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A road map from single-cell transcriptome to patient classification for the immune response to trauma
Tianmeng Chen, Matthew J. Delano, Kong Chen, Jason L. Sperry, Rami A. Namas, Ashley J. Lamparello, Meihong Deng, Julia Conroy, Lyle L. Moldawer, Philip A. Efron, Patricia Loughran, Christopher Seymour, Derek C. Angus, Yoram Vodovotz, Wei Chen, Timothy R. Billiar
Tianmeng Chen, Matthew J. Delano, Kong Chen, Jason L. Sperry, Rami A. Namas, Ashley J. Lamparello, Meihong Deng, Julia Conroy, Lyle L. Moldawer, Philip A. Efron, Patricia Loughran, Christopher Seymour, Derek C. Angus, Yoram Vodovotz, Wei Chen, Timothy R. Billiar
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Research Article Immunology Inflammation

A road map from single-cell transcriptome to patient classification for the immune response to trauma

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Abstract

Immune dysfunction is an important factor driving mortality and adverse outcomes after trauma but remains poorly understood, especially at the cellular level. To deconvolute the trauma-induced immune response, we applied single-cell RNA sequencing to circulating and bone marrow mononuclear cells in injured mice and circulating mononuclear cells in trauma patients. In mice, the greatest changes in gene expression were seen in monocytes across both compartments. After systemic injury, the gene expression pattern of monocytes markedly deviated from steady state with corresponding changes in critical transcription factors, which can be traced back to myeloid progenitors. These changes were largely recapitulated in the human single-cell analysis. We generalized the major changes in human CD14+ monocytes into 6 signatures, which further defined 2 trauma patient subtypes (SG1 vs. SG2) identified in the whole-blood leukocyte transcriptome in the initial 12 hours after injury. Compared with SG2, SG1 patients exhibited delayed recovery, more severe organ dysfunction, and a higher incidence of infection and noninfectious complications. The 2 patient subtypes were also recapitulated in burn and sepsis patients, revealing a shared pattern of immune response across critical illness. Our data will be broadly useful to further explore the immune response to inflammatory diseases and critical illness.

Authors

Tianmeng Chen, Matthew J. Delano, Kong Chen, Jason L. Sperry, Rami A. Namas, Ashley J. Lamparello, Meihong Deng, Julia Conroy, Lyle L. Moldawer, Philip A. Efron, Patricia Loughran, Christopher Seymour, Derek C. Angus, Yoram Vodovotz, Wei Chen, Timothy R. Billiar

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Figure 11

Six signatures define 2 patient subtypes associated with different prognosis.

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Six signatures define 2 patient subtypes associated with different progn...
(A) Schema describing the workflow for Figure 11 and Supplemental Figure 10. SG, Groups clustered based on Signature scores. (B) Trauma patients were clustered into 2 subtypes (SG1 vs. SG2) using the signature score matrix. (C and D) Time-to-event analyses (event = recovery). (C) Kaplan-Meier curve was plotted by the 2 subtypes to visualize 28-day recovery. Log-rank P value is shown. (D) Hazard ratio of the subtypes after adjusting potential covariates using Cox proportional hazards model. Compared with SG2 (shown as the reference), SG1 is significantly associated with slower recovery after adjusting for the potential covariants. (E–H) Burn/sepsis patients were clustered into 2 subtypes, and Kaplan-Meier curve was plotted to visualize 28-day survival. Log-rank P value is shown. (B–D) Trauma data set (n = 167). (E and F) Burn data set (n = 241). (G and H) Sepsis data set (n = 479).

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