Biology subjects

Hendriks, G.-J.

Publications and source records attributed to Hendriks, G.-J..

6 recordsLinked to original sources

bioRxiv2025

RNA BaseCode: Long-Read RNA Sequences with Short-Read Accuracy

RNA sequencing has revolutionized the life sciences by enabling comprehensive profiling of gene expression across tissues and individual cells. Most mammalian genes produce multiple isoforms with distinct functional consequences, yet their accurate detection and quantification remain challenging. Here we present RNA BaseCode, a synthetic long-read RNA sequencing method that combines the high accuracy and quantitative precision of short-read sequencing with internal random barcodes to reconstruct full-length RNA sequences. We applied RNA BaseCode to purified RNA to benchmark its performance against short-read and long-read RNA sequencing methods. RNA BaseCode demonstrated superior basecalling accuracy, higher quantitative precision, and an improved ability to reconstruct full-length transcripts compared to long-read sequencing. This method enables comprehensive characterization of RNA isoform expression at scale, providing a powerful and broadly applicable tool for investigating isoform regulation across biological contexts as well as the roles of these isoforms in health and disease.

genomics↗

bioRxiv2021

Molecular spikes: a gold standard for single-cell RNA counting

Molecule counting is central to single-cell sequencing, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes, novel RNA spike-ins containing inbuilt unique molecular identifiers that we use to identify critical experimental and computational conditions for accurate RNA counting across single-cell RNA-sequencing methods. The molecular spikes are a new gold standard that can be widely used to validate RNA counting in single cells.

genomics↗

bioRxiv2020

Deducing transcriptional kinetics and molecular functions of long non-coding RNAs using allele-sensitive single-cell RNA-sequencing

An increasing number of studies have demonstrated the regulatory importance of long non-coding RNAs (lncRNAs), yet little is known about their transcriptional dynamics and it remains challenging to determine their regulatory functions. Here, we used allele-sensitive single-cell RNA-seq (scRNA-seq) to demonstrate that lncRNAs have lower burst frequencies with twice as long duration between bursts, compared to mRNAs. Additionally, we observed an increased cell-to-cell variability in lncRNA expression that was due to more sporadic bursting (lower frequency) with larger numbers of RNA molecules being produced. Exploiting heterogeneity in asynchronously growing cells, we identified and experimentally validated lncRNAs with cell-state specific functions involved in cell cycle progression and apoptosis. Finally, utilizing allele-resolved RNA expression, we identified cis functioning lncRNAs and observed that knockdown of these lncRNAs modulated either transcriptional burst frequency or size of the nearby protein-coding gene. Collectively, our results identify distinct transcriptional regulation of lncRNAs and we demonstrate a role for lncRNAs in the regulation of transcriptional bursting of mRNAs.

genomics↗

bioRxiv2019

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5 unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

genomics↗

bioRxiv2019

State transitions of a developmental oscillator

Gene expression oscillators can structure biological events temporally and spatially. Different biological functions benefit from distinct oscillator properties. Thus, finite developmental processes rely on oscillators that start and stop at specific times; a poorly understood behavior. Here, we have characterized a massive gene expression oscillator comprising >3,700 genes in C. elegans larvae. We report that oscillations initiate in embryos, arrest transiently after hatching and in response to perturbation, and cease in adults. Experimental observation of the transitions between oscillatory and non-oscillatory states at a resolution where we can identify bifurcation points reveals an oscillator operating near a Saddle Node on Invariant Cycle (SNIC) bifurcation. These findings constrain the architecture and mathematical models that can represent this oscillator. They also reveal that oscillator arrests occur reproducibly in a specific phase. Since we find oscillations to be coupled to developmental processes, including molting, this characteristic of SNIC bifurcations thus endows the oscillator with the potential to halt larval development at defined intervals, and thereby execute a developmental checkpoint function.

systems biology↗

bioRxiv2019

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of monoallelic expression resulting from transcriptional bursting and how it compared to the amounts of monoallelic expression of autosomal genes observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain frequent observations of autosomal monoallelic gene expression in cells. Importantly, the burst frequency largely determined the fraction of cells with monoallelic expression, whereas both burst frequency and size contributed to allelic imbalance. Allelic observations deviate from the expected when analysed across heterogeneous groups of cells, suggesting that allelic modelling can provide an unbiased assessment of heterogeneity within cells. Finally, large numbers of cells are required for analyses of allelic imbalance to avoid confounding observations from transcriptional bursting. Altogether, our results shed light on the implications of transcriptional burst kinetics on allelic expression patterns and phenotypic variation between cells.

genomics↗