A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain
Preprint — published as 10.1186/s13195-019-0524-x · Paper page
Abstract
Alzheimer Disease (AD) is the most common form of dementia. This neurodegenerative disorder is associated with neuronal death and gliosis heavily impacting the cerebral cortex. AD has a substantial but heterogeneous genetic component, presenting both Mendelian and complex genetic architectures. Using bulk RNA-seq from parietal lobes and deconvolution methods, we previously reported that brains exhibiting different AD genetic architecture exhibit different cellular proportions. Here, we sought to directly investigate AD brain changes in cell proportion and gene expression using single cell resolution. To do so, we generated unsorted single-nuclei RNA-sequencing data from brain tissue. We leveraged tissue donated from a carrier of a Mendelian genetic mutation and two family members who suffer from AD, but do not have the same mutation. We evaluated alternative alignment approaches to maximize the titer of reads, genes and cells with high quality. In addition, we employed distinct clustering strategies to determine the best approach to identify cell clusters that reveal neuronal and glial cell types and avoid artifacts such as sample and batch effects. We propose an approach to cluster cells that reduces biases and enable further analyses. We identified distinct types of neurons, both excitatory and inhibitory, and glial cells, including astrocytes, oligodendrocytes, and microglia among others. In particular, we identified a reduced proportion of excitatory neurons in the Mendelian mutation carrier, but a similar distribution of inhibitory neurons. Furthermore, we investigated whether single-nuclei RNA-seq from human brains recapitulate the expression profile of Disease Associated Microglia (DAM) discovered in mouse models. We also determined that when analyzing human single-nuclei data it is critical to control for biases introduced by donor specific expression profiles. In conclusion, we propose a collection of best practices to generate a highly-detailed molecular cell atlas of highly informative frozen tissue stored in brain banks. Importantly, we have developed a new web application to make this unique single-nuclei molecular atlas publicly available.
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Studies of single nucleus RNA sequencing from human brain reported either no detectable population structure in microglial cells or a maximum of four transcriptionally distinct microglial subsets.
“Recently, studies of single nucleus RNA sequencing from human brain reported either no detectable population structure in microglial cells7,8 or a maximum of four transcriptionally distinct microglial subsets9.”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [8]
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Cites
Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · see citing works
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095 · see citing works
Functional organization of the transcriptome in human brain
Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H. (2008) · Nature Neuroscience · doi:10.1038/nn.2207 · Cited by 752 · see citing works
Friedman, B. A., Srinivasan, K., Ayalon, G., Meilandt, W. J., Lin, H., Huntley, M. A., Cao, Y., Lee, S.-H., Haddick, P. C. G., Ngu, H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.066 · Cited by 662 · see citing works
A transcriptomic atlas of aged human microglia
Olah, M., Patrick, E., Villani, A.-C., Xu, J., White, C. C., Ryan, K. J., Piehowski, P., Kapasi, A., Nejad, P., Cimpean, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02926-5 · Cited by 482 · see citing works
Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
McKenzie, A. T., Wang, M., Hauberg, M. E., Fullard, J. F., Kozlenkov, A., Keenan, A., Hurd, Y. L., Dracheva, S., Casaccia, P., Roussos, P., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-27293-5 · Cited by 481 · see citing works
Jacobs, H. I. L., Van Boxtel, M. P. J., Jolles, J., Verhey, F. R. J., & Uylings, H. B. M. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.06.009 · Cited by 293 · see citing works
Rice, H. C., de Malmazet, D., Schreurs, A., Frere, S., Van Molle, I., Volkov, A. N., Creemers, E., Vertkin, I., Nys, J., Ranaivoson, F. M., et al. (2019) · Science · doi:10.1126/science.aao4827 · Cited by 281 · see citing works
Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V., & Oldham, M. C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0216-z · Cited by 198 · see citing works
Jaffe, A. E., Shin, J., Collado-Torres, L., Leek, J. T., Tao, R., Li, C., Gao, Y., Jia, Y., Maher, B. J., Hyde, T. M., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3898 · Cited by 156 · see citing works
Alzheimer's Disease: The Downside of a Highly Evolved Parietal Lobe?
Bruner, E., & Jacobs, H. I. L. (2013) · Journal of Alzheimer's Disease · doi:10.3233/jad-122299 · Cited by 87 · see citing works
Flagged first, then by citations
Research works printed but not linked: 23
- Benaglia T, Chauveau D, Hunter DR, Young DS (2010) mixtools: An R Package for Analyzing Mixture Models. 32: Doi 10.18637/jss.v032.i06 · DOI
- Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36: 411-420 Doi 10.1038/nbt.4096 · DOI · Paper page
- Cabeza R, Ciaramelli E, Olson IR, Moscovitch M (2008) The parietal cortex and episodic memory: an attentional account. Nat Rev Neurosci 9: 613-625 Doi 10.1038/nrn2459 · DOI
- Chen R, Wu X, Jiang L, Zhang Y (2017) Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity. Cell Rep 18: 3227-3241 Doi 10.1016/j.celrep.2017.03.004 · DOI · Paper page
- De Strooper B, Annaert W (2010) Novel research horizons for presenilins and gamma-secretases in cell biology and disease. Annu Rev Cell Dev Biol 26: 235-260 Doi 10.1146/annurev-cellbio- 100109-104117 · DOI
- Grindberg RV, Yee-Greenbaum JL, McConnell MJ, Novotny M, O'Shaughnessy AL, Lambert GM, Arauzo-Bravo MJ, Lee J, Fishman M, Robbins GEet al (2013) RNA-sequencing from single nuclei. Proc Natl Acad Sci U S A 110: 19802-19807 Doi 10.1073/pnas.1319700110 · DOI · Paper page
- Herculano-Houzel S (2014) The glia/neuron ratio: how it varies uniformly across brain structures and species and what that means for brain physiology and evolution. Glia 62: 1377-1391 Doi 10.1002/glia.22683 · DOI · Paper page
- Hu P, Fabyanic E, Kwon DY, Tang S, Zhou Z, Wu H (2017) Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single- Nucleus RNA-Seq. Mol Cell 68: 1006-1015 e1007 Doi 10.1016/j.molcel.2017.11.017 · DOI · Paper page
- Ji Z, Ji H (2016) TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 44: e117 Doi 10.1093/nar/gkw430 17 · DOI
- Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth Bet al (2017) A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell 169: 1276-1290 e1217 Doi 10.1016/j.cell.2017.05.018 · DOI · Paper page
- LaFerla FM, Oddo S (2005) Alzheimer's disease: Abeta, tau and synaptic dysfunction. Trends Mol Med 11: 170-176 Doi 10.1016/j.molmed.2005.02.009 · DOI
- Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, Wildberg A, Gao D, Fung HL, Chen Set al (2016) Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352: 1586-1590 Doi 10.1126/science.aaf1204 · DOI · Paper page
- Li WV, Li JJ (2018) An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun 9: 997 Doi 10.1038/s41467-018-03405-7 · DOI
- Li Z, Del-Aguila JL, Dube U, Budde J, Martinez R, Black K, Xiao Q, Cairns NJ, Dominantly Inherited Alzheimer N, Dougherty JDet al (2018) Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure. Genome Med 10: 43 Doi 10.1186/s13073-018-0551-4 · DOI · Paper page
- Lindeboom J, Weinstein H (2004) Neuropsychology of cognitive ageing, minimal cognitive impairment, Alzheimer's disease, and vascular cognitive impairment. Eur J Pharmacol 490: 83-86 Doi 10.1016/j.ejphar.2004.02.046 · DOI
- Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C (2017) Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14: 979-982 Doi 10.1038/nmeth.4402 · DOI · Paper page
- Saunders A, Macosko E, Wysoker A, Goldman M, Krienen F, de Rivera H, Bien E, Baum M, Wang S, Goeva Aet al (2018) Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain. bioRxiv: Doi https://doi.org/10.1101/299081 · DOI
- Selkoe DJ (2001) Alzheimer's disease: genes, proteins, and therapy. Physiol Rev 81: 741-766 Doi 10.1152/physrev.2001.81.2.741 · DOI · Paper page
- Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan Set al (2018) Shared and distinct transcriptomic cell types across neocortical areas. Nature 563: 72-78 Doi 10.1038/s41586-018-0654-5 · DOI · Paper page
- Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YTet al (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362: Doi 10.1126/science.aat8464 · DOI · Paper page
- Wang J, Chen L, Chen Z, Zhang W (2015) RNA-seq based transcriptomic analysis of single bacterial cells. Integr Biol (Camb) 7: 1466-1476 Doi 10.1039/c5ib00191a · DOI
- Xu C, Su Z (2015) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31: 1974-1980 Doi 10.1093/bioinformatics/btv088 37 · DOI
- Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu Jet al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8: 14049 Doi 10.1038/ncomms14049 · DOI · Paper page
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Mancuso, R., Fattorelli, N., Martinez-Muriana, A., Davis, E., Wolfs, L., Van Den Daele, J., Geric, I., Preman, P., Serneels, L., Poovathingal, S., et al. (2022) · doi:10.1101/2022.07.07.499139 · Cited by 169 · see citing works
- Authors
- Del-Aguila, J. L., Li, Z., Dube, U., Mihindukulasuriya, K. A., Budde, J. P., Fernandez, M. V., Ibanez, L., Bradley, J., Wang, F., Bergmann, K., Davenport, R., Morris, J. C., Holtzman, D. M., Perrin, R. J., Benitez, B. A., Dougherty, J., Cruchaga, C., & Harari, O.
- Publisher
- openRxiv
- Published
- 2019-03-30
- Cited by
- 3
- References
- 12
- Type
- Preprint
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