Skip to main content

Thank you for visiting nature.com. 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.

  • Technical Report
  • Published:

Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies

Nature Immunology volume 19, pages 636–644 (2018)Cite this article

Subjects

Abstract

Transcriptome profiling is widely used to infer functional states of specific cell types, as well as their responses to stimuli, to define contributions to physiology and pathophysiology. Focusing on microglia, the brain’s macrophages, we report here a side-by-side comparison of classical cell-sorting-based transcriptome sequencing and the ‘RiboTag’ method, which avoids cell retrieval from tissue context and yields translatome sequencing information. Conventional whole-cell microglial transcriptomes were found to be significantly tainted by artifacts introduced by tissue dissociation, cargo contamination and transcripts sequestered from ribosomes. Conversely, our data highlight the added value of RiboTag profiling for assessing the lineage accuracy of Cre recombinase expression in transgenic mice. Collectively, this study indicates method-based biases, reveals observer effects and establishes RiboTag-based translatome profiling as a valuable complement to standard sorting-based profiling strategies.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: RiboTag analysis reveals that Cx3cr1Cre mice but not Cx3cr1CreER animals display rearrangements in neurons.
Fig. 2: Comparison of cell-sorting-based protocol and the RiboTag method for profiling microglia.
Fig. 3: Microglial isolation results in cell activation.
Fig. 4: Microglia transcriptomes, but not translatomes, include cargo-derived mRNAs and mRNAs sequestered in nuclei and P bodies.
Fig. 5: Analysis of microglia isolated from mice challenged with LPS.

Similar content being viewed by others

References

  1. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex tissues. Proc. Natl Acad. Sci. USA 106, 13939–13944 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).

    Article  PubMed  CAS  Google Scholar 

  6. Jung, S. et al. Analysis of fractalkine receptor CX3CR1 function by targeted deletion and green fluorescent protein reporter gene insertion. Mol. Cell. Biol. 20, 4106–4114 (2000).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Davalos, D. et al. ATP mediates rapid microglial response to local brain injury in vivo. Nat. Neurosci. 8, 752–758 (2005).

    Article  PubMed  CAS  Google Scholar 

  8. Goldmann, T. et al. A new type of microglia gene targeting shows TAK1 to be pivotal in CNS autoimmune inflammation. Nat. Neurosci. 16, 1618–1626 (2013).

    Article  PubMed  CAS  Google Scholar 

  9. 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  PubMed  PubMed Central  CAS  Google Scholar 

  10. Orthgiess, J. et al. Neurons exhibit Lyz2 promoter activity in vivo: Implications for using LysM-Cre mice in myeloid cell research. Eur. J. Immunol. 46, 1529–1532 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. Kim, K.-W. et al. In vivo structure/function and expression analysis of the CX3C chemokine fractalkine. Blood 118, e156–e167 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Fonseca, M. I. et al. Cell-specific deletion of C1qa identifies microglia as the dominant source of C1q in mouse brain. J. Neuroinflammation 14, 48 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  15. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  CAS  Google Scholar 

  16. A-Gonzalez, N. et al. Phagocytosis imprints heterogeneity in tissue-resident macrophages. J. Exp. Med. 214, 1281–1296 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Derrien, T. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Braunschweig, U. et al. Widespread intron retention in mammals functionally tunes transcriptomes. Genome Res. 24, 1774–1786 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Khong, A. et al. The stress granule transcriptome reveals principles of mRNA accumulation in stress granules. Mol. Cell 68, 808–820.e5 (2017).

    Article  PubMed  CAS  Google Scholar 

  20. Hubstenberger, A. et al. P-body purification reveals the condensation of repressed mRNA regulons. Mol. Cell 68, 144–157.e5 (2017).

    Article  PubMed  CAS  Google Scholar 

  21. Tebaldi, T. et al. Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells. BMC Genomics 13, 220 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Bahar Halpern, K. et al. Nuclear retention of mRNA in mammalian tissues. Cell Rep. 13, 2653–2662 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Hirrlinger, J. et al. Split-cre complementation indicates coincident activity of different genes in vivo. PLoS One 4, e4286 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wolf, Y. et al. Brown-adipose-tissue macrophages control tissue innervation and homeostatic energy expenditure. Nat. Immunol. 18, 665–674 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Boutej, H. et al. Diverging mRNA and protein networks in activated microglia reveal SRSF3 suppresses translation of highly upregulated innate immune transcripts. Cell Rep. 21, 3220–3233 (2017).

    Article  PubMed  CAS  Google Scholar 

  26. Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329.e6 (2017).

    Article  PubMed  CAS  Google Scholar 

  27. Tilgner, H. et al. Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res. 22, 1616–1625 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank all members of the Jung laboratory for discussion, the staff of the Weizmann Animal Facility, members of the FACS facility for expert advice, G. Friedlander for help with bioinformatics, and C. Glass for sharing sequencing data. S.J. was supported by the Israeli Science Foundation (887/11), an Infect-ERA grant, the European Research Council (Adv ERC 340345), and the Deutsche Forschungsgemeinschaft (CRC/TRR167 ‘NeuroMac’). I.B and M.G. were supported by the Deutsche Forschungsgemeinschaft DFG-SFB 1052/1: ‘Obesity mechanisms’ (projects A09 and B09).

Author information

Authors and Affiliations

  1. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    Zhana Haimon, Alon Volaski, Sigalit Boura-Halfon, Diana Varol, Anat Shemer, Simon Yona, Eyal David, Louise Chappell-Maor & Steffen Jung

  2. Carl Ludwig Institute of Physiology, University of Leipzig, Leipzig, Germany

    Johannes Orthgiess

  3. Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel

    Binyamin Zuckerman & Igor Ulitsky

  4. Institute of Anatomy, University of Leipzig, Leipzig, Germany

    Ingo Bechmann & Martin Gericke

Authors
  1. Zhana Haimon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alon Volaski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Johannes Orthgiess
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sigalit Boura-Halfon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diana Varol
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anat Shemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Simon Yona
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Binyamin Zuckerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eyal David
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Louise Chappell-Maor
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ingo Bechmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Martin Gericke
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Igor Ulitsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Steffen Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.H. and S.J. conceived the project and designed the experiments; Z.H., A.V. and J.O. performed the experiments; L.C.-M. performed RNA-seq and E.D. analyzed the data. I.U. and B.Z. performed bioinformatics analysis. S.Y., A.S., D.V., S.B.-H., I.B. and M.G. advised on experiments; Z.H. and S.J. wrote the paper; S.J. supervised the project.

Corresponding author

Correspondence to Steffen Jung.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Enrichment and de-enrichment of cell-specific genes in TAM-treated Cx3cr1CreER:Rpl22HA brains after IP.

Graphs showing normalized reads of example genes from heat map in Fig. 1c, showing cell-specific genes of Microglia (A), Astrocytes (B), Neurons (C) and Oligodendrocytes (D) enrichement in input, IP IgG control and IP HA samples of TAM-treated Cx3cr1CreER:Rpl22HA brains after IP. Each dot represents an individual mouse, n = 3, lines represent mean.

Supplementary Figure 2 Immunohistochemistry analysis of brains of reporter animals.

(A) Microscopic analysis of hypothalamus sections from Cx3cr1Cre:R26-YFP mice (left panel) and Cx3cr1CreER:R26-YFP mice (TAM treated (right panel) or not (middle panel), stained for IBA-1, GFP and DAPI, showing neuronal expression of GFP in Cx3cr1Cre brains and microglia-restricted GFP expression in Cx3cr1CreER brains. The animals analyzed are F1 offspring of the intercross of homozygote Cx3cr1Cre or Cx3cr1CreER animals and homozygote R26-YFP mice. Representative of 2 independent experiments. (B) Immuno-fluorescent staining of 30µm thick brain sections of cortex (upper panels) and cerebellum (lower panels) of Cx3cr1Cre:Rpl22HA (left) and Cx3cr1CreER:Rpl22HA animals, either untreated (middle) or TAM-treated (right), stained for IBA1, HA and NeuN, showing neuronal expression of HA in Cx3cr1Cre brains, and microglia-restricted HA expression in TAM-treated Cx3cr1CreER brains. Scale bar cortex = 100µm, scale bar cerebellum = 200µm. Representative of 2 independent experiments.

Supplementary Figure 3 Gating strategy for microglial isolation.

FACS dot plots representing gating strategy for sorting of microglia. Cells were gated for single cells by FSC-W and FSC-A; monocytes, neutrophils and dead cells were excluded by gating on Ly6C/G (Gr1)– DAPI– cells; final microglia gate was on CD11b+ CD45int cells.

Supplementary Figure 4 Microglial gene expression profiles are comparable across different retrieval methods.

Graphs showing normalized reads of example genes from cluster I of Fig. 2b, showing microglial genes at comparable reads number, indicating that retrieval methods are comparable. n = 3, lines represent mean.

Supplementary Figure 5 Microglia activation signature is robust, reproducible and induced by cell extraction from tissue.

(A) Heatmap clustered by K-means clustering, comparing direct IP to whole mRNA retrieved from sorted cells extracted with (+coll) or without (-coll) enzymatic digestion in the isolation protocol, revealing 4 clusters similar to Fig. 2b. n = 4 individual mice (one repeat of IP-HA was removed due to technical reasons). (B) Correlation plots of average of log2 normalized reads (n = 4) plotting sorted cells with enzymatic digestion on the Y axis and sorted cells without enzymatic digestion on the X axis (left panel) or IP on the X axis (right panel). (C) Correlation matrix combining two independent experiments, showing high reproducibility of the results.

Source data

Supplementary Figure 6 Analysis of microglia isolated from mice challenged with LPS.

(A) Heatmap of RNA-seq data comparing RiboTag to sorting with LPS treatment, taking significantly changed genes (fold change>2, p-value<0.05, as calculated by DESeq2 statistical analysis). n = 3 for PBS group, n = 4 for LPS group. (B) Graphs showing normalized reads of example genes taken from cluster I from Fig. S6A, showing similar levels of microglia signature genes among samples. Each dot represents an individual mouse, n = 3 for PBS group, n = 4 for LPS group, lines represent mean.

Source data

Supplementary Figure 7 Expression profiles of selected genes across brain cell populations.

Non-microglia mRNAs suggesting cargo contamination (Fig. 5f)Data obtained from (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) 9. (Zhang, Y. et al. J. Neurosci. 34, 11929–11947 (2014)).

Supplementary information

Supplementary Figures and Supplementary Table 1

Supplementary Figures 1–7 and Supplementary Table 1

Reporting Summary

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haimon, Z., Volaski, A., Orthgiess, J. et al. Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies. Nat Immunol 19, 636–644 (2018). https://doi.org/10.1038/s41590-018-0110-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-018-0110-6

This article is cited by

Search

Advanced search

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