Abstract
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.
Highlights
The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states
In the present study we describe Smart-seq-total, an RNA-sequencing method that delivers a broad picture of the total cellular RNA content
Using Smart-seq-total, we analyzed the content of hundreds of human and mouse cells and showed that the noncoding RNA content of cells significantly differs across cell types and dynamically changes throughout the vital processes of a cell, such as cell cycle and cell differentiation
Summary
The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. 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, enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells. None of the existing methods are able to simultaneously quantify all RNA types within a cell This limits the ability to map the regulatory connection between coding and noncoding transcripts within a cell and motivates the need for the development of novel singlecell technologies capable of assaying both poly(A)+ and poly(A)À RNA, irrespective of transcript length. Smart-seqtotal simultaneously quantifies the levels of mRNA alongside other RNA types in the same cell, which permits: 1) the annotation of cell types and states based on mRNA and integration of this data with the existent single-cell RNA-sequencing (scRNA-seq) datasets, and 2) the discovery of noncoding regulatory patterns of the respective states
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