Identification of Four Biomarkers of Human Skin Aging by Comprehensive Single Cell Transcriptome, Transcriptome, and Proteomics

Rui Mao; Yunying Wang; Fan Wang; Lei Zhou; Sha Yan; Shanshan Lu; Wei Shi; Yiya Zhang

doi:10.3389/fgene.2022.881051

Identification of Four Biomarkers of Human Skin Aging by Comprehensive Single Cell Transcriptome, Transcriptome, and Proteomics

Front Genet. 2022 Aug 23:13:881051. doi: 10.3389/fgene.2022.881051. eCollection 2022.

Authors

Rui Mao^{1

2}, Yunying Wang³, Fan Wang¹, Lei Zhou¹, Sha Yan¹, Shanshan Lu^{4

5}, Wei Shi^{1

6}, Yiya Zhang^{1

2

6}

Affiliations

¹ Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
² Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, China.
³ Department of Dermatology, The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
⁴ Research Center of Carcinogenesis and Targeted Therapy, Xiangya Hospital, Central South University, Changsha, China.
⁵ The Higher Educational Key Laboratory for Cancer Proteomics and Translational Medicine of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.
⁶ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.

Abstract

Background: Aging is characterized by the gradual loss of physiological integrity, resulting in impaired function and easier death. This deterioration is a major risk factor for major human pathological diseases, including cancer, diabetes, cardiovascular disease and neurodegenerative diseases. It is very important to find biomarkers that can prevent aging. Methods: Q-Exactive-MS was used for proteomic detection of young and senescence fibroblast. The key senescence-related molecules (SRMs) were identified by integrating transcriptome and proteomics from aging tissue/cells, and the correlation between these differentially expressed genes and well-known aging-related pathways. Next, we validated the expression of these molecules using qPCR, and explored the correlation between them and immune infiltrating cells. Finally, the enriched pathways of the genes significantly related to the four differential genes were identified using the single cell transcriptome. Results: we first combined proteomics and transcriptome to identified four SRMs. Data sets including GSE63577, GSE64553, GSE18876, GSE85358, and qPCR confirmed that ETF1, PLBD2, ASAH1, and MOXD1 were identified as SRMs. Then the correlation between SRMs and aging-related pathways was excavated and verified. Next, we verified the expression of SRMs at the tissue level and qPCR, and explored the correlation between them and immune infiltrating cells. Finally, at the single-cell transcriptome level, we verified their expression and explored the possible pathway by which they lead to aging. Briefly, ETF1 may affect the changes of inflammatory factors such as IL-17, IL-6, and NFKB1 by indirectly regulating the enrichment and differentiation of immune cells. MOXD1 may regulate senescence by affecting the WNT pathway and changing the cell cycle. ASAH1 may affect development and regulate the phenotype of aging by affecting cell cycle-related genes. Conclusion: In general, based on the analysis of proteomics and transcriptome, we identified four SRMs that may affect aging and speculated their possible mechanisms, which provides a new target for preventing aging, especially skin aging.

Keywords: fibroblast; proteomics; senescence; single cell transcriptome analysis; skin aging; transcriptome.