The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. However, current single-cell RNA-sequencing (RNA-seq) methods are unable to simultaneously monitor all forms of RNA transcripts at the single-cell level, and thus deliver only a partial snapshot of the cellular RNAome. Here we describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and noncoding RNA from a single cell. Smart-seq-total does not require splitting the RNA content of a cell and allows the incorporation of unique molecular identifiers into short and long RNA molecules for absolute quantification. It outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, thus enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T, and MCF7 cells, as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. By analyzing the coexpression patterns of both noncoding RNA and mRNA from the same cell, we were able to discover new roles of noncoding RNA throughout essential processes, such as cell cycle and lineage commitment during embryonic development. Moreover, we show that independent classes of short-noncoding RNA can be used to determine cell-type identity.
Single cell RNA-seq analysis identifies a noncoding RNA mediating resistance to sorafenib treatment in HCC