2026-09-30 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research-news/202609/t20260925_1201512.shtml
- https://www.cell.com/neuron/abstract/S0896-6273(26)00690-2
OpenFISHは、同一断面の空間トランスクリプトミクスとMALDI-MSIの統合を可能にする OpenFISH enables same-section spatial transcriptomics and MALDI-MSI integration
Xinyang Li ∙ Yuan Huang ∙ Shuo Wang ∙ … ∙ Qing-Feng Wu ∙ Woo-ping Ge ∙ Lihui Duan
Neuron Published:September 29, 2026
DOI:https://doi.org/10.1016/j.neuron.2026.09.007
Highlights
- OpenFISH is an open-source, cost-effective ST platform at single-cell resolution
- OpenFISH enables same-section spatial transcriptomics and MALDI-MSI integration
- OpenFISH resolves cell-type-associated and compartment-level metabolic patterns
- Exploratory 5xFAD analysis reveals cell-type-resolved metabolic alterations
Summary
Integrating high-resolution spatial transcriptomics with metabolomics is essential for linking cellular identity to metabolic state, yet same-section, single-cell alignment is limited by incompatible substrates, chemistries, and imaging. We introduce OpenFISH, an open-source, low-cost, modular spatial transcriptomics platform and a matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI)-compatible workflow. Through optimized tissue handling and a guided registration pipeline, OpenFISH enables cell-type-aware co-mapping of transcripts and metabolites. We demonstrate its utility by revealing cell-type-specific metabolic heterogeneity in the central nervous system and compartment-level metabolic zonation in the hippocampus. Exploratory 5xFAD experiments identify concordant cell-type-associated metabolic changes, including prominent phospholipid changes in microglia, metabolic shifts in excitatory neurons masked in aggregate analyses, and elevated lysophosphatidylethanolamine-related features in oligodendrocytes. OpenFISH also independently quantifies transposable-element activation after immune challenge and identifies candidate striatal D1-neuron patterning changes in Reeler mutants. This work provides an open, low-cost, and customizable framework for same-section spatial multi-omics.
