Spatial Discovery
Applications
We’ve rebranded some of our products, learn more ›
CODEX® is now PhenoCycler, Phenoptics™ is now Phenolmager.
Scale Up Your Spatial Discoveries Via Spatial Phenotyping
Achieve novel breakthroughs in spatial biology by the virtue of speed. More speed translates to more phenotypes, more biomarkers, more samples and ultimately more significance. The power of spatial phenotyping allows you to visualize and quantify dozens of biomarkers in a single tissue sample while maintaining cellular and sub-cellular detail. Shifting between different marker combinations on the same tissue section reveals unexpected patterns and associations that are not evident with other methodologies. It also allows you to see how the same cell types will behave differently based on their cellular microenvironments and identify distinct cellular clustering patterns which may be predictive of clinical outcomes. These “contextual phenotypes” based on adjacencies between cell types are providing researchers with new insights into inflammatory processes, tumor progression, and revealing novel targets for therapy.
Every Cell Matters. Defy the Odds and Find a 1 in a Million Cell
Detection of rare cells with PhenoCycler. Left: T-SNE population map of 63’056 cells clustered from image shown above. A single cell population of 44 cells (0.07% of total) is indicated in cyan. Right: anatomical data from the same experiment confirming the Ker14/Ker8 phenotype of rare cell population.
Using unbiased cell phenotyping with PhenoCycler® (formerly CODEX®) to study breast cancer in human FFPE tissue, Akoya’s applications team was able to independently recapitulate the discovery of a rare epithelial cell type, demonstrating the ability of the PhenoCycler technology to resolve rare cell populations via unrestricted imaging of large tissue samples.
Learn how you can discover rare cell types with whole-tissue, single-cell imaging
Context Matters: Discover Cellular Neighborhoods
Representative Voronoi diagrams of cellular neighborhoods (CNs) selected to show the nine different CNs (left) and corresponding seven-color image (right).
Dr. Garry Nolan and his team at Stanford University collaborated with the University of Bern to conduct deep single-cell phenotyping and spatial analysis on a cohort of colorectal cancer FFPE samples using PhenoCycler. As a result, the team discovered nine distinct cellular neighborhoods, each uniquely composed of certain immune and cancer cell types. These cellular neighborhoods were found to interact with one another in a manner that correlated with disease progression and prognosis.
Case Studies
How Spatial Phenotyping Can Uncover Novel Insights in Tissue Biology
What role do cellular interactions play in promoting or suppressing disease? In areas such as oncology, immunology, and neurology, spatial phenotyping can provide new insights.
The Tumor Neighborhood Watch: How Spatial Architecture and Cellular Neighborhoods Correlate to Colorectal Cancer Outcomes
Most recent single-cell and spatial biology studies have focused on the network of interactions between different cell types and their spatial context. However, studying tumor biology at two different levels — the interacting cell types as well as the tissue regions within which they are organized — can give further insight into tumor progression and immunotherapy response.
Spatial Multiomics Webinar Series
In this multi-part webinar series, our expert speakers review analytical frameworks and algorithms to integrate imaging-based single-cell spatial phenotyping data with complementary transcriptomic and genomic datasets.
