Genome biologyGenomicsPeer-reviewed journal article
Highly Multiplexed Single-Cell Full-Length cDNA Sequencing of human immune cells with 10X Genomics and R2C2
Abstract
Single cell transcriptome analysis elucidates facets of cell biology that have been previously out of reach. However, the high-throughput analysis of thousands of single cell transcriptomes has been limited by sample preparation and sequencing technology. High-throughput single cell analysis today is facilitated by protocols like the 10X Genomics platform or Drop-Seq which generate cDNA pools in which the origin of a transcript is encoded at its 5 or 3 end. These cDNA pools are most often analyzed by short read Illumina sequencing which can identify the cellular origin of a transcript and what gene it was transcribed from. However, these methods fail to retrieve isoform information. In principle, cDNA pools prepared using these approaches can be analyzed with Pacific Biosciences and Oxford Nanopore long-read sequencers to retrieve isoform information but current implementations rely heavily on Illumina short-reads for analysis in addition to long reads. Here, we used R2C2 to sequence and demultiplex 12 million full-length cDNA molecules generated by the 10X Chromium platform from [~]3000 peripheral blood mononuclear cells (PBMCs). We used these reads to - independent from Illumina data - cluster cells into B cells, T cells, and Monocytes and generate isoform-level transcriptomes for these cell types. We also generated isoform-level transcriptomes for all single cells and used this information to identify a wide range of isoform diversity between genes. Finally, we also designed a computational workflow to extract paired adaptive immune receptors - T cell receptor and B cell receptor (TCR and BCR) - sequences unique to each T and B cell. This work represents a new, simple, and powerful approach that - using a single sequencing method - can extract an unprecedented amount of information from thousands of single cells.
- organism: human
- assay: scRNA-seq
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: database / resource
- approach: experimental assay
- Citations
- 92
- FWCI
- 4.65
- Field percentile
- top 3%
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