Spacemarkers novel AI method identifies locations, interactions among genes in and around tumors
SpaceMarkers, a new machine learning software developed by researchers at the Johns Hopkins Convergence Institute and the Johns Hopkins Kimmel Cancer Center, can identify molecular interactions among distinct types of cells in and around a tumor.
SpaceMarkers harnesses the information available through spatial transcriptomics — a technology advancing the ability to measure gene expression in tissue samples based on their location in cells. Understanding the molecular profile of individual cells and the impact of intercellular interactions in the tumor microenvironment (cells in and around tumors) is crucial to distinguish the determinants of tumor progression.
The study introducing SpaceMarkers and its applications across diverse types of cancer was published as the cover article in the April 2023 issue of the journal Cell Systems.
SpaceMarkers works by identifying regions of high activity from individual cell types seen in spatial transcriptomic data, explains lead study author Atul Deshpande, Ph.D., M.S., a postdoctoral researcher in the Fertig Lab at The Johns Hopkins University. Regions with high activity from two cell types are identified as the sites of cell-to-cell interaction. Then, the algorithm identifies molecular changes from the interaction of the two cell types.
The software has two modes of operation, Deshpande says. One is a simple, differential expression mode that identifies genes with significantly higher expression in the sites of cell-to-cell interaction, suggesting that this interaction causes genes to express at higher rates. However, the software does not take into consideration the spatial variations in the cell populations. The second (the residual mode) identifies genes with significantly higher expression after accounting for all cell populations identified in the spatial transcriptomic data.
Investigators tested SpaceMarkers using spatial transcriptomics data (measurements of gene expression in tissue samples based on their location in cells) from several clinical samples of pancreatic, breast and liver cancers. The software was validated by identifying genes known to affect interactions in tumor and immune cells.
