iPEX enables micrometre-resolution deep spatial proteomics via tissue expansion
Article Date: 12 November 2025
Article URL: https://www.nature.com/articles/s41586-025-09734-0
Article Image: (not provided)
Summary
iPEX is a new spatial proteomics workflow that combines hydrogel tissue expansion with MALDI mass spectrometry imaging (MSI) and complementary LC–MS/MS to reach micrometre-scale, deep proteomic mapping across tissue sections. By physically expanding tissue (hydrogel expansion) the authors increase effective spatial resolution for MALDI‑MSI peptide detection, enabling robust identification and localisation of peptides and proteins across layers in mouse retina, cerebellum, whole brain sections, intestine, liver and human cerebral organoids.
The paper provides validation (MS2/tandem spectra), cross-platform comparisons, external LC–MS/MS libraries and demonstrates applications to a 5xFAD Alzheimer’s disease mouse model where early, region-specific proteomic and mitochondrial/lipid alterations (including ACAA1-related lipid metabolism changes) are uncovered. Data and code are available via Supplementary Datasets, GitHub and Zenodo.
Key Points
- iPEX uses hydrogel-driven tissue expansion to improve spatial resolution of MALDI‑MSI to the micrometre scale, boosting peptide detectability and localisation precision.
- Method validated across tissues (mouse retina, cerebellum, brain, intestine, liver) and human cerebral organoids with MS2 confirmation and correlation to immunofluorescence and LC–MS/MS libraries.
- Demonstrated reproducibility and dynamic range improvements versus conventional MALDI‑MSI, with pixel-level peptide/protein maps and clustering analyses.
- Applied to 5xFAD Alzheimer’s model brains to reveal region-specific proteomic changes, mitochondrial alterations and lipid metabolism disruptions—highlighting ACAA1 involvement. External datasets support selected findings.
- All reference libraries, raw lipidomics, code and analysis scripts are shared (Supplementary Datasets, GitHub: zouyilonglab/ipex, and Zenodo DOIs), enabling reuse and follow-up studies.
Content summary
The work details an end-to-end iPEX pipeline: sample embedding and expansion, MALDI‑MSI acquisition (including timsTOF fleX MALDI-2 support), peptide annotation using LC–MS/MS-derived libraries, MS2 validation and spatial clustering. Extensive extended data show method development, validation of peptide localisation (including synaptic and extra-somatic structures), application across multiple organs and organoids, and specific biological discoveries in Alzheimer’s model mice. The authors pair proteomic maps with lipidomics, imaging and ultrastructural analyses to support mechanistic claims. Data and code availability are emphasised, with datasets and analysis code deposited on Zenodo and GitHub.
Context and relevance
Spatial proteomics has been limited by a trade-off between molecular depth and spatial resolution. iPEX addresses this by physically enlarging tissues so MALDI imaging can resolve and identify peptides at near‑cellular/micrometre scale while preserving proteomic depth. This bridges expansion microscopy concepts with mass-spectrometry-based molecular mapping, enabling new studies of tissue microarchitecture, disease progression and cell-type-specific molecular changes that were previously hard to access with bulk or lower-resolution approaches.
Why should I read this?
Quick and dirty: if you care about where proteins live inside tissues (not just which proteins are present), this paper is a game-changer. It gives a practical route to micrometre-level protein maps, comes with code and data, and actually shows biological payoffs — from synapse-level protein localisation in retina to early Alzheimer’s-associated changes. Saves you time: the authors did the heavy lifting and shared the resources, so you can adapt iPEX rather than reinvent the wheel.
Author note
Punchy take: iPEX is a solid technical advance that meaningfully narrows the gap between high‑resolution imaging and deep proteomics. For researchers in spatial omics, neuroscience, pathology or mass spectrometry it’s highly relevant and worth digging into the methods and supplementary datasets.