Abstract
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.
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Data availability
The snRNA-Seq data analyzed in this study are available from https://www.synapse.org/ with synapse IDs: syn18485175 and syn21126462 for cohorts 1 and 2, respectively. Accessing these data requires submitting a Data Use Certificate through the AMP–AD website. Clinical data were available in the corresponding publications. The scRNA-Seq data from the embryonic cortex and the scRNA-Seq data from the entorhinal cortex are also available from the Gene Expression Omnibus54 with accession numbers GSE103723 and GSE138852.
Code availability
Our R scripts, which are available as Supplementary material, can be used to fully reproduce our results. Our code is also publicly available at https://bitbucket.org/habilzare/alzheimer/src/master/code/senescence/Shiva/.
Change history
16 December 2021
In the version of this article initially appearing online, the Supplementary Code file was missing, and has been included as of 16 December 2021.
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Acknowledgements
This work is supported by NIH/NIA (R01AG068293, R01AG057896, U01AG046170, RF1AG057440, R01AG057907, K99AG061259; P30AG062421; RF1AG051485, R21AG059176, and RF1AG059082 and T32AG021890), Cure Alzheimer’s Fund, New Vision Research Charleston Conference for Alzheimer’s Disease, and Veterans Affairs (IK2BX003804). We obtained ROSMAP data from the AD Knowledge Portal (https://adknowledgeportal.synapse.org). Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data generation was supported by National Institute on Aging (NIA) and grants RF1AG57473, P30AG10161, R01AG15819, R01AG17917, U01G46152, U01AG61356 and RF1AG059082. Additional phenotypic ROSMAP data can be requested at https://www.radc.rush.edu. We acknowledge the Texas Advanced Computing Center (TACC) at the University of Texas at Austin for providing high-performance computing resources: http://www.tacc.utexas.edu. We acknowledge the Biggs Institute Brain Bank and Massachusetts ADRC for providing postmortem human tissue for analyses. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Contributions
All authors edited and approved the final manuscript. In addition, S.K.D. developed code, designed and performed in silico experiments, performed all bioinformatic and statistical analyses, prepared figures and drafted the manuscript; J.W. provided tissue from the Biggs Brain Bank, performed p19 and AT8 IHC validation and wrote IHC methods; E.S. performed immunofluorescence validation (p19, AT8 and lamin B1), performed confocal microscopy and wrote the corresponding methods; E.B. performed blinded analyses of IHC and immunofluorescence and wrote the corresponding methods; F.A. performed immunofluorescence (p19, AT8 and Map2) validation and wrote corresponding methods; B.F. provided oversight to F.A. and provided Lamin b1 expertise; B.W. performed RNAScope; R.E.B. requested and acquired brain tissue from MGH ADRC, performed confocal microscopy, provided oversight to B.W., generated RNAscope figures and wrote RNAscope methods, RNAscope data interpretation; T.C.O. developed methods to analyze lamin b1 expression, performed blinded lamin b1 analyses and wrote lamin B1 analyses methods; Y.Z. provided access and expertise on cohort 2 snRNA-Seq data; P.S.A. provided access and expertise on cohort 2 snRNA-Seq data; M.C. provided access and expertise on cohort 2 snRNA-Seq data; P.H.S. provided bioinformatics consultation and validation; P.X. performed cell clustering analyses and wrote corresponding methods; M.W. performed cell clustering analyses and wrote the corresponding methods; B.Z. provided oversight to P.X. and provided bioinformatic consultation; H.Z. provided oversight to S.K.D. (bioinformatic experimental design and development), acquired access to all datasets, designed and confirmed bioinformatic data analyses and interpreted results; M.E.O. conceived the project, acquired funding, provided supervision (E.S., E.B. and T.C.O.), analyzed and interpreted histology data, interpreted bioinformatic data, and drafted manuscript and submitted the manuscript.
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Patent applicant: Wake Forest University Health Sciences. Name of inventor: Miranda Orr. Application number: 63/199,927 & 63/261,630. Status of application: Pending. The specific aspect of manuscript covered in patent application: data from this manuscript was used to file a patent, ‘Biosignature and therapeutic approach for neuronal senescence’.
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Extended data figures and tables
Extended Data Fig. 1 Prominent senescent cell types in prefrontal cortex of the embryonic control.
Cell types and counts represented in the senescent cell population discovered in (A) CSP, (B) SIP and (C) SRP. The cutoff and statistical test definitions are the same as in Fig. 1. Cell populations: astrocytes [Ast], blood cells [Blood], Cajal-Retzius cells [Cajal], endothelial cells [Endo], excitatory neurons [Ext], immune cells [Immune], inhibitory neuron [Inh], microglia [Micro], neural stem cells [NSC], and oligodendrocyte precursor cells [Oligo] were classified in the original publication.
Extended Data Fig. 2 Prominent senescent cell types in the dorsal lateral prefrontal cortex in Cohort 2.
Cell types and counts represented in the senescent cell population discovered in (A) CSP, (B) SIP and (C) SRP with n = 57,857. The cutoff, statistical test and abbreviations definitions are the same as in Fig. 1.
Extended Data Fig. 3 Overlap between senescent and NFT neurons.
Each vertical bar represents the number of neurons in Cohort 1 that express the eigengenes marked by green circles below the bar. Each row at the bottom corresponds to an eigengene, and the number of neurons expressing that eigengene is shown in the right end on each row. The probability distributions of multi-set intersections have been calculated and the significance was tested using a hypergeometric test. The scale bar at top right shows the level of significance for each intersection. The largest p-value is −232 in log10 scale, which corresponds to the intersection between SRP and CSP expressing cells.
Extended Data Fig. 4 Prominent senescent cell types using CellAge, GO and KEGG gene lists in Cohort 1.
Cell types and counts represented in the senescent cell population discovered in (A) CellAge, (B) GO and (C) KEGG. The cutoff, statistical test and abbreviations definitions are the same as in Fig. 1.
Extended Data Fig. 5 Overlap between senescent cell populations.
Each vertical bar represents the number of senescent cells in Cohort 1 that express the senescence eigengenes, marked by green circles below the bar. Each row at the bottom corresponds to a senescence eigengene, and the number of senescent cells expressing that eigengene is shown at the end of each row. The probability distributions of multi-set intersections have been calculated and the significance was tested using a hypergeometric test. The scale bar at top right shows the level of significance for each intersection. The largest p-value is-260 in log10 scale corresponding to the intersection of SRP and CSP.
Extended Data Fig. 6 Excitatory neurons are the prominent senescent cell types based on CDKN2D in (A) Cohort 1 and (B) Cohort 2.
Cell types and counts represented in the senescent cell population using only CDKN2D. The cutoff, statistical tests and abbreviations definitions are the same as in Fig. 1.
Extended Data Fig. 7 RNAscope reveals higher CDKN2D expression in postmortem brains from cases with AD than age-matched control brains.
A. CDKN2D negative and positive control probe signal. B. CDKN2D RNAscope on three separate AD cases (n = 3) compared to a representative age-matched non-demented control (n = 3) (refer to Supplementary Table 5 for case characteristics. Scale bar 50 μm.
Extended Data Fig. 8 CDKN2D RNAscope co-localized with neuronal marker, HuD.
Postmortem AD tissue was processed for RNAscope with CDKN2D (green) and co-labeled for total nuclei (DAPI, gray) and neurons (HuD, cyan)/ Merged image display strong overlap between CDKN2D and neurons, but not other cell types (that is, blue and green co-localization with infrequent green co-localization in nuclei without HuD staining). Scale bar 10 μm. Representative images from postmortem human brains (n = 3 control and n = 3 AD cases).
Supplementary information
Supplementary Table 1
This file contains Supplemental Tables 1-3 combined into a single excel workbook file with multiple Tabs. Supplementary Table 1 contains the gene lists used to create each of the eigengene; Supplementary Table 2 (multiple tabs) contains eigengene expression data from every cell analyzed across datasets. Supplementary Table 3 (multiple tabs) contains data describing the overlap between cells expressing the senescence eigengenes and those expressing the NFT eigengenes for both cohorts (1 and 2) using both NFT eigengenes (Dunckley and Garcia).
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Dehkordi, S.K., Walker, J., Sah, E. et al. Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nat Aging 1, 1107–1116 (2021). https://doi.org/10.1038/s43587-021-00142-3
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DOI: https://doi.org/10.1038/s43587-021-00142-3
