Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Abstract

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.

Keywords: intercellular signaling; ligand-receptor interactions; omics integration; pathways; signaling network.

Figure 1. The composition and workflow of OmniPath

1. Database contents with the respective number of resources in parentheses.

2. Workflow and design: OmniPath is based on four major types of resources, and the pypath Python package combines the resources to build five databases. The databases are available by the database builder software pypath, the web resource at https://omnipathdb.org/, the R package OmnipathR, the Python client omnipath, the Cytoscape plug‐in and can be exported to formats such as Biological Expression Language (BEL).

Figure 2. The composition and representation of the intercellular signaling network

We assigned intercellular communication roles to proteins based on evidence from multiple resources. In all panels: —transmitter; —receiver.

1. Schematic illustration of the intercellular communication roles and their possible connections. Cells are physically connected by proteins forming tight junctions (1), gap junctions (2), and other adhesion proteins (3); they release vesicles which can be taken up by other cells (4); some receptors form complexes (5) to detect secreted ligands (6); transporters might also be affected by factors released by other cells (8); enzymes released into the extracellular space act on ligands and the extracellular matrix (7, 9); cells release the components of the extracellular matrix and bind to the matrix by adhesion proteins (10).

2. The main intercellular communication roles (x axis) and the major contributing resources (y axis). Size of the dots represents the number of proteins annotated to have a certain role in a given resource. The darker areas represent the overlaps (proteins annotated in more than one resource for the same role) while the lighter color denotes those unique to that resource.

3. The intercellular communication network. The circle segments represent the eight main intercellular communication roles. The edges are proportional to the number of interactions in the OmniPath PPI network connecting proteins of one role to the other.

4. Number of unique, directed transmitter–receiver (e.g., ligand–receptor) connections by resources. Bars on the right show the coverage of each resource on a textbook dataset of 131 well‐known ligand–receptor interactions.

Figure EV4. Example of the intercell query in the OmniPath web service

Each category has a parent category and a database of origin. The scope of a category is either “generic” (e.g., ligand) or “specific” (e.g., interleukin). The aspect is either “locational” or “functional”. Further attributes show whether the protein is a signal transmitter or a receiver, and whether it is secreted, or a transmembrane or peripheral protein of the plasma membrane.

Figure 3. Quantitative description of the network, complex, and enzyme–PTM databases

1. A–C

   Networks by interaction types and the network datasets within the PPI network. (A) Number of nodes and interactions. The light dots represent the shared nodes and edges (in more than one resource), while the dark ones show their total numbers. (B) Causality: number of connections by direction and effect sign. (C) Coverage of the networks on various groups of proteins. Dots show the percentage of proteins covered by network resources for the following groups: cancer driver genes from COSMIC and IntOGen, kinases from kinase.com, phosphatases from Phosphatome.net, receptors from the Human Plasma Membrane Receptome (HPMR) and transcription factors from the TF census. Gray bars show the number of proteins in the networks. The information for individual resources is in Figs EV1 and EV2, Appendix Fig S1.

2. D–G

   On each panel, the bottom rows represent the combined complex and enzyme–PTM databases contained in OmniPath (D, E). Number of complexes (D) and enzyme–PTM (E) interactions by resource. (F) Enzyme–PTM relationships by PTM type. (G) Enzyme–PTM interactions by their target. Light, medium, and dark dots represent the number of enzyme–PTM relationships targeting the enzyme itself, another protein within the same molecular complex or an independent protein, respectively.

Figure EV1. Quantitative description of the PPI network by resource

1. Number of nodes and interactions. The light dots represent the shared nodes and edges (in more than one resource), while the dark ones show their total numbers.

2. Causality: number of connections by direction and effect sign.

3. Coverage of the networks on various groups of proteins. Dots show the percentage of proteins covered by network resources for the following groups: cancer driver genes from COSMIC and IntOGen, kinases from kinase.com, phosphatases from Phosphatome.net, receptors from the Human Plasma Membrane Receptome (HPMR) and transcription factors from the TF census. Gray bars show the number of proteins in the networks.

Figure EV2. Quantitative description of the transcriptional network by resource

1. A–C

   Panels and notations are the same as on Fig EV1.

Figure EV3. Example of the annotations query in the OmniPath web service

For the protein mTOR a large variety of information is available from different databases. The “record_id” binds together the fields of the record from the original resource. Each field has a “label” and a “value”.

Figure 4

Examples of tools for omics data analysis that can be applied with the prior knowledge available in OmniPath.

Figure EV5. OmniPath‐based NicheNet analysis to predict over‐expressed ligands in SARS‐CoV‐2 infection potentially affecting the expression of inflammatory response related genes in Calu3 cells

1. Most significantly enriched gene sets after SARS‐CoV‐2 infection on the Calu3 cell line. Inflammatory response is highlighted in red.

2. Results of NicheNet’s ligand activity analysis: Number of over‐expressed ligands after SARS‐CoV‐2 infection and their potential to predict the inflammatory response gene set based on the Pearson correlation coefficient. The top 12 ranked ligands, out of a total of 117 over‐expressed ligands, were selected.

3. Regulatory potential of the top ranked ligands and target genes from the inflammatory response program based on NicheNet’s prior knowledge model.

4. Ligand–receptor interaction potential based on NicheNet’s prior knowledge model between the top ranked ligands and the receptors expressed in the Calu3 cell line.

Figure 5. Illustrations of the integrated analysis of inter‐ and intracellular signaling

1. Inter‐ and intracellular signaling interactions linking the top predicted ligands over‐expressed after SARS‐CoV‐2 infection to their potential immune response targets in the Calu3 cell line. Top ranked ligands (orange) connect to their potential receptors (turquoise) that trigger an intracellular cascade until reaching TFs (purple), that in turn regulate the expression of the target genes (blue). Signaling intermediates (gray) connect receptors to TFs across their shortest path.

2. Intercellular connections and their downstream effect in UC compared with healthy control. Top: communication network of five cell types reconstructed from scRNA‐Seq; the thickness of the edges is proportional to the number of intercellular connections. Bottom: condition‐specific ligand–receptor connections between myofibroblasts and regulatory T cells trigger an immunosuppressive versus an inflammatory signaling in T cells, in healthy and UC, respectively.

3. Condition‐specific connections between myofibroblast ligands (upper semicircles, black) and Treg cell receptors (lower semicircles, colored by pathways) in ulcerative colitis (right) and healthy control (left). Pathway annotations from SignaLink. Immune—innate immune response, RTK—receptor tyrosine kinase, TLR—Toll‐like receptor.