Biology subjects

Vandesompele, J.

Publications and source records attributed to Vandesompele, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A false sense of circularity: hidden variability in RNase R enzymes confounds reliable circRNA validation

Circular RNAs (circRNAs) are an emerging class of RNA molecules involved in development and disease. They are generated through back-splicing from pre-mRNA. RNaseR treatment, which degrades linear RNA while preserving circRNAs, is widely used for circRNA validation and enrichment. This short report demonstrated differential performance of two RNaseR enzymes from different suppliers, despite identical experimental conditions, indicating that these enzymes should not be considered interchangeable across suppliers. Importantly, these findings highlight that without demonstrating efficient linear RNA degradation, unchanged circRNA abundance after RNase R treatment is inherently uninformative and may contribute to false-positive circRNA validation or inadequate circRNA enrichment. We therefore highlight that RNaseR digestion should be consistently verified using a linear control, particularly when changing enzyme suppliers.

molecular biology↗

bioRxiv2026

Introducing the digital PCR data essentials standard to harmonize data structure for clinical and research use

Digital PCR (dPCR) is a powerful technology for absolute quantification of nucleic acids, valued for its accuracy, sensitivity, and repeatability. Yet, the commercialization of different instruments with proprietary software has introduced challenges to data analysis, interoperability, and comparability. Therefore, we present the Digital PCR Data Essentials Standard (DDES) - a lightweight, human- and machine-readable, and cross-platform data standard developed in collaboration with the dPCR community. The standard consists of three file types designed to enable both manual inspection and automated analysis: (i) a main file summarizing experiment and reaction-level (meta-)data; (ii) an assay file describing targets and detection chemistry, and (iii) intensity files capturing partition-level raw fluorescence data per reaction. DDES supports a wide range of current dPCR applications, including singleplex and multiplex assays, endpoint and real-time readouts, and will be curated to implement future dPCR developments. By harmonizing the data structure, DDES lays out the foundation for FAIR dPCR data practices and supports improved software compatibility, collaborative and reproducible research, and future dPCR data repositories.

bioinformatics↗

bioRxiv2025

Deconvoluting fiber type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

Muscle fibers are the dominant, multinucleated cell type in skeletal muscle. In humans, they can be classified as slow (type 1) and fast (type 2) fibers, traditionally based on distinct properties of their contractile machinery. Slow and fast fibers are characterized by shared and specific cellular complexities that are pivotal for their adaptive capacity to exercise or role in metabolic disease progression. In this era of omics research, there is a critical need to accurately infer muscle fiber type proportions from bulk tissue omics datasets, as single-fiber approaches are not feasible in large-scale or retrospective studies. Here we present FibeRtypeR, an easy-to-use web application to accurately estimate fiber type proportions from bulk transcriptomics and proteomics datasets: https://muscleapps.ugent.be. FibeRtypeR exploits transcriptomics and proteomics profiles of 1000 fiber-typed individual human skeletal muscle fibers as a reference dataset and is validated against paired immunohistochemical fiber type determinations in 160 muscle biopsies. We show that FibeRtypeR can be applied to public datasets, illustrating the application potential across a wide range of biological contexts such as aging, disease and exercise training. This new freely accessible computational tool will prove valuable to the skeletal muscle research community. Key pointsO_LIBulk muscle omics datasets lack fiber type specific information C_LIO_LIOur new tool, FibeRtypeR, leverages in-house collected single-fiber profiles allowing for accurate fiber type inference C_LIO_LIFibeRtypeR is methodologically robust across omics technologies and workflows C_LIO_LIWe host FibeRtypeR as an intuitive open-access Shiny app, applicable to new and publicly available transcriptomics and proteomics datasets C_LI

physiology↗

bioRxiv2025

Cross-platform digital PCR evaluation of bovine papilloma virus quantification: introducing PCR-ValiPal for Standardized guided assay validation

Digital Polymerase chain reaction (dPCR) enables precise and absolute quantification of nucleic acids by partitioning samples into thousands of individual PCR micro-reactions. While it minimizes the need for standard curves and enhances reproducibility compared to qPCR, thorough assay validation remains crucial. We introduce PCR-ValiPal, a user-friendly web application that standardizes dPCR assay validation steps and streamlines calculations of limit of blank (LOB), limit of detection (LOD), limit of quantification (LOQ), precision, trueness, and linearity in accordance with International Organization for Standardization (ISO) 20395:2019. To demonstrate PCR-ValiPals capabilities and the value of method-specific optimization, we use it to validate a novel three-color PCR assay for Bovine Papillomavirus (BPV) types 1 and 2, comparing four platforms: Naica (droplet dPCR), QIAcuity (microwell dPCR), LOAA (real-time dPCR), and CFX96 (qPCR). Using synthetic standards, we assess the platforms performance under identical assay conditions. Naica and QIAcuity showed lower LOB and LOQ values, along with minimal bias for BPV-1, while LOAA demonstrated stable but negative bias. Although qPCR exhibited the highest sensitivity for BPV-2, it was less sensitive at low concentrations for BPV-1. These results underscore the value of method-specific optimization and the usefulness of PCR-ValiPal.

molecular biology↗

bioRxiv2024

Intact messenger RNA exists in human blood plasma and urine, and their purified macromolecular compartments

It is generally assumed that extracellular long RNA molecules in biofluids are fragmented. Few studies have indirectly hinted at the existence of possibly functional, intact long RNA transcripts. In search for such RNA molecules, we developed a long-read full transcript sequencing workflow for low-input and low-quality samples. We applied our method to human blood plasma, urine, and their isolated macromolecular compartments, in parallel with total RNA sequencing. This approach enabled us to find intact messenger RNA molecules in human biofluids and macromolecular compartments. We showed that the full-length transcriptome of human urine and blood plasma differs, but we also reveal intact messenger RNA molecules shared between biofluids. In addition, we show that these intact molecules are differentially distributed over fractionated macromolecular compartments. This study provides a foundation for future extracellular RNA studies to elucidate the human biofluid full-length transcriptome.

molecular biology↗

bioRxiv2023

Beyond Myosin Heavy Chains: Ribosomal Specialization Drives Human Skeletal Muscle Fiber Heterogeneity

Skeletal muscle is an inherently heterogenous tissue comprised primarily of myofibers, which are historically classified into three distinct fiber types in humans: one "slow" (type 1) and two "fast" (type 2A and type 2X), delineated by the expression of myosin heavy chain isoforms (MYHs). However, heterogeneity between and within traditional fiber types remains underexplored. Indeed, whether MYHs are the main classifiers of skeletal muscle fibers has not been examined in an unbiased manner. Through the development and application of novel transcriptomic and proteomic workflows, applied to 1050 and 1038 single muscle fibers from human vastus lateralis, respectively, we show that MYHs are not the only principal drivers of skeletal muscle fiber heterogeneity. Instead, metabolic, ribosomal, and cell junction proteins are a source of multi-dimensional variation between skeletal muscle fibers. Furthermore, whilst slow and fast fiber clusters can be identified, described by their contractile and metabolic profiles, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers at an omics level. Moreover, MYH-based classifications do not adequately describe the phenotype of skeletal muscle fibers in one of the most common genetic muscle diseases, nemaline myopathy, with fibers shifting towards a non-oxidative phenotype independently of MYH-based fiber type. We also characterize novel transcriptomic and proteomic features of slow and fast skeletal muscle fibers, including identifying several muscle fiber type-specific polypeptides, termed microproteins, encoded by transcripts annotated as non-coding RNA. Overall, our data indicates that skeletal muscle fiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.

physiology↗

bioRxiv2023

Flexible Methods for Standard Error Calculation in digital PCR Experiments

Digital PCR (dPCR) is a highly accurate and precise technique for the quantification of target nucleic acid(s) in a biological sample. This digital quantification relies on the binomial or Poisson distribution to estimate the amount of target molecules based on positive and negative partitions. However, the implementation of these distributions require adherence to underlying assumptions that are often neglected, leading to a suboptimal (too optimistic) variance estimation of the target concentration, especially when considering the multiple sources of variation in experimental dPCR setups. Moreover, these parametric methods cannot be easily used for downstream statistical inference when more advanced analysis are required, such as for copy number variation. We evaluated the performance of three new statistical methods (BootsVar, NonPVar, BinomVar) in both simulations and real-life datasets for target and variance estimation in dPCR setups while taking into account a combination of commonly observed sources of experimental variability that can interfere with the underlying assumptions of the current parametric methods. The results demonstrate the capability of the new methods for variance estimation and present a more accurate reflection of the true variability over the classical binomial approach. In addition, these statistical methods are flexible and generic in the way that they work well for the variance estimation of non-linear statistics that work with ratios (e.g. CNV) and for multiplex dPCR setups. In this study, we provide guidelines when to use the binomial-assumption based methods and when the non-parametric one is better to achieve more accurate variance estimates.

bioinformatics↗

bioRxiv2022

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

genomics↗

bioRxiv2022

Blocking unwanted sequences by target specific high-affinity binding oligonucleotides during transcriptome library preparation

RNA sequencing has become the gold standard for transcriptome analysis but has an inherent limitation of challenging quantification of low-abundant transcripts. In contrast to microarray technology, RNA sequencing reads are proportionally divided in function of transcript abundance. Therefore, low-abundant RNAs compete against highly abundant - and sometimes non-informative - RNA species. We developed an easy-to-use strategy based on high-affinity RNA-binding oligonucleotides to block reverse transcription and PCR amplification of specific RNA transcripts, thereby substantially reducing their abundance in the final sequencing library. To demonstrate the broad application potential of our method, we applied it to different transcripts and library preparation strategies, including YRNAs in small RNA sequencing of human blood plasma, mitochondrial rRNAs in both 3 end sequencing and long-read sequencing, and MALAT1 in single-cell 3 end sequencing. We demonstrate that the blocking strategy is highly efficient, reproducible, specific, and generally results in better transcriptome coverage and complexity. Furthermore, our method does not require modifications of the library preparation procedure apart from simply adding blocking oligonucleotides to the RT reaction and can thus be easily integrated into virtually any RNA sequencing library preparation protocol.

molecular biology↗

bioRxiv2022

Performance evaluation of three DNA sample tracking tools in a whole exome sequencing workflow

IntroductionNext-generation sequencing applications are becoming indispensable for clinical diagnostics. These experiments require numerous wet and dry lab steps, each one increasing the probability of a sample swap or contamination. Therefore, an identity confirmation at the end of the process is recommended to ensure the right data is used for each patient. MethodsWe tested three commercially available, SNP based sample tracking kits in a diagnostic workflow to evaluate their ease of use and performance. The coverage uniformity, on-target specificity, sample identification and genotyping performance were determined to assess the reliability and the cost-effectiveness of each kit. Results and discussionHands-on time and manual steps are almost identical for the kits from pxlence and Nimagen. The Swift kit has an extra purification step, making it the longest and most demanding protocol. Furthermore, the Swift kit failed to correctly genotype 26 out of the 46 samples. The Nimagen kit identified all but one sample and the pxlence kit unambiguously identified all samples, making it the most reliable and robust kit of this evaluation. The Nimagen kit showed poor on-target mapping rates, resulting in deeper sequencing needs and higher sequencing costs compared to the other two kits. Our conclusion is that the Human Sample ID kit from pxlence is the most cost-effective of the three tested tools for DNA sample tracking and identification. Key pointsO_LIKits from pxlence and Nimagen are easy to use. C_LIO_LIUnambiguous identification of all samples possible with the pxlence kit. C_LIO_LIOnly 20 out of 46 samples were correctly identified with the Swift kit. C_LIO_LIPoor on-target rates for the Nimagen kit results in higher sequencing costs. C_LI

genomics↗

bioRxiv2021

Automatic end-to-end analysis of high-throughput in vitro cell culture screening by HTSplotter

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, available as web tool and Python module, which performs automatic data analysis and visualisation of either endpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements in order to identify experiment type and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HAS) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

bioinformatics↗

bioRxiv2021

Performance of RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control (exRNAQC) study

The use of blood-based extracellular RNA (cell-free RNA; exRNA) as clinical biomarker requires the implementation of a validated procedure for sample collection, processing, and profiling. So far, no study has systematically addressed the pre-analytical variables affecting transcriptome analysis of exRNAs. In the exRNAQC study, we evaluated ten blood collection tubes, three time intervals between blood draw and downstream processing, and eight RNA purification methods using the supplier-specified minimum and maximum biofluid input volumes. The impact of these pre-analytics on deep transcriptome profiling of both small and messenger RNA from healthy donors plasma or serum was assessed for each pre-analytical variable separately and for interactions between a selected set of pre-analytics, resulting in 456 extracellular transcriptomes. Making use of 189 synthetic spike-in RNAs, the processing and analysis workflow was controlled. When comparing blood collection tubes, so-called preservation tubes do not stabilize exRNA well, and result in variable RNA concentration and sensitivity (i.e., the number of detected RNAs) over time, as well as compromised reproducibility. We also document large differences in RNA purification kit performance in terms of sensitivity, reproducibility, and observed transcriptome complexity, and demonstrate interactions between specific blood collection tubes, purification kits and time intervals. Our results are summarized in 11 performance metrics that enable an informed selection of the most optimal sample processing workflow for a given experiment. In conclusion, we put forward robust quality control metrics for exRNA quantification methods with validated standard operating procedures (SOPs), representing paramount groundwork for future exRNA-based precision medicine applications.

genomics↗

bioRxiv2021

Custom long non-coding RNA capture enhances detection sensitivity in different human sample types

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

genomics↗

bioRxiv2021

The long non-coding RNA SAMMSON is essential for uveal melanoma cell survival

PurposeLong non-coding RNAs (lncRNAs) can exhibit cell-type and cancer-type specific expression profiles, making them highly attractive as therapeutic targets. Pan-cancer RNA sequencing data revealed broad expression of the SAMMSON lncRNA in uveal melanoma (UM), the most common primary intraocular malignancy in adults. Currently, there are no effective treatments for UM patients with metastatic disease, resulting in a median survival time of 6-12 months. We aimed to investigate the therapeutic potential of SAMMSON inhibition in UM. Experimental DesignThe impact of antisense oligonucleotide (ASO)-mediated SAMMSON inhibition was evaluated in a panel of UM cell lines and patient derived xenograft (PDX) models. Cell proliferation and apoptosis were quantified in vitro and in vivo and complemented with molecular profiles established through RNA-sequencing. SAMMSON interaction partners were identified using ChIRP-MS. ResultsSAMMSON inhibition impaired the growth and viability of a genetically diverse panel of uveal melanoma cell lines. These effects were accompanied by an induction of apoptosis and were recapitulated in two uveal melanoma PDX models through subcutaneous ASO delivery. SAMMSON pulldown revealed several candidate interaction partners, including various proteins involved in mitochondrial translation. Consequently, inhibition of SAMMSON impaired global protein translation levels and mitochondrial function in uveal melanoma cells. ConclusionSAMMSON expression is essential for uveal melanoma cell survival. ASO-mediated silencing of SAMMSON may provide an effective treatment strategy to treat primary and metastatic uveal melanoma patients.

cancer biology↗

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↗

bioRxiv2020

Recurrent chromosomal imbalances provide selective advantage to human embryonic stem cells under enhanced replicative stress conditions

Human embryonic stem cells (hESCs) and embryonal tumors share a number of common features including a compromised G1/S checkpoint. Consequently, these rapidly dividing hESCs and cancer cells undergo elevated levels of replicative stress which is known to induce genomic instability causing chromosomal imbalances. In this context, it is of interest that long-term in vitro cultured hESCs exhibit a remarkable high incidence of segmental DNA copy number gains, some of which are also highly recurrent in certain malignancies such as 17q gain (17q+). The selective advantage of DNA copy number changes in these cells has been attributed to several underlying processes including enhanced proliferation. We hypothesized that these recurrent chromosomal imbalances become rapidly embedded in the cultured hESCs through a replicative stress driven Darwinian selection process. To this end, we compared the effect of hydroxyurea induced replicative stress versus normal growth conditions in an equally-mixed cell population of isogenic euploid and 17q+ hESCs. We could show that 17q+ hESCs rapidly overtook normal hESCs. Our data suggest that recurrent chromosomal segmental gains provide a proliferative advantage to hESCs under increased replicative stress, a process that may also explain the highly recurrent nature of certain imbalances in cancer.

cancer biology↗

bioRxiv2020

When DNA gets in the way in RNA-seq experiments, a sequel

Using a newly developed method dubbed SILVER-Seq--enabling extracellular RNA sequencing (exRNA-seq) directly from a small volume of human serum or plasma-- Yan et al. recently reported in Current Biology a potential exRNA biomarker for the early diagnosis of Alzheimers disease [1]. After the publication of the initial paper describing the SILVER-Seq method [2], we reported our concern regarding potential DNA contamination in their datasets [3]. Although the authors replied they were able to successfully treat RNA samples with DNase to avoid such contamination, they did not address our observations of the majority of reads without evidence of being derived from RNA, nor documented verified absence of DNA after DNase treatment [4]. To assess whether the newly data generated may suffer from DNA contamination, we downloaded the publicly available sequencing data and evaluated two quality control metrics (i.e., fraction of exonic and splice reads), which were not reported in the paper. We found that both quality metrics were much lower than expected for RNA-seq data (6.28% exonic and 0.478% splice reads), in line with our previous findings on the first SILVER-Seq paper. These observations suggest the data and results presented by Yan et al. are affected by DNA contamination, an issue that may be inherent to the SILVER-Seq technology.

molecular biology↗

bioRxiv2020

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

BackgroundThe methylation pattern of cfDNA, isolated from liquid biopsies, is gaining substantial interest for diagnosis and monitoring of diseases. We have evaluated the impact of type of blood collection tube and time delay between blood draw and plasma preparation on bisulfite-based cfDNA methylation profiling. Methods15 tubes of blood were drawn from three healthy volunteer subjects (BD Vacutainer K2E EDTA spray tubes, Streck Cell-Free DNA BCT tubes, PAXgene Blood ccfDNA tubes, Roche Cell-Free DNA Collection tubes and Biomatrica LBgard blood tubes in triplicate). Samples were either immediately processed or stored at room temperature for 24 or 72 hours before plasma preparation. DNA fragment size was evaluated by capillary electrophoresis. Reduced representation bisulfite sequencing was performed on the cell-free DNA isolated from these plasma samples. We evaluated the impact of blood tube and time delay on several quality control metrics. ResultsAll preservation tubes performed similar on the quality metrics that were evaluated. Furthermore, a considerable increase in cfDNA concentration and the fraction of it derived from NK cells was observed after a 72-hour time delay in EDTA tubes. ConclusionThe methylation pattern of cfDNA is robust and reproducible in between the different preservation tubes. EDTA tubes processed as soon as possible, preferably within 24 hours, are the most cost effective. If immediate processing is not possible, preservation tubes are valid alternatives.

genomics↗