Inferring a spatial code of cell-cell interactions across a whole animal body

Abstract

Cell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans’ cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.Graphical abstract

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

“…Ranks per Communicating Cell Types *2. Physical distance, measured by Euclidean distance between the closest cell types, was already reported to be an appropriate proxy of cell pair communication activity 65 . Other measures can be the neighbourhood enrichment or spatial co-occurrence of cells 24,66 .…”

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

“…As we demonstrated here, OmniPath is able to deliver the input knowledge for different data analysis tools, such as CellPhoneDB (Efremova et al, 2020), NicheNet (Browaeys et al, 2019), CellChat (Jin et al, 2021), ICELLNET (Noël et al, 2021), NATMI (Hou et al, 2020), cell2cell (preprint: Armingol et al, 2020a), and CARNIVAL (Liu et al, 2019) to infer communication between (Armingol et al, 2020b) and within cell types. For some of the analysis tools, we provide dedicated software integration and workflows (https://workflows.omnipathdb.org/).…”

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

“…For instance, our strategy can be readily applied to time series data by considering time points as the contexts, and to spatial transcriptomic datasets, by previously defining cellular niches or neighborhoods as the contexts. Moreover, we have also included Tensor-cell2cell as a part of our previously developed tool cell2cell 60 , enabling previous functionalities such as employing any list of LR pairs (even including protein complexes), multiple visualization options, and personalizing the communication scores to account for other signaling effects such as the (in)activation of downstream genes in a signaling pathway 23,61 , which could lead to different biological interpretations 57 . Lastly, we demonstrated that Tensor-cell2cell stands as a fast, low-memory and accurate method (Figure 3), which can be substantially accelerated when a GPU is available (Figure 3a).…”

“…Tensor-cell2cell is implemented in our cell2cell suite 65 , which is available in a GitHub repository (https://github.com/earmingol/cell2cell). All the code and input data used for the analyses are available online in a Code Ocean capsule for reproducible runs (https://doi.org/10.24433/CO.0051950.v2).…”