This Knowledge Base serves as a technical resource specifically to answer common questions and assist with troubleshooting; NanoU is the primary source for manuals, guides, and other documentation for CellScape PSP systems and products.
For additional assistance, email support.spatial@bruker.com.
The CellScape PSP platform generates quantitative spatial proteomics data with best-in-class resolution from tissue sections using cyclic multiplexed immunofluorescence imaging. Each experiment produces multichannel whole-slide images capturing the spatial distribution and expression levels of protein biomarkers at single-cell and subcellular resolution. This includes fluorescence intensity data per marker, cell segmentation maps, and positional information for each cell across the tissue architecture.
Data outputs are saved in OME-TIFF format, compatible with a wide range of third-party image analysis tools. When using the EpicIF™ workflow, the system also collects repeated imaging cycles from the same tissue section, enabling deep phenotyping through high-plex protein detection. The result is a rich dataset that supports detailed analyses of cell types, states, interactions, and spatial organization within the tissue microenvironment.
The CellScape PSP platform outputs image data in the OME-TIFF format, enabling compatibility with a variety of third-party image analysis and quantification tools. Image acquisition, assay design, and instrument automation are facilitated via the CellScape Navigator software, while downstream image analysis can be performed using a variety of open-source and commercial platforms.
Common image analysis tools for tasks such as image visualization and quantification include QuPath, HALO by Indica Labs, and Visiopharm. For advanced cell segmentation, deep learning-based pipelines such as StarDist, DeepCell, and Cellpose are often used, and these tools can identify cell boundaries, classify cell types, and quantify protein expression per cell across spatial coordinates.
Users can also analyze CellScape data using custom image analysis pipelines and open-access libraries available in R, MATLAB, and Python (e.g., Scanpy, Squidpy, Seurat) to conduct multidimensional data exploration, supervised and unsupervised clustering, neighborhood analysis, and other spatial analyses. The open data format and high-resolution output from the CellScape platform make it easy to tailor analysis workflows to specific research needs.
Yes, data from the CellScape PSP platform can be integrated with transcriptomic or genomic data to support multiomic analyses. The platform generates spatially resolved, high-plex protein expression data at single-cell resolution, which can complement transcriptomic datasets—such as bulk RNA-seq, single-cell RNA-seq, or spatial transcriptomics—by providing protein-level validation and insight into post-transcriptional regulation and cell signaling.
Because the CellScape platform outputs data in standard formats like OME-TIFF and provides quantitative fluorescence intensity measurements, these datasets can be aligned with other omics layers using computational tools and shared spatial coordinates. This allows researchers to map transcriptomic signatures to spatially defined cell phenotypes, investigate correlations between RNA and protein expression, or explore how genomic alterations influence the proteomic landscape within the tissue microenvironment.
Integration typically involves bioinformatics platforms which support multiomic data harmonization and spatial analysis. This cross-modal capability makes CellScape PSP a powerful tool for building comprehensive, spatially contextualized biological models.
Data produced from the CellScape PSP platform enables researchers to answer a wide range of spatial biology questions. With best-in-class resolution, users can identify and map diverse cell types and states based on protein expression and co-expression of various biomarkers, revealing how these populations are distinct and distributed across tissue compartments. The platform allows detailed analysis of spatial relationships, such as where specific cells reside relative to anatomical landmarks or how immune cells are positioned around tumor cells as well as their relative infiltration state. With its unmatched flexibility, researchers can also explore direct or inferred cell–cell interactions, uncovering potential communication between neighboring cell types. Additionally, CellScape PSP supports profiling of the tissue microenvironment, helping to characterize the cellular composition and heterogeneity of complex structures like tumors or inflamed tissues. The system also enables the detection of spatial gradients and zonation patterns—important for understanding processes like immune infiltration or tissue remodeling—and facilitates comparison of spatial architecture across disease states, treatment conditions, or time points. Altogether, CellScape PSP provides a powerful means to study not just what cells are present, but where and how they function within their native spatial context.
For additional analysis support, please contact your FAS at support.spatial@bruker.com.
We also offer outsourced data analysis as part of our Canopy Multiomics Service offering.