Spatial signatures—a new standard for predictive value
Spatial signatures are a defined set of measurable features that capture spatial relationships and protein co-expression within specific cellular subsets and can be evaluated across samples. The Akoya PhenoImager HT 2.0 helps researchers identify and characterize spatial signatures at scale, enabling the development of spatially predictive signatures to inform drug development, patient selection, precision medicine, and biomarker research.
Spatial signatures can be based on the presence or absence of a spatial signature, proximity of different signatures to one another, density of a spatial signature, or the combination of these features.
Visualize
Reveal the spatial organization of cells across entire tissue sections with multispectral imaging. The PhenoImager HT 2.0 makes complex tumor–immune architecture visible, enabling researchers to detect meaningful spatial patterns that form the foundation of predictive biomarker development.
Quantify
Convert spatial complexity into structured, reproducible data. The PhenoImager HT 2.0 enables quantification of cell types, marker expression, and spatial relationships, allowing researchers to measure subtle differences across samples and generate the robust spatial metrics required for predictive modeling.
Predict
Leverage spatial signatures—such as proximity, density, and colocalization—to identify biomarkers linked to treatment response and clinical outcomes. The PhenoImager HT 2.0 provides the spatial data needed to build predictive insights that advance precision healthcare.
Spatial signatures outperform other biomarker modalities in predicting immunotherapy response
A large-scale meta-analysis of data from more than 50 studies, ten types of cancer, and outcome data from more than 8,000 patients published in JAMA Oncology showed that spatial phenotyping measured by mIF more accurately predicts patient response to anti-PD-1/PD-L1 therapy than other biomarker assays, including PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), and gene expression profiling (GEP).
An integrated imaging workflow with data interoperability
To develop spatial signatures at scale, the PhenoImager HT 2.0 brings together onboard spectral unmixing, rapid imaging, and manageable data outputs in a streamlined workflow.
Technology
Accurate mIF analysis is often complicated with issues such as tissue autofluorescence and spectral overlap.
Our patented multispectral imaging MSI technology and spectral unmixing isolates autofluorescence. This increases spatial phenotyping accuracy by up to 50% compared to phenotyping cells without spectral unmixing.
Data analysis
Quanterix’ versatile QPTIFF file format means manageable file sizes. Gigabyte-sized files are portable and preserve high-quality images. Files seamlessly integrate into the PhenoImager HT 2.0 image analysis software suite that includes Phenochart and inForm, most common third party analysis platforms, and open-source solutions such as QuPath.
The first multi-institutional analytical demonstration of a spatial biology workflow
The MITRE Study established the high reproducibility and robustness of the PhenoImager HT 2.0 for spatial phenotyping in clinical and translational research.
Ready-to-use, optimized panels for the PhenoImager HT 2.0
The PhenoCode Signature Panels streamline staining workflows and take a third of the time to develop a predictive assay. One additional marker can be added to a five-plex panel to address a specific research question.
Publications featuring the PhenoImager HT 2.0
Fast, high-throughput multispectral imaging system with onboard spectral unmixing for scalable spatial phenotyping
Tissue microarrays and tissue sections
440–780 nm
Brightfield and fluorescence (multispectral or color)