Spatial Proteomics for Biomarker Discovery and Validation

Proteins are the functional unit of biology. Nearly 90% of FDA-approved drugs target them¹. Cell identity, immune phenotype, pathway activation, and disease state are defined at the protein level. And yet, for most of the last decade, the dominant spatial biology tools have measured RNA. The rise of spatial proteomics, named Nature Methods 2024 Method of the Year, reflects a growing recognition that RNA alone is not enough². A more complete understanding of tissue biology requires accurate measurement of proteins within their native spatial context. 

• The Rise of Spatial Proteomics
• Why Spatial Proteomics Matters
• Choosing the Right Spatial Proteomics Approach for Your Biological Question
• Spatial Proteomics Reveals a Hidden Driver of Treatment Resistance in Primary CNS Lymphoma
• Spatial Proteomics Enables Alzheimer’s Biomarker Discovery and Subcellular Validation
• Spatial Proteomics Is No Longer Optional
• Frequently Asked Questions for Spatial Proteomics
• References

The Rise of Spatial Proteomics

The Nature Methods recognition reflected a growing consensus that the spatial organization of proteins in tissue is critical to understanding disease biology and therapeutic response. Cellular neighborhoods, tissue architecture, and the proximity of one cell type to another determine whether a tumor evades treatment, whether an immune response resolves or persists, or whether a neuron survives or degenerates. 

Spatial proteomics has advanced rapidly due to improvements in multiplex imaging, spatial multiomics, and downstream computational analysis, transforming it from a specialized research technique into a scalable approach for biomarker discovery and translational research.

Why Spatial Proteomics Matters

Proteins ultimately determine biological function. Their abundance, localization, modification state, and interactions define cellular behavior in ways that gene expression alone cannot fully capture. While RNA provides valuable insight into biological programs, protein-level measurements reveal what is actually present and functioning within tissue. 

This distinction matters more than it might initially appear. A growing body of evidence shows that mRNA and protein measurements do not always align in biologically important tissues and disease contexts^3,4. Median mRNA-to-protein correlations across human tissues sit around 0.46, highlighting that transcript levels often do not accurately predict protein abundance⁵. Direct protein measurement is therefore essential for understanding the biological processes that ultimately drive cell function and disease. 

But measuring proteins alone is not enough. Spatial context is equally important. Dissociation-based methods generate valuable molecular information but remove the tissue architecture that shapes cellular behavior. Spatial proteomics preserves those relationships, enabling researchers to study proteins within their native biological environment. 

Spatial proteomics enables researchers to answer questions that other methods cannot: Which pathways are truly active within tissue, not merely transcriptionally primed? How do immune, stromal, and tumor cell populations interact spatially within the microenvironment? These insights emerge only when proteins are measured within their biological context. 

Answering these questions requires more than simply detecting proteins. Researchers must be able to measure protein abundance accurately, characterize complex cellular phenotypes, develop panels that evolve with new biological insights, and generate data at a scale that reflects the complexity of tissue biology. As a result, selecting the right spatial proteomics approach depends on the specific scientific objectives of the study. 

Technology	Best Used For	Key Question Answered
Traditional IHC/IF	Single-marker tissue validation	Is marker X present in this tissue?
Flow Cytometry	Cell population characterization	Which cell populations are present?
CellScape® XR Platform	Quantitative subcellular spatial proteomics for rigorous, reproducible spatial phenotyping	Where are protein-defined cell phenotypes located, and how do they interact in tissue?
GeoMx® DSP	Mechanistic biomarker discovery	Which pathways drive biology within specific tissue regions?

Choosing the Right Spatial Proteomics Approach for Your Biological Question

Researchers should evaluate technologies based on how well they align with the objectives of the study, whether that objective is quantitative spatial phenotyping, biomarker validation, mechanistic discovery, or multiomic exploration. For researchers focused on quantitative spatial proteomics, single-cell spatial phenotyping, and biomarker validation, the CellScape^® XR platform serves as the primary validation platform, while GeoMx^® DSP complements the workflow with discovery-scale spatial multiomics and PTM profiling. 

The CellScape XR Platform: Flexible spatial proteomics for rapid and quantitative spatial phenotyping

The CellScape XR platform is a quantitative spatial proteomics platform for single-cell and subcellular protein analysis in intact tissue. It is designed to identify spatial phenotypes, measure protein abundance, and reveal how specific cell populations are organized and interacting across whole tissue sections. While many approaches classify protein expression as simply positive or negative, biology is rarely binary. Protein expression exists on a continuum, and meaningful cellular phenotypes are often defined by subtle differences in low, medium, or high expression levels that can influence cell state, function, and disease biology. By combining directly conjugated primary antibodies, the gold standard for quantitative reproducibility, with best-in-class resolution and high dynamic range imaging, the CellScape XR platform enables researchers to accurately measure these expression differences in their spatial context. This allows for confident identification of not just which proteins are present, but at what level, in which cells, and within which tissue compartments. The ability to quantitatively resolve spatial phenotypes across a range of expression states provides a deeper understanding of tissue biology and is what makes the CellScape XR platform particularly well suited for phenotype discovery, biomarker validation, and the spatially resolved single-cell analyses required in translational and clinical research. 

Equally important, the CellScape XR platform is centered around an open and flexible assay ecosystem designed to support spatial proteomics without constraining biological discovery. Leveraging EpicIF™ signal removal technology and access to more than 6,000 off-the-shelf, directly conjugated fluorescent antibodies, the CellScape XR platform enables researchers to design panels around their biology rather than platform limitations. For researchers who want to move quickly, VistaPlex^® Multiplexing Assay Kits provide extensively validated, ready-to-run panels purpose-built for spatial immune profiling and tissue architecture, offering curated spatial phenotyping markers in a format that accelerates time-to-data without sacrificing rigor. When biology requires targets beyond the kit, researchers can expand existing panels using commercially available IHC-validated antibodies that arrive pre-conjugated and ready to use. Expanding a panel becomes a matter of weeks rather than the months or years of validation that custom conjugation and qualification typically demands on alternative multiplex immunofluorescence platforms. And when a target of interest has no commercially available conjugated antibody, VistaFlex™ Antibody Labeling Kits powered by Proteintech^® Group make it straightforward to label an antibody and incorporate it into an existing panel, ensuring that platform flexibility extends to custom and proprietary targets.  

For researchers evaluating spatial proteomics platforms, the CellScape XR platform provides a quantitative, flexible approach for mapping protein-defined phenotypes and validating spatial biomarker findings in intact tissue.

GeoMx DSP: Unmatched discovery multiomics to profile from cells to tissue structures for spatial breakthroughs at scale

The GeoMx Digital Spatial Profiler (DSP) enables mechanistic biomarker discovery by 

combining whole-transcriptome profiling with more than 1,200 protein targets and 130+ post-translational modifications (PTMs) within the same tissue regions. It is designed for studies where researchers need to understand not only which cell types are present, but which biological pathways and molecular processes are driving the observed biology. By integrating RNA, protein, and PTM measurements within a single workflow, GeoMx DSP moves researchers beyond descriptive molecular profiling to direct measurement of pathway activity. 

GeoMx DSP begins with biology. Researchers select tissue regions most relevant to their biological question ­ — whether tumor versus stroma, immune-enriched versus immune-excluded, or diseased versus adjacent normal — and focus profiling depth on those regions. This biology-driven approach isolates the cell populations and tissue compartments most relevant to disease, revealing which pathways are truly active rather than merely possible. 

Automated workflows, support for multiple tissue samples per slide, and throughput of 20 – 30 slides per week enable GeoMx DSP to scale mechanistic discovery across large cohorts. Researchers can efficiently generate reproducible spatial multiomic data across diverse tissue specimens, providing a confident path from biomarker discovery to downstream validation.

Studies such as these illustrate the power of discovery-scale spatial multiomics. However, identifying a biomarker or pathway is often only the beginning. Researchers must still determine where those findings occur within tissue, which cell populations are involved, and how they contribute to disease biology. As a result, many researchers need both biomarker discovery and spatial validation to fully understand disease biology. 

From Discovery to Validation: A Unified GeoMx DSP to CellScape XR Spatial Proteomics Workflow

A major challenge in biomarker development is moving from high-plex protein discovery to scalable single-cell spatial validation. GeoMx DSP and the CellScape XR platform address this challenge through a complementary workflow that leverages the strengths of each technology. GeoMx DSP enables unbiased, high-plex protein and PTM profiling across large patient cohorts, helping researchers identify biologically relevant targets that would be difficult to include in predefined panels. This discovery power comes with tradeoffs in spatial resolution and throughput. Once key targets are identified, the CellScape XR platform enables researchers to translate those findings into high-resolution, single-cell spatial analysis at scale. Using the same validated antibody clones and tissue samples, targets discovered on GeoMx DSP can move directly into the CellScape XR platform without reformulation or revalidation. The CellScape XR platform’s flexible assay design also allows researchers to rapidly incorporate newly discovered or rare protein targets, preserving biological continuity while accelerating the path from discovery to validation.

Want to explore how GeoMx DSP and the CellScape XR platform fit your research? Download the GeoMx DSP brochure or the CellScape XR platform brochure to learn more.

Spatial Proteomics Reveals a Hidden Driver of Treatment Resistance in Primary CNS Lymphoma

In a recent study from the Cancer Science Institute of Singapore at the National University of Singapore, the combination of discovery on GeoMx DSP with validation on the CellScape XR platform produced a clinically actionable insight into treatment resistance in primary central nervous system lymphoma (PCNSL), demonstrating the value of combining mechanistic discovery with spatial validation within a unified workflow.  

PCNSL is an aggressive brain lymphoma with no approved therapies for refractory or recurrent disease⁶. Using the GeoMx Whole Transcriptome Atlas, the team profiled CD68+ macrophage-enriched regions across 65 DLBCL and 17 PCNSL samples. Pathway enrichment analysis revealed glycolysis, cholesterol homeostasis, and lipid metabolism as PCNSL-specific programs, and computational deconvolution mapped the macrophage signature onto TREM2+ lipid-laden macrophage clusters; a population that had not been evaluated in PCNSL despite its abundance⁷. 

To confirm that signature at the protein level and determine whether it was spatially driving clinical outcomes, the team leveraged the CellScape XR platform. Quantitative single-cell imaging across 35+ protein markers on archival FFPE tissue sections, combining VistaPlex Spatial Immune Profiling Multiplexing Assay Kits with custom lipid-laden macrophage targets including GPNMB and CD36, confirmed lipid-laden macrophages were significantly more abundant in PCNSL than control tissue (p = 0.0016). The spatial precision of the CellScape XR platform was what made the clinical finding possible: single-cell interaction analysis revealed that regulatory T cells showed the strongest spatial attraction to lipid-laden macrophages, and patients in whom these cell types had closer physical contact were significantly less likely to achieve complete remission. The finding was invisible at the RNA level and undetectable by any non-spatial approach. 

Key Finding

GeoMx DSP identified a previously unrecognized lipid-laden macrophage population in PCNSL. The CellScape XR platform revealed that spatial interactions between these macrophages and regulatory T cells were associated with treatment outcome. Together, the platforms transformed a molecular observation into a clinically relevant finding.

Spatial Proteomics Enables Alzheimer’s Biomarker Discovery and Subcellular Validation

A collaboration with Dr. Miranda Orr at Washington University in St. Louis demonstrates both the speed and the spatial resolution that the GeoMx DSP to CellScape XR platform workflow delivers in practice. Using GeoMx DSP, the team screened more than 640 protein targets across AT8+ neurons with neurofibrillary tangles and AT8− normal neurons in Alzheimer’s disease hippocampal tissue, identifying significant differences in tau phosphorylation at multiple residues along with additional neurodegeneration-linked biomarkers. GeoMx DSP produced a ranked, spatially contextualized list of candidates. The next step was determining how those biomarkers were distributed within tissue at a higher resolution and which specific neuronal populations were driving the observed biology.  

The CellScape XR platform provided that level of spatial detail. A custom 30-plex panel built from the same antibody clones identified in the GeoMx DSP screen was deployed on the same hippocampal tissue sections. The CellScape XR platform’s best-in-class resolution, and high dynamic range imaging enabled quantitative visualization of subcellular pTau localization within individual neurons, revealing distinct phosphorylation patterns at the soma and neurite level. All antibodies identified through GeoMx DSP discovery were successfully incorporated into validated CellScape XR panels, enabling completion of the entire workflow — from panel design to subcellular spatial results — in under four weeks.  

Key Finding

GeoMx DSP identified phosphoprotein biomarkers associated with Alzheimer’s disease. The CellScape XR platform localized those biomarkers at subcellular resolution, revealing distinct phosphorylation patterns within individual neurons and providing a more refined view of disease-associated protein biology. 

Spatial Proteomics Is No Longer Optional

The questions that define modern biology — why one patient responds to treatment and another does not, what makes a cell immunosuppressive in one location and not another, which pathway is driving disease versus which is merely expressed — are questions that require proteins to be measured in the spatial context where they function. This growing recognition was reflected when Nature Methods named spatial proteomics its 2024 Method of the Year. The tools to act on it are here now. 

Whether the starting point is mechanistic biomarker discovery with GeoMx DSP, quantitative spatial proteomics and single-cell spatial phenotyping with the CellScape XR platform, or both running as a unified workflow, spatial proteomics is no longer a specialized technique for a handful of labs. It is becoming the standard for researchers seeking to connect molecular discovery, cellular phenotype, and clinical outcome within the same tissue sample. 

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For research use only. Not for use in diagnostic procedures.

Frequently Asked Questions for Spatial Proteomics

References

1. Santos R, et al. A comprehensive map of molecular drug targets. Nature Reviews Drug Discovery. 2017;16(1):19–34. doi:10.1038/nrd.2016.230
2. Method of the Year 2024: Spatial Proteomics. Nature Methods. 2024;21:2195–2196. doi: 10.1038/s41592-024-02565-3
3. Harnik Y, Buchauer L, et al. Spatial discordances between mRNAs and proteins in the intestinal epithelium. Nature Metabolism. 2021;3:1680–1693. doi: 10.1038/s42255-021-00504-6
4. Ye Z, et al. Single-cell proteomic landscape of the developing human brain. Nature Biotechnology. 2026;44:1391–1404. doi:10.1038/s41587-025-02980-7
5. Jiang L, Wang M, Lin S, et al. (GTEx Consortium). A quantitative proteome map of the human body. Cell. 2020;183(1):101–117. doi: 10.1016/j.cell.2020.08.036 
6. Ambady, P, et al. Relapsed and refractory primary CNS lymphoma: treatment approaches in routine practice. Annals of Lymphoma. 2021;5:18. doi:10.21037/aol-21-20
7. Liang Hong, ML, et al. Spatial multi-omics identify an immunosuppressive lipid-laden macrophage niche in primary CNS lymphoma. bioRxiv. Posted February 20, 2026. doi: 10.64898/2026.02.19.705289