Blood samples collected under anesthesia can be used as a source of non-diseased controls for immune-based assays

Abstract

Recruiting very young, healthy children to serve as age-matched controls in research presents substantial ethical and practical challenges. One potential approach to address this issue is to recruit healthy children who are referred for elective procedures under general anesthesia. As infants are typically anesthetized using volatile anesthetics before cannula insertion for additional drug administration, blood samples become readily accessible after the onset of drug-induced coma. However, since prolonged exposure to inhaled anesthetic agents is known to have immune-modulating effects that could affect their suitability as experimental controls, we aimed to investigate whether immune changes are also present in samples collected immediately after gas induction in children undergoing elective dental procedures. The composition and transcriptional profile of whole blood immune cells were assessed using multiparameter flow cytometry and bulk RNA-sequencing, respectively. Cryopreserved PBMCs were used to study changes in the phenotype of polyclonally activated CD4⁺ T cells by single-cell RNA sequencing using the 10x Genomics (Pleasanton, CA, USA) platform. We report that inhaled anesthetic induction with a combination of nitrous oxide and sevoflurane has minimal effect on immune system composition and transcriptional profiles, and does not alter the phenotype of CD4⁺ T cells activated with staphylococcal enterotoxin B (SEB). However, we observed increased absolute cell counts in specific leucocyte populations. We conclude that blood samples collected during elective procedures under general anesthesia may represent a valuable opportunity for recruiting healthy children for research studies, depending on the intended assays.

Keywords: anaesthesia; blood; children; controls; immune cells.

Figure 1

Minimal changes in immune system composition after sevoflurane induction. (A) Agglomerative hierarchical clustering analysis of changes in cell population frequency is represented as a heatmap. Patient IDs are shown vertically on the right-hand side of the heatmap (y-axis), and cell populations are shown horizontally below the heatmap (x-axis). Samples before induction are shown in grey, and samples after induction are shown in black. (B) Principal component analysis (PCA) of blood samples from 10 individuals (P1–P10) collected before and after inhaled anesthetic induction, indicated as “pre” and “post”, respectively. The two principal components (PCs) are shown. Samples from the same individual are joined by a line. (C) Multiple paired t-tests were performed with correction for multiple comparisons using the Bonferroni–Dunn method. The threshold for a significant mean difference in cell frequency before and after inhaled anesthetic induction is represented by a dotted line at y = 1.3 (alpha = 0.05).

Figure 2

Whole blood RNA-sequencing analysis shows minor changes in gene expression after sevoflurane administration. (A) Volcano plot showing log2 fold change (x-axis) and the logarithm of the corrected p-value (y-axis) for 14,590 protein-coding genes identified in 19 samples. (B) Agglomerative hierarchical clustering analysis of changes in cell population frequency is represented as a heatmap. Patient IDs are shown vertically on the right-hand side of the heatmap (y-axis), and the 100 most variable genes are shown horizontally below the heatmap (x-axis). Samples before induction are shown in grey, and samples after induction are shown in black. (C) PCA of blood samples from 10 individuals (P1–P10) collected before and after inhaled anesthetic induction, indicated as “pre” and “post”, respectively. The two principal components (PCs) are shown. Samples from the same individual are connected by a line.

Figure 3

The transcriptional profile of activated CD4⁺ T cells is unaltered after sevoflurane induction. (A) Clustering analysis of unstimulated (purple) and SEB-stimulated (green) CD4⁺ T cells from four donors projected onto two dimensions using t-SNE. (B) SEB-stimulated CD4⁺ T cells colored by time point (pre-SEVO, blue; post-SEVO, orange). (C) Graph-based clustering of SEB-stimulated CD4⁺ T cells. (D) Bar chart showing the proportion of cells in each cluster by time point (pre-SEVO, grey; post-SEVO, black). Chi-squared tests performed on all four clusters between pre- and post-SEVO samples showed no significant differences.