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

Authors
  • Haimon, Zhana1
  • Volaski, Alon1
  • Orthgiess, Johannes2
  • Boura-Halfon, Sigalit1
  • Varol, Diana1
  • Shemer, Anat1
  • Yona, Simon1
  • Zuckerman, Binyamin3
  • David, Eyal1
  • Chappell-Maor, Louise1
  • Bechmann, Ingo4
  • Gericke, Martin4
  • Ulitsky, Igor3
  • Jung, Steffen5
  • 1 Department of Immunology, Weizmann Institute of Science, Rehovot, Israel. , (Israel)
  • 2 Carl Ludwig Institute of Physiology, University of Leipzig, Leipzig, Germany. , (Germany)
  • 3 Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel. , (Israel)
  • 4 Institute of Anatomy, University of Leipzig, Leipzig, Germany. , (Germany)
  • 5 Department of Immunology, Weizmann Institute of Science, Rehovot, Israel. [email protected] , (Israel)
Type
Published Article
Journal
Nature Immunology
Publisher
Springer Nature
Publication Date
Jun 01, 2018
Volume
19
Issue
6
Pages
636–644
Identifiers
DOI: 10.1038/s41590-018-0110-6
PMID: 29777220
Source
Medline
License
Unknown

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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