Spatial Phenotyping Techniques and Applications

Protein biomarkers are critical indicators of disease and cellular activity. They help distinguish immune and tumor cell populations, reveal how these cells are arranged, and define neighborhoods that shape disease progression and treatment response. Advances in multiplex imaging now allow researchers to detect and analyze these markers directly within the spatial context of intact tissues.

The importance of spatial phenotyping

Popular techniques like single-cell sequencing, flow cytometry, and immunohistochemistry provide valuable insights into cell types, states, and abundance. But these methods involve tissue dissociation or are limited in the number of biomarkers they can detect, which constrains their ability to capture spatial organization or complex cell states. Spatial phenotyping overcomes these limitations by using multiplex imaging that combines high-plex biomarker detection with single-cell resolution microscopy. This approach preserves the architecture of intact tissues while providing detailed information on biomarker expression, spatial organization, cell classes, and interactions. With these insights, researchers can uncover contextual phenotypes, clustering patterns, microenvironmental influences, and additional features that define the cells of interest.

Spatial phenotyping tools

Spatial phenotyping is supported by an expanding suite of techniques and technologies. Among them, antibody-based platforms such as multiplex immunofluorescence, cyclic immunofluorescence, co-detection by indexing (CODEX), and iterative bleaching extends multiplexity (IBEX) enable simultaneous detection of dozens of proteins with high-resolution single-cell or subcellular mapping.^1,2 Other methods, such as imaging mass cytometry (IMC) and multiplex ion beam imaging (MIBI), use antibodies tagged with metal isotopes, which are detected by mass spectrometry to avoid spectral overlap and enable highly multiplexed analysis at subcellular resolution.^1,3 DNA-barcoded antibodies, as in spatial CITE-seq, integrate protein and RNA detection on the same tissue.⁴ Nanoscale imaging approaches such as SUM-PAINT extend this toolkit by achieving resolution below 10 nm with virtually unlimited multiplexing.^1,3

Mass spectrometry (MS) imaging provides an antibody-free alternative, capturing proteoforms and post-translational modifications by analyzing either intact proteins directly or digested peptides that reconstruct protein identities.^2,4 Liquid chromatography-mass spectrometry (LC-MS)-based proteomics applies pixel-by-pixel or region-of-interest strategies with laser microdissection to detect thousands of proteins with growing sensitivity.¹ More recently, multiscale approaches such as deep visual proteomics integrate microscopy, artificial intelligence, and MS to link protein localization, abundance, and modifications directly from tissues.^1,3 Each of these complementary methods balances resolution, multiplexing, and throughput to offer researchers powerful ways to study tissue organization, protein networks, and disease.

Biomarkers in the tumor microenvironment

Nowhere is this level of spatial analysis more important than in the tumor microenvironment (TME). This environment contains diverse cell types and states that shift dynamically. Earlier efforts often relied on single biomarkers or small panels to study these heterogeneous diseases, but these approaches failed to capture the adaptive nature of cancer.⁵ The high multiplexing capabilities of spatial phenotyping now make it possible to study this complexity directly. Another essential aspect of the TME is understanding how immune cells interact within tumors. Guidance in the field emphasizes that understanding human immunity requires shifting focus from blood to tissues, the primary sites where immune cells reside and responses occur.⁶ This highlights why mapping cells in their native context is so critical, since the tumor microenvironment is shaped by immune infiltration, stromal remodeling, and malignant cell behavior within a shared tissue space.³

Because the TME is so dynamic, identifying high-quality protein biomarkers in situ is central to the value of these approaches.⁷ Comparisons of normal and diseased tissues reveal shifts in protein abundance, changes in cell subtypes, and the emergence of new cellular neighborhoods that serve as biomarkers of disease. These spatially defined biomarkers improve diagnosis and prognosis, support patient stratification, and help match patients with the most effective therapies. They also advance drug discovery through the identification of novel targets and allow investigators to monitor treatment effects and immune responses directly within tissues. At the single-cell level, changes in protein abundance and localization generate biomarker-based guidance that brings precision medicine closer to routine practice.

Applications across different cancers demonstrate how the TME shapes therapy response. In head and neck squamous cell carcinoma, researchers mapped immune, metabolic, and stress markers, uncovering heterogeneous neighborhoods with competing niches of activation and progression.⁸ In triple-negative breast cancer, spatial profiling defined inflamed, excluded, and ignored phenotypes that predicted prognosis and response to anti-PD1 therapy, with inflamed tumors responding best.⁹ Broader studies across different types of breast cancer have shown how spatially organized immune niches and suppressive patterns influence outcomes, demonstrating the importance of spatial phenotyping to identify features that drive disease and guide immunotherapy strategies.¹⁰

Challenges and clinical potential

Despite continual growth, spatial phenotyping still faces obstacles to realizing its full potential and achieving broader clinical adoption. Current in situ proteomic platforms are unable to profile the full proteome.⁷ This limitation means that important low-abundance proteins, signaling pathways, or post-translational modifications may go undetected, leaving researchers with only a partial view of cellular states and processes. Other key challenges include accurate cell segmentation, reliance on protein abundance alone, and the lack of fast, reliable biomarker validation pipelines.^7,11 These issues can slow the transition of spatial phenotyping into clinical practice. Additionally, the high costs of instrumentation, specialized reagents, and data analysis can further hinder adoption.¹¹ Progress in the field will depend on overcoming these barriers, and on advances that combine abundance, localization, and colocation data in ways that can move spatial profiling toward routine use in precision medicine.

Emerging applications and future directions

Spatial phenotyping also has an important role in many applications outside of cancer. In infectious disease research, spatial phenotyping has enabled the identification of cells infected with the Epstein-Barr virus alongside immune lineages and activation states.¹² In neuroscience, spatial analyses showed how amyloid-β oligomers and plaques differentially associate with astrocyte and microglial activation, as well as neuronal pathology, in Alzheimer’s disease models.¹³ Studies on asthma and chronic obstructive pulmonary disease (COPD) have revealed distinct spatial organizations of immune, epithelial, and matrix cells, indicating inflammatory remodeling in asthma, fibrotic remodeling in COPD, and vascular contributions in both conditions.¹⁴

These diverse applications highlight the need for approaches that extend past single-modality analysis. Multiomic strategies link spatial proteomics with other layers such as genomics, transcriptomics, epigenomics, and metabolomics to provide a multidimensional view of cell states and tissue organization. Recent cancer research has shown how combining spatial proteomics with additional spatial omics can uncover cellular ecosystems, molecular transitions, and disease mechanisms that would otherwise be missed.¹ Many commercial systems are now changing into multiomic platforms that integrate proteomics with additional modalities.¹¹ This integrative strategy provides a more comprehensive understanding of complex tissues and supports biological discovery and clinical translation.

Spatial phenotyping is rapidly becoming a core approach for studying human biology and disease. Mapping protein biomarkers directly in intact tissues allows investigators to characterize the complexity of cellular neighborhoods in ways unmatched by other methods. Continued improvements in multiplexing, analysis, and integration with multiomics will be key to moving these insights into the clinic, where spatial phenotyping has the potential to shape precision medicine across cancer, infectious disease, neuroscience, and other disease areas.

References

1. Hu B, Zhu J, Zhao F. The evolving landscape of spatial proteomics technologies in the AI age. Fundam Res. 2024; Published online December 4, 2024. doi: 10.1016/j.fmre.2024.11.023

2. Lee Y, Lee M, Shin Y, Kim K, Kim T. Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications. Int J Mol Sci. 2025;26(9):3949. doi: 10.3390/ijms26093949

3. Bungaro C, Guida M, Apollonio B. Spatial proteomics of the tumor microenvironment in melanoma: current insights and future directions. Front Immunol. 2025;16:1568456. Published 2025 May 15. doi: 10.3389/fimmu.2025.1568456

4. Horvath P, Coscia F. Spatial proteomics in translational and clinical research. Mol Syst Biol. 2025;21(6):526-530. doi: 10.1038/s44320-025-00101-9

5. Barker AD, Alba MM, Mallick P, Agus DB, Lee JSH. An Inflection Point in Cancer Protein Biomarkers: What was and What's Next. Mol Cell Proteomics. 2023;22(7):100569. doi: 10.1016/j.mcpro.2023.100569

6. Farber DL. Tissues, not blood, are where immune cells function. Nature. 2021;593(7859):506-509. doi: 10.1038/d41586-021-01396-y

7. Chen Y, Guo J. Multiplexed Single-Cell in Situ Protein Profiling. ACS Meas Sci Au. 2022;2(4):296-303. doi:10.1021/acsmeasuresciau.2c00011

8. Jhaveri N, Ben Cheikh B, Nikulina N, et al. Mapping the spatial proteome of head and neck tumors: key immune mediators and metabolic determinants in the tumor microenvironment. Gen Biotechnol. 2023;2(5):418-434. doi: 10.1089/genbio.2023.0029

9. Hammerl D, Martens JWM, Timmermans M, et al. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer. Nat Commun. 2021;12(1):5668. doi: 10.1038/s41467-021-25962-0

10. Hasan M, Kim Silva A, Abousamra S, et al. New Spatial Phenotypes from Imaging Uncover Survival Differences for Breast Cancer Patients. ACM BCB. 2024;2024:17. doi: 10.1145/3698587.3701333

11. Nieszporek A, Wierzbicka M, Khan A, et al. Spatial profiling technologies for research and clinical application in head and neck squamous cell cancers. Curr Res Biotechnol. 2025;10:100321. doi: 10.1016/j.crbiot.2025.100321

12. Nikulina N, Ben Cheikh B, Braubach O, et al. Highly multiplexed, single-cell spatial phenotyping of Epstein-Barr virus infected tissues. J Immunol. 2022;208(Suppl 1):126.11. doi: 10.4049/jimmunol.208.Supp.126.11

13. Tang J, Huang H, Muirhead RCJ, et al. Associations of amyloid-β oligomers and plaques with neuropathology in the AppNL-G-F mouse. Brain Commun. 2024;6(4):fcae218. doi: 10.1093/braincomms/fcae218

14. Khalfaoui L, Moore RM, Villasboas JC, et al. Spatial phenotyping of human bronchial airways in obstructive lung disease. Respir Res. 2025;26(1):232. doi: 10.1186/s12931-025-03315-5