Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for ‘orphan’ receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.

High-throughput, automated ELISA-based protein-protein interaction (PPI) screen of secreted proteins and the extracellular domain (ECD) region of cell-surface single-transmembrane proteins. 564 secreted and ECD proteins were produced in two different multimerized versions, 'bait' and 'prey', and every combination was tested for binding (i.e., 564 x 564 = 318,096). The read-out of the assay is colorimetric and plates are read at O.D. 650 nm. Experiments were performed in triplicate. Background was determined by averaging the binding signal from all 564 'prey' proteins on each 'bait'. The signal of each protein was divided by the background to determine fold-over-background binding (F.O.B.). Both raw and processed data are included in Wojtowicz et al_DataS4. 

Multiple sequence alignment (MSA) of the 564 secreted and ECD proteins included in the screen was performed using MUltiple Sequence Comparison by Log- Expectation (MUSCLE) (https://www.ebi.ac.uk/Tools/msa/muscle/) and Multiple Alignment using Fast Fourier Transform (MAFFT) (https://mafft.cbrc.jp/alignment/server/) online resources and analyzed using both first iteration and  second iteration parameters (MUSCLE) and the default parameters (MAFFT) (https://www.ebi.ac.uk/Tools/msa/muscle/). MSA files were submitted to the Interactive Tree of Life (iTOL) (https://itol.embl.de/), an agglomerative hierarchical clustering algorithm, to build a cluster hierarchy and generate phylogenetic trees. Screen data were analyzed alongside the phylogenetic trees to predict additional PPIs between subfamily members that may have been missed in the screen (false negatives). MSA data files are provided as follows: Wojtowicz et al_DataS6 (MUSCLE, first iteration), Wojtowicz et al_DataS7 (MUSCLE, second iteration) and Wojtowicz et al_DataS8 (MAFFT).

MSA sequence files can be used to generate and visualize phylogenetic trees. We use the Interactive Tree of Life (iTOL) (https://itol.embl.de/), an agglomerative hierarchical clustering algorithm, to build a cluster hierarchy and generate the phylogenetic trees.