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

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

PROVIDER: PRJNA733165 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR14668217.fastq.gz	Fastqsanger.gz
	SRR14668218.fastq.gz	Fastqsanger.gz
	SRR14668219.fastq.gz	Fastqsanger.gz
	SRR14668220.fastq.gz	Fastqsanger.gz

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

Project description: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.

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Unsupervised cell interaction profiling based on multiplet RNA sequencing reveals major architectural differences between small intestinal and colonic epithelium.

Project description:Cellular identity in complex multicellular organisms is strictly maintained over the course of life. This control is achieved in part by the organ structure itself, such that neighboring cells influence each other’s identity. However, large-scale investigation of the cellular interactome has been technically challenging. Here, we develop CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using particle swarm optimization. We use CIM-seq to define the cell interaction landscape of the mouse small intestinal and colonic epithelium uncovering both known and novel interactions. Specifically, we find that the general architecture of the stem cell niche is radically different between the two tissues. In small intestine, the stem-paneth cell interaction forms an exceptionally strong and exclusive niche, in which paneth cells provide Wnt ligands1. In colonic epithelium, no similar compartment exists to support stem cells, and Wnt signaling is provided by a mesenchymal cell layer. However, colonic stem cells are associated with a subset of goblet cells expressing the wound healing marker Plet1, suggesting an additional level of structural control in the colon. These results identify novel cellular interactions specific for the colonic stem cell niche and shed light on a previously unappreciated complexity of the tissue organization and biology of the colon. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

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