Significance Exceptionally long-lived people such as supercentenarians tend to spend their entire lives in good health, implying that their immune system remains active to protect against infections and tumors. However, their immunological condition has been largely unexplored. We profiled thousands of circulating immune cells from supercentenarians at single-cell resolution and identified CD4 T cells that have cytotoxic features. This characteristic is very unique to supercentenarians, because generally CD4 T cells have helper, but not cytotoxic, functions under physiological conditions. We further profiled their T cell receptors and revealed that the cytotoxic CD4 T cells were accumulated through clonal expansion. The conversion of helper CD4 T cells to a cytotoxic variety might be an adaptation to the late stage of aging.

Abstract Supercentenarians, people who have reached 110 y of age, are a great model of healthy aging. Their characteristics of delayed onset of age-related diseases and compression of morbidity imply that their immune system remains functional. Here we performed single-cell transcriptome analysis of 61,202 peripheral blood mononuclear cells (PBMCs), derived from 7 supercentenarians and 5 younger controls. We identified a marked increase of cytotoxic CD4 T cells (CD4 cytotoxic T lymphocytes [CTLs]) as a signature of supercentenarians. Furthermore, single-cell T cell receptor sequencing of 2 supercentenarians revealed that CD4 CTLs had accumulated through massive clonal expansion, with the most frequent clonotypes accounting for 15 to 35% of the entire CD4 T cell population. The CD4 CTLs exhibited substantial heterogeneity in their degree of cytotoxicity as well as a nearly identical transcriptome to that of CD8 CTLs. This indicates that CD4 CTLs utilize the transcriptional program of the CD8 lineage while retaining CD4 expression. Indeed, CD4 CTLs extracted from supercentenarians produced IFN-γ and TNF-α upon ex vivo stimulation. Our study reveals that supercentenarians have unique characteristics in their circulating lymphocytes, which may represent an essential adaptation to achieve exceptional longevity by sustaining immune responses to infections and diseases.

Supercentenarians are rare individuals who reach 110 y of age. They are endowed with high resistance to lethal diseases such as cancer, stroke, and cardiovascular disease (1⇓⇓–4). Demographers in Canada estimated that the chance of living more than 110 y is as low as 1 in 100,000 (http://www.forum.umontreal.ca/forum_express/pages_a/demo.htm). According to the population census covering the whole territory of Japan in 2015 (http://www.stat.go.jp/english/data/kokusei/2015/pdf/outline.pdf), the number of centenarians was 61,763, of which only 146 were supercentenarians. A distinctive feature of supercentenarians is a long healthy lifespan, maintaining relatively high cognitive function and physical independence even after 100 y of age (5, 6). In other words, many supercentenarians can spend almost their entire lives in good health due to the delayed onset of age-related diseases and compression of morbidity (7). Therefore, supercentenarians can be considered a good model of successful aging, and understanding their attributes would be beneficial for superaging societies.

Many functions of the immune system show a progressive decline with age, a phenomenon known as immunosenescence, leading to a higher risk of infection, cancer, and autoimmune diseases (8, 9). A low level of inflammation is the best predictor of successful aging at extreme old age, indicating the importance of maintaining the immune system (10). Age-related alterations are apparent in 2 primary lymphoid organs, thymus and bone marrow, which are responsible for the development of mature lymphocytes (11). In particular, elderly hematopoietic stem cells in bone marrow exhibit a myeloid-biased differentiation potential (12, 13), which causes changes in the cell population of peripheral blood.

Numerous studies have examined age-related alterations in whole blood and peripheral blood mononuclear cells (PBMCs), derived from healthy donors in a wide range of age groups. Fluorescence activated cell sorting (FACS) and transcriptome sequencing technologies, which are extensively used to profile circulating immune cells, have revealed that the population makeup and expression levels of peripheral lymphocytes change dynamically with age. For example, the absolute number and percentage of peripheral blood CD19 B cells decrease with age (14⇓–16). Naïve T cell numbers tend to decrease according to age, whereas antigen-experienced memory T cell numbers increase with concomitant loss of costimulation factors CD27 and CD28 (17). This tendency is more pronounced for CD8 T cells in cytomegalovirus seropositive donors (18). In parallel, transcriptome studies have reported a large number of age-associated genes in bulk peripheral blood that can be used to predict “transcriptomic age” (19). However, most of the studies targeted donors from young to 100 y old, and the circulating immune cells in supercentenarians remain largely unexplored.

Single-cell transcriptomic methods have rapidly evolved in recent years. The accuracy of quantifying gene expression and the number of cells captured per experiment have been dramatically improved (20, 21). These methods have been applied to various subjects such as finding signatures of aging in the human pancreas (22), observing infiltrating T cells in tumors (23, 24), and characterizing diversity of cell types during brain development (25). Here we profiled circulating immune cells in supercentenarians at single-cell resolution and identified unique signatures in supercentenarians that could characterize healthy aging.

Discussion Here, we identified signatures of supercentenarians in circulating lymphocytes by using single-cell transcriptome analyses. In particular, CD4 CTLs were strongly expanded with distinct expression profiles including the activation of GZMA, GZMB, GZMH, PRF1, NKG7 (TIA-1), GNLY, CD40LG, KLRG1, KLRB1, and ITGAL (CD11A) and the suppression of CCR7, CD27, CD28, and IL7R (Figs. 3D, 4A, and 5 B and F). The results of single-cell TCR repertoire analysis of 2 supercentenarians suggest that the cell state transition of CD4 T cells is at least partially explained by clonal expansion due to repeated stimulation with the same antigen. Here we discuss potential functions of CD4 CTLs in the late stage of aging in terms of protective roles against tumor development and viral infections. The primary function of CD4 T cells, generally called helper T cells, is the regulation of immune responses using various cytokines, rather than direct elimination of target cells using cytotoxic molecules. Nevertheless, the presence of CD4 T cells with cytotoxic features, namely CD4 CTLs, has been repeatedly reported in humans and mice (32, 34, 39). The reported fractions of CD4 CTLs are generally as low as a few percent of the total CD4 T cells in healthy PBMCs (32, 40), whereas the size of the CD4 CTL fraction in the supercentenarians analyzed was on average 25% of T cells, as measured by RNA-Seq and supported by the independent FACS measurements (Fig. 4 B and E). More intriguingly, 5 out of 7 supercentenarians analyzed by FACS had more GZMB+ than GZMB− CD4 T cells (Fig. 4D). The physiological role of the expanded CD4 CTLs remains unclear in humans, however a recent single-cell transcriptome study identified tumor-infiltrating CD4 CTLs in human hepatocellular carcinoma (23). In addition, several studies demonstrate that CD4 CTLs have the ability to directly kill tumor cells and eradicate established tumors in an MHC class II-dependent manner in mouse models (41, 42). Importantly, CD8 CTLs recognize class I MHC molecules present in nearly all cells. In contrast CD4 CTLs recognize class II MHC molecules, which are usually absent in normal nonimmune cells, but present in a subset of tumor cells (43). This indicates that CD4 CTLs might contribute tumor immunity against established tumors and may have an important role in immunosurveillance, helping to identify and remove incipient tumor cells abnormally activating class II MHC molecules. Another potential function of CD4 CTLs is antiviral immunity. A growing number of studies have demonstrated the direct cytotoxic activity, protective roles, and the associated induction of CD4 CTLs against various viruses such as dengue virus, influenza virus, hepatitis virus, CMV (cytomegalovirus), and HIV (44⇓⇓⇓–48). Clonally expanded CD4 CTLs with virus-specific TCRs have been identified in dengue virus-positive donors (40). The association of CD4 CTLs with virus infection suggests that CD4 CTLs have accumulated in supercentenarians at least partially through clonal expansions triggered by repeated viral exposure. Although some important genes such as CRTAM and ADGRG1 (GPR56) have been reported (34, 49), the exact molecular mechanism of the conversion from CD4 helper T cells to CD4 CTLs is still unclear. Our transcriptome data show the striking similarity of gene expression and differentiation between CD4 CLTs and CD8 CTLs (Fig. 5 D and E), suggesting that CD4 CTLs use the CD8 transcriptional program internally, while retaining CD4 expression on the cell surface. Indeed, CD4 CTLs extracted from supercentenarians produced IFN-γ and TNF-α upon ex vivo stimulation (Fig. 6J). This agrees with the previous finding that CD4 helper T cells can be reprogrammed into CD4 CTLs by the loss of ThPOK (also known as ZBTB7B), the master regulator of CD4/CD8 lineage commitment, with concomitant activation of CD8 lineage genes (50). The reinforcement of the cytotoxic ability by the conversion of CD4 T cells in supercentenarians might be an adaptation to the late stage of aging, in which the immune system needs to eliminate abnormal or infected cells more frequently. It should be noted, however, that antigens recognized by clonally expanded TCRs are not known, and further work is required to characterize CD4 CTLs in supercentenarians.

Materials and Methods Human Blood Samples. All experiments using human samples in this study were approved by the Keio University School of Medicine Ethics Committee (approval no. 20021020) and the ethical review committee of RIKEN (approval no. H28-6). Informed consent was obtained from all donors. Fresh whole blood from supercentenarians, their offspring residing with them, and unrelated donors was collected in 2-mL tubes containing ethylene diamine tetraacetic acid (EDTA). PBMCs were isolated from whole blood within 8 h of sample collection by using SepMate-15 tubes (STEMCELL Technologies) with Ficoll-Paque Plus (GE Healthcare Life Sciences) according to the manufacturer’s instructions. Briefly, each blood sample was diluted with an equal volume of PBS plus 2% FBS, added into a SepMate tube, and centrifuged at 1,200 × g for 10 min at room temperature. Enriched mononuclear cells were washed with PBS plus 2% FBS and twice centrifuged at 300 × g for 8 min. Cell numbers and viability were measured using a Countess II Automated Cell Counter (Thermo Fisher Scientific). Single-Cell Library Preparation. Single-cell libraries were prepared from freshly isolated PBMCs by using Chromium Single Cell 3ʹ v2 Reagent Kits (26). The cells and kit reagents were mixed with gel beads containing barcoded oligonucleotides (UMIs) and oligo dTs (used for reverse transcription of polyadenylated RNAs) to form reaction vesicles called gel bead-in-emulsions (GEMs). The barcoded cDNAs in each GEM were pooled for PCR amplification, and adapter and sample indices were added. Single-cell libraries were sequenced with paired-end reads on the Illumina HiSEq. 2500 platform, with mostly 1 sample per lane. The remaining PBMCs were suspended in CELLBANKER cryopreservation medium (ZENOAQ), and stored at −80 °C. Single-Cell Data Processing. The analysis pipelines in Cell Ranger version 2.1.0 were used for sequencing data processing. FASTQ files were generated using cellranger mkfastq with default parameters. Then, cellranger count was run with–transcriptome = refdata-cellranger-GRCh38-1.2.0 for each sample, in which reads had been mapped on the human genome (GRCh38/hg38) using STAR (version 2.5.1b) (51) and UMIs were counted for each gene. The outputs of cellranger count for individual samples were integrated using cellranger aggr with–normalize = mapped, in which read depths are normalized based on the confidently mapped reads. This command also runs principal component analysis (PCA), tSNE, and k-means clustering algorithms to visualize clustered cells in 2D space. The output of cellranger aggr was loaded into R by using an R package, Cell Ranger R Kit (version 2.0.0), developed by 10× Genomics (http://cf.10xgenomics.com/supp/cell-exp/rkit-install-2.0.0.R). Log-normalized expression values of all annotated genes were calculated using 2 functions, normalize_barcode_sums_to_median and log_gene_bc_matrix, implemented in the R package. Analysis of B Cell Subsets. Cells categorized in the B cell cluster by the k-means clustering were extracted and saved as a file using the save_cellranger_matrix_h5 function in the R package Cell Ranger R Kit. This file was loaded into cellranger reanalyze to rerun PCA, tSNE, and k-means (k = 3) clustering algorithms. Wilcoxon rank sum test was applied to compare percentages of B cells between the supercentenarians and controls using the wilcox.test function in R. Analysis of T Cell Subsets. The Seurat R package (version 2.3.0) was used to analyze T cell subsets (TC1 and TC2). The outputs of cellranger count were loaded using the Read10X function. Cells clustered in TC1 and TC2 by the Cell Ranger analysis pipelines were extracted, and principal components were calculated using RunPCA function. The first 16 principal components, based on the manual inspection of the elbow plot (PCElbowPlot), were used for cell clustering (using the FindClusters function with resolution 0.05) and tSNE visualization (using RunTSNE). Differentially expressed genes were identified using the FindAllMarkers function, and the top 20 genes were visualized in a heatmap using the DoHeatmap function. CD4 CTL, CD8 CTL, and γδ T cell clusters were manually defined in the interactive mode of the tSNE plot by using the TSNEPlot function with do.identify = TRUE, based on the expression of marker genes. Wilcoxon rank sum test was applied to compare percentages of T cell subtypes between the supercentenarians and controls using the wilcox.test function in R. Analysis of T Cells in Young Donors from a Public Dataset. The single-cell RNA-Seq data of PBMCs derived from 45 healthy donors were generated and released by van der Wijst et al. (33). Freely available UMI count data for T cells were downloaded and visualized in 2D space (tSNE) based on the expression profile using the Seurat R package (version 2.3.0). Age information of individual donors was extracted from Supplementary Table 8 of the original paper (32). Pseudotime Analysis. Monocle 2 (version 2.4.0) was used to estimate a pseudotemporal path of T cell differentiation (35). Cells clustered in TC1 and TC2 by Cell Ranger analysis pipelines were loaded to create a Monocle object using the newCellDataSet function implemented in Monocle 2. The cells were ordered in pseudotime along a trajectory using reduceDimension with the DDRTree method and orderCells functions. Mean log-normalized expression values of selected marker genes were calculated for each bin from 0 to 12 pseudotime points. Antibodies and Flow Cytometric Analysis. Cryopreserved PBMCs were thawed and suspended in FACS buffer (1× Hank’s balanced salt solution with 2% FBS and 0.2% NaN3). Monoclonal antibodies specific for human CD3ε (UCHT1 and HIT3a), CD4 (RPA-T4), CD8 (RPA-T8), CD19 (HIB19), CD14 (M5E2), CD16 (B73.1), CD56 (B159), GzmB (GB11), Perforin (δG9), and Granulysin (RB1), were purchased from BD Pharmingen. Cell numbers were counted using a Countess II Automated Cell Counter. For intracellular staining, cells were fixed and permeabilized with IntraPrep Permeabilization Reagent (Beckman Coulter) according to the manufacturer’s protocols. Cells were analyzed using FACSAria III and FACSAria SORP cell sorters (BD Biosciences) with FlowJo Software (version 10.4.2). Single-Cell TCR Analysis. RosetteSep Human CD4+ T Cell Enrichment Mixture with SepMate-15 (STEMCELL Technologies) was used to remove non-CD4+ T cells from fresh whole blood. A single-cell transcriptome library was prepared from the enriched CD4+ T cells by using the Chromium Single Cell 5ʹ Library Kit (10× Genomics) with 50 ng of cDNA amplified product. A single-cell TCR library was prepared using Chromium Single Cell V(D)J Enrichment Kits, Human (10× Genomics). The libraries were sequenced with paired-end 150-bp reads on the Illumina HiSEq. 2500 platform. Analysis pipelines in Cell Ranger version 3.0.2 (updated version was used for the 5′ single-cell and TCR libraries from version 2.1.0 used for the 3′ single-cell libraries) were used for the sequencing data processing. TCR data were processed by running cellranger vdj with–reference = refdata-cellranger-vdj-GRCh38-alts-ensembl-2.0.0 to assemble TCR alpha and beta chains and determine clonotypes. Transcriptome data were processed by running cellranger count with–transcriptome = refdata-cellranger-GRCh38-1.2.0. The Seurat R package (version 2.3.0) was used for cell clustering (FindClusters) and tSNE visualization (RunTSNE). Three control datasets of T cell clonotypes analyzed by the same 10× Genomics kits were downloaded from the 10× Genomics websites below (a simple registration is needed). T cells: http://cf.10xgenomics.com/samples/cell-vdj/3.0.0/vdj_v1_hs_pbmc2_t/vdj_v1_hs_pbmc2_t_clonotypes.csv

CD4 T cells: http://cf.10xgenomics.com/samples/cell-vdj/2.2.0/vdj_v1_hs_cd4_t/vdj_v1_hs_cd4_t_clonotypes.csv

CD8 T cells: http://cf.10xgenomics.com/samples/cell-vdj/2.2.0/vdj_v1_hs_cd8_t/vdj_v1_hs_cd8_t_clonotypes.csv Cell Culture and Stimulation with PMA and Ionomycin. Cryopreserved PBMCs and CD4+ T cells were thawed at 37 °C and washed once with X-VIVO 20 (Lonza). The cells were centrifuged at 500 × g for 5 min, resuspended in X-VIVO 20 at a concentration of 1.0 × 106 cells/mL, and incubated at 37 °C in 5% CO 2 for 16 h. The cells were stimulated with cell activation mixture containing PMA and ionomycin (BioLegend) and brefeldin A (eBioscience), and incubated for 6 h. The stimulated cells were harvested, washed once with 500 μL of PBS with 2% FBS, and treated with Human BD Fc Block (BD Biosciences). Dead cells were stained with Fixable Viability Dye eFluor 780 (eBioscience). Cell-surface proteins were stained with specific antibodies against CD3ε (UCHT1), CD4 (RPA-T4), and CD8 (RPA-T8). The cells were fixed and permeabilized with Fixation/Permeabilization solution and BD Perm/Wash buffer (BD Biosciences). Intracellular molecules were stained with specific antibodies against GzmB (GB11), IFN-γ (4S.B3), and TNF-α (Mab11) or isotype controls, IgG1 κ (×40), IgG1 κ (MOPC-21), and IgG2b κ (28⇓⇓⇓⇓⇓⇓⇓–36). Cells were analyzed using FACSCanto II (BD Biosciences) with FlowJo Software (version 10.4.2). All FACS antibodies were purchased from BD Pharmingen. Statistical Analysis. The statistical significance of differences between supercentenarians and controls were determined by a 2-sided Wilcoxon rank sum test using the wilcox.test function in R. P values are indicated with an asterisk (*P < 0.05) in the figures. Data Availability. Raw UMI counts and normalized expression values for single-cell RNA-Seq are publicly available at http://gerg.gsc.riken.jp/SC2018/. Individual sequencing read data will be available on request under the condition of approval of the ethics committee of Keio University and material transfer agreement.

Acknowledgments We thank all the donors who participated in this study. We also thank RIKEN GeNAS for the sequencing of the single-cell libraries, Dr. Shigeki Hirabayashi for advice on collecting human blood samples, and Dr. Monique van der Wijst for advice on using their single-cell RNA-Seq dataset. This work was supported by a research grant from the Japanese Ministry of Education, Culture, Sports, Science and Technology to the RIKEN Center for Integrative Medical Sciences and research grants for Keio University Global Initiative Research Projects.

Footnotes Author contributions: K.H., T. Sasaki, G.P., A.M., I.T., Y.A., N. Hirose, and P.C. designed research; K.H., T.K., T.I., N. Hayatsu, Y.M., H.Y., T.T., T. Sasaki, T. Suzuki, Y.O., H.S., J.W.S., A.M., I.T., H.O., Y.A., and N. Hirose performed research; K.H., M.V., G.P., and P.C. analyzed data; and K.H. and M.V. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

Data deposition: Raw UMI counts and normalized expression values for single-cell RNA-Seq are publicly available at http://gerg.gsc.riken.jp/SC2018.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1907883116/-/DCSupplemental.