Multiregion transcriptomic profiling of the primate brain reveals signatures of aging and the social environment.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
08
01
2022
accepted:
05
10
2022
pubmed:
25
11
2022
medline:
2
12
2022
entrez:
24
11
2022
Statut:
ppublish
Résumé
Aging is accompanied by a host of social and biological changes that correlate with behavior, cognitive health and susceptibility to neurodegenerative disease. To understand trajectories of brain aging in a primate, we generated a multiregion bulk (N = 527 samples) and single-nucleus (N = 24 samples) brain transcriptional dataset encompassing 15 brain regions and both sexes in a unique population of free-ranging, behaviorally phenotyped rhesus macaques. We demonstrate that age-related changes in the level and variance of gene expression occur in genes associated with neural functions and neurological diseases, including Alzheimer's disease. Further, we show that higher social status in females is associated with younger relative transcriptional ages, providing a link between the social environment and aging in the brain. Our findings lend insight into biological mechanisms underlying brain aging in a nonhuman primate model of human behavior, cognition and health.
Identifiants
pubmed: 36424430
doi: 10.1038/s41593-022-01197-0
pii: 10.1038/s41593-022-01197-0
pmc: PMC10055353
mid: NIHMS1884457
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1714-1723Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R00AG051764
Organisme : NIGMS NIH HHS
ID : R35 GM124827
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG018023
Pays : United States
Organisme : NIA NIH HHS
ID : R00 AG051764
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG046139
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG019610
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01MH096875
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01MH118203
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : T32AG000057
Organisme : NIA NIH HHS
ID : R01 AG060931
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201000029C
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
ID : R35GM124827
Organisme : NINDS NIH HHS
ID : U24 NS072026
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH096875
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH121260
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : U01MH121260
Organisme : NIMH NIH HHS
ID : R01 MH118203
Pays : United States
Organisme : NIH HHS
ID : P40 OD012217
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG016574
Pays : United States
Organisme : NIA NIH HHS
ID : K99 AG075241
Pays : United States
Organisme : NCI NIH HHS
ID : HHSN261200800001E
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG032990
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Center for Research Resources (NCRR)
ID : P40OD012217
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R01AG060931
Organisme : NINDS NIH HHS
ID : R01 NS080820
Pays : United States
Organisme : NIA NIH HHS
ID : RC2 AG036547
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS097537
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG006786
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
ID : R01NS097537
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019).
pubmed: 31501588
Chiou, K. L. et al. Rhesus macaques as a tractable physiological model of human ageing. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190612 (2020).
pubmed: 32951555
pmcid: 7540946
Hernandez-Pacheco, R. et al. Managing the Cayo Santiago rhesus macaque population: the role of density. Am. J. Primatol. 78, 167–181 (2016).
pubmed: 25597512
Schneider, J. A., Arvanitakis, Z., Leurgans, S. E. & Bennett, D. A. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann. Neurol. 66, 200–208 (2009).
pubmed: 19743450
pmcid: 2812870
Blair, J. A. et al. Individual case analysis of postmortem interval time on brain tissue preservation. PLoS ONE 11, e0151615 (2016).
pubmed: 26982086
pmcid: 4794172
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).
pubmed: 32669715
pmcid: 7757734
Deleidi, M., Jäggle, M. & Rubino, G. Immune aging, dysmetabolism, and inflammation in neurological diseases. Front. Neurosci. 9, 172 (2015).
pubmed: 26089771
pmcid: 4453474
Mecocci, P. et al. A long journey into aging, brain aging, and Alzheimer’s disease following the oxidative stress tracks. J. Alzheimers Dis. 62, 1319–1335 (2018).
pubmed: 29562533
pmcid: 5870006
Sul, J. H., Martin, L. S. & Eskin, E. Population structure in genetic studies: confounding factors and mixed models. PLoS Genet. 14, e1007309 (2018).
pubmed: 30589851
pmcid: 6307707
Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).
pubmed: 30478440
Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).
pubmed: 27756721
Scheff, S. W., Price, D. A., Schmitt, F. A. & Mufson, E. J. Hippocampal synaptic loss in early Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 27, 1372–1384 (2006).
pubmed: 16289476
Poulin, S. P. et al. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Res. 194, 7–13 (2011).
pubmed: 21920712
pmcid: 3185127
Binder, E. B. et al. Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nat. Genet. 36, 1319–1325 (2004).
pubmed: 15565110
Sinclair, D., Fillman, S. G., Webster, M. J. & Weickert, C. S. Dysregulation of glucocorticoid receptor co-factors FKBP5, BAG1 and PTGES3 in prefrontal cortex in psychotic illness. Sci. Rep. 3, 3539 (2013).
pubmed: 24345775
pmcid: 3866598
Blair, L. J. et al. Accelerated neurodegeneration through chaperone-mediated oligomerization of tau. J. Clin. Invest. 123, 4158–4169 (2013).
pubmed: 23999428
pmcid: 3784538
Halbgebauer, S. et al. Modified serpinA1 as risk marker for Parkinson’s disease dementia: analysis of baseline data. Sci. Rep. 6, 26145 (2016).
pubmed: 27184740
pmcid: 4868992
Ebbert, M. T. W. et al. Conserved DNA methylation combined with differential frontal cortex and cerebellar expression distinguishes C9orf72-associated and sporadic ALS, and implicates SERPINA1 in disease. Acta Neuropathol. 134, 715–728 (2017).
pubmed: 28808785
pmcid: 5647251
Chai, Z., Zheng, P. & Zheng, J. Mechanism of ARPP21 antagonistic intron miR-128 on neurological function repair after stroke. Ann. Clin. Transl. Neurol. 8, 1408–1421 (2021).
pubmed: 34047500
pmcid: 8283178
Cooper-Knock, J. et al. Mutations in the glycosyltransferase domain of GLT8D1 are associated with familial amyotrophic lateral sclerosis. Cell Rep. 26, 2298–2306 (2019).
pubmed: 30811981
pmcid: 7003067
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
pubmed: 21593866
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
pubmed: 27104977
Greenwood, A. K. et al. The AD Knowledge Portal: a repository for multi-omic data on Alzheimer’s disease and aging. Curr. Protoc. Hum. Genet. 108, e105 (2020).
pubmed: 33085189
pmcid: 7587039
Izgi, H. et al. Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity. eLife 11, e68048 (2022).
Holland, P. W. H. & Takahashi, T. The evolution of homeobox genes: implications for the study of brain development. Brain Res. Bull. 66, 484–490 (2005).
pubmed: 16144637
Bergsland, M. et al. Sequentially acting Sox transcription factors in neural lineage development. Genes Dev. 25, 2453–2464 (2011).
pubmed: 22085726
pmcid: 3243056
Gould, E. How widespread is adult neurogenesis in mammals? Nat. Rev. Neurosci. 8, 481–488 (2007).
pubmed: 17514200
Diederich, N. J., James Surmeier, D., Uchihara, T., Grillner, S. & Goetz, C. G. Parkinson’s disease: is it a consequence of human brain evolution? Mov. Disord. 34, 453–459 (2019).
pubmed: 30759321
pmcid: 6593760
Pouladi, M. A., Morton, A. J. & Hayden, M. R. Choosing an animal model for the study of Huntington’s disease. Nat. Rev. Neurosci. 14, 708–721 (2013).
pubmed: 24052178
Finch, C. E. & Austad, S. N. Commentary: is Alzheimer’s disease uniquely human? Neurobiol. Aging 36, 553–555 (2015).
pubmed: 25533426
Yerbury, J. J. et al. Walking the tightrope: proteostasis and neurodegenerative disease. J. Neurochem. 137, 489–505 (2016).
pubmed: 26872075
Nativio, R. et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease. Nat. Neurosci. 21, 497–505 (2018).
pubmed: 29507413
pmcid: 6124498
Kinney, J. W. et al. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement. 4, 575–590 (2018).
Vicario-Orri, E., Opazo, C. M. & Muñoz, F. J. The pathophysiology of axonal transport in Alzheimer’s disease. J. Alzheimers Dis. 43, 1097–1113 (2015).
pubmed: 25147115
Eschbach, J. & Dupuis, L. Cytoplasmic dynein in neurodegeneration. Pharmacol. Ther. 130, 348–363 (2011).
pubmed: 21420428
Glass, C. K., Saijo, K., Winner, B., Marchetto, M. C. & Gage, F. H. Mechanisms underlying inflammation in neurodegeneration. Cell 140, 918–934 (2010).
pubmed: 20303880
pmcid: 2873093
Wan, Y.-W. et al. Meta-analysis of the Alzheimer’s disease human brain transcriptome and functional dissection in mouse models. Cell Rep. 32, 107908 (2020).
pubmed: 32668255
pmcid: 7428328
Kumar, S. et al. Extent of dorsolateral prefrontal cortex plasticity and its association with working memory in patients with Alzheimer disease. JAMA Psychiatry 74, 1266–1274 (2017).
pubmed: 29071355
pmcid: 6583382
Upright, N. A. & Baxter, M. G. Prefrontal cortex and cognitive aging in macaque monkeys. Am. J. Primatol. 83, e23250 (2021).
Prater, K. E. et al. Subtype transcriptomic profiling of myeloid cells in Alzheimer disease brain illustrates the diversity in active microglia phenotypes. Preprint at bioRxiv https://doi.org/10.1101/2021.10.25.465802 (2021).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
pubmed: 34616062
pmcid: 8494640
Luebke, J., Barbas, H. & Peters, A. Effects of normal aging on prefrontal area 46 in the rhesus monkey. Brain Res. Rev. 62, 212–232 (2010).
pubmed: 20005254
Raible, D. W. & McMorris, F. A. Induction of oligodendrocyte differentiation by activators of adenylate cyclase. J. Neurosci. Res. 27, 43–46 (1990).
pubmed: 2174977
Perry, V. H. & Holmes, C. Microglial priming in neurodegenerative disease. Nat. Rev. Neurol. 10, 217–224 (2014).
pubmed: 24638131
Niraula, A., Sheridan, J. F. & Godbout, J. P. Microglia priming with aging and stress. Neuropsychopharmacology 42, 318–333 (2017).
pubmed: 27604565
Lowsky, D. J., Olshansky, S. J., Bhattacharya, J. & Goldman, D. P. Heterogeneity in healthy aging. J. Gerontol. A Biol. Sci. Med. Sci. 69, 640–649 (2014).
pubmed: 24249734
Belsky, D. W. et al. Quantification of biological aging in young adults. Proc. Natl Acad. Sci. USA 112, E4104–E4110 (2015).
pubmed: 26150497
pmcid: 4522793
Snyder-Mackler, N. et al. Social determinants of health and survival in humans and other animals. Science 368, eaax9553 (2020).
pubmed: 32439765
pmcid: 7398600
Blomquist, G. E., Sade, D. S. & Berard, J. D. Rank-related fitness differences and their demographic pathways in semi-free-ranging rhesus macaques (Macaca mulatta). Int. J. Primatol. 32, 193–208 (2011).
Snyder-Mackler, N., Somel, M. & Tung, J. Shared signatures of social stress and aging in peripheral blood mononuclear cell gene expression profiles. Aging Cell 13, 954–957 (2014).
pubmed: 24956926
pmcid: 4172541
Testard, C. et al. Rhesus macaques build new social connections after a natural disaster. Curr. Biol. 31, 2299–2309 (2021).
pubmed: 33836140
pmcid: 8187277
McColgan, P., Joubert, J., Tabrizi, S. J. & Rees, G. The human motor cortex microcircuit: insights for neurodegenerative disease. Nat. Rev. Neurosci. 21, 401–415 (2020).
pubmed: 32555340
Ohm, T. G. The dentate gyrus in Alzheimer’s disease. Prog. Brain Res. 163, 723–740 (2007).
pubmed: 17765747
Jiji, S., Smitha, K. A., Gupta, A. K., Pillai, V. P. M. & Jayasree, R. S. Segmentation and volumetric analysis of the caudate nucleus in Alzheimer’s disease. Eur. J. Radiol. 82, 1525–1530 (2013).
pubmed: 23664648
Wilson, R. S. et al. Loneliness and risk of Alzheimer disease. Arch. Gen. Psychiatry 64, 234–240 (2007).
pubmed: 17283291
Holwerda, T. J. et al. Feelings of loneliness, but not social isolation, predict dementia onset: results from the Amsterdam Study of the Elderly (AMSTEL). J. Neurol. Neurosurg. Psychiatry 85, 135–142 (2014).
pubmed: 23232034
Cadar, D. et al. Individual and area-based socioeconomic factors associated with dementia incidence in England: evidence from a 12-year follow-up in the English Longitudinal Study of Ageing. JAMA Psychiatry 75, 723–732 (2018).
pubmed: 29799983
pmcid: 6145673
Berard, J. A four-year study of the association between male dominance rank, residency status, and reproductive activity in rhesus macaques (Macaca mulatta). Primates 40, 159–175 (1999).
pubmed: 23179538
Zannas, A. S. et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 16, 266 (2015).
pubmed: 26673150
pmcid: 4699359
Zannas, A. S. Epigenetics as a key link between psychosocial stress and aging: concepts, evidence, mechanisms. Dialogues Clin. Neurosci. 21, 389–396 (2019).
pubmed: 31949406
pmcid: 6952744
Harvanek, Z. M., Fogelman, N., Xu, K. & Sinha, R. Psychological and biological resilience modulates the effects of stress on epigenetic aging. Transl. Psychiatry 11, 601 (2021).
pubmed: 34839356
pmcid: 8627511
Miller, G. E., Cohen, S. & Ritchey, A. K. Chronic psychological stress and the regulation of pro-inflammatory cytokines: a glucocorticoid-resistance model. Health Psychol. 21, 531–541 (2002).
pubmed: 12433005
Snyder-Mackler, N. et al. Social status alters immune regulation and response to infection in macaques. Science 354, 1041–1045 (2016).
pubmed: 27885030
pmcid: 5498102
Snyder-Mackler, N. et al. Social status alters chromatin accessibility and the gene regulatory response to glucocorticoid stimulation in rhesus macaques. Proc. Natl Acad. Sci. USA 116, 1219–1228 (2019).
pubmed: 30538209
Kessler, M. J. & Rawlins, R. G. A 75-year pictorial history of the Cayo Santiago rhesus monkey colony. Am. J. Primatol. 78, 6–43 (2016).
pubmed: 25764995
Missakian, E. A. Genealogical and cross-genealogical dominance relations in a group of free-ranging rhesus monkeys (Macaca mulatta) on Cayo Santiago. Primates 13, 169–180 (1972).
Widdig, A. et al. Low incidence of inbreeding in a long-lived primate population isolated for 75 years. Behav. Ecol. Sociobiol. 71, 18 (2017).
pubmed: 28018027
Finch, C. E. & Austad, S. N. Primate aging in the mammalian scheme: the puzzle of extreme variation in brain aging. Age 34, 1075–1091 (2012).
pubmed: 22218781
pmcid: 3448989
Roth, G. S. et al. Aging in rhesus monkeys: relevance to human health interventions. Science 305, 1423–1426 (2004).
pubmed: 15353793
Kessler, M. J., Rawlins, R. G. & London, W. T. The hemogram, serum biochemistry, and electrolyte profile of aged rhesus monkeys (Macaca mulatta). J. Med. Primatol. 12, 184–191 (1983).
pubmed: 6680144
Hoffman, C. L., Higham, J. P., Mas-Rivera, A., Ayala, J. E. & Maestripieri, D. Terminal investment and senescence in rhesus macaques (Macaca mulatta) on Cayo Santiago. Behav. Ecol. 21, 972–978 (2010).
pubmed: 22475990
pmcid: 2920293
Kessler, M. J., Turnquist, J. E., Pritzker, K. P. & London, W. T. Reduction of passive extension and radiographic evidence of degenerative knee joint diseases in cage-raised and free-ranging aged rhesus monkeys (Macaca mulatta). J. Med. Primatol. 15, 1–9 (1986).
pubmed: 3701835
Nussey, D. H., Froy, H., Lemaitre, J.-F., Gaillard, J.-M. & Austad, S. N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).
pubmed: 22884974
Bronikowski, A. M. et al. Aging in the natural world: comparative data reveal similar mortality patterns across primates. Science 331, 1325–1328 (2011).
pubmed: 21393544
pmcid: 3396421
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Testard, C. et al. Social connections predict brain structure in a multidimensional free-ranging primate society. Sci. Adv. 8, eabl5794 (2022).
pubmed: 35417242
pmcid: 9007502
DeCasien, A. R. et al. Evolutionary and biomedical implications of sex differences in the primate brain transcriptome. Preprint at bioRxiv https://doi.org/10.1101/2022.10.03.510711 (2022).
Wong, K.-S. & Pang, H.-M. Simplifying HT RNA quality & quantity analysis. Genet. Eng. Biotechnol. News 33, 17 (2013).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
Warren, W. C. et al. Sequence diversity analyses of an improved rhesus macaque genome enhance its biomedical utility. Science 370, eabc6617 (2020).
pubmed: 33335035
pmcid: 7818670
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).
pubmed: 26925227
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
pubmed: 21169378
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
pubmed: 21478889
pmcid: 3083463
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
pubmed: 21653522
pmcid: 3137218
Lipatov, M., Sanjeev, K., Patro, R. & Veeramah, K. Maximum likelihood estimation of biological relatedness from low coverage sequencing data. Preprint at bioRxiv https://doi.org/10.1101/023374 (2015).
Alexa, A., Rahnenführer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607 (2006).
pubmed: 16606683
Alexa, A. & Rahnenführer, J. topGO: enrichment analysis for Gene Ontology. Bioconductor https://doi.org/10.18129/B9.bioc.topGO (2019).
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
pubmed: 19617889
pmcid: 3159387
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).
Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).
pubmed: 18385116
pmcid: 2278096
Naqvi, S. et al. Conservation, acquisition, and functional impact of sex-biased gene expression in mammals. Science 365, eaaw7317 (2019).
pubmed: 31320509
pmcid: 6896219
Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X. & Jensen, L. J. DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83–89 (2015).
pubmed: 25484339
Wang, M. et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci. Data 5, 180185 (2018).
pubmed: 30204156
pmcid: 6132187
De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).
pubmed: 30084846
pmcid: 6080491
Allen, M. et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci. Data 3, 160089 (2016).
pubmed: 27727239
pmcid: 5058336
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
pubmed: 20513432
pmcid: 2898526
Dunn, P. K. & Smyth, G. K. dglm: double generalized linear models. R package version 1.8.4 https://CRAN.R-project.org/package=dglm (2020).
Herrero, J. et al. Ensembl comparative genomics resources. Database 2016, bav096 (2016).
pubmed: 26896847
pmcid: 4761110
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 102409
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
pubmed: 30787437
pmcid: 6434952
Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
pubmed: 28818938
pmcid: 5894354
Hennig, B. P. et al. Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol. G3 8, 79–89 (2018).
pubmed: 29118030
Korneliussen, T. S. & Moltke, I. NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).
pubmed: 26323718
pmcid: 4673978
Hanghøj, K., Moltke, I., Andersen, P. A., Manica, A. & Korneliussen, T. S. Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding. Gigascience 8, giz034 (2019).
pubmed: 31042285
pmcid: 6488770
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).
pubmed: 25420514
pmcid: 4248462
Hedrick, P. W. & Lacy, R. C. Measuring relatedness between inbred individuals. J. Hered. 106, 20–25 (2015).
pubmed: 25472983
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).
pubmed: 30954476
pmcid: 6625319
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
pubmed: 26060301
pmcid: 4466750
Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).
pubmed: 30545855
pmcid: 6900982
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
pubmed: 31178118
pmcid: 6687398
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198
pmcid: 6456269
Miller, J. A. et al. Common cell type nomenclature for the mammalian brain. eLife 9, e59928 (2020).
pubmed: 33372656
pmcid: 7790494
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021).
pubmed: 34004146
pmcid: 8195859
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
pubmed: 30643263
pmcid: 6340744
Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science 365, eaax1971 (2019).
pubmed: 31488706
pmcid: 7428862
McKenzie, A. T. et al. Brain cell type specific gene expression and co-expression network architectures. Sci. Rep. 8, 8868 (2018).
pubmed: 29892006
pmcid: 5995803
Chen, L. et al. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ 6, e4600 (2018).
pubmed: 29629248
pmcid: 5885979
Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).
pubmed: 26490707
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
pubmed: 20808728
pmcid: 2929880
Anderson, J. A. et al. High social status males experience accelerated epigenetic aging in wild baboons. eLife 10, e66128 (2021).
pubmed: 33821798
pmcid: 8087445