High-throughput total RNA sequencing in single cells using VASA-seq

Abstract

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.

Single-cell Total RNA Miniaturized sequencing (STORM-seq) reveals differentiation trajectories of primary human fallopian tube epithelium

“…Compared to bulk total RNA-seq, STORM-seq detects approximately 4,700 (SD = 2,284) fewer genes across sequencing depths of 0.1-1 million reads, suggesting that STORM-seq provides complex libraries in single cells, approaching the complexity achieved in bulk methods ( Figure 1B ). STORM-seq is a full-length, single-cell protocol and we sought to compare how our method compares to recently published, bespoke single-cell total RNA-seq methods, SMART-seq total and VASA-seq 9,21 , as well as a 3’ biased protocol like 10x Genomics. We generated HEK293T single-cell libraries as a common cell type to compare across technologies.…”

“…Briefly, single-end reads were trimmed using default parameters with the following modifications: 1) --trim-n, 2) --length 18, 3) specifying two adapter sequences as -a AAAAAAAAAA -a TTTTTTTTTT, and 4) --fastqc. VASA-seq data were pre-processed and trimmed using TrimGalore (v0.6.0) and cutadapt (v2.10) using provided scripts in Salmen et al 21 . SMART-seq2 K562 data were trimmed using TrimGalore (v0.6.3).…”

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells

“…The methods outlined to estimate RNA velocity lack extensibility and flexibility to adapt to these more complicated, real-world scenarios. Emerging technologies like VASA-seq 19 , which have greater sensitivity for unspliced RNA detection, may also provide sufficient signal to fit more complex models (e.g., going beyond two promoter states 20 ); thus, a framework is necessary to rapidly test and update model assumptions.…”

High precision, high throughput generation of droplets containing single cells

“…Moreover, many applications require the co-encapsulation of more than one cell/particle into a droplet. For example, for single cell mRNA sequencing [12][13][14] and single-cell secretory analysis [15][16][17], the co-encapsulation of exactly one cell and one functional bead is required; For cell-cell interaction and functional screening, two distinct cell types need to be paired one-to-one in every High precision, high throughput generation of droplets containing single cells droplet [18][19][20][21]; For cell-cell communication profiling, two types of cells plus one or two distinct types of beads are needed to construct an assay in each droplet [22][23][24]. In these cases, the limitation brought by the Poisson distribution is even more pronounced.…”