Genome ResearchBioinformaticsPeer-reviewed journal article
A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging
Jacob C. KimmeliD, Lolita PenlandiD, Nimrod Daniel RubinsteiniD, David G. HendricksoniD, David R. KelleyiD, Adam Z. Rosenthal
Abstract
BackgroundAging is a pleiotropic process affecting many aspects of organismal and cellular physiology. Mammalian organisms are composed of a constellation of distinct cell type and state identities residing within different tissue environments. Due to technological limitations, the study of aging has traditionally focused on changes within individual cell types, or the aggregate changes across cell types within a tissue. The influence of cell identity and tissue environment on the trajectory of aging therefore remains unclear.\n\nResultsHere, we perform single cell RNA-seq on >50,000 individual cells across three tissues in young and aged mice. These molecular profiles allow for comparison of aging phenotypes across cell types and tissue environments. We find transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we compute a trajectory and magnitude of aging for each cell type. We find that cell type exerts a larger influence on these measures than tissue environment.\n\nConclusionIn this study, we use single cell RNA-seq to dissect the influence of cell identity and tissue environment on the aging process. Single cell analysis reveals that cell identities age in unique ways, with some common features of aging shared across identities. We find that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: trajectory inference
- research focus: differential expression
- approach: statistical model
- approach: matrix factorization
- approach: optimal transport
- Citations
- 184
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