Schematic overview of the 3 major technological classes that enable spatially resolved proteome profiling in tissues. On the left, sequencing-based spatial proteomics uses oligonucleotide-barcoded antibodies — e.g., CITE-seq (67), cellular indexing of transcriptomes and epitopes by sequencing (spatial-CITE-seq) (12), spatially resolved CITE-seq (SM-omics) (68), and spatial multi-omics — to jointly capture RNA and protein signals, which are subsequently decoded through next-generation sequencing to provide spatially indexed protein maps. The middle panel shows imaging-based approaches — e.g., cyclic immunofluorescence (cycIF); co-detection by indexing (CODEX), CosMx (69), and CosMx spatial molecular imager; iterative bleaching extends multiplexing (IBEX) (20); and imaging mass cytometry (IMC) (70) — which rely on iterative staining, imaging, and signal cycling or multiplexed ion-based detection to achieve high-plex, subcellular-resolution protein localization. On the right, MS-based spatial proteomics integrates tissue microdissection or imaging MS — e.g., laser capture microdissection coupled with liquid chromatography–tandem MS (LCM-LC-MS/MS), deep visual proteomics (DVP) (73), and filter-aided expanded proteomics (FAXP) (74) — to spatially quantify thousands of proteins. TOF, time of flight. Collectively, these platforms enable reconstruction of spatial protein abundance, posttranslational modification patterns, and cell-cell interaction landscapes across tissue architectures.