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Analyzing 2.5 Million Cells with PhenoCycler-Fusion: Explore the Dataset

From the early stages of planning to research and development to execution, Akoya is extremely excited to roll out the first data captured by the PhenoCycler-Fusion platform. Since launch, PhenoCycler-Fusion has generated lots of excitement, setting a new standard in spatial biology for high-speed imaging of whole slides at single-cell and subcellular resolution.

The PhenoCycler-Fusion boasts significant capabilities empowering researchers to make spatial insights. The prevailing belief is that spatial biology can be applied to 3 distinct research areas: discovery, translational, and clinical research, each with a highly specific set of requirements.

Discovery research relies upon unbiased imaging of each cell across an entire tissue section. In translational research, the focus is on speed and throughput to create sample sizes large enough to generate statistical confidence in the predictive ability of the biomarkers being developed. And finally in the clinical stages of research, it’s about proven capabilities – consistently reproducible and actionable results derived from the insights of the earlier phases.

PhenoCycler-Fusion has the power to address the needs across the spatial biology continuum and we are excited to put it to the test. Within this data set you will be able to explore different cell types across the tonsil tissue including T cells, B cells, myeloid cells, epithelium and endothelium, and connective tissues. You will also be able to take a tour of the various analyses used to visualize high-dimensional datasets.

Highly multiplexed tissue image of a human tonsil (FFPE) captured on the PhenoCycler-Fusion

Click here to view a fully interactive data set of highly multiplexed human tonsil (FFPE) tissue captured on PhenoCycler-Fusion.

The data shown here were acquired in less than 1 day and consisted of 2,591,479 cells, each of which displayed distinct combinatorial antibody labeling patterns. Analysis of these data begins with accurate segmentation of cell nuclei, followed by unsupervised Leiden clustering and annotation of cell phenotypes.

pcf dataset umap 2

Included are 13 annotated phenotypes that comprised the main cellular constituents of this tonsil tissue. Each phenotype is shown in UMAP plots, where a circle corresponds to a cell and its color represents the cell phenotype. For clarity, the UMAP shows only a fraction of the total cell population. While viewing the data set, click on a circle within the UMAP plot to find the precise location of this cell in the tonsil tissue or toggle data layers to see the distributions of individual cell phenotypes.

There is also an option to view cell phenotypes as a heatmap – a simplified and annotated result where cell phenotypes (columns) are listed against imaged biomarker intensities (rows). Each phenotype displays a unique biomarker labeling pattern.

Heatmap of PhenoCycler-Fusion dataset

Once inside the dataset, feel free to click on a column within the heatmap to visualize the spatial distributions of different cellular phenotypes against the tissue background.

A radial plot has also been generated that summarizes the number and proportion of cell phenotypes across the tonsil tissue.

Lastly, there is an option to explore single-cell spatial connectivity. Single-cell spatial phenotyping affords investigators the ability to characterize and quantify cells within their tissue context. With this approach it is possible to map any type of connectivity between the 2.5 million cells that are present in this tonsil tissue. These types of spatial analyses are currently transforming our understanding of tissue biology by uncovering complex interactions and organizational principles, like cellular neighborhoods.

We have just begun to scratch the surface of what PhenoCycler-Fusion can do for spatial biology, but Akoya’s applications team is eager to push the limits of PhenoCycler-Fusion and continue to demonstrate its flexibility and scalability to meet the needs of today and tomorrow.

You can start exploring the dataset yourself or get in touch with us to learn how you can generate powerful data from your own tissue samples with PhenoCycler-Fusion.

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