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. 2022 Feb 3:13:807539.
doi: 10.3389/fimmu.2022.807539. eCollection 2022.

Monitoring Pre- and Post-Operative Immune Alterations in Patients With Locoregional Colorectal Cancer Who Underwent Laparoscopy by Single-Cell Mass Cytometry

Chuanyong Zhou  1 , Zaozao Wang  1 , Beihai Jiang  1 , Jiabo Di  1 , Xiangqian Su  1
Affiliations

Monitoring Pre- and Post-Operative Immune Alterations in Patients With Locoregional Colorectal Cancer Who Underwent Laparoscopy by Single-Cell Mass Cytometry

Chuanyong Zhou et al. Front Immunol. .

Abstract

Surgical excision is currently the principal therapy for locoregional colorectal cancer (CRC). However, surgical trauma leads to controlled tissue damage, causing profound alterations in host immunity and, in turn, affecting post-operative outcomes. Surgery-induced immune alterations in CRC remain poorly defined. Here, single-cell mass cytometry was applied to serial blood samples collected pre-operatively, and on days 1, 3, and 7 post-operatively from 24 patients who underwent laparoscopic surgical resection of CRC to comprehensively monitor the perioperative phenotypic alterations in immune cells and dynamics of immune response. Characterization of immune cell subsets revealed that the post-operative immune response is broad but predominantly suppressive, supported by the decreases in total frequencies of circulating T cells and natural killer (NK) cells, as well as decreased HLA-DR expression on circulating monocytes. The proportion of T cells significantly decreased on day 1 and recovered to the pre-surgical level on day 3 after surgery. The frequency of monocytes was significantly elevated on day 1 after surgery and declined to baseline level on day 3. NK cells temporarily contracted on post-operative day 3. T cells, monocytes, DCs, NK cells, and B cells were partitioned into phenotypically different single-cell clusters. The dynamics of single-cell clusters were different from those of the bulk lineages. T cell clusters in the same response phase fluctuate inconsistently during the perioperative period. Comparing to the baseline levels, the frequencies of CD11b(+)CD33(+)CD14(+)CD16(-) classical monocytes expanded followed by contraction, whereas CD11b(+)CD33(+)CD14(high)CD16(low) intermediate monocytes remained unchanged; HLA-DR expression in monocytes were significantly reduced; the frequencies of intermediate CD56(bright)CD16(+) NK cell subsets increased; and the percentage of memory B lymphocytes were elevated after surgery. Post-operative pro- and anti-inflammatory cytokines were both altered. Furthermore, perioperative immune perturbations in some of the cell subsets were unrecovered within seven days after surgery. Chronological monitoring major immune lineages provided an overview of surgery-caused alterations, including cell augments and contractions and precisely timed changes in immune cell distribution in both innate and adaptive compartments, providing evidence for the interaction between tumor resection and immune modulation.

Keywords: immunosuppression; laparoscopy; locoregional colorectal cancer; perioperative immune alterations; single-cell mass cytometry.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identification of the main immune components in CRC. (A) Immune cell lineages and respective subpopulations of interest for comprehensive immunophenotyping. (B) Experimental strategy used in this study. (C) FIt-SNE of immune cells colored by PhenoGraph clusters. (D) Heatmap showing the normalized expression of the markers for PhenoGraph clusters. Clusters are grouped by surface marker expression profiles. The cluster numbers and relative frequencies are exhibited on the right. (E) FIt-SNE plots of normalized marker expression for 10,000 cells randomly selected from all patients. (F) FIt-SNE plots highlighting the distribution of major cell lineages. (G) Boxplots showing the frequencies of each immune cell lineage at different time point. *p < 0.05, **p < 0.01 by Krustal-Wallis analysis with post-hoc Dunn test. P-value adjusted with the Benjamini-Hochberg method. NK, natural killer; DC, dendritic cell.
Figure 2
Figure 2
In-depth characterization of the T cell compartment. (A) Heatmap showing normalized expression of the markers for the 24 T cell clusters. Clusters are grouped by marker expression patterns. The cluster numbers and relative frequencies are displayed on the right. (B) FIt-SNE plots of normalized marker expression for 10,000 T cells randomly selected from all patients. (C) Exemplary identification of Treg from a representative patient. PBMCs were labeled with the indicated antibodies, examined by flow cytometry, and then analyzed by Cytobank. The Tregs were identified through the indicated strategy. (D) Surface marker combinations used to identify T cell subsets. (E, F) Histograms showing the expression of indicated functional markers on CD8(+) T cells (E) and CD4(+) T cells (F).
Figure 3
Figure 3
T cell fluctuation during perioperative period. (A) FIt-SNE plots highlighting the distribution of T cells at different time points. (B, C) Boxplots showing the frequencies of the CD8(+) T cell clusters (B) and the CD4(+) T cell clusters (C) during the perioperative period. (D, E) Boxplots showing the mean expression levels of CTLA-4 (D) and PD-1 (E) in T cells during the perioperative period. (F) Boxplots showing the frequencies of Treg cells during the perioperative period. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 by Krustal-Wallis analysis with post-hoc Dunn test. P-value adjusted with the Benjamini-Hochberg method.
Figure 4
Figure 4
Characterization of perioperative monocytes and DCs. (A) Heatmap showing normalized expression of the markers for the 19 monocytes and DC clusters. Clusters are grouped by marker expression patterns. The cluster numbers and relative frequencies are displayed on the right. (B) Surface marker combinations used to identify monocytes and DC subsets. (C) FIt-SNE of monocytes and DCs colored by PhenoGraph clusters at different time points. (D) Boxplots showing the frequencies of the classical and intermediate monocytes during the perioperative period. (E) Boxplots showing the mean expression levels of HLA-DR in monocytes during the perioperative period. (F) Cluster-specific analysis of HLA-DR expression in monocytes shown by boxplots. (G, H) Boxplots showing the frequencies of the mDCs (G) and the pDCs (H) during the perioperative period. (I, J) Boxplots showing the cluster-specific mean expression of HLA-DR in mDCs (I) and in pDCs (J) during the perioperative period. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 by Krustal-Wallis analysis with post-hoc Dunn test. P-value adjusted with the Benjamini-Hochberg method.
Figure 5
Figure 5
In-depth analysis of NK cell subsets during the perioperative period. (A) Heatmap showing normalized expression of the markers for the 16 NK cell clusters. Clusters are grouped by marker expression patterns. The cluster numbers and relative frequencies are displayed on the right. (B) Surface marker combinations used to identify NK cell subsets. (C) FIt-SNE plots highlighting the distribution of NK cells at different time points. (D) Boxplots showing the frequencies of NK cell clusters during the perioperative period. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 by Krustal-Wallis analysis with post-hoc Dunn test. P-value adjusted with the Benjamini-Hochberg method.
Figure 6
Figure 6
Perioperative serum cytokine levels. Boxplots showing the perioperative serum cytokine secretion measured by ProcartaPlex™. IL, interleukin; IFN-γ, interferon-γ; TNF-α, tumor necrosis factor-α. *p < 0.05, ****p < 0.0001 by Krustal-Wallis analysis with post-hoc Dunn test. P-value adjusted with the Benjamini-Hochberg method.
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