Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons

This article has been updated


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Single nuclei isolation experimental workflow.
Figure 2: Quality control of nuclei isolation.
Figure 3: FACS of single nuclei.
Figure 4: qPCR and Bioanalyzer quality control analysis of total mRNA, single-nucleus cDNA synthesis and a single-nucleus NexteraXT RNA-seq library.
Figure 5: Overall characteristics of mapping and expression.
Figure 6: Behavior of ERCC spike-in controls, sensitivity and detection limit estimation.
Figure 7: Biological variation and technical noise stratified by relative expression of genes.
Figure 8: The use of 3′ bias as a quality control assay for cDNA.
Figure 9: Read depth across the GAPDH gene.
Figure 10: Nuclei captured from several neuronal and glial cell types.

Similar content being viewed by others

Change history

  • 02 March 2016

     In the version of this article initially published, the name of author Martijn Kelder was misspelled as 'Martin Kelder.' The error has been corrected in the HTML and PDF versions of the article.


  1. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  Google Scholar 

  2. Kurimoto, K., Yabuta, Y., Ohinata, Y. & Saitou, M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protoc. 2, 739–752 (2007).

    Article  CAS  Google Scholar 

  3. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  Google Scholar 

  4. Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  Google Scholar 

  5. Tang, F. et al. RNA-seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535 (2010).

    Article  CAS  Google Scholar 

  6. Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).

    Article  CAS  Google Scholar 

  7. Citri, A., Pang, Z.P., Sudhof, T.C., Wernig, M. & Malenka, R.C. Comprehensive qPCR profiling of gene expression in single neuronal cells. Nat. Protoc. 7, 118–127 (2012).

    Article  CAS  Google Scholar 

  8. Qiu, S. et al. Single-neuron RNA-seq: technical feasibility and reproducibility. Front. Genet. 3, 124 (2012).

    Article  CAS  Google Scholar 

  9. Lovatt, D., Bell, T. & Eberwine, J. Single-neuron isolation for RNA analysis using pipette capture and laser capture microdissection. Cold Spring Harb. Protoc. doi:10.1101/pdb.prot072439 (2015).

  10. 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).

    Article  CAS  Google Scholar 

  11. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  Google Scholar 

  12. Huang, H.L. et al. Trypsin-induced proteome alteration during cell subculture in mammalian cells. J. Biomed. Sci. 17, 36 (2010).

    Article  Google Scholar 

  13. Grindberg, R.V. et al. RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807 (2013).

    Article  CAS  Google Scholar 

  14. Barthelson, R.A., Lambert, G.M., Vanier, C., Lynch, R.M. & Galbraith, D.W. Comparison of the contributions of the nuclear and cytoplasmic compartments to global gene expression in human cells. BMC Genomics 8, 340 (2007).

    Article  Google Scholar 

  15. Cheng, J. et al. Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science 308, 1149–1154 (2005).

    Article  CAS  Google Scholar 

  16. Schwanekamp, J.A. et al. Genome-wide analyses show that nuclear and cytoplasmic RNA levels are differentially affected by dioxin. Biochim. Biophys. Acta 1759, 388–402 (2006).

    Article  CAS  Google Scholar 

  17. Trask, H.W. et al. Microarray analysis of cytoplasmic versus whole cell RNA reveals a considerable number of missed and false positive mRNAs. RNA 15, 1917–1928 (2009).

    Article  CAS  Google Scholar 

  18. Jiang, Y., Matevossian, A., Huang, H.S., Straubhaar, J. & Akbarian, S. Isolation of neuronal chromatin from brain tissue. BMC Neurosci. 9, 42 (2008).

    Article  Google Scholar 

  19. Birnie, G.D. Isolation of nuclei from animal cells in culture. Methods Cell Biol. 17, 13–26 (1978).

    Article  CAS  Google Scholar 

  20. Schroeder, A. et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 7, 3 (2006).

    Article  Google Scholar 

  21. Dounce, A.L., Witter, R.F., Monty, K.J., Pate, S. & Cottone, M.A. A method for isolating intact mitochondria and nuclei from the same homogenate, and the influence of mitochondrial destruction on the properties of cell nuclei. J. Biophys. Biochem. Cytol. 1, 139–153 (1955).

    Article  CAS  Google Scholar 

  22. Hymer, W.C. & Kuff, E.L. Isolation of nuclei from mammalian tissues through the use of Triton X-100. J. Histochem. Cytochem. 12, 359–363 (1964).

    Article  CAS  Google Scholar 

  23. Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    Article  CAS  Google Scholar 

  24. Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  25. Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  26. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).

    Article  CAS  Google Scholar 

  27. Rabani, M. et al. High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies. Cell 159, 1698–1710 (2014).

    Article  CAS  Google Scholar 

  28. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. (in the press).

  29. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  CAS  Google Scholar 

  30. DeLuca, D.S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    Article  CAS  Google Scholar 

  31. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  Google Scholar 

  32. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Bioinformatics Action 17, 2 (2013).

    Google Scholar 

  33. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  Google Scholar 

  34. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  35. Bolger, A.M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  36. Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9, 99–107 (2006).

    Article  CAS  Google Scholar 

  37. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  Google Scholar 

Download references


R.S.L. was funded by NIH-1RC1 HG005471, NIH-2R01 HG003647; R.S.L. and F.H.G. by Transformative R01MH095741; F.H.G. by the Jeffry M. and Barbara Picower Foundation, the Mathers Foundation, the McDonnell Foundation and the National Institute of Mental Health (NIMH); and M.J.M. by a Crick-Jacobs Junior Fellowship. K.B. is supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1144086. The authors thank the Allen Institute for Brain Science founders, Paul G. Allen and Jody Allen, for their vision, encouragement and support. Research was supported in part by the Allen Institute for Brain Science.

Author information

Authors and Affiliations



R.S.L., R.V.G., M.J.M., S.R.K., F.H.G., E.S.L. and J.L. contributed to design of the project. S.R.K., M.N., B.J.L., J.A.E., R.H. and J.K.M. carried out experiments. P.V., K.B., S.B.L., S.P., M.K., J.M., J.K.M., N.S., B.D.A., F.D.F. and R.H.S. contributed to sequencing data analysis. R.S.L., R.V.G., S.R.K. and M.N. wrote the manuscript with contributions from all the authors.

Corresponding authors

Correspondence to Rashel V Grindberg or Roger S Lasken.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 NeuN immunostaining of neuronal nuclei.

(A-B) NeuN staining was conducted using a NeuN-Alexa Fluor (AF) 488 directly conjugated antibody according to the conditions outlined in PROCEDURE, step 6. (A, B) NeuN-AF488 staining was bright and readily apparent in some of the isolated nuclei (arrow), as illustrated in a sample examined prior to FACS. However, low levels of background fluorescence were detected in most of the DAPI-positive nuclei examined (arrowheads, A, B), making discernment between NeuN-positive and negative nuclei difficult. (C) In contrast, NeuN detection by staining with an unconjugated primary antibody and AF-594 conjugated secondary antibody produced staining with minimal background. Note that NeuN-negative/DAPI-positive nuclei (arrowhead) can clearly be distinguished from NeuN-positive/DAPI-positive nuclei (arrow) prior to FACS. (D) Representative image of NeuN-positive nuclei collected after FACS using the gating strategy illustrated in (E-F). (E-F) General gating strategy for collection of NeuN-labeled neuronal nuclei. (E) To capture NeuN-positive nuclei, DAPI-positive events were first collected and subsequently passed through a series of gates (FSC-H x FSC-W, SSC-H x SSC-W) that excluded nuclei aggregates. NeuN-positive events outlined by the NeuN-positive gate exhibited high AF-594 fluorescence and were clearly distinguishable from NeuN-negative events. (F) Staining with an AF-594 isotype control showed that very few events exhibited high AF-594 fluorescence, indicating little non-specific background labeling.

Supplementary Figure 2 Read distribution across introns and exons.

Read coverage across exons and introns as described in RNA-Seq Analysis: Quality control based on coverage across exons and introns. Most exons (red) are covered by several reads, but there are only few introns (blue) that are fully covered. The introns that are fully covered are relatively small (< 1KB) compared to several large exons have reads that span across the entire exon. As the histogram details, most introns have less than 10% of their body covered by a read. Although there are several thousand exons that are also only partially covered, the distribution for exons is highly skewed towards fully covered with nearly 4000 exons having at least 1x coverage across their entire length.

Supplementary Figure 3 Spearman correlation stratified by relative expression in Total RNA-100pg-2.

The spearman correlation coefficients across the different samples based on TPM values for Ensembl genes are stratified by the relative expression of genes in Total RNA-100pg-2 (Fig. 7). There is higher variability and lower correlation between genes that are lowly expressed. For the highly expressed genes, the highest correlation coefficients are shared amongst the Total RNA samples, but several neuronal and non-neuronal nuclei also show significant correlation.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Tables 1–4 (PDF 1643 kb)

Supplementary Methods

R code files. (ZIP 1391 kb)

Supplementary Note

Sequence of adapters and primers used for trimming. (TXT 1 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krishnaswami, S., Grindberg, R., Novotny, M. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat Protoc 11, 499–524 (2016).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing