Abstract
Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell–cell and ligand–receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell–cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand–receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell–cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.
Highlights
Added dimensions can come into play when it is necessary to compare cell–cell signaling in tissues between experimental conditions, over time during growth or remodeling, or between disparate tissue systems in which the same cell type annotations are not necessarily present
Connectome has been used to explore native signaling in human lung[9] and to identify aberrant signaling in pulmonary arterial hypertension (PAH)[10], chronic obstructive pulmonary disease (COPD)[11], and COVID-1912
Differential tissue connectomics. (A) Schematic showing comparison of a perturbed multicellular system against a known control or reference set of interactions. (B) Assuming we are only interested in those edges in which either the ligand or the receptor changes due to perturbation, each edge in a differential systems comparison falls into one of four distinct styles: the ligand and receptor are either both up, both down, or some combination. (If edges are to be considered in which only the ligand or receptor change, there are eight distinct categories of edge shift.) Dual ligand/receptor increase or decrease are consistent with edge activation or deactivation, respectively
Summary
Cell-to-cell communication is a major driver of cell differentiation and physiological function governing organ development, homeostasis, and response to injury. The combination of single-cell sequencing data with ligand–receptor mapping is a promising approach to exploring, understanding, and reverse-engineering complex tissue systems-biology for biologic, therapeutic, and regenerative efforts. This manuscript formalizes and disseminates the techniques first published by Raredon et al.[1], presenting open-source software to the wider community facilitating exact recapitulation of this analysis. Mechanisms contribute to the connectome; and weighted—i.e. interaction edges can be assigned quantitative values These properties make data mining and data visualization substantially more complex than in some other genres of network science. Connectome is a multi-purpose tool designed to create ligand–receptor mappings in single-cell data, to identify non-random patterns representing signal, and to provide biologically-informative visualizations of these patterns. Detailed vignettes and instructions for use are published at https://msraredon.github.io/Connectome/
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