GSE183167
CoLoC-seq, a high-throughput approach to profile organelle transcriptomes
Proper RNA localization is essential for normal gene expression in apparently all organisms. Detailed understanding of the extent of this phenomenon and the comprehensive profiling of subcellular transcriptomes rely on genome-wide, deep sequencing-based approaches. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the RNA-seq of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from rampant false-positives since incompletely digested contaminant RNAs can be erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat while preserving the key advantages of fractionation-based techniques. Instead of relying on the apparent abundance of individual RNA species in purified organelles, CoLoC-seq leverages their characteristic depletion dynamics in a gradient of exogenously added RNase, with or without intact organellar membranes, to distinguish between genuinely resident transcripts and merely abundant contaminants. Our generic approach robustly performed on human mitochondria and is in principle applicable to any membrane-bounded organelles, including plastids, vacuoles, and extracellular vesicles. Overall design: The dataset includes two ordered series of human mitochondrial CoLoC-seq samples (biological replicates) and two corresponding series of mock CoLoC-seq samples (biological replicates). CoLoC-seq replicate #1 includes 9 samples, CoLoC-seq replicate #2 includes 10 samples, mock CoLoC-seq replicate #1 includes 9 samples, mock CoLoC-seq replicate #2 includes 10 samples. Each series begins with a reference sample corresponding to an untreated control ("0 µg/ml RNase A").