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.

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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

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

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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.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Steffen Jung.

Integrated supplementary information

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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)).

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