [ { "name": "SpaceM", "url": "https://github.com/alexandrovteam/SpaceM", "role": "Open-source implementation of spatial registration and single-cell metabolomic data processing." } ], "resources": { "code_available": true, "code_url": "https://github.com/alexandrovteam/SpaceM", "method_available": true, "method_url": "https://doi.org/10.1038/s41592-021-01198-0" }, "scope": { "designed_for": [ "In situ metabolic profiling of heterogeneous cell populations at single-cell resolution.", "Linking metabolic states to cell morphology and spatial position." ], "requirements": [ "Compatible microscopy, cell segmentation, and MALDI-MSI measurements from the same spatial field.", "Accurate spatial registration between optical images and MSI coordinates." ], "considerations": [ "Cell-level profiles depend on segmentation and registration accuracy.", "MALDI pixel size, analyte delocalization, and ion suppression can influence per-cell assignments." ] }, "ask_keywords": [ "single cell metabolomics", "maldi single cells", "connect microscopy with mass spectrometry imaging", "spatially resolved cell metabolomics", "spacem" ], "sample_types": [ "cells" ], "biological_contexts": [ "broadly_applicable" ], "organisms": [], "related_strategies": [ "msi-guided-molecular-networking" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because the publication introduces a transferable multimodal strategy that uses spatial registration to convert MSI measurements into single-cell metabolic profiles." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_verified": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "stable-isotope-labeling-molecular-networking", "name": "Stable-isotope labeling–assisted molecular networking", "summary": "A biosynthetic discovery strategy that combines stable-isotope precursor feeding with LC-MS/MS molecular networking to identify structurally related metabolites that incorporate a defined biosynthetic building block.", "description": "Stable-isotope labeling–assisted molecular networking layers biosynthetic incorporation information onto MS/MS similarity. A labeled precursor such as a stable-isotope amino acid is supplied to a producing organism, labeled mass shifts are detected in metabolite features, and molecular networking groups related MS/MS spectra. The combination helps distinguish biosynthetically connected analogues and can provide sequence or building-block information that molecular networking alone does not supply.", "publication": { "title": "Combining Stable Isotope Labeling and Molecular Networking for Biosynthetic Pathway Characterization", "doi": "10.1021/acs.analchem.5b01934", "url": "https://pubs.acs.org/doi/10.1021/acs.analchem.5b01934", "citation": "Klitgaard A, Nielsen JB, Frandsen RJN, Andersen MR, Nielsen KF. Combining Stable Isotope Labeling and Molecular Networking for Biosynthetic Pathway Characterization. Analytical Chemistry 87, 6520–6526 (2015).", "journal": "Analytical Chemistry", "year": 2015 }, "purpose": { "primary": "stable_isotope_tracing", "secondary": [ "natural_product_discovery", "molecular_family_discovery", "structure_elucidation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "stable_isotope_tracing" ], "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda" ], "ion_mobility": "not_required" }, "components": [ "sample_preparation", "chromatography", "data_acquisition", "preprocessing", "molecular_networking", "annotation" ], "workflow_steps": [ { "title": "Select and feed a stable-isotope precursor", "description": "Add a labeled biosynthetic building block to the culture under conditions that permit incorporation into specialized metabolites, while retaining appropriate unlabeled controls." }, { "title": "Detect isotope incorporation", "description": "Compare labeled and unlabeled LC-MS data to identify features with mass shifts consistent with incorporation of the supplied precursor." }, { "title": "Acquire and compare MS/MS spectra", "description": "Collect fragmentation spectra for candidate labeled and related metabolites." }, { "title": "Build a molecular network", "description": "Organize structurally related spectra by MS/MS similarity and overlay isotope-incorporation evidence to recognize families sharing the labeled building block.", "tool_slugs": [ "gnps" ] }, { "title": "Infer biosynthetic or structural relationships", "description": "Use the combined label pattern and fragmentation relationships to prioritize analogues, infer building-block composition, and guide targeted structural validation." } ], "tools": [ { "slug": "gnps", "role": "Molecular networking of related MS/MS spectra." } ], "resources": { "method_available": true, "method_url": "https://pubs.acs.org/doi/10.1021/acs.analchem.5b01934" }, "scope": { "designed_for": [ "Linking natural-product analogues to a supplied biosynthetic precursor.", "Characterizing families of peptide or other metabolites where isotope incorporation provides interpretable biosynthetic information." ], "requirements": [ "A biologically plausible labeled precursor and culture system capable of incorporating it.", "Matched labeled and unlabeled controls and sufficient mass accuracy to resolve the expected isotope shift." ], "considerations": [ "Label incorporation may be incomplete, diluted by metabolism, or redistributed into downstream metabolites.", "Molecular similarity and label incorporation support a biosynthetic relationship but do not by themselves establish complete pathway genetics or final structure." ] }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included as an Analytical Strategy because stable-isotope incorporation adds a biosynthetic evidence layer to molecular-network relationships and is transferable beyond the original fungal system. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [ "microbial_culture" ], "biological_contexts": [ "fungal", "microbial" ], "organisms": [ "Aspergillus nidulans" ], "related_strategies": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "target-decoy-controlled-dia-metabolomics", "name": "Target-decoy-controlled DIA metabolomics", "summary": "Use DDA-derived assay libraries and decoy scoring to control false discoveries during targeted DIA extraction.", "description": "DIAMetAlyzer links assay construction, chromatographic extraction and statistical validation. Its distinguishing contribution is error-controlled extraction, rather than merely combining acquisition modes.", "publication": { "title": "DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics", "doi": "10.1038/s41467-022-29006-z", "year": 2022, "journal": "Nature Communications", "citation": "Alka et al. DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics. Nature Communications (2022)." }, "purpose": { "primary": "metabolite_annotation", "secondary": [] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted" ], "components": [ "data_acquisition", "annotation", "quality_control", "statistics" ], "ask_keywords": [ "DIAMetAlyzer", "DIA", "false discovery rate", "decoy", "assay library", "targeted extraction" ], "workflow_steps": [ { "title": "Construct target and decoy assays", "description": "Build assays from DDA spectra with fragment annotations.", "tool_slugs": [ "openms", "sirius" ] }, { "title": "Extract DIA chromatograms", "description": "Use the assay library to extract candidate peak groups.", "tool_slugs": [ "openms" ] }, { "title": "Score and filter results", "description": "Estimate false-discovery rates before interpreting quantified features.", "tool_slugs": [ "openms" ] } ], "tools": [ { "slug": "openms", "role": "Hosts the DIAMetAlyzer workflow." }, { "slug": "sirius", "role": "Fragment annotation for assay construction." } ], "resources": { "code_available": true, "code_url": "https://github.com/oliveralka/DIAMetAlyzer_additional_code", "method_available": true, "method_url": "https://doi.org/10.1038/s41467-022-29006-z" }, "scope": { "designed_for": [ "Assay-driven DIA quantification with statistical error control." ], "requirements": [ "Compatible DDA library and DIA measurements." ], "considerations": [ "Library coverage limits extraction.", "Extraction FDR does not establish definitive chemical identity." ] }, "related_strategies": [ "dadia-hybrid-acquisition" ], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Distinct from DaDIA acquisition design: this card describes target-decoy statistical validation. Primary paper checked; software not executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-19", "updated_at": "2026-09-19", "last_editorial_review": "2026-09-19" } }, { "$schema": "../../schemas/strategy.schema.json", "slug": "taxonomically-informed-annotation", "name": "Taxonomically informed metabolite annotation", "summary": "A metabolite-annotation strategy that reranks candidate natural-product structures by combining spectral or in-silico annotation evidence with the taxonomic distance between the study organism and known producers of candidate compounds.", "description": "Taxonomically informed scoring adds biological provenance to conventional candidate ranking. Candidate structures are first generated from spectral libraries or in-silico annotation. Known organism–compound associations are then used to calculate taxonomic relevance, allowing candidates reported from related organisms to receive additional support without treating taxonomy as structural proof.", "publication": { "title": "Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation", "doi": "10.3389/fpls.2019.01329", "url": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.01329/full", "citation": "Rutz A, Dounoue-Kubo M, Ollivier S, et al. Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation. Frontiers in Plant Science 10, 1329 (2019).", "journal": "Frontiers in Plant Science", "year": 2019 }, "purpose": { "primary": "metabolite_annotation", "secondary": [ "natural_product_discovery", "dereplication" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "acquisition": {}, "components": [ "data_acquisition", "preprocessing", "annotation", "metadata_analysis", "statistics" ], "workflow_steps": [ { "title": "Generate candidate structural annotations", "description": "Use spectral-library or in-silico annotation to generate ranked candidate structures for MS/MS features." }, { "title": "Retrieve candidate biological-source information", "description": "Collect taxonomic provenance for candidate natural products from curated structure–organism resources.", "tool_slugs": [ "lotus", "natural-products-atlas" ] }, { "title": "Calculate taxonomic relevance", "description": "Compare the taxonomy of the analyzed organism with reported producers of each candidate and convert the distance into an annotation-support score." }, { "title": "Rerank candidates", "description": "Combine chemical and taxonomic evidence while retaining the original annotation evidence and uncertainty." } ], "tools": [ { "slug": "lotus", "role": "Structure–organism occurrence context." }, { "slug": "natural-products-atlas", "role": "Natural-product taxonomic provenance, especially microbial compounds." } ], "resources": { "code_available": true, "code_url": "https://github.com/oolonek/taxo_scorer", "method_available": true, "method_url": "https://doi.org/10.3389/fpls.2019.01329" }, "scope": { "designed_for": [ "Improving candidate ranking when reliable organism identity and candidate source-occurrence information are available." ], "requirements": [ "A taxonomically identified source organism.", "Candidate structures with linked biological-source metadata." ], "considerations": [ "Taxonomy is supporting evidence and cannot establish structure.", "Published occurrence databases are incomplete and biased toward studied taxa.", "The linked contemporary resources illustrate ways to implement the strategy; they are not all software used in the original publication." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because taxonomy is an independent biological evidence layer added to conventional chemical annotation. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [], "biological_contexts": [ "plant", "microbial", "fungal" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "three-dimensional-molecular-cartography", "name": "3D molecular cartography", "summary": "A spatial metabolomics strategy that combines systematic surface sampling, LC-MS/MS, coordinate-aware metadata and 2D/3D molecular mapping to reconstruct the distribution of metabolites and environmental chemicals across physical objects.", "description": "3D molecular cartography extends metabolomics beyond conventional bulk samples by attaching each extraction to a physical coordinate. LC-MS data are processed and annotated, then mapped onto photographs or 3D surface models so chemical distributions can be explored spatially. Cartographical snapshots preserve visualization settings for reproducibility.", "publication": { "title": "3D molecular cartography using LC–MS facilitated by Optimus and 'ili software", "doi": "10.1038/nprot.2017.122", "url": "https://www.nature.com/articles/nprot.2017.122", "citation": "Protsyuk I, Melnik AV, Nothias L-F, et al. 3D molecular cartography using LC–MS facilitated by Optimus and 'ili software. Nature Protocols 13, 134–154 (2018).", "journal": "Nature Protocols", "year": 2018 }, "purpose": { "primary": "spatial_metabolomics", "secondary": [ "environmental_metabolomics", "molecular_discovery" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "spatial_metabolomics" ], "acquisition": {}, "components": [ "sample_preparation", "chromatography", "data_acquisition", "preprocessing", "annotation", "data_sharing" ], "workflow_steps": [ { "title": "Define a spatial sampling scheme", "description": "Record coordinates for reproducible sampling across a surface, organism or object." }, { "title": "Extract and profile each location", "description": "Analyze spatially indexed extracts by LC-MS/MS with appropriate blanks and quality controls." }, { "title": "Process and annotate features", "description": "Generate a quantitative feature table and molecular annotations while preserving spatial sample identifiers." }, { "title": "Map molecular features onto the physical model", "description": "Join feature abundance to sampling coordinates and visualize the chemical distribution on a 2D image or 3D model.", "tool_slugs": [ "ili" ] }, { "title": "Share cartographical snapshots", "description": "Preserve the mapped view and visualization settings so collaborators can reproduce the spatial interpretation." } ], "tools": [ { "slug": "ili", "role": "Interactive 2D/3D molecular cartography visualization." } ], "resources": { "method_available": true, "method_url": "https://www.nature.com/articles/nprot.2017.122" }, "scope": { "designed_for": [ "Mapping extractable small molecules over complex surfaces at scales larger than pixel-based MSI.", "Environmental, ecological, built-environment and organism-surface chemistry." ], "requirements": [ "A reproducible coordinate system connecting each extraction to the mapped object.", "Sufficient sampling density for the spatial question." ], "considerations": [ "Spatial resolution is determined by sampling geometry and is much coarser than many MSI methods.", "Observed distributions integrate extraction, LC-MS detectability and sampling effects." ] }, "related_strategies": [], "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "Included because spatial coordinates and physical-model reconstruction add a transferable experimental layer to LC-MS metabolomics. Navigator review checked publication identity and methodological scope; linked software was not independently benchmarked." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "sample_types": [], "biological_contexts": [ "environmental", "plant", "human", "broadly_applicable" ], "organisms": [] }, { "$schema": "../../schemas/strategy.schema.json", "slug": "tissue-isotope-imaging", "name": "Tissue isotope imaging with iso-imaging", "aliases": [ "iso-imaging" ], "summary": "Combine stable-isotope nutrient tracing with MALDI imaging to map labeling patterns within tissue rather than averaging an entire section.", "publication": { "title": "Spatially resolved isotope tracing reveals tissue metabolic activity", "doi": "10.1038/s41592-021-01378-y", "year": 2022, "journal": "Nature Methods", "citation": "Wang L, Xing X, Zeng X, et al. Nature Methods 19, 223-230 (2022)." }, "purpose": { "primary": "stable_isotope_tracing", "secondary": [ "spatial_metabolomics" ] }, "platforms": [ "imaging_ms" ], "components": [ "sample_preparation", "data_acquisition", "metadata_analysis" ], "tools": [ { "name": "IsoScope", "url": "https://github.com/xxing9703/Isoscope", "role": "Analyze and display spatial isotope-labeling patterns." } ], "workflow_steps": [ { "title": "Introduce and measure the nutrient tracer", "description": "Use the published nutrient-infusion design and measure circulating tracer labeling." }, { "title": "Image tissue isotopologues", "description": "Acquire MALDI ion images while retaining tissue coordinates." }, { "title": "Validate signals and map labeling", "description": "Check assignments against LC-MS and visualize isotope fractions or enrichment with IsoScope.", "external_tools": [ { "name": "IsoScope", "url": "https://github.com/xxing9703/Isoscope" } ] } ], "scope": { "designed_for": [ "Spatial nutrient-use measurements in tracer-labeled mammalian tissues." ], "requirements": [ "A designed stable-isotope labeling experiment, not an ordinary unlabeled imaging dataset.", "MSI resolving the relevant isotopologues and matched anatomical information.", "Companion LC-MS measurements for signal validation and tracer normalization." ], "considerations": [ "Label incorporation alone is not an absolute reaction-flux measurement.", "Overlapping ions, isotope abundance and sampling time affect interpretation.", "The published tissue implementation is not generally single-cell resolved." ] }, "ask_keywords": [ "spatial isotope tracing", "tissue isotope imaging", "nutrient labeling across tissue", "iso imaging", "isotopologue maps" ], "related_strategies": [ "kinetic-flux-profiling", "spacem-single-cell-metabolomics" ], "resources": { "code_available": true, "code_url": "https://github.com/xxing9703/Isoscope", "method_available": true, "method_url": "https://doi.org/10.1038/s41592-021-01378-y" }, "provenance": { "submitted_by": "curator", "author_verified": false, "notes": "The 2022 paper formalizes this iso-imaging workflow; no claim to first use of all isotope imaging. Distinct from unlabeled spatial profiling and bulk kinetic flux inference. Single-cell isotope extensions require separate evaluation." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-29", "updated_at": "2026-09-29", "last_verified": "2026-09-29", "last_editorial_review": "2026-09-29" } } ]