Cohort characteristics and single-cell analysis of PBMCs in young and aged adults
To generate a comprehensive immune cell atlas reflecting cellular and systemic adaptations resulting from age and/or COVID-19 infection, we integrated scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq of single-cell PBMC suspensions collected from 3 separate cohorts (Fig. 1A, 1B, and Table S1A–G). In cohort-1, comprising young healthy adults (YA) (20–45 years old) and aged healthy adults (AA) (≥60 years old), we combined CyTOF (n = 10) and scATAC-seq (n = 10) with scRNA-seq (n = 16) and scTCR/BCR-seq (n = 16); in cohort-2, comprising young healthy (YH) individuals (30–45 years old), aged healthy (AH) individuals (≥60 years old), young COVID-19 onset patients (YCO) (30–50 years old) and aged COVID-19 onset patients (ACO) (≥70 years old), we performed CyTOF analysis (n = 8); and in cohort-3, comprising YH individuals, AH individuals, young recovered COVID-19 patients (YCR) (30–50 years old) and aged recovered COVID-19 patients (ACR) (≥70 years old), we performed scRNA-seq (n = 22) (Fig. 1B). By combining scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq analysis, we created a comparative framework detailing the impact of aging on cell type distribution and immune cell functions at the transcriptional, proteomic, and chromatin accessibility levels in cohort-1. In cohort-2, we measured single-cell protein expression using a 26-marker CyTOF panel to discover early cellular changes in incipient COVID-19 patients and how those changes were affected by age. Finally, in cohort-3, we compared cellular differences between young and aged recovered COVID-19 patients by scRNA-seq analysis (Fig. 1B).
Figure 1 Schematic illustration of the collection and data processing of PBMC from young and aged group. (A) Flowchart overview of PBMC collection in young and aged adults followed by scRNA-seq, mass cytometry, scATAC-seq and scTCR/BCR-seq experiments. (B) Schematic illustration of experimental cohorts; cohort-1: young and aged adults, cohort-2: young and aged healthy individuals, young and aged adults with COVID-19 onset, cohort-3: young and aged healthy individuals, young and aged adults recovered from COVID-19, matched with analysis as indicated: single-cell proteomic data from CyTOF studies, gene expression data from scRNA-seq studies, chromosomal accessibility data from scATAC-seq, and TCR and BCR repertoire data from scTCR/BCR-seq. (C) t-SNE projections of PBMCs derived from scRNA-seq data in cohort-1. (D) Heatmaps showing scaled expression of discriminative gene sets for each cell type and cell subset. Color scheme is based on z-score distribution from −3 (purple) to 3 (yellow)
We analyzed PBMC single-cell suspensions by CyTOF for the protein expression of several lineage-, activation- and trafficking-associated markers and converted them to barcoded scRNA-seq libraries using 10x Genomics for downstream scRNA-seq, scATAC-seq and scTCR/BCR-seq analysis. CellRanger software and the Seurat package were used for initial processing of the sequencing data. Quality metrics included numbers of unique molecular identifiers (UMIs), genes detected per cell, and reads aligned that were comparable across different research subjects. We identified red blood cells (RBCs), megakaryocytes (MEGAs) and five major immune cell lineages (TCs, NKs, BCs, MCs and DCs) based on the expression of canonical lineage markers and other genes specifically upregulated in each cluster (Figs. 1C, 1D and S1A–C). In accordance with the scRNA-seq results, we identified five immune cell lineages (TCs, NKs, BCs, MCs and DCs) in CyTOF using t-distributed stochastic neighbor embedding (t-SNE), an unbiased dimensionality reduction algorithm (See Table S2 for a list of antibodies) (Fig. S2A–D). Cell-type-specific marker genes were determined by differential gene expression values between clusters positioned and visualized in a t-SNE plot (Figs. S1 and S2). The definition of cell types in clusters in the t-SNE maps was comparable between old and young individuals (Figs. S1B and 2B) both by scRNA-seq and CyTOF, indicating that the cell type identity was not altered with age.
Figure 2 Changes in cell proportions during aging. (A) Bar chart of the relative percentage of immune cell types derived from scRNA-seq data in PBMCs. (B) Bar chart of the relative percentage of immune cell subsets derived from scRNA-seq data in PBMCs. The focused cell-subsets have been marked red. (C) Pie charts showing relative cluster abundances derived from mass cytometry data in the YA and AA groups. (D) Percentage of CD4 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (E) Percentage of CD8 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (F) Percentage of CD4 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (G) Percentage of CD8 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (H) Bar chart of the relative percentage of CD4+ T cell subsets derived from scRNA-seq data in PBMCs. (I) Bar chart of the relative percentage of CD8+ T cell subsets derived from scRNA-seq data in PBMCs. (J) Bar chart of the relative percentage of CD4+ T cell subsets derived from mass cytometry data in CD45+ cells. (K) Bar chart of the relative percentage of CD8+ T cell subsets derived from mass cytometry data in CD45+ cells. (L) Percentage of CD14 monocytes in PBMCs from YA (n = 8) and AA (n = 8) groups. (M) Percentage of CD14 monocytes in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (N) Bar chart of the relative percentage of DC subsets derived from scRNA-seq data in PBMCs. (O) Bar chart of the relative percentage of DC subsets derived from mass cytometry data in CD45+ cells. P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups