Teamwork makes the dream work and for any team to work, communication is key. The same goes for the trillions of cells inside our body.
Cells are not lonely, isolated building blocks, but are working as a team, where they constantly crosstalk and behave in sync through the binding of thousands of ligands and corresponding receptors, triggering a cascade of gene responses. All to serve the most important team mission - keeping our whole body healthy.
Why add cell-cell communication inference to our multi-omics platform, CDIAM?
The study of cell-cell communication has primed new understandings about many aspects of biology. In the maternal-fetal interface research, decidualization was found to enhance the ability to communicate with the fetus as most of the receptors and ligands up-regulated during decidualization have their counterpart expressed in trophoblast cells . Meanwhile, macrophages were found to frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, suggesting its important role in immune defense and immune pathogenesis .
Now we are lucky to have high-throughput single-cell sequencing technologies coming along. The massive new inflow of single-cell data has sparked an increased interest in inferring cell-cell communication. Many computational tools have been developed for this purpose, such as CellChat, CellPhoneDB, Connectome, Crosstalk Scores, Single-cellSignalR#,… .
With that being said, what is the gap? Even when we have abundant tools, most infer cell-cell communication from the single-cell RNA-seq data. Apparently, for every insight, we need a validation. We currently lack a complete workspace to validate the signals across multiple omics types, say validating the crosstalk of macrophages-CoV targets in the spatial, proteomics, and metabolomics data.
It’s good to do all those things in a single platform. That is why we integrated Intercellular Signaling Prediction or Cell-cell Communication Inference in the CDIAM Multi-Omics Studio (http://c-diam.com ), our multi-omics analysis software-as-a-service, a.k.a. web application.
This time, we started with CellPhoneDB following so many requests from our users. The integration supports any biologists and bioinformaticians to interactively submit their single-cell data for intercellular signaling prediction with CellPhoneDB as the underlying method, then visualize, compare, and validate results (such as up/down-regulated ligand-receptor pairs and pairs of cell types that crosstalk) across multi-omics data.
What is CellPhoneDB - skip this if you are already a pro
CellPhoneDB is a curated database for receptors, ligands, and their interactions. Databases from which CellPhoneDB obtains the information are: UniProt, Ensembl, PDB, the IMEx consortium, IUPHAR. ,
Together with a database, CellPhoneDB is also a computational tool. It statistically quantifies the expression of ligands and receptors in each cell population in single-cell RNA-seq data and selects the interactions that are unique between them - letting us know about the unique function and regulation of such cell populations or cell types. ,
CellPhoneDB scoring system:
(1) Truncated Mean—average expression of transmitter and receivers, the minimum expression (by default) of heteromeric complex of subunits
(2) P-values—significance identified via permutation of cell cluster labels to determine a null distribution of means for each receiver-transmitter interaction ,
Besides its robustness, another great characteristic of CellPhoneDB is that “it considers the heteromeric composition of the ligands and receptors”. This is important because the function of a receptor is subject to the expression and assembly of multiple subunits. In fact, a lot of receptors with antagonist function shares subunits, e.g., interleukin family.,
What is supported for Intercellular Signaling Prediction in CDIAM?
CellPhoneDB is the first algorithm in the Intercellular Signaling Prediction function. Keep in mind that this is just the start. We are constantly adding more methods to CDIAM. The platform can be easily integrated with new methods thanks to its microservice-based architecture. Ping us to request a method in your wish list!
Interactive data input with a UI
CDIAM supports a point-and-click interface to import your single-cell data and run CellPhoneDB. Here’s the required format: an H5AD object with cell type annotations. It will be even greater if you include sample information in your submitted data.
Interaction plots, DE plots, and tables
You can interactively display the CellPhoneDB results by tables and a range of plot types, by which you can get a quick answer to the following questions:
· What cell types interact with each other? What are the quantified levels of interaction? What ligand-receptor pairs account for the interaction between two cell types?
· What interactions are common among multiple samples?
· Comparing two groups (say Disease and Control), are there any up/down-regulated ligand-receptor pairs? Is there any difference in the levels of interaction among cell types?
Summarizing common interactions across datasets
You can submit multiple single-cell datasets from different studies, keep them in a Project, run multiple CellPhoneDB analyses, and summarize results. From that, you can identify the most active ligand-receptor pairs and pairs of cell types with most interactions across different experiments/ studies.
Validating cell-cell communication with spatial colocalization
Spatial omics, with its capabilities to study colocalization, have created avenues for dissecting cell–cell communication. As a multi-omics analysis platform, CDIAM also incorporates an interactively accessible spatial database (covering Visium, MERFISH, GeoMx, CosMx and more) for users to validate insights from other omics, which include your CellPhoneDB/cell-cell communication results.
You can query ligands, receptors, or any genes/proteins' expression across this database and quantify colocalization scores.
Get access to CDIAM
For a closer look at CDIAM and the cell-cell communication function, please refer to this Application Report, where we performed meta-analysis on available Parkinson’s Disease omics datasets. Our CellPhoneDB analysis found that communications between immune cells with other cells were significantly upregulated in PD patients. The report also identified Parkinson’s Disease targets and biomarkers for prioritization through our Pathway2Targets and Biomarker2Validate pipelines, so check it out!