This study introduces a novel spatial proteomics strategy called Cross-link Assisted Spatial Proteomics (CLASP), which addresses the limitations of existing methods in resolving sub-organelle compartments and characterizing membrane-associated proteomes. Using human mitochondria as a model system, CLASP enables the high-resolution mapping of mitochondrial sub-compartments and provides insights into membrane protein topologies. The method was validated using biochemical and imaging-based approaches, confirming its ability to identify mitochondria-associated proteins and revise previous localization data. CLASP was also applied to synaptic vesicles, demonstrating its applicability to various sub-cellular compartments. CLASP leverages cross-linking mass spectrometry (XL-MS) to capture protein-protein interactions and spatial relationships. By using a defined cross-linker (DSSO), CLASP achieves a labeling radius of approximately 4 nm, allowing for precise spatial resolution. The method was tested on intact mitochondria and synaptic vesicles, revealing detailed sub-compartmental localization data. For example, CLASP identified 244 reliable localization markers (LMs) in mitochondria, enabling the mapping of 97 previously unannotated proteins and revising the topology of 24 membrane proteins. Additionally, CLASP discovered three new mitochondria-associated proteins. CLASP was further validated using a different cross-linker (DSBSO), which allowed for the enrichment of cross-linked proteins and improved spatial resolution. The DSBSO-based CLASP confirmed 431 out of 542 spatially annotated proteins from the original DSSO dataset, demonstrating the method's robustness and reproducibility. The study also showed that CLASP can correct existing topology annotations, such as for TMEM126A, which was previously misclassified as an IMM protein but was found to localize to the IMS. A Python tool was developed to automate CLASP predictions, using XL-MS data and protein localization annotations from Swiss-Prot. This tool enables the selection of LMs and spatial predictions, improving the accuracy of CLASP results. The study highlights the potential of CLASP as a powerful tool for spatial proteomics, offering high-resolution insights into protein localization and membrane topology. CLASP's ability to provide detailed sub-compartmental information makes it a valuable method for understanding cellular organization and function.This study introduces a novel spatial proteomics strategy called Cross-link Assisted Spatial Proteomics (CLASP), which addresses the limitations of existing methods in resolving sub-organelle compartments and characterizing membrane-associated proteomes. Using human mitochondria as a model system, CLASP enables the high-resolution mapping of mitochondrial sub-compartments and provides insights into membrane protein topologies. The method was validated using biochemical and imaging-based approaches, confirming its ability to identify mitochondria-associated proteins and revise previous localization data. CLASP was also applied to synaptic vesicles, demonstrating its applicability to various sub-cellular compartments. CLASP leverages cross-linking mass spectrometry (XL-MS) to capture protein-protein interactions and spatial relationships. By using a defined cross-linker (DSSO), CLASP achieves a labeling radius of approximately 4 nm, allowing for precise spatial resolution. The method was tested on intact mitochondria and synaptic vesicles, revealing detailed sub-compartmental localization data. For example, CLASP identified 244 reliable localization markers (LMs) in mitochondria, enabling the mapping of 97 previously unannotated proteins and revising the topology of 24 membrane proteins. Additionally, CLASP discovered three new mitochondria-associated proteins. CLASP was further validated using a different cross-linker (DSBSO), which allowed for the enrichment of cross-linked proteins and improved spatial resolution. The DSBSO-based CLASP confirmed 431 out of 542 spatially annotated proteins from the original DSSO dataset, demonstrating the method's robustness and reproducibility. The study also showed that CLASP can correct existing topology annotations, such as for TMEM126A, which was previously misclassified as an IMM protein but was found to localize to the IMS. A Python tool was developed to automate CLASP predictions, using XL-MS data and protein localization annotations from Swiss-Prot. This tool enables the selection of LMs and spatial predictions, improving the accuracy of CLASP results. The study highlights the potential of CLASP as a powerful tool for spatial proteomics, offering high-resolution insights into protein localization and membrane topology. CLASP's ability to provide detailed sub-compartmental information makes it a valuable method for understanding cellular organization and function.