biostudies-literatureGiroux PFrench National Cancer InstituteFrench National Research AgencyFrench Government Ministry of Education Research and Technology101493https://www.ebi.ac.uk/biostudies/studies/S-EPMC12933558biostudies-literatureUnknown25(3)Cell-cell communications are widely explored to understand tissue homeostasis and diseases. Numerous computational tools have been developed to infer cellular interactions from transcriptomic or proteomic expression data. However, proteins often carry post-translational modifications (PTMs) that can induce conformational switches and alter their functional properties. A key challenge remains to incorporate PTM data in the inference and analysis of cellular interactions. Here, we propose an extension of our previously published tool BulkSignalR to integrate PTM information in ligand-receptor interactions and downstream pathway predictions. This new functionality is compatible with bulk and single-cell data, and it supports all types of PTMs. Based on two illustrative datasets, we show that Molecular & cellular proteomics : MCPPost-Transcriptional Modification Integration for Ligand-Receptor Cellular Network Inference.PMC12933558ANR-21-CE13-0011-03FreGATE-pDC 2020-038Maillard MColinge JGiroux PfalsePost-Transcriptional Modification Integration for Ligand-Receptor Cellular Network Inference.Cell-cell communications are widely explored to understand tissue homeostasis and diseases. Numerous computational tools have been developed to infer cellular interactions from transcriptomic or proteomic expression data. However, proteins often carry post-translational modifications (PTMs) that can induce conformational switches and alter their functional properties. A key challenge remains to incorporate PTM data in the inference and analysis of cellular interactions. Here, we propose an extension of our previously published tool BulkSignalR to integrate PTM information in ligand-receptor interactions and downstream pathway predictions. This new functionality is compatible with bulk and single-cell data, and it supports all types of PTMs. Based on two illustrative datasets, we show that 2025-01-01T00:00:00Z2025 Dec2026-07-16T21:32:51.407Z2026-07-12T03:07:48.132ZS-EPMC129335584142305010.1016/j.mcpro.2025.101493