Jo Vandesompele

Bio: Jo Vandesompele is an academic researcher from Ghent University. The author has contributed to research in topics: Neuroblastoma & microRNA. The author has an hindex of 88, co-authored 383 publications receiving 59368 citations. Previous affiliations of Jo Vandesompele include Washington University in St. Louis & Ghent University Hospital.

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Abstract: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

Murine M cells express annexin V specifically

Abstract: The specialized epithelium covering the lymphoid follicles of Peyer's patches in the gut mediates transcytosis of antigens to the underlying immune cells, mainly through the membranous, or M, cells. At present, the molecular processes involved in the mucosal immune response, and in antigen transport across the follicle-associated epithelium (FAE) and M cells, are poorly understood. To characterize FAE and M cells, we compared the gene expression profiles of small intestine FAE and villus epithelium (VE) in BALB/c mice by microarray analysis; 91 genes were found to be up-regulated and four down-regulated at least two-fold (p < 0.01) in the FAE. The differential expression of a subset of these genes was shown to be confirmed by quantitative RT-PCR. Using immunohistochemistry on BALB/c Peyer's patches, cathepsin H and clusterin expression was increased in the FAE compared to the VE. Moreover, we demonstrated M cell-specific expression of annexin V, which has recently been reported to be important in endocytic transport and membrane scaffolding, suggesting that annexin V has a function in M cell-mediated transcytosis. Copyright © 2006 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Selection of reference genes for quantitative real-time PCR in equine in vivo and fresh and frozen-thawed in vitro blastocysts

Abstract: Application of reverse transcription quantitative real-time polymerase chain reaction is very well suited to reveal differences in gene expression between in vivo and in vitro produced embryos. Ultimately, this may lead to optimized equine assisted reproductive techniques. However, for a correct interpretation of the real-time PCR results, all data must be normalized, which is most reliably achieved by calculating the geometric mean of the most stable reference genes. In this study a set of reliable reference genes was identified for equine in vivo and fresh and frozen-thawed in vitro embryos. The expression stability of 8 candidate reference genes (ACTB, GAPDH, H2A/I, HPRT1, RPL32, SDHA, TUBA4A, UBC) was determined in 3 populations of equine blastocysts (fresh in vivo, fresh and frozen-thawed in vitro embryos). Application of geNorm indicated UBC, GAPDH, ACTB and HPRT1 as the most stable genes in the in vivo embryos and UBC, RPL32, GAPDH and ACTB in both in vitro populations. When in vivo and in vitro embryos were combined, UBC, ACTB, RPL32 and GAPDH were found to be the most stable. SDHA and H2A/I appeared to be highly regulated. Based on these results, the geometric mean of UBC, ACTB, RPL32 and GAPDH is to be recommended for accurate normalization of quantitative real-time PCR data in equine in vivo and in vitro produced blastocysts.

Flexible, scalable, and efficient targeted resequencing on a benchtop sequencer for variant detection in clinical practice.

Abstract: The release of benchtop next-generation sequencing (NGS) instruments has paved the way to implement the technology in clinical setting. The need for flexible, qualitative, and cost-efficient workflows is high. We used singleplex-PCR for highly efficient target enrichment, allowing us to reach the quality standards set in Sanger sequencing-based diagnostics. For the library preparation, a modified NexteraXT protocol was used, followed by sequencing on a MiSeq instrument. With an innovative pooling strategy, high flexibility, scalability, and cost-efficiency were obtained, independent of the availability of commercial kits. The approach was validated for ∼250 genes associated with monogenic disorders. An overall sensitivity (>99%) similar to Sanger sequencing was observed in combination with a positive predictive value of >98%. The distribution of coverage was highly uniform, guaranteeing a minimal number of gaps to be filled with alternative methods. ISO15189-accreditation was obtained for the workflow. A major asset of the singleplex PCR-based enrichment is that new targets can be easily implemented. Diagnostic laboratories have validated assays available ensuring that the proposed workflow can easily be adopted. Although our platform was optimized for constitutional variant detection of monogenic disease genes, it is now also used as a model for somatic mutation detection in acquired diseases.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Cutadapt removes adapter sequences from high-throughput sequencing reads

Abstract: When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Abstract: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.