Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Carl Ludwig Institute of Physiology, University of Leipzig, Leipzig, Germany.
Nat Immunol. 2018 Jun;19(6):636-644. doi: 10.1038/s41590-018-0110-6. Epub 2018 May 18.
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.
转录组谱分析被广泛用于推断特定细胞类型的功能状态，以及它们对刺激的反应，以确定对生理学和病理生理学的贡献。本文聚焦于小胶质细胞，即大脑中的巨噬细胞，我们报告了经典的基于细胞分选的转录组测序和“RiboTag”方法的并排比较，该方法避免了从组织环境中回收细胞，并提供了翻译组测序信息。传统的全细胞小胶质细胞转录组被发现受到组织解离、货物污染和从核糖体上隔离的转录本引入的假象的显著污染。相反，我们的数据突出了 RiboTag 分析在评估转基因小鼠中 Cre 重组酶表达的谱系准确性方面的附加值。总的来说，这项研究表明了基于方法的偏差，揭示了观察者效应，并确立了基于 RiboTag 的翻译组谱分析作为标准基于分选的分析策略的有价值的补充。
