Eliminating senescent cells is an important strategy to improve and alleviate ageing-related diseases. CAR-T cell therapy is a promising new treatment method for cancer and tumours. CAR-T cell therapy induces engineered T cells to construct chimeric antigen receptors (CARS) against tumour targets, such as chimeric antigen receptor CAR-T cells, in the treatment of chronic lymphocytic leukaemia. Drawing on the target specificity of CAR-T, the concept of CAR has gone beyond tumour research, and CAR-T cell therapy provides a new idea for anti-ageing, such as the use of CAR-T targeted removal of senescent cells to alleviate diseases in autoimmune diseases and anti-ageing. In 2020, CAR-T was successfully applied to eliminate senescent cells by targeting uPAR, a cell surface protein that is widely induced during ageing. uPAR-CAR-T cells can specifically eliminate senescent cells with high uPAR expression in vitro and in vivo and alleviate upAR-related liver fibrosis while anti-ageing and anti-inflammation. Its combination with other drugs prolongs the survival time of mice with lung adenocarcinoma, confirming the therapeutic potential of CAR-T cells for ageing-related diseases. Our team mainly focuses on the heart and lung, aiming to find new targets for anti-ageing, and design new and better CAR-T products to provide the possibility of alleviating ageing and its related diseases.

Four data about the heart were downloaded, with 1 from Tabula Muris Senis and 3 from Gene Expression Omnibus (GEO) database.

• A: Mouse heart (Tabula Muris Senis)
• B: Rat aorta (GSE137869)
• C: Monkey coronary artery (GSE117715)
• D: Monkey aorta (GSE117715)

Data were downloaded from Tabula Muris Senis. A total of 17489 mouse lung scRNA from 16 individuals of different ages were collected.

Tips: GEO database (https://www.ncbi.nlm.nih.gov/geo/ ) includes microarray chips, next-generation sequencing, and other forms of high-throughput genomic data. Tabula Muris Senis (https://tabula-muris-senis.ds.czbiohub.org/) includes Single-cell and Bulk RNA-sequencing of different organs across the mouse lifespan.

In the mouse heart and lung data, 1- and 3-month-old cells were defined as young cells, and 18, 21, 24, 30 month-old cells were defined as senescent cells. Limma 3.52.1 was used for differential gene expression analysis. The other datasets were divided into two groups, aged & young, according to the annotation of the datasets and were subject to DEGSeq2 1.36.0 for differential gene expression analysis. Genes significantly (p < 0.05) upregulated genes (LogFC > 0) in senescent samples were considered as potential markers

A web crawler was constructed using Python BeautifulSoup and Selenium packages to automate the search process of the subcellular location partition of the UniProt database. Genes whose products are not found on the plasma membrane were excluded.

For heart data, 12 membrane proteins were up-regulated in 4 data, 51 membrane proteins were up-regulated in 3 data, and a total of 63 genes entered the follow-up screening.

For lung data, we did not do this process. However, 14 genes were up-regulated and entered the follow-up screening.

Expression patterns of the potential markers should be relatively tissue-specific. UCSC database and human protein atlas were used to determine the 11 tissue-clean genes.

Target genes cannot be highly expressed in T cells. Mouse Cell Atlas of Zhejiang University was used to discard the genes highly expressed (rank < 5 in T cells) in T cells.

Combined with literature analysis and other previous data set analysis, the final 6 potential targets were identified (Anxa3, Icam1, Vsir, Tspan8, Cyba, Fxyd5).

Cell subset analysis was performed on the four datasets using Seurat 4.1.1, and each subset was manually fine-tuned based on SingleR 3.11 & Celldex 3.15 to obtain the expression level of each target in the cell subset.