Immune cell temporal specific signatures clock track recovery status of critical injury patients

Authors:
TeDing Chang

Body of Abstract:
BACKGROUND

Despite major advances in resuscitation and supportive care, trauma remains a leading cause of death and disability worldwide, particularly among young adults. Critical illness following severe trauma represents one of the most complex and dynamic immunological conditions encountered in modern intensive care. Survivors frequently experience prolonged recovery characterized by immune dysregulation, secondary infections, and MODS. These adverse outcomes highlight the need to understand not only the magnitude but also the temporal dynamics of immune responses during recovery from critical injury.

METHODS

We conducted a large, prospective, multicenter study enrolling 243 critically injured trauma patients and 57 healthy volunteers. Using sorted immune cell populations including T cells, monocytes, and PMNs, we constructed a comprehensive, time-resolved transcriptomic cohort of immune responses from injury onset through clinical recovery or death.

We applied three complementary analytical approaches: DEGs, WGCNA, and SLIDE to select the recovery related gene signature. From these analyses, we identified nine gene signatures associated with complicated recovery and used them to train an immune recovery clock.

We developed a novel framework, termed Temporal Gap (TempoGap), to quantify the deviation between the predicted and actual post-injury time, thereby providing a metric of immune recovery delay or acceleration. To model temporal progression, we employed the LASSO regression, optimized through 5-fold cross-validation. Model stability and performance variability were estimated via 500 bootstrapped iterations, aggregated into an ensemble of LASSO-based temporal predictors.

RESULTS

All TempoGap models showed significant associations with recovery duration. Notably, the T cell latent factor–based TempoGap demonstrated the strongest predictive effect (HR = 1.5, p < 0.001), indicating that higher expression of this temporal module was associated with faster recovery. Given the importance of early prognostication, we further evaluated model performance within the first week post-injury. The models retained substantial predictive power during this period, with the monocyte complicated recovery gene TempoGap model showing the best performance on day six (HR = 2.0, p < 0.05). Importantly, TempoGap scores exhibited only weak correlations with conventional clinical indicators, suggesting that TempoGap captures unique biological dimensions of immune recovery not reflected by traditional scoring systems. CONCLUSIONS In summary, we established an immune recovery clock model and introduced TempoGap, a novel temporal deviation metric that predicts recovery status in critically injured patients. This approach offers a conceptual and analytical foundation for precision monitoring of immune recovery, enabling early outcome prediction and guiding targeted interventions to promote recovery after critical injury.