Establishing a Single-cell Spatial Multiomic Profiling Workflow using the Akoya PhenoCycler-Fusion

Understanding how different cell types function and communicate with each other in disease states provides the opportunity to identify new treatment options for a range of different disorders. Our knowledge of how cells change in diseases has been transformed in recent years by the ability to study and profile individual single cells at previously unparalleled depth. However, these methods usually rely on isolating cells by breaking up or dissociating the tissue in question, meaning that the spatial location of the individual cells within the tissue is lost. Hence, the precise position of cells and the composition of their local neighbourhood within the tissue often remains unknown. Furthermore, profiling single cells often requires fresh tissue samples, limiting the ability to study biopsy samples from several diseases where fresh tissue is not readily available.

To address these issues, the new field of spatial biology has rapidly developed over the past few years. The Akoya PhenoCycler-Fusion instrument and Visiopharm analysis software are at the forefront of the technological and analytical developments in spatial biology. The PhenoCycler-Fusion uses advanced microscope technology to allow researchers to study hundreds of proteins and genes expressed by millions of single cells in previously acquired and stored tissue sections without dissociating the tissue, meaning the spatial location and neighbourhood of each cell can be precisely mapped. Visiopharm software allows scientists to analyse the complex information generated by the PhenoCycler-Fusion in a user-friendly and reproducible manner, generating new insights into how the spatial location and functions of specific cell populations and their neighbours might change between healthy and diseased tissue.

This funding would enable us to install the Akoya PhenoCycler-Fusion instrument and Visiopharm analysis software at the University of Edinburgh for the first time. We will spend time setting up a new spatial biology workflow and training researchers how to best use this powerful technology. Once established, a number of renowned experts at the University will use this workflow to study a range of diseases including liver, brain, kidney, skin, infections and cancer. We anticipate this will provide novel insights into potential new treatments for these diseases. We also expect that establishing this spatial biology approach at the University will provide exciting new opportunities for research into other diseases in the future.

Single cell approaches to deeply phenotype pathogenic cell types at previously unparalleled resolution are transforming our understanding of human diseases across the entire MRC remit. However, these approaches rely on the dissociation of fresh tissue, meaning that spatial information is lost and that archival tissue with linked clinical metadata can often not be used for such studies. Hence, using larger cohorts of archival tissue to map pathogenic cells to their tissue environment and understand how they interact with neighbouring cells promises to herald the next wave of biological insights.

The Akoya PhenoCycler-Fusion ultra high-plex spatial biology system enables simultaneous mapping of over 100 RNA and protein molecules at single cell resolution in archival tissue sections, providing new insights into cellular organisation and cell-to-cell communication in tissue homeostasis and disease. This will be coupled with Visiopharm analysis software, enabling streamlined user-friendly analysis of the high dimensionality multiomic imaging data generated from the PhenoCycler-Fusion to spatially map millions of single cells, identify rare cell subpopulations and study the neighbourhoods and intercellular communication networks of different cell types.

We will establish this system and analytical workflows at the University of Edinburgh for the first time and utilise this to study tissue from diverse diseases including liver, brain, kidney, skin, infection and cancer. This work will identify new therapeutic targets for these diseases as well as informing novel biomarker discovery and precision medicine efforts. Furthermore, once established, the cutting-edge spatial biology workflow will be applicable and available to a wider range of research studies in Edinburgh and beyond.