![]() ![]() Finally, we used the full-length coverage to determine cell-type-specific splicing patterns, with an emphasis on heart morphogenesis and blood development. Higher coverage of intronic regions in the full-length VASA-seq dataset led to more accurate RNA velocity measurements 25, 26 across differentiation trajectories. Indeed, VASA-seq’s increased sensitivity led to the discovery of several cell-type-specific marker genes and non-polyadenylated histone gene expression patterns, which were used to accurately determine cell cycle stage across tissues. The analysis revealed layers of biological information that have been absent from recently published resources 20, 21, 22, 23, 24. Our resource provides a comprehensive analysis of mammalian post-implantation development by characterizing the total transcriptome at single-cell resolution. Next, we used VASA-seq to sample more than 30,000 single cells from mouse post-implantation embryos at the following developmental stages: embryonic day (E) 6.5, E7.5, E8.5 and E9.5. To our knowledge, VASA-seq is the only technology to combine excellent sensitivity, full-length coverage of total RNA and high throughput. We first benchmarked VASA-seq against state-of-the-art methods using cultured cells. To overcome these challenges, we developed ‘vast transcriptome analysis of single cells by dA-tailing’ (VASA-seq), which captures both non-polyadenylated and polyadenylated transcripts across their length in both plate and droplet microfluidic formats. ![]() Furthermore, neither full-length nor whole-transcriptome methods 16, 17, 18 have been adapted to high-throughput droplet-based platforms, which offer at least one order-of-magnitude gain in throughput compared to plate-based methods 19. This prevents differential expression of non-coding RNAs and alternative splicing (AS) and alternative promoter (AP) usage analyses.įull-length transcriptome sequencing methods 12, 13 have enabled AS profiling of polyadenylated RNA species at single-cell resolution 10, 14, 15, but the exact quantification of splicing events is hampered by the lack of strand and unique molecular identifier (UMI) information along the whole gene body. This results in the detection of short fragments (~400–600 base pairs) immediately adjacent to the poly(A) tail or at the 5′ end of the transcript, and, thus, remaining sequences in polyadenylated RNA molecules and the spectrum of non-polyadenylated transcripts are undetected. Although state-of-the-art scRNA-seq methods are sufficiently sensitive to quantify and determine cell states with high accuracy 8, 9, 10, 11, most methods rely on the hybridization of barcoded oligo-dT primers to the poly(A) sequences of polyadenylated transcripts for RNA capture and complementary DNA (cDNA) synthesis. Initial technologies were applied to small numbers of individual cells 1, 2, 3, 4 and were subsequently adapted to droplet microfluidics to sample thousands to millions of single cells 5, 6, 7. Single-cell RNA sequencing (scRNA-seq) has transformed understanding of cellular complexity over the last decade. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The method is compatible with both plate-based formats and droplet microfluidics. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. ![]()
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