Clinical recovery from surgery correlates with single-cell immune signatures

Abstract

Delayed recovery from surgery causes personal suffering and substantial societal and economic costs. Whether immune mechanisms determine recovery after surgical trauma remains ill-defined. Single-cell mass cytometry was applied to serial whole-blood samples from 32 patients undergoing hip replacement to comprehensively characterize the phenotypic and functional immune response to surgical trauma. The simultaneous analysis of 14,000 phosphorylation events in precisely phenotyped immune cell subsets revealed uniform signaling responses among patients, demarcating a surgical immune signature. When regressed against clinical parameters of surgical recovery, including functional impairment and pain, strong correlations were found with STAT3 (signal transducer and activator of transcription), CREB (adenosine 3',5'-monophosphate response element-binding protein), and NF-κB (nuclear factor κB) signaling responses in subsets of CD14(+) monocytes (R = 0.7 to 0.8, false discovery rate <0.01). These sentinel results demonstrate the capacity of mass cytometry to survey the human immune system in a relevant clinical context. The mechanistically derived immune correlates point to diagnostic signatures, and potential therapeutic targets, that could postoperatively improve patient recovery.

Trial registration: ClinicalTrials.gov NCT01578798.

Fig. 1. Consort chart summarizes patient recruitment

Two hundred and fifty-one patients were assessed for eligibility, 50 were consented, 39 underwent total hip arthroplasty under the approved protocol, and 32 completed the study. Six patients were included in the pilot study, and 26 patients were included in the main study.

Fig. 2. Mass tag barcoding enables the longitudinal analysis of the cellular immune response in peripheral blood of patients undergoing surgery

A. Experimental workflow. Whole blood samples from six patients undergoing primary hip arthroplasty were collected 1 h before surgery (baseline, BL), and 1 h, 24 h, 72 h, and 6 weeks after surgery. Following red blood cell lysis, leukocyte samples from each patient were barcoded using a unique combination of palladium isotopes (panel 1). Barcoded samples were pooled, stained with a panel of 31 antibodies (panel 2, Table S1), and analyzed by mass cytometry (panel 3). Raw mass cytometry data were normalized for signal variation over time (33) (panel 4), de-barcoded (25) (panel 5) and analyzed (panel 6). B. Assay validation in surgical patients. Ten intracellular signaling responses to surgery were quantified for four immune cell subsets (neutrophils, CD14⁺ MCs, CD4⁺ and CD8⁺ T cells). Signal induction for each signaling molecule was calculated as the difference of inverse hyperbolic sine medians between samples obtained at baseline and at 1 h, 24 h, 72 h, and 6 weeks after surgery (“arcsinh ratio”). Five of 10 phospho-proteins (pSTAT1, pSTAT3, pSTAT5, pCREB, pP38) displayed reproducible changes at 1 h, 24 h, or 72 h after surgery compared to baseline. Results are shown as means ± SEM. SAM Two class paired was used for statistical analysis (** indicates a false discovery rate q< 0.01).

Fig. 3. Surgery induces a redistribution of major immune cell-types and a 6-fold expansion of HLA-DR^low CD14⁺ monocytes

A. Frequencies of neutrophils, CD14⁺ MCs, cDCs, pDCs, NK cells, B cells, CD4⁺ T cells, and CD8⁺ T cells are depicted for 26 patients 1 h, 24 h, 72 h, and 6 weeks after surgery. Cell-types were identified by manual gating (Fig. S2). Neutrophil frequency was quantified as percent of total hematopoietic cells (CD61CD235). All other cell frequencies are expressed as percent total of mononuclear cells (CD45⁺CD66). Significant changes occurred for all cell types (**q<0.01, SAM Two class paired). Results are shown as mean fold change (± SEM). B. Visual representation of unsupervised hierarchical clustering. Results are shown for CD45⁺CD66 immune cells. The analysis used 21 cell surface markers (Table S1). Major immune cell compartments are contoured (Fig. S4). Contoured in red are CD14⁺ MCs. The color scale indicates median intensity of CD14 expression. C. CD14⁺MCs were clustered into HLA-DR^hi (yellow), HLA-DR^mid (green), and HLA-DR^low (blue) subsets. The color scale indicates the median intensity of HLA-DR expression. D-G. Histogram plots. Arrows designate histograms of HLA-DR expression for CD14⁺MC clusters (red) against HLA-DR background expression in all CD45⁺CD66 cells (blue). H-K. CD14⁺MC cell cluster frequencies 1 h before and 1 h, 24 h, and 72 h after surgery. Expansion of all CD14⁺MC clusters (H) was attributable to the expansion of the HLA-DR^mid (J) and HLA-DR^low (K) CD14⁺MC clusters. Results are shown as mean fold change (± SEM).

Fig. 4. Surgery induces time-dependent and cell-type specific activation of immune signaling networks

A. A heat map depicting hand-gated major immune cell subsets (rows, Fig. S2) and sampling times after surgery (columns). Within each block, changes in phosphorylation state of 11 intracellular signaling proteins (y-axis) are individually depicted for 26 patients (x-axis). The color scale indicates changes in phospho-signal median intensity (arcsinh ratio) compared to baseline. B. Heat map depicting for each signaling protein, cell subset, and time point whether phosphorylation signals significantly increased (yellow, q<0.01, SAM Two class paired), decreased (blue, q<0.01), or remained unchanged (black, q>0.01). The color scale indicates mean fold-change of the signaling responses compared to baseline. Signaling responses in CD14⁺MCs and CD4⁺ T cells were most prominent (red). C. Pearson correlation coefficients between changes in phosphorylation states of 11 signaling proteins in CD14⁺MCs at 1 h, 24 h, and 72 h after surgery were determined. Correlations within each (solid lines) and across (dash lines) time point(s) are depicted as black (|R|>0.7) and gray lines (|R|>0.5). D. Signaling modules in CD14⁺MCs at 1 h, 24 h, and 72 h were identified by cutting the dendrograms of clustered correlation coefficients (Fig. S7) using a threshold of R>0.7. E. At 72 h, module 1 split into modules 1a (pNF-κB, prpS6, pCREB) and 1b (pSTAT1) that correlated with each other (R=0.46, red line). At 24 h, module 2 split into modules 2a (pMAPKAP2, pP38) and 2b (pERK, pP90RSK) that correlated with each other (R=0.45, red line).

Fig. 5. The rate of surgical recovery varies greatly among patients

A-C. Heat maps depict the recovery parameters (A) postoperative fatigue, (B) hip function, and (C) pain for individual patients over the 6-week observation period. Postoperative fatigue was assessed with the Surgical Recovery Scale (SRS; 0–100 = worst-best function) (67). Pain and impairment of hip function were assessed with adapted versions of the Western Ontario and McMaster Universities Arthritis Index (WOMAC, pain 0–40 = no pain-worst imaginable pain; function 0–60 = no impairment-severe functional impairment) (64). The heat maps reflect significant variability for extent and rate of recovery across all three outcome domains. D-F. Box plots depict medians and interquartile ranges of (D) SRS, (E) WOMAC function, and (F) WOMAC pain scores (bars indicate 10^th and 90^th percentiles). An inset graph in panel f depicts the median daily analgesic consumption expressed as the dose equivalent of intravenous hydromorphone. Graphical information regarding pain and analgesic consumption are jointly presented, as these variables are inter-dependent. G-I. Clinical recovery parameters were derived to quantify rate of recovery for the three outcomes. Derived parameters were (G) time to 50% recovery from postoperative fatigue, (H) time to mild functional impairment of the hip, and (I) time to mild pain. Bars indicate median and interquartile range; open circles indicate individual data points.

Fig. 6. STAT3, CREB, and NF-κB signaling in CD14⁺MC subsets strongly correlate with surgical recovery

A. CD45⁺CD66 cells obtained at BL and at 1 h, 24 h, and 72 h after surgery were clustered using an unsupervised approach (35) (panels 1 and 2, Fig. 3B). Immune features, which include frequencies and signaling responses of 11 phospho-proteins, were derived for every cluster (panel 3). SAM Quantitative was used to detect significant correlations between immune features and parameters of clinical recovery (q<0.01, panel 4). Cell cluster phenotypes were identified using cell surface marker expression (panel 5). B. Significant correlations were obtained for STAT3 signaling in cluster A (left panel), CREB signaling in cluster B (middle panel), and NF-κB signaling in cluster C (right panel) with recovery from postoperative fatigue, functional impairment of the hip, and resolution of pain. Clusters A and B were CD14⁺HLA-DR^low MCs; cluster C was CD14⁺HLA-DR^hi MCs (Fig. S8). C. Cells were hand-gated using 12 surface markers (blue line). Representative 2D plots are shown for one patient at 24 h (upper panel) and 1 h (middle and lower panels) after surgery. Percent cells in parent gate are shown. Cells contained in Clusters A, B, or C (blue shadow) are overlaid onto the entire cell population (gray). D. Significant correlations between signaling responses and parameters of clinical recovery identified using an unsupervised approach were replicated with hand-gated data. Depicted are regression lines and 95% confidence intervals (solid and dashed lines), Spearman’s ranked correlation coefficients, false discovery rates (q), and p-values.