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Nuclei isolation of multiple brain cell types for omics interrogation


We present a nuclei isolation protocol for genomic and epigenomic interrogation of multiple cell type populations in the human and rodent brain. The nuclei isolation protocol allows cell type-specific profiling of neurons, microglia, oligodendrocytes, and astrocytes, being compatible with fresh and frozen samples obtained from either resected or postmortem brain tissue. This 2-day procedure consists of tissue homogenization with fixation, nuclei extraction, and antibody staining followed by fluorescence-activated nuclei sorting (FANS) and does not require specialized skillsets. Cell type-specific nuclei populations can be used for downstream omic-scale sequencing applications with an emphasis on epigenomic interrogation such as histone modifications, transcription factor binding, chromatin accessibility, and chromosome architecture. The nuclei isolation protocol enables translational examination of archived healthy and diseased brain specimens through utilization of existing medical biorepositories.

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Fig. 1: Schematic of cell type-specific nuclei isolation from frozen brain tissue.
Fig. 2: FANS gating strategy for sorting of cell type-specific nuclei populations.
Fig. 3: Enhancer atlases from ex vivo microglia and PU.1 nuclei.

Data availability

The processed ATAC-seq and H3K27ac ChIP-seq tracks for the Schwannoma myeloid cells has been made available as part of a previously published UCSC genome browser session (hg19) containing ATAC-seq, ChIP-seq, and PLAC-seq datasets for brain cell types1: ATAC-seq and ChIP-seq datasets for ex vivo microglia and brain-derived PU.1 nuclei were previously published1,28 and are available on dbGap (


  1. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    CAS  Article  Google Scholar 

  2. Breuss, M. W. et al. Somatic mosaicism in the mature brain reveals clonal cellular distributions during cortical development. Preprint at bioRxiv (2020).

  3. Gallagher, M. D. & Chen-Plotkin, A. S. The post-GWAS era: from association to function. Am. J. Hum. Genet. 102, 717–730 (2018).

    CAS  Article  Google Scholar 

  4. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  Article  Google Scholar 

  5. Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).

    CAS  Article  Google Scholar 

  6. Marzi, S. J. et al. A histone acetylome-wide association study of Alzheimer’s disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat. Neurosci. 21, 1618–1627 (2018).

    CAS  Article  Google Scholar 

  7. Nativio, R. et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease. Nat. Genet. 52, 1024–1035 (2020).

    CAS  Article  Google Scholar 

  8. Sun, W. et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397.e11 (2016).

    CAS  Article  Google Scholar 

  9. Heinz, S. et al. Transcription elongation can affect genome 3D structure. Cell 174, 1522–1536.e22 (2018).

    CAS  Article  Google Scholar 

  10. Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21, 207–226 (2020).

    CAS  Article  Google Scholar 

  11. Fang, R. et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 26, 1345–1348 (2016).

    CAS  Article  Google Scholar 

  12. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    CAS  Article  Google Scholar 

  13. Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

    CAS  Article  Google Scholar 

  14. Wolf, S. A., Boddeke, H. W. G. M. & Kettenmann, H. Microglia in physiology and disease. Annu. Rev. Physiol. 79, 619–643 (2017).

    CAS  Article  Google Scholar 

  15. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    CAS  Article  Google Scholar 

  16. Ramamurthy, E. et al. Cell type-specific histone acetylation profiling of Alzheimer’s disease subjects and integration with genetics. Preprint at bioRxiv (2020).

  17. Hrvatin, S., Deng, F., O’Donnell, C. W., Gifford, D. K. & Melton, D. A. MARIS: method for analyzing RNA following intracellular sorting. PLoS ONE 9, e89459 (2014).

    Article  Google Scholar 

  18. Carlin, A. F. et al. Deconvolution of pro- and antiviral genomic responses in Zika virus-infected and bystander macrophages. Proc. Natl Acad. Sci. USA 115, E9172–e9181 (2018).

    CAS  Article  Google Scholar 

  19. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    CAS  Article  Google Scholar 

  20. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    CAS  Article  Google Scholar 

  21. Lu, T. et al. REST and stress resistance in ageing and Alzheimer’s disease. Nature 507, 448–454 (2014).

    CAS  Article  Google Scholar 

  22. Siegmund, K. D. et al. DNA methylation in the human cerebral cortex is dynamically regulated throughout the life span and involves differentiated neurons. PLoS ONE 2, e895 (2007).

    Article  Google Scholar 

  23. Spalding, K. L., Bhardwaj, R. D., Buchholz, B. A., Druid, H. & Frisén, J. Retrospective birth dating of cells in humans. Cell 122, 133–143 (2005).

    CAS  Article  Google Scholar 

  24. Koshi-Mano, K. et al. Neuron-specific analysis of histone modifications with post-mortem brains. Sci. Rep. 10, 3767 (2020).

    CAS  Article  Google Scholar 

  25. van der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019).

    Article  Google Scholar 

  26. Policicchio, S. S. et al. Fluorescence-activated nuclei sorting (FANS) on human post-mortem cortex tissue enabling the isolation of distinct neural cell populations for multiple omic profiling. (2020).

  27. Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020).

    CAS  Article  Google Scholar 

  28. Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).

    Article  Google Scholar 

  29. Nation, D. A. et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat. Med. 25, 270–276 (2019).

    CAS  Article  Google Scholar 

  30. Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R. & Zlokovic, B. V. Blood–brain barrier: from physiology to disease and back. Physiol. Rev. 99, 21–78 (2019).

    CAS  Article  Google Scholar 

  31. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    CAS  Article  Google Scholar 

  32. Mathys, H. et al. Temporal tracking of microglia activation in neurodegeneration at single-cell resolution. Cell Rep. 21, 366–380 (2017).

    CAS  Article  Google Scholar 

  33. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS  Article  Google Scholar 

  34. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  Article  Google Scholar 

  35. Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature 503, 487–492 (2013).

    CAS  Article  Google Scholar 

  36. Link, V. M. et al. Analysis of genetically diverse macrophages reveals local and domain-wide mechanisms that control transcription factor binding and function. Cell 173, 1796–1809.e17 (2018).

    CAS  Article  Google Scholar 

  37. Gosselin, D. et al. Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell 159, 1327–1340 (2014).

    CAS  Article  Google Scholar 

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We thank M. P. Pasillas for technical assistance and scientific discussions. A.N. was supported by the Alzheimer’s Association (grant no. AARF-18-531498), the Altman Clinical & Translational Research Institute at UCSD (National Center for Advancing Translational Sciences, supported by NIH grant no. KL2TR001444-6), a pilot project grant from UCSD Shiley-Marcos ADRC 1P30AG062429, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. J.C.M.S. was supported by the Interdisciplinary Research Fellowship in NeuroAIDS (NIH R25MH081482-12) and the HNRC CSPAR Developmental Core award (NIH 5P30MH062512-18). B.R.F. was supported by the National Institutes of Health (NIH) grant, 1F30AG062159-01. These studies were carried out with grant support to C.K.G. from the NIH R01 NS096170, R01 AG056511, and R01 AG061060-01, and from the Cure Alzheimer’s Fund Gifford Neuroinflammation Consortium CAF 20183159.

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Authors and Affiliations



A.N., J.C.M.S., and C.K.G. conceptualized the study; A.N. and J.C.M.S. optimized the methodology with input from B.R.F.; A.N. acquired and analyzed nuclei ChIP-seq and ATAC-seq datasets; A.N., J.C.M.S., B.R.F., and C.K.G. wrote the manuscript.

Corresponding authors

Correspondence to Alexi Nott, Johannes C. M. Schlachetzki or Christopher K. Glass.

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The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Dimitrios Davalos, Elvira Mass, and Cindy van Velthoven for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Key references using this protocol

Nott, A. et al. Science 366, 1134−1139 (2019):

Breuss, M. W. et al. Preprint at bioRxiv (2020):

Key data used in this protocol

Nott, A. et al. Science 366, 1134-1139 (2019):

Gosselin, D. et al. Science 356, eaal3222 (2017):

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Nott, A., Schlachetzki, J.C.M., Fixsen, B.R. et al. Nuclei isolation of multiple brain cell types for omics interrogation. Nat Protoc 16, 1629–1646 (2021).

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