Abstract
A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.
Highlights
Single-cell RNA sequencing is a widely adopted method for profiling transcriptomic heterogeneity in health and disease [1]
High-throughput single-cell full-length transcriptome sequencing with Chromium 10x We modified the standard Chromium scRNA-seq protocol (10x Genomics) to better amplify the full-length cDNA
Since the throughput of long-read platforms is still limited compared to Illumina sequencing platforms, we subsample 10–20% of the 10x Chromium generated Gel Bead-in-Emulsions (GEMs) after reverse transcription (Fig. 1A), similar to the method recently described by Lebrigand et al [9]
Summary
Single-cell RNA sequencing (scRNA-seq) is a widely adopted method for profiling transcriptomic heterogeneity in health and disease [1]. Limited sequencing throughput introduces a trade-off between the per-cell sequencing depth and the number of cells or genes processed Protocols such as ScISOr-Seq [6] sequence all processed cells necessitating either shallow depth per cell or high cost, while RAGE-Seq [7] focuses on specific transcripts rather than the whole transcriptome. On top of the current protocol limitations, another pressing issue is the lack of data analysis pipelines for long-read transcriptome data, especially for single cells. Tools such as ScNapBar [10] and SiCeLoRe [9] focus on cell barcode and UMI assignment, while others such as FLAIR [11] and TALON [12] lack the ability to process single-cell data. New protocols and computational pipelines to overcome these limitations are needed
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