Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons
Open accessAbstract
A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at -80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.
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The central nervous system of older mice is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over- or underrepresented.
“The central nervous system of older mice is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over-or underrepresented (Darmanis et al., 2015;Grindberg et al., 2013;Krishnaswami et al., 2016;Lake et al., 2016;Lake et al., 2017;Tasic et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as Krishnaswami et al., 2016
The Introduction (page unavailable) says that CNS cells are difficult to isolate intact because neurons are highly interconnected. It does not compare older and younger mice or say that dissociation causes cell types to be over- or underrepresented. (LLM, conf=0.85)
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- Authors
- Krishnaswami, S. R., Grindberg, R. V., Novotny, M., Venepally, P., Lacar, B., Bhutani, K., Linker, S. B., Pham, S., Erwin, J. A., Miller, J. A., Hodge, R., McCarthy, J. K., Kelder, M., McCorrison, J., Aevermann, B. D., Fuertes, F. D., Scheuermann, R. H., Lee, J., Lein, E. S., Schork, N., McConnell, M. J., Gage, F. H., & Lasken, R. S.
- Affiliations
- J. Craig Venter Institute (US)
- ETH Zurich (CH)
- Salk Institute for Biological Studies (US)
- Allen Institute for Brain Science (US)
- University of Virginia (US)
- Corresponding author
- Grindberg, Rashel V
- Lasken, Roger S
- ORCID
- Venue
- Nature Protocols
- Published
- 2016-02-18
- Volume
- 11
- Issue
- 3
- Pages
- 499-524
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC4941947
- OpenAlex
- W2279350325
- Cited by
- 445
- References
- 11
- Citations by year
- 2026: 26 · 2025: 47 · 2024: 47 · 2023: 57 · 2022: 63 · 2021: 63 · 2020: 57 · 2019: 26 · 2018: 23 · 2017: 25 · 2016: 10 · 2012: 1
- FWCI
- 14.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH095741)
- National Human Genome Research Institute (R01HG003647, RC1HG005471)
- National Cancer Institute (P30CA014195)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- single-nucleus RNA sequencing, nuclear transcriptome, postmortem brain, neurons, nuclei isolation, SmartSeq2, cDNA library construction
- MeSH terms
- Autopsy, Brain, Cell Fractionation, Cell Nucleolus, Humans, Neurons, RNA, Tissue Preservation, Gene Library, Sequence Analysis, RNA, DNA, Complementary, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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