Trans-omics analysis of post-injury thromboinflammation plasma identifies endotypes and trajectories in trauma patients

Understanding and managing the complexity of trauma-induced thromboinflammation necessitates an innovative, data-driven approach. This study leveraged a trans-omics analysis of 759 longitudinal samples from 118 trauma patients and 97 healthy controls to illuminate molecular endotypes and trajectories that underpin patient outcomes. We hypothesized that unsupervised trans-omics profiling would reveal underlying clinical differences in injured patients that may present with similar clinical characteristics but ultimately have different outcomes. Here, we used proteomics and metabolomics to profile longitudinal plasma samples from trauma patients and healthy controls. Omics-based patient states were defined to map unique pathophysiologic states encountered by trauma patients over time. Then, patients were endotyped according to their longitudinal trajectory through trauma omics states, and injury patterns and outcomes were compared. Importantly, endotypes without significant differences in injury patterns yet with different clinical outcomes were identified. Organ failure among these similarly injured patients was predicted with higher accuracy using omics markers over injury covariates. Patients who presented with elevated proteosome activation, catabolism, and superoxide formation were vulnerable to heart and lung failure, and ALI, respectively. Additionally, hypoxia, RBC lysis, and hydrolase omics markers out-predicted injury covariates for mortality and intensive care across all trauma patients. Injury and outcome patterns persisted in an independent validation cohort of 333 patients from the Trauma Activation Protocol trial following trajectory prediction using a single, early timepoint. This strategy aligns with our understanding that trauma patients, despite similar clinical presentation, might harbor vastly different biological responses and outcomes. Further, this work presents a novel framework for personalized trauma patient treatment by mapping patient trajectory through injury and recovery.

Methods
Data are proteomics and metabolomics intensity values from trauma patient plasma processed via mass spectrometry. For proteomics, plasma samples were tryptic digested using S-Trap 96-well plates. Peptides were lyophilized, resuspended in 0.1% formic acid, and loaded onto Evotips. Peptides were analyzed using the Evosep One system coupled to a timsTOF Pro mass spectrometer (diaPASEF mode) via the nano-electrospray ion source. Raw DIA files were searched in Spectronaut using a project-specific spectral library, and data were presented as units of relative intensity. 

For metabolomics, frozen plasma aliquots (10 µL) were extracted 1:25 in ice-cold extraction solution (methanol:acetonitrile: water 5:3:2 v/v/v). Samples were vortexed for 30 min at 4℃ prior to centrifugation for 10 min at 15,000g at 4℃. Analyses were performed using a Vanquish UHPLC coupled online to a Q Exactive mass spectrometer (ThermoFisher). Samples were analyzed using a 1-minute and 5-minute gradient-based method, spectra were searched in Maven, and data were presented as units of relative intensity.