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

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Abstract

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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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.

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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.

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Acknowledgements

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

Authors

Contributions

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.

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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)

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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). https://doi.org/10.1038/nprot.2016.015

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