Heterogeneity is a hallmark of cancer progression, characterized by genetic, transcriptional, and protein expression variability, as well as dynamic interactions within the tumor microenvironment. Primary tumors often harbor multiple, spatially distinct cellular subpopulations, complicating efforts to achieve comprehensive molecular characterization. Recent progress in molecular diagnostics and sequencing technologies has played a pivotal role in revealing the complex genetic architecture of various cancers. While Next-Generation Sequencing (NGS) has enhanced molecular profiling, traditional bulk approaches obscure spatial and single-cell resolution. Emerging spatially resolved and single-cell technologies allow the investigation of tumors within their tissue architecture. Among these, Laser Capture Microdissection (LCM) enables precise isolation of defined regions for downstream multi-omics analysis, preserving spatial context. This protocol offers an automated analysis-driven microscopy workflow for spatial-omics profiling. By leveraging high-content imaging, with scalable resolution, and A.M.I.CO. image cytometry platform, it enables phenotype-driven image acquisition and precise spatial targeting across diverse sample types. The spatial relocalization procedure integrated in A.M.I.CO. provides the physical coordinates of phenotypically defined cells or regions inside a sample for LCM. This protocol supports an intelligent analysis-driven acquisition, offering a reproducible, scalable, and tissue-context-aware workflow for several biomedical applications.