Mass Spectrometry Imaging — Spatial Metabolomics
Summary
End-to-end MSI spatial metabolomics: from imzML to FDR-controlled ion-image annotations and anatomically-segmented, region-compared metabolite maps.
When to use
Use when you have mass-spectrometry imaging data (imzML, e.g. MALDI/DESI) and want spatially-resolved metabolite annotations — pixel preprocessing and m/z alignment, FDR-controlled spatial annotation, spatial segmentation, and region-wise comparison.
When NOT to use
- The data is not MS-imaging.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — preprocess_imaging
Goal: load imzML, peak pick, align m/z across pixels
EDAM operation: operation_3215
Inputs: imzML · Outputs: feature-table
Candidate leaf skills: mass-spectrometry-imaging-data-import (primary), mass-spectrometry-peak-detection-and-alignment, spatial-pixel-coordinate-alignment, spectral-peak-alignment-across-pixels, imzml-metadata-parsing
Tools (primary): Cardinal, CardinalIO, R, matter, BiocManager
Other candidate tools: BiocParallel, Cardinal 3.6, Python, imzML Writer, imzML Scout, msconvert, pewpew, pewlib, pewpew (pew²)
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.1c02138, 10.1021/acs.analchem.4c06520, 10.1093/bioinformatics/btv146, 10.1529/biophysj.103.038422
Stage 2 — spatial_annotation
Goal: annotate ion images to metabolites with FDR control
EDAM operation: operation_3803
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: maldi-imaging-mass-spectrometry-data-interpretation (primary), spatial-metabolomics-feature-annotation, imaging-mass-spectrometry-ion-identification, mass-spectral-feature-annotation, m-z-metabolite-annotation-mapping, metabolite-annotation-at-scale
Tools (primary): Google Colab, Python 3, METASPACE, CellProfiler 3.0.0, Fiji (December 22 2015), Python 3 with requirements.txt
Other candidate tools: SpaMTP, R, Seurat, Cardinal, pandas, h5py, Graph-attention autoencoder, scanpy, SMART, RefineLipids, SearchAnnotations, dplyr, HMDB Database, Lipidmaps Database
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.4c06210, 10.1038/s41592-021-01198-0, 10.1101/2024.10.14.618269, 10.1101/2024.10.31.621429v1
Stage 3 — segmentation
Goal: spatial segmentation / clustering into regions
EDAM operation: operation_3432
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: spatial-segmentation-shrunken-centroids (primary), spatial-spectral-array-processing, cardinal-object-structure-understanding
Tools (primary): SpaMTP, dplyr, R, Cardinal, Seurat
Other candidate tools: BiocParallel, matter
Grounding: 3 KB(s); DOIs: 10.1093/bioinformatics/btv146, 10.1101/2024.10.14.618269, 10.1101/2024.10.31.621429v1
Stage 4 — region_statistics
Goal: compare metabolite intensities across spatial regions
EDAM operation: operation_3659
Inputs: tsv, tsv · Outputs: tsv
Candidate leaf skills: bioinformatic-object-conversion (primary), spatial-coordinate-mapping-msi, spot-level-intensity-aggregation
Tools (primary): SpaMTP, R, Cardinal, Seurat
Other candidate tools: spatialMETA, spatialmeta.pp.filter_cells_sm
Grounding: 3 KB(s); DOIs: 10.1038/s41467-025-63915-z, 10.1101/2024.10.14.618269, 10.1101/2024.10.31.621429v1
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.