Cell type-specific transcriptomics are changing our understanding of tissue and organ development, physiology and disease. However, cell type-specific RNA sequencing (RNA-seq) requires tissue dissociation through either microdissection or fluorescence-activated cell sorting. These methods are laborious and can lead to considerable cell death. Additionally, the transcriptional profile of a cell greatly depends on its context and can change during the dissociation process. To address these issues, Eric Miska and colleagues redesigned in vivo thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) to allow for the direct sequencing of cell type-specific transcriptomes. Their approach, which they term SLAMseq in tissue (SLAM-ITseq), uses 4-thiouracil tags that are reverse transcribed to guanine instead of adenine during sequencing library preparation. The authors combined this with a mismatch-aware algorithm to align and quantify the conversions and, consequently, measure the expression levels of the metabolically tagged transcripts. Moreover, by controlling the tissue-specific expression of the enzyme responsible for 4-thiouracil tagging of RNA, they were able to identify cell type-specific transcriptomes across a variety of murine tissues. As this method involves bulk RNA-seq library preparation, it eliminates the need for tissue dissociation. Thus, SLAM-ITseq improves the accessibility and applicability of in vivo metabolic labelling of RNA for studying cell type-specific transcriptomics in animals.
