T-RHEX-RNAseq – a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. We present RAG-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. RAG-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which is essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with oligo dT primers, rRNA depletion via Depletion of Abundant Sequences by Hybridization (DASH), and linear amplification, RAG-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights RAG-seq’s superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.

show abstract

Simulation study of factors affecting the accuracy of transcriptome models under complex environments

Eiju,

Hashida,

Maeda

et al. 2024

Preprint

View full text Add to dashboard Cite

Characterization of molecular responses in real and complex field environments is essential for understanding the environmental response of plants. Field transcriptomics, i.e. modelling large amounts of transcriptomic and meteorological data, is the most comprehensive method of studying gene expression dynamics in complex environments. However, it is not clear what factors influence the accuracy of field transcriptome models. In this study, a novel simulation system was developed. Using the system, we performed a large-scale simulation to reveal the factors affecting the accuracy of the models. We found that the factors that had the greatest impact on the accuracy are, in order of importance, the expression pattern of the gene, the number of samples in the training data, the diurnal coverage of the training data, and the temperature coverage of the training data. Validation using actually measured transcriptome data showed similar results to the simulations. Our simulation system and the analysis results will be helpful for developing efficient sampling strategies for training data and for generating simulated data for benchmarking new modelling methods. It will also be valuable to dissect the relative importance of various factors behind transcriptome dynamics in the real environment.Key messageNovel simulation system revealed how prediction accuracy of field transcriptome was affected by number and diversity of training data

show abstract

T-RHEX-RNAseq – a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells

Cited by 3 publications

References 34 publications

The di-leucine motif in the host defense peptide LL-37 is essential for initiation of autophagy in human macrophages

The di-leucine motif in the host defense peptide LL-37 is essential for initiation of autophagy in human macrophages

RAG-seq: NSR-primed and Transposase Tagmentation-mediated Strand-specific Total RNA Sequencing in Single Cells

Simulation study of factors affecting the accuracy of transcriptome models under complex environments

Contact Info

Product

Resources

About