Transcriptomic and proteomic assessment of radiation injury and dose-rate dependency in white blood cells

Ionizing radiation elicits complex cellular responses that are influenced not only by total dose but also by the rate at which the dose is delivered. Understanding how dose rate modulates molecular outcomes is important for accurate risk assessment. In this study, we apply an integrative multi-omics approach combining transcriptomic and proteomic profiling while adjusting for covariates to investigate how differential dose rates of ionizing radiation alter gene and protein expression in human lymphocytes. Particular emphasis is placed on identifying dose-rate-specific alterations in key molecular pathways. Peripheral blood from 14 healthy donors (8 males, 6 females) was irradiatedex vivowith x-rays at 0.05 Gy min^-1(DR1) and 1.0 Gy min^-1(DR2) across a dose range from 0 to 6 Gy. Gene expression was assessed using TempO-Seq™, and relative protein abundance was determined by mass spectrometry. Differential expression analysis was conducted using edgeR and limma, adjusting for sex, age, and leukocyte counts (false discovery rate < 0.05). Multi-omics integration was performed using regularised canonical correlation analysis (rCCA) implemented in mixOmics, followed by Reactome pathway enrichment analysis. We identified 2477 and 2612 differentially expressed genes at DR1 and DR2, respectively, and 368 and 386 differentially expressed proteins. To assess dose discrimination, we examined sample separation in the space defined by the average canonical variates from transcriptomic and proteomic datasets using rCCA. Covariate adjustment improved dose discrimination, particularly above 0.5 Gy. Using a correlation cut-off threshold of 0.5 in rCCA, 212 (DR1) and 276 (DR2) highly correlated gene-protein pairs were identified. DR2 exposure was associated with stronger enrichment of stress-related pathways, including unfolded protein response, senescence and oncogenic kinase signalling. In contrast, DR1 induced enrichment of pathways associated with immune engagement, including antigen presentation. At both dose rates, transcriptomic changes highlighted upstream regulatory processes (chromatin modelling) and proteomic changes captured downstream functional pathways such as immune activity and apoptosis. The multi-omics approach with covariate adjustment revealed key radiation-responsive pathways and dose-rate-dependent molecular differences, highlighting the value of integrating transcriptomic and proteomic data to better understand radiation effects.