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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 10

Generation and validation of 6 CD14+ monocyte signatures.

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Generation and validation of 6 CD14+ monocyte signatures.
(A) RNA profil...
(A) RNA profile of pairwise DEGs (Bonferroni-adjusted P < 0.05 and fold change ≥ 2) between 7 CD14+ monocyte clusters. Columns represent the average gene expression for each cluster. Genes (rows) are clustered into 6 signatures (C1~C6). Single-cell transcriptomic data were collected from 38 samples harvested at 4 different time points as shown in Figure 8A. (B) Enriched regulons for the signatures shown in A. Hypergeometric P value was computed. Only the relationships with Benjamini-Hochberg–adjusted P < 0.05 with FE ≥ 2 and the number of overlapping genes ≥ 5 are shown. Relationships are color-coded by top enriched gene modules (with highest FE). (C) Validation of the 6 signatures in published trauma data set (37 healthy controls vs. longitudinal data from 167 patients). Expression of each signature along timeline (up to 28 days after injury) is shown. Smoothing lines were fitted by Loess regression. The vertical dotted line labels the 24-hour time point after injury after injury. (D) Statistical quantification of the differences between 2 recovery statuses (complicated vs. noncomplicated) shown in C using Wilcoxon’s test. The sampled time points were binned into 7 time points (12 h, 1 d, 4 d, 7 d, 14 d, 21 d, 28 d) after injury. The significant time bin for each signature (Wilcoxon’s P < 0.05) is shown.

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