By the Metabolomics Team (Orbitrap/HRAM), Creative Proteomics
The Metabolomics Team comprises method scientists, QC specialists, and data engineers with combined HRAM/Orbitrap platform experience. We design SSTs, define QC cadences.
Standardization in HRAM metabolomics isn't a single parameter file—it's governance. If you manage LC–MS runs or own translational program timelines, what matters is consistent sample handling, system suitability before injections, a planned QC cadence with pooled QCs and blanks, bridging across batches, and full parameter/version logging that yields annotation-ready evidence. This ultimate guide distills those practices into reusable checklists and neutral decision logic for Orbitrap-class HRAM workflows.
Key takeaways
- Standardization means end-to-end governance: SST, planned QC cadence, bridging, and transparent parameter/version logs.
- Orbitrap/HRAM excels at high mass accuracy/resolution and quality MS/MS; plan around isomers, matrix effects, and incomplete MS/MS.
- "Annotation-ready" ≠ confirmed ID: provide feature-centric evidence fields and traceable versions aligned to MSI expectations.
- Define RFP/SOW deliverables in Minimum/Recommended/Optional tiers to reduce rework and improve audit readiness.
- Use vendor-agnostic tools (XCMS, MZmine, MS-DIAL, MetaboAnalystR) and flexible QC-based normalization (QC-RLSC, RALPS) to keep pipelines reproducible.
What "standardised HRAM metabolomics" means (and why it matters)
In practice, a standardized HRAM metabolomics workflow is built on five governance pillars:
- System suitability testing (SST) before running study samples.
- A planned QC cadence with pooled QC samples and blanks, plus bridging to maintain cross-batch comparability.
- Chromatography and acquisition choices that are documented and aligned to downstream annotation goals.
- Reproducible preprocessing with versioned software and parameter manifests.
- Transparent reporting that packages evidence per feature (not just names) and records methods and decisions.
Some service providers document these governance elements as part of routine RUO workflows—for example, pooled QC placement maps and analysis manifests with software versions. See the internal explainer on LC–MS setup for context: LC–MS setup for untargeted metabolomics.
According to 2024 guidance in QComics for LC–MS metabolomics, sequential QC assessments and transparent reporting are key to reproducibility. The mQACC framework similarly frames QC cadence and documentation as living guidance rather than fixed numeric prescriptions; see Mosley et al., 2024 QA/QC framework.
Where HRAM/Orbitrap performs best (and realistic limits to plan around)
Orbitrap-class HRAM systems provide sub-ppm mass accuracy and high resolving power, which improves m/z precision and reduces false positives during library matching; they also deliver strong MS/MS quality for structural evidence. In workflows that pair RP and HILIC and run in both polarities, you'll achieve broad biochemical coverage.
Plan around realistic limits:
- Isomeric species may require orthogonal separation or authentic standards; report MSI confidence levels appropriately.
- Matrix effects and ion suppression necessitate pooled QC placement and blanks, with QC-based drift correction post-run.
- Multi-batch comparability depends on bridging samples and robust cross-batch alignment.
For confidence frameworks, the MSI convention still applies: Level 1 requires ≥2 orthogonal properties matched to an authentic standard under identical conditions, whereas putative Levels 2/3 rely on library matches without standards. Representative recent applications explain these levels clearly (2020–2025), such as MSI guidance in longitudinal metabolomics.
System suitability before you run samples (a vendor-neutral SST checklist)
Before you inject study samples, implement an SST to document system readiness without prescribing hard numeric thresholds. Use this vendor-neutral checklist:
- Calibration/status verification (record instrument status, date/time, relevant references).
- Reference mix check (expected ions present, retention times, signal consistency, chromatographic stability).
- Solvent blank(s) to detect contaminants.
- Carryover check with a post-high standard blank.
- Retention time and sensitivity trend checks during conditioning.
- Contamination notes and corrective actions (e.g., column wash protocols).
- Documentation fields: instrument ID, LC method version, column lot, mobile phase lots, software versions, operator, dates.
Recent guidance emphasizes sequential QC evaluation and transparent reporting, not universal thresholds; see QComics recommendations (2024) and QA/QC reporting guidance (2022).
Sample prep best practices by matrix (plasma, tissue, cells, urine)
Across matrices, cold-chain control, minimal time-to-quench, consistent extraction, and reduced freeze–thaw are essential. Just as important: record deviations and batch context—documentation beats imagined perfection.
- Plasma: Keep anticoagulant type consistent; separate and freeze promptly; standardize protein precipitation and dilution; document hemolysis assessment.
- Tissue: Define quench and homogenization protocols; normalize to mass; record storage time, cryogenic milling parameters, and extraction ratios.
- Cells: Quench metabolism rapidly (e.g., cold solvent), control cell counts and confluence; document lysis, extraction solvent, and cleanup steps.
- Urine: Normalize to specific gravity or creatinine where appropriate; monitor salt content and pH; track sample timing and storage conditions.
For matrix-specific templates and further reading, see the internal resource: Metabolomics sample pre-processing and development.
Chromatography & acquisition choices that shape data usability
Your LC–MS decisions directly affect downstream usability and annotation odds. Think of it this way: you're trading coverage, spectral quality, and run time.
- RP vs HILIC: Pair methods for complementary coverage—HILIC for polar metabolites; RP for mid-to-nonpolar classes.
- Polarity: Positive mode favors amines/lipids; negative suits acids/phosphorylated species; dual-polarity increases coverage but doubles acquisition.
- MS/MS strategy: DDA provides high-quality spectra for abundant features; DIA broadens coverage with more complex deconvolution; align acquisition rules to your annotation plan.
For neutral setup guidance, see LC–MS setup for untargeted metabolomics.
QC cadence and batch planning in an HRAM metabolomics workflow
Plan a QC cadence rather than set a universal frequency. Pooled QCs monitor drift in RT, intensity, and m/z accuracy; blanks reveal contaminants and carryover; bridging samples maintain cross-batch comparability. This QC planning is foundational to any HRAM metabolomics workflow.
- Pooled QCs: Aliquoted mix of study samples placed regularly across each batch for monitoring and enabling QC-based correction.
- Blanks: Solvent and extraction blanks interspersed to detect carryover/contaminants; deploy remediation and document outcomes.
- Bridging samples: Technical replicates injected across batches to support alignment and between-batch adjustments.
Evidence supports flexible, documented cadence over fixed intervals. See mQACC framework (Mosley et al., 2024) and QC-based correction methods like QC-RLSC (CRAN manual) and RALPS normalization (2023).
What QC diagnostics to include in reports (transparent, audit-ready in RUO)
Include interpretable panels with clear provenance so reviewers can trace decisions:
- QC clustering (e.g., PCA with pooled QCs), RT drift plots, internal standard stability.
- Missingness overview by feature and sample; pre/post-normalization RSD distributions for QC samples.
- Blanks/carryover evidence and remediation notes.
- Rerun/exclusion decision log with rationale; versioned summary of methods and parameters.
QComics emphasizes transparent reporting of QC steps and decisions; see QComics recommendations (2024).
From raw files to feature tables: preprocessing that stays reproducible
Reproducible preprocessing means recording enough context to rerun the pipeline with the same decisions.
- Peak detection and chromatogram building; retention-time alignment; deconvolution; isotope/adduct grouping; gap-filling; filtering.
- Parameter manifest: record software names and versions (XCMS, MZmine, MS-DIAL, MetaboAnalystR) and key parameters; timestamp and operator.
- Cross-batch alignment: use robust algorithms to match features when offsets exist (e.g., correlation-informed optimal transport or RT/m/z/FI correspondence).
Authoritative resources and tool docs:
- XCMS vignette (Bioconductor)
- MZmine workflow documentation
- MetaboAnalystR 4.0 (Nature Communications, 2024) and tutorials
- Cross-batch alignment research: GromovMatcher (eLife, 2024) or RT/m/z/FI matching (Analytical Chemistry, 2022).
What "annotation-ready" really means (evidence, not just names)
Annotation-ready does not mean confirmed identification. It means the package contains traceable evidence per feature to enable downstream library/database matching and transparent review.
- Consistent feature ID; m/z; retention time; polarity; chromatographic method ID; peak area/height; detection quality metrics.
- MS/MS availability and link to spectra; adduct and isotope labels; ion grouping relationships.
- Library/database identifiers used with version/date (e.g., HMDB, KEGG, MoNA/GNPS, in-house AMRT libraries); spectral match metadata if available.
- Software and pipeline versions; key parameter manifest; date/time stamps; operator.
- MSI reporting level per feature: Level 1 requires ≥2 orthogonal properties vs. putative Levels 2/3.
Deliverables to define in your RFP/SOW (vendor-neutral, annotation-ready)
Define deliverables up front to reduce rework and clarify audit expectations. Use scope-dependent tiers.
Minimum deliverables (common across HRAM projects): raw data access terms (vendor format and/or agreed open format), feature table with field dictionary, sample metadata template aligned to repository standards, a QC diagnostics pack, and an analysis manifest (software versions + key parameters + databases/libraries). Recommended additions include MS/MS evidence exports (e.g., MGF per feature) and mapping tables (adduct/isotope groupings; cross-batch correspondence). Optional items, if in scope, are pathway/network outputs or code notebooks that reproduce preprocessing.
Anchors for this approach include mQACC QA/QC reporting guidance (2022), QComics transparency recommendations (2024).
Common failure modes (and how standardisation prevents rework)
- Drift and RT instability: Mitigate with pooled QC cadence, SST verification, and QC-based correction.
- Carryover and contamination: Detect via blanks and reference mix checks; document remediation and criteria for reruns.
- Insufficient MS/MS coverage for annotation: Align acquisition strategy (DDA/DIA) and triggering rules to annotation goals; consider inclusion lists for critical classes.
- Metadata gaps and parameter inconsistency: Enforce manifest templates and versioning; maintain rerunnable pipelines.
- Cross-batch misalignment: Define a bridging plan and apply alignment algorithms with documented acceptance criteria.
These mitigations are foundational to a robust HRAM metabolomics workflow.
View the methods primer / platform overview
If you're aligning teams on governance, QC cadence, preprocessing manifests, and annotation-ready evidence fields, share this guide and standardize your next RFP/SOW using the checklists above. View the methods primer / platform overview to explore vendor-neutral service scopes and deliverables aligned to a standardized HRAM metabolomics workflow.
