Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells

Abstract

Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes 1-11 , but applying this technology to single cells has proven to be difficult, with less than ten single-cell transcriptomes having been analyzed thus far 12,13. Although single splicing events have been described for ≤200 single cells with statistical confidence 14,15 , full-length mRNA analyses for hundreds of cells have not been reported. Singlecell short-read 3′ sequencing enables the identification of cellular subtypes 16-21 , but full-length mRNA isoforms for these cell types cannot be profiled. We developed a method that starts with bulk tissue and identifies single-cell types and their full-length RNA isoforms without fluorescence-activated cell sorting. Using single-cell isoform RNA-Seq (ScISOr-Seq), we identified RNA isoforms in neurons, astrocytes, microglia, and cell subtypes such as Purkinje and Granule cells, and cell-typespecific combination patterns of distant splice sites 6-9,22,23. We used ScISOr-Seq to improve genome annotation in mouse Gencode version 10 by determining the cell-type-specific expression of 18,173 known and 16,872 novel isoforms. Unlike sorting-based methods (Supplementary Fig. 1a), ScISOr-Seq identifies isoforms in >1,000 single cells from bulk tissue without cell sorting by combining two technologies (Fig. 1a). We used microfluidics to amplify full-length cDNA from single cells in a sample. cDNA produced from each single cell was barcoded to enable cell-of-origin identification and then split into two pools, with one pool being used for short-read Illumina 3′ sequencing to measure gene expression and the other pool being used for long-read sequencing and isoform identification. Short-read 3′ sequencing provided molecular counts for each gene and cell, which enabled clustering of cells and cell type assignment using cell-type-specific markers. Long-read sequencing with Pacific Biosciences (PacBio) 1,2,4,5 or Oxford Nanopore 3 was used to identify full-length RNA isoforms. Single-cell barcodes were also present in long reads and could be used to determine the individual

A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

“…Even in the domain of single-cell RNA-seq, which is currently thriving on short singlereads for molecule counting, long-read formats are beginning to be applied, aiming to capture the richness of isoform variation and regulation on a per-cell and per-cell-type basis 47 . That said, short-read transcriptomes will surely continue to play a prominent role for short RNA class substrates, for intractably degraded RNAs, and, increasingly, in biological settings where a few long-read transcriptomes can provide a reference against which larger numbers of companion short-read samples can be quantified.…”

Enabling high-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing

“…Despite the benefits, the combination of UMIs with long-read sequencing is relatively unexplored, and only recently has it been applied with PacBio sequencing, but without profiling the error of the generated consensus sequences 23,24 . For ONT sequencing the raw error rate of 5-25% 25 has, until now, made it difficult to efficiently extract UMI sequences and confidently determine the true UMI sequences necessary for read binning.…”

Illuminating the dark side of the human transcriptome with TAMA Iso-Seq analysis

“…While there have been many studies using long read RNA sequencing for transcriptome discovery 5,6,7,8,9 , the tools used for processing long read data suffer from limitations that severely reduce the sensitivity and specificity of transcriptome exploration. These strategies either rely on orthogonal information which biases gene discovery and are only available for a small number of species 10 (Talon 11 , TAPIS 6 , SQANTI 12 ) or on algorithms with serious theoretical limitations 13,14 .…”