Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity

Abstract

Quantifying aging rate is important for evaluating age-associated decline and mortality. A blood single-cell RNA sequencing dataset for seven supercentenarians (SCs) was recently generated. Here, we generate a reference 28-sample aging cohort to compute a single-cell level aging clock and to determine the biological age of SCs. Our clock model placed the SCs at a blood biological age to between 80.43 and 102.67 years. Compared to the model-expected aging trajectory, SCs display increased naive CD8⁺ T cells, decreased cytotoxic CD8⁺ T cells, memory CD4⁺ T cells, and megakaryocytes. As the most prominent molecular hallmarks at the single-cell level, SCs contain more cells and cell types with high ribosome level, which is associated with and, according to Bayesian network inference, contributes to a low inflammation state and slow aging of SCs. Inhibiting ribosomal activity or translation in monocytes validates such translation against inflammation balance revealed by our single-cell aging clock.

Fig. 1.. Single-cell RNA sequencing (scRNA-seq) landscape of human blood peripheral blood mononuclear cell (PBMC) during aging.

(A) Study design and flow chart of the human blood PBMC scRNA-seq aging clock analysis. The scRNA-seq data from SE, Chinese young cohort (CYCT), Wuhan cohort (WHCT), Japanese old cohort (JOCT), and supercentenarian (SC) cohort are merged to classify cell types, and then cell type annotations are transferred to additional cohorts by TOSICA for further analysis. The green bars show the age range of each cohort. (B) Two-dimensional uniform manifold approximation and projection (UMAP) visualization of scRNA-seq data from PBMCs of all cohorts. Each point is a cell; colors are based on subtype annotation defined by the Louvain clustering algorithm and marker genes. (C) Expression levels of significantly differentially expressed genes for eight major cell types. The color and size of each dot represent the expression level and cell fraction of the marker genes, respectively. (D) Proportion of cell types shown in (C), in different cohorts. Cell type frequency is labeled in white text.

Fig. 2.. Single-cell RNA sequencing (scRNA-seq)–based single-cell composition aging clock and age delay of supercentenarians (SCs).

(A) Spearman rank correlation coefficient (RCC) of age to proportion of cell types with RCC > 0.25 across all SE samples. (B) Variable importance in projection (VIP) values of cell types with VIP ≥ 1 in partial least square regression (PLSR) clock model. (C) PLSR clock model trained and cross-validated on cell type proportions in SE + Guangdong Medical University (GM) samples. Each dot represents one individual, colored by cohort. (D) Chronological age and age-corrected PLSR (based on 28 SE + GM samples) predicted age of independent cohorts. Each dot represents one individual. (E) PLSR (based on 28 SE + GM samples)–predicted age-corrected difference between chronological and predicted age (cAgeDiff) of individuals with mild and severe COVID-19 compared to age-matched healthy controls. Student’s t test P value is shown on the top. (F) PLSR (based on 28 SE + GM samples)–predicted cAgeDiff of COVID-19-positive patients at convalescence and progression stages. (G) PLSR (based on 28 SE + GM samples)–predicted cAgeDiff of systemic lupus erythematosus (SLE) patients of managed, flare, and treated states compared with age-matched healthy controls. Each point represents one individual, and the error bar represents mean and SD. Student’s t test P values are shown on the top. (H) Anti-correlation between the proportions of CD8-CTL and CD8-Naive cells in CD8⁺ T cells across SE + GM and SC samples. Pearson’s correlation coefficient and P value are shown.

Fig. 3.. Aging-related cell type proportion and gene expression changes delayed in supercentenarians (SCs).

(A) Illustration of the identification and definition of SC delayed age-related changes in cell type proportions and gene expressions. (B) Partial least square regression (PLSR) age predictor-trained and cross-validated on pseudobulk transcriptome in SE + GM samples. Each dot represents one individual, colored by cohort. (C) Age-corrected PLSR model-predicted age of each SC sample based on the pooled gene expression in each cell type. The maximum and minimum predicted ages of each SC are marked in white and black, respectively. (D and E) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched in SC delayed age-up and delayed age-down genes in different cell types according to linear regression model (LR) model-predicted gene expression in SCs (Fisher test FDR < 0.01 scaled according to the legend). (F) Circos plot showing the top 150 interactions mediated by ligand-receptor pairs between cell types significantly changed with age Pearson correlation coefficient (PCC) P < 0.05. The outer ring displays color-coded cell types according to Fig. 1B, and the inner ring represents the ligand-receptor interacting pairs. The line width and arrow width are proportional to t test FDR. Colors of edges indicate the changes with age: blue, age-down; red, age-up. (G) Circos plot of the SC delayed age-up or age-down interactions between cell types. Blue, SC delayed age-up; red, SC delayed age-down.

Fig. 4.. Relationship between ribosome and inflammation across single cells and cell types during aging.

(A) Dot plot of ribosome gene expression levels and inflammatory score for each cell in SE and supercentenarian (SC) cohorts. Combined Pearson correlation coefficient (PCC) and P value in SE and SC are labeled. (B) Dot plot of average ribosome gene expression levels against inflammatory score for all cell types. Combined rank correlation coefficient (RCC) and P value in SE and SC are labeled. (C) Heatmap of the total ribosome gene expression level in each cell type of each individual. Rank correlation coefficients (RCCs) with age in 28 SE + Guangdong Medical University (GM) samples and t test P value between SC and predicted value are shown on the right. (D) Heatmap of the high ribosome state frequency in each cell type. RCCs with age and t test P value between SC and SE are shown on the right. The significant RCCs are labeled by *. The maximum, median, and minimum RCCs are marked in white, gray, and black, respectively. (E) Circos plot of the down-regulated interactions in high versus low ribosome state (t test, P < 0.001). (F) Bayesian networks inferred among the four factors in HL-LR cell types and LI-HR cell types in 28 SE + GM samples. A directed edge denotes that the occurrence of the target node is dependent on that of the source node. (G) Heatmap of the correlation between inflammation score, ribosome levels, age, and age-corrected difference between chronological and predicted age (cAgeDiff) in each cell type of 28 SE + GM samples. The mean level of inflammation and ribosome is labeled on the right. (H) Quantitative polymerase chain reaction (PCR) shows the transcript changes of IL-6 and IL-8 in primary monocyte cells treated with vehicle and cycloheximide (CHX) (100 μg/ml) at various time points. **P < 0.01; ***P < 0.001 (t test). (I) Schematic summary of the ribosome and inflammation balance as a hallmark of blood single-cell aging and delayed aging in SCs.