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.