Jul 23, 2019
GRAND-SLAM - The Method for quantification of new and old RNA
Life Sciences, Diagnostics/Biomarker
- Labelling with SLAM-seq, TUC-seq or TLapse-seq
- Fully quantitative analyses of RNA (incl. single cell)
- Vertebrates, insects, plants & yeast, in vitro & in vivo
The abundance of all expressed RNAs can be determined by RNA-seq experiments on genome scale. All mRNAs of a sample are fragmented, transcribed into cDNA and millions of these fragments are then analyzed by next-generation sequencing. The number of sequencing „reads“ obtained for a specific mRNA can be used as a measure of its expression strength. If the research question involves studying rapid processes, only a small part of changes in the transcription or degradation rate are reflected in total RNA quantities. RNAs with different turn-over rates (in particular mRNAs with medium and long half-lives) are affected to different degrees. This distorts the results drastically. In order to mitigate this problem, SLAM-seq, TUC-seq or TimeLapse-seq methods were developed and published recently. They describe the combination of metabolic RNA labeling and single nucleotide conversions chemical nucleotide conversion for measuring the RNA newly transcribed by cells in a defined time window. This is based on RNA-seq and exploiting the introduced SNCnucleotide substitutions.
This technology is based on a computational approach called GRAND-SLAM which provides a powerful tool for the analysis of expression data generated by SLAM-seq, TUC-seq and/or TimeLapse-seq. The unique feature of GRAND-SLAM is to enable fully quantitative analyses of such data to determine a) the new-to-total ratio of RNA (NTR) for each gene representing the percentage of RNA transcribed during the period of labeling b) regulation of mRNAs over time c) RNA half-lives and d) changes based on RNA stability or on altered RNA synthesis rates.
- Metabolic labelling using SLAM-seq, TUC-seq or TimeLapse-seq with GRAND-SLAM is applicable to all major model organisms including vertebrates, insects, plants and yeast, in vitro as well as in vivo.
- SLAM-seq and GRAND-SLAM are applicable to single cell RNA sequencing (scSLAM-seq)1
- The additional temporal dimension of such experiments enables to pursue completely new types of research questions.
- SLAM-seq, TUC-seq or TimeLapse-seq with GRAND SLAM combined with CRISPR-based perturbations will greatly improve the sensitivity of the approaches to decipher the molecular mechanisms with major implications for developmental biology, infection and cancer.
Proof of concept
Erhard F et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 2019