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An unsupervised and broadly applicable method for physical cell interaction profiling of complex tissues

ABSTRACT: Cellular identity in complex multicellular organisms is controlled in part by the physical organization of cells. However, large-scale investigation of the cellular interactome has been technically challenging. Here we develop Cell Interaction by Multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell types presented in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types using machine learning. Contrary to previous deconvolution-based methods, CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing the data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium, or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results from different tissue types demonstrate that CIM-seq is broadly applicable to profile cell type interactions in different tissue types using in an unsupervised manner.

ORGANISM(S): Mus musculus

PROVIDER: GSE175664 | GEO | 2021/05/28

REPOSITORIES: GEO

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