The immune system operates at the scale of the whole organism in mammals. We currently lack experimental approaches to systematically track and study organism-wide molecular processes in mice. Here we describe an integrated toolkit for measuring gene expression in whole tissues, 3-prime mRNA extension sequencing, that is applicable to most mouse organs and any mouse model of interest. Further, the methods of RNA-seq described in this protocol are broadly applicable to other sample types beyond whole organs, such as tissue samples or isolated cell populations. We report procedures to collect, store and lyse a dozen organ types using conditions compatible with the extraction of high-quality RNA. In addition, we detail protocols to perform high-throughput and low-cost RNA extraction and sequencing, as well as downstream data analysis. The protocol takes 5 d to process 384 mouse organs from collecting tissues to obtaining raw sequencing data, with additional time required for data analysis and mining. The protocol is accessible to individuals with basic skills in (i) mouse perfusion and dissection for sample collection and (ii) computation using Unix and R for data analysis. Overall, the methods presented here fill a gap in our toolbox for studying organism-wide processes in immunology and physiology.
Subscribe to Journal
Get full journal access for 1 year
only $21.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The example datasets used in this protocol are available in the Gene Expression Omnibus under accession number GSE138103.
All scripts are available at https://github.com/chevrierlab/PME-seq.
Kadoki, M. et al. Organism-level analysis of vaccination reveals networks of protection across tissues. Cell 171, 398–413.e21 (2017).
Mackay, L. K. & Prier, J. E. Mapping organism-wide immune responses. Trends Immunol. 39, 1–2 (2018).
Masopust, D., Sivula, C. P. & Jameson, S. C. Of mice, dirty mice, and men: using mice to understand human immunology. J. Immunol. 199, 383–388 (2017).
Churchill, G. A. et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat. Genet. 36, 1133–1137 (2004).
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580 (2013).
Tang, T. et al. A mouse knockout library for secreted and transmembrane proteins. Nat. Biotechnol. 28, 749 (2010).
Sage, P. T. et al. Suppression by TFR cells leads to durable and selective inhibition of B cell effector function. Nat. Immunol. 17, 1436–1446 (2016).
Hou, S. et al. FoxP3 and Ezh2 regulate Tfr cell suppressive function and transcriptional program. J. Exp. Med. 216, 605–620 (2019).
Shultz, L. D. et al. Humanized mouse models of immunological diseases and precision medicine. Mamm. Genome 30, 123–142 (2019).
Weigert, R., mkova, M., Parente, L., Amornphimoltham, P. & Masedunskas, A. Intravital microscopy: a novel tool to study cell biology in living animals. Histochem. Cell Biol. 133, 481–491 (2010).
Reinhardt, L. R., Khoruts, A., Merica, R., Zell, T. & Jenkins, M. K. Visualizing the generation of memory CD4 T cells in the whole body. Nature 410, 101 (2001).
Southern, P. J., Blount, P. & Oldstone, M. B. Analysis of persistent virus infections by in situ hybridization to whole-mouse sections. Nature 312, 555–558 (1984).
Treweek, J. B. et al. Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping. Nat. Protoc. 10, 1860–1896 (2015).
Tainaka, K. et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159, 911–924 (2014).
Pan, C. et al. Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat. Methods 13, 859–867 (2016).
Kubota, S. I. et al. Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep. 20, 236–250 (2017).
Steinert, E. M. et al. Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161, 737–749 (2015).
Hou, P. et al. Genome editing of CXCR4 by CRISPR/cas9 confers cells resistant to HIV-1 infection. Sci. Rep. 5, 15577 (2015).
Reed, E. et al. Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques. Front. Genet. 10, 150 (2019).
Alpern, D. et al. BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome Biol. 20, 71 (2019).
Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).
Shishkin, A. A. et al. Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325 (2015).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171 (2014).
Avraham, R. et al. A highly multiplexed and sensitive RNA-seq protocol for simultaneous analysis of host and pathogen transcriptomes. Nat. Protoc. 11, 1477–1491 (2016).
McLaughlin, L. W., Romaniuk, E., Romaniuk, P. J. & Neilson, T. The effect of acceptor oligoribonucleotide sequence on the T4 RNA ligase reaction. Eur. J. Biochem. 125, 639–643 (1982).
Hafner, M. et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA 17, 1697–1712 (2011).
Fuchs, R. T., Sun, Z., Zhuang, F. & Robb, B. G. Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PloS One 10, e0126049 (2015).
Lecanda, A. et al. Dual randomization of oligonucleotides to reduce the bias in ribosome-profiling libraries. Methods 107, 89–97 (2016).
Anderson, K. G. et al. Intravascular staining for discrimination of vascular and tissue leukocytes. Nat. Protoc. 9, 209–222 (2014).
Korin, B., Dubovik, T. & Rolls, A. Mass cytometry analysis of immune cells in the brain. Nat. Protoc. 13, 377 (2018).
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2013).
Shen-Orr, S. S. & Gaujoux, R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr. Opin. Immunol. 25, 571–578 (2013).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Okonechnikov, K., Conesa, A. & García-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2017).
Li, B. et al. A comprehensive mouse transcriptomic BodyMap across 17 tissues by RNA-seq. Sci. Rep. 7, 4200 (2017).
We thank M. Kadoki, T. Matsui and S. Tay for helpful discussions. This work was supported by an Overseas Research Fellowship from the Astellas Foundation for Research on Metabolic Disorders (M.T.), an NIH Director’s New Innovator Award DP2 (AI145100), an Institutional Research Grant (IRG-16-222-56) from the American Cancer Society, the University of Chicago Medicine Comprehensive Cancer Center Support Grant (P30 CA14599), the Elliot and Ruth Sigal MRA Young Investigator Award (571146) and funds from the Pritzker School of Molecular Engineering (N.C.).
The authors declare no competing interests.
Peer review information Nature Protocols thanks Zhou Xing and the other anonymous reviewer(s) 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.
Key references using this protocol
Kadoki, M. et al. Cell 171, 398–413 (2017): https://doi.org/10.1016/j.cell.2017.08.024
Sage, P. T. et al. Nat. Immunol. 17, 1436–1446 (2016): https://doi.org/10.1038/ni.3578
Hou, S. et al. J. Exp. Med. 216, 605 (2019): https://doi.org/10.1084/jem.20181134
Integrated supplementary information
Supplementary Fig. 1 Setup of the peristaltic pump and associated materials to perform transcardial perfusion followed by tissue collections.
a, Materials for the peristaltic pump setup. Shown are all equipment, materials and associated catalog numbers. Before using a vacutainer, the opposite end of the butterfly needle is cut off and discarded. b, Peristaltic pump ready to use for perfusion. Tubing is linked to the vacutainer through a connector (Gilson, cat. no. F1179941). On the opposite side of the tubing, the tubing is connected to another piece of tubing by a connector (Gilson, cat. no. F117987), and the connection is wrapped with parafilm. c, Example setup for transcardial perfusion followed by tissue collection. In this case, Vesphene is used as a disinfectant for decontamination purposes when working with infectious materials.
Supplementary Fig. 1 and Supplementary Methods.
Suite of scripts (1 of 3) to use for RNA extraction automation using the Agilent Bravo liquid handling platform.
Suite of scripts (2 of 3) to use for RNA extraction automation using the Agilent Bravo liquid handling platform.
Suite of scripts (3 of 3) to use for RNA extraction automation using the Agilent Bravo liquid handling platform.
About this article
Cite this article
Pandey, S., Takahama, M., Gruenbaum, A. et al. A whole-tissue RNA-seq toolkit for organism-wide studies of gene expression with PME-seq. Nat Protoc 15, 1459–1483 (2020). https://doi.org/10.1038/s41596-019-0291-y