High-throughput total RNA sequencing in single cells using VASA-seq.
Fredrik SalmenJoachim De JongheTomasz S KaminskiAnna AlemanyGuillermo E ParadaJoe Verity-LeggAyaka YanagidaTimo N KohlerNicholas BattichFloris van den BrekelAnna L EllermannAlfonso Martinez AriasJennifer NicholsMartin HembergFlorian HollfelderAlexander van OudernaardenPublished in: Nature biotechnology (2022)
Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. 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. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. 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. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. 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.
Keyphrases
- single cell
- rna seq
- high throughput
- induced apoptosis
- cell cycle
- cell cycle arrest
- genome wide
- heart failure
- atrial fibrillation
- depressive symptoms
- stem cells
- oxidative stress
- cell death
- endoplasmic reticulum stress
- electronic health record
- machine learning
- small molecule
- pregnant women
- mesenchymal stem cells
- real time pcr
- loop mediated isothermal amplification
- deep learning
- artificial intelligence
- pregnancy outcomes