Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life

Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life

The decline of CD8 + T cell functions contributes to deteriorating health with aging, but the mechanisms that underlie this phenomenon are not well understood. We use single-cell RNA sequencing with both cross-sectional and longitudinal samples to assess how human CD8 + T cell heterogeneity and tran...

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Bibliographic Details
Published in:Nature communications Vol. 13; no. 1; p. 5128
Main Authors: Lu, Jian, Ahmad, Raheel, Nguyen, Thomas, Cifello, Jeffrey, Hemani, Humza, Li, Jiangyuan, Chen, Jinguo, Li, Siyi, Wang, Jing, Achour, Achouak, Chen, Joseph, Colie, Meagan, Lustig, Ana, Dunn, Christopher, Zukley, Linda, Chia, Chee W., Burd, Irina, Zhu, Jun, Ferrucci, Luigi, Weng, Nan-ping
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01-09-2022
Nature Publishing Group
Nature Portfolio
Subjects:
13/31
38/39
38/91
45/23
49
631/1647/514/2254
631/250/1619/554/1834/1269
631/250/2152/1566
631/250/2502
Age
Aging
CD8 antigen
Flow cytometry
Gene expression
Gene sequencing
Heterogeneity
HIV
Human immunodeficiency virus
Humanities and Social Sciences
Infections
Learning algorithms
Lymphocytes
Lymphocytes T
Machine learning
multidisciplinary
Mutation
Science
Science (multidisciplinary)
Subpopulations
Transcriptomes
Transcriptomics
Vaccination
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Summary:The decline of CD8 + T cell functions contributes to deteriorating health with aging, but the mechanisms that underlie this phenomenon are not well understood. We use single-cell RNA sequencing with both cross-sectional and longitudinal samples to assess how human CD8 + T cell heterogeneity and transcriptomes change over nine decades of life. Eleven subpopulations of CD8 + T cells and their dynamic changes with age are identified. Age-related changes in gene expression result from changes in the percentage of cells expressing a given transcript, quantitative changes in the transcript level, or a combination of these two. We develop a machine learning model capable of predicting the age of individual cells based on their transcriptomic features, which are closely associated with their differentiation and mutation burden. Finally, we validate this model in two separate contexts of CD8 + T cell aging: HIV infection and CAR T cell expansion in vivo. The characterisation of T cells during aging is important to predict functional outcomes in vaccination or infection. Here the authors use flow cytometry and scRNA sequencing to transcriptionally age CD8 T cells and then use a machine learning model to interpret cell age from transcriptional profiles.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-32869-x