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Background Multiplex immunofluorescence (mIF) has become a leading application in spatial biology for the investigation of the tumor microenvironment (TME). While mIF is widely used, current workflows remain limited by variability in image acquisition and labor-intensive manual processes. To address these challenges, we developed an end-to-end, high-throughput spatial biology workflow by integrating the ZEISS SlideStream automated imaging software and Vizgen’s pre-optimized InSituPlex assay. These integrated solutions enable high throughput, fully automated, and reproducible detection of biomarkers across a broad dynamic range, improving efficiency and scalability. To ensure reliability and consistency, we conducted a multisite verification study demonstrating high concordance and reproducibility across sites. This approach provides researchers with a streamlined, automated approach for spatially resolved immune profiling, advancing translational research in immuno-oncology.Methods A 4-plex OmniVUE panel including CD3, Ki67, Granzyme-B and CK/Sox10 was utilized to stain formalin-fixed paraffin-embedded (FFPE) TMA sections including tonsil, lymph node, colon and melanoma tissue cores at multiple locations. Serial slides were stained on different Leica Bond RX instruments and imaged on the ZEISS Axioscan 7 featuring the SlideStream software in an automated fashion, using a pre-established scan profile and automatic tissue detection. Acquired images were qualitatively assessed and quantitatively analyzed using the STARVUE Image Data Science Platform by measuring positive cell density as well as fluorescence intensity in positive cells.Results The integration of ZEISS SlideStream automated imaging software with Vizgen’s highly reproducible InSituPlex mIF assays resulted in a robust and consistent workflow across multiple study sites. Measured cell densities demonstrated exceptionally high concordance between sites, confirming the precision of automated acquisition, segmentation, and analysis across different locations and operators. Staining quality was both qualitatively and quantitatively comparable across all locations. Variation in fluorescence signal and background intensity levels did not affect the cell density quantifications, highlighting the robustness of the STARVUE™ AI driven image analysis pipeline.Conclusions This end-to-end Spatial Biology workflow demonstrates highly reproducible results across locations, establishing a robust and reliable process for translational research. The reproducibility and reliability of the integrated imaging and assay platforms enables the generation of comprehensive biomarker data with minimal variability across samples. By streamlining image acquisition and analysis, this workflow significantly enhances both the speed and quality of spatial data generation. As a result, it holds strong potential to accelerate spatial biology research and enable more precise profiling of the tumor microenvironment.