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

[https://doi.org/10.5061/dryad.d51c5b0dn](https://doi.org/10.5061/dryad.d51c5b0dn)

## Description of the data and file structure

Plasma was collected from trauma (severely injured) patients, which was processed for and quantified by mass spectrometry-based proteomics and metabolomics. Data underwent supervised analyses to compare differences among hemorrhagic shock and tissue injury groups, followed by unsupervised analyses to determine omics-driven endotypes to determine differences in trauma severity and clinical outcomes among these sub-phenotypes. In this directory is Data.file.S1.data_COMBAT.xlsx which contains proteomics and metabolomics data (clinical data available upon request) as well as .Rmd file which contains R scripts that were used for modeling and analyses.

### Files and variables

#### File: Table.S16.data_COMBAT.xlsx

**Description:** Proteomics..., During enrollment for this study, explicit consent was obtained from each participant for the use and publication of their de-identified data into the public domain. This data is de-identified through the use of an anonymized identifier, and contains no personally identifiable information.