Biology subjects

Verboom, K.

Publications and source records attributed to Verboom, K..

3 recordsLinked to original sources

bioRxiv2020

Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

Technological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

molecular biology↗

bioRxiv2019

A comprehensive dataset of TLX1 positive ALL-SIL lymphoblasts and primary T-cell acute lymphoblastic leukemias

Most currently available transcriptome data of T-cell acute lymphoblastic leukemia (T-ALL) are based on polyA[+] RNA sequencing methods thus lacking non-polyadenylated transcripts. Here, we present the data of polyA[+] and total RNA sequencing in the context of in vitro TLX1 knockdown in ALL-SIL cells and a primary T-ALL cohort. We extended this dataset with ATAC sequencing and H3K4me1 and H3K4me3 ChIP sequencing data to map putative gene regulatory regions. In this data descriptor, we present a detailed report of how the data were generated and which bioinformatics analyses were performed. Through several technical validations, we showed that our sequencing data are of high quality and that our in vitro TLX1 knockdown was successful. We also validated the quality of the ATAC and ChIP sequencing data and showed that ATAC and H3K4me3 ChIP peaks are enriched at transcription start sites. We believe that this comprehensive set of sequencing data can be reused by others to further unravel the complex biology of T-ALL in general and TLX1 in particular.

molecular biology↗

bioRxiv2018

SMARTer single cell total RNA sequencing

Single cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3 end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

genomics↗