Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology

Shiva Kazempour Dehkordi, Jamie Walker, Eric Sah, Emma Bennett, Farzaneh Atrian, Bess Frost, Benjamin Woost, Rachel E. Bennett, Timothy C. Orr, Yingyue Zhou, Prabhakar S. Andhey, Marco Colonna, Peter H. Sudmant, Peng Xu, Minghui Wang, Bin Zhang, Habil Zare, Miranda E. Orr

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Senescent cells contribute to pathology and dysfunction in animal models1. Their sparse distribution and heterogenous phenotype have presented challenges to their detection in human tissues. We developed a senescence eigengene approach to identify these rare cells within large, diverse populations of postmortem human brain cells. Eigengenes are useful when no single gene reliably captures a phenotype, like senescence. They also help to reduce noise, which is important in large transcriptomic datasets where subtle signals from low-expressing genes can be lost. Each of our eigengenes detected ∼2% senescent cells from a population of ∼140,000 single nuclei derived from 76 postmortem human brains with various levels of Alzheimer’s disease (AD) pathology. More than 97% of the senescent cells were excitatory neurons and overlapped with neurons containing neurofibrillary tangle (NFT) tau pathology. Cyclin-dependent kinase inhibitor 2D (CDKN2D/p19) was predicted as the most significant contributor to the primary senescence eigengene. RNAscope and immunofluorescence confirmed its elevated expression in AD brain tissue. The p19-expressing neuron population had 1.8-fold larger nuclei and significantly more cells with lipofuscin than p19-negative neurons. These hallmark senescence phenotypes were further elevated in the presence of NFTs. Collectively, CDKN2D/p19-expressing neurons with NFTs represent a unique cellular population in human AD with a senescence-like phenotype. The eigengenes developed may be useful in future senescence profiling studies as they identified senescent cells accurately in snRNA-Seq datasets and predicted biomarkers for histological investigation.

Original languageEnglish
Pages (from-to)1107-1116
Number of pages10
JournalNature Aging
Issue number12
StatePublished - Dec 2021


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