AgeAnno: a knowledgebase of single-cell annotation of aging in human

Abstract

Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.

Figure 1.

Database content and construction of AgeAnno. (A) Public resources used in AgeAnno. (B) Dynamic functional annotations of aging-related genes in AgeAnno. (C) User interface of AgeAnno. Users can perform data queries and browse through multiple paths. AgeAnno supports browsing, searching, and downloading information on aging-related genes.

Figure 2.

Examples from functional analyses of AgeAnno. (A) VIP expression in inhibitory neurons of different age groups. VIP is significant downregulated with the aging process. (B) Radar plot of variation coefficient of VIP in each cell type between mid-group and youth group. (C) Comparison of VIP variation coefficient between different age groups (mid versus youth, old versus mid). (D) Top 5 enriched biological pathways of VIP. (E) Circular network plot of the brain of the mid group. (F) Circular network plot of the brain of the old group. Nodes with different colors represent different cell types, while the edges represent L–R interactions between two cell types. (G) RSS of FOS in different cell types. (H) Sunburst plot of immune cell components and proportion in blood in youth group. (I) Sunburst plot of immune cell components and proportion in the blood of the old group. (J) SOX9 showed significantly increased chromatin accessibility in astrocytes in aged brain. (K) TF footprint plot of different age groups. Red line represents the mid group and blue line represents old group.

Figure 3.

The main functions and usages of AgeAnno. (A) The top navigation bar of the main functions in AgeAnno. (B) On the ‘Home’ page, users can perform data queries through two paths: ‘Search by gene symbol/Entrez ID’ and ‘Search by tissue type’. (C) The functional analyses included in AgeAnno. Users can query and visualize genes in different functional analysis categories. (D) The ‘Link’ function includes published aging-related algorithms and tools. (E) Data download function. (F) ‘Help’ function contains a brief introduction of AgeAnno and its functions. (G) Statistics information of samples and annotations in AgeAnno.