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

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. Overall design: Deconvolution of transcriptional profiles from cell clusters of 2 or more cells (multiplets) identifying their constituent cell types.