A high-throughput, automated, and reproducible mIF workflow for spatial biology enabled by ZEISS slide stream and Vizgen's InSituPlex® assays

Multiplex immunofluorescence (mIF) has become a cornerstone technique in spatial biology for investigating the tumor microenvironment. Despite its widespread use, existing mIF workflows are often hindered by variability in image acquisition and dependence on labor-intensive manual steps. To overcome these limitations, we developed a fully integrated, high-throughput spatial biology workflow combining the highly standardized and automated ZEISS spatial biology platform with Vizgen's pre-optimized InSituPlex assay. Together, these deliver high-throughput, fully automated, and reproducible biomarker detection across a broad dynamic range, enhancing both efficiency and scalability. To validate these claims, we conducted a multisite verification study. A 4-plex OmniVUE panel including CD3, Ki67, Granzyme-B and CK/Sox10 was utilized to stain multiple tissue indications on formalin-fixed paraffin-embedded (FFPE) sections. On all sites, the slides underwent the same predefined and automated routine for staining using Leica Bond RX instruments and imaging using ZEISS Axioscan 7 spatial biology slide scanners. Acquired images were seamlessly uploaded to Mindpeak and analyzed with integrated STARVUE algorithms optimized for Vizgen InSituPlex assays imaged on Axioscan scanners, providing quantitative analysis for densities and fluorescence intensities of positive cells. Analysis of the results demonstrated excellent concordance between sites, confirming the precision and accuracy of the combined workflow across multiple operators and instruments. Qualitatively, staining was highly comparable across all sites, and the limited variations in fluorescence signal and background did not affect the accuracy of the STARVUE™ AI algorithms. Quantitatively, the presented workflow exhibits high reproducibility and repeatability of results across locations, providing a reliable solution for translational research with amplified speed and quality of data generation. As a result, it offers significant potential to advance spatial biology from translational research to clinical applications