Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response

Target enrichment using parallel nanoliter quantitative PCR amplification

Background: Next generation targeted resequencing is replacing Sanger sequencing at high pace in routine genetic diagnosis. The need for well validated, high quality enrichment platforms to complement the bench-top next generation sequencing devices is high. Results: We used the WaferGen Smartchip platform to perform highly parallelized PCR based target enrichment for a set of known cancer genes in a well characterized set of cancer cell lines from the NCI60 panel. Optimization of PCR assay design and cycling conditions resulted in a high enrichment efficiency. We provide proof of a high mutation rediscovery rate and have included technical replicates to enable SNP calling validation demonstrating the high reproducibility of our enrichment platform. Conclusions: Here we present our custom developed quantitative PCR based target enrichment platform. Using highly parallel nanoliter singleplex PCR reactions makes this a flexible and efficient platform. The high mutation validation rate shows this platform’s promise as a targeted resequencing method for multi-gene routine sequencing diagnostics

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data

A unified censored normal regression model for qPCR differential gene expression analysis

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as undetermined . Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision

Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quanti cation. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cuflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but speci c gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set

ZnT3 mRNA levels are reduced in Alzheimer's disease post-mortem brain

<p>Abstract</p> <p>Background</p> <p>ZnT3 is a membrane Zn²⁺transporter that is responsible for concentrating Zn²⁺into neuronal presynaptic vesicles. Zn²⁺homeostasis in the brain is relevant to Alzheimer's disease (AD) because Zn²⁺released during neurotransmission may bind to Aβ peptides, accelerating the assembly of Aβ into oligomers which have been shown to impair synaptic function.</p> <p>Results</p> <p>We quantified ZnT3 mRNA levels in Braak-staged human post mortem (pm) brain tissue from medial temporal gyrus, superior occipital gyrus, superior parietal gyrus, superior frontal gyrus and cerebellum from individuals with AD (n = 28), and matched controls (n = 5) using quantitative real-time PCR. ZnT3 mRNA levels were significantly decreased in all four cortical regions examined in the AD patients, to 45-60% of control levels. This reduction was already apparent at Braak stage 4 in most cortical regions examined. Quantification of neuronal and glial-specific markers in the same samples (neuron-specific enolase, NSE; and glial fibrillary acidic protein, GFAP) indicated that loss of cortical ZnT3 expression was more pronounced, and occurred prior to, significant loss of NSE expression in the tissue. Significant increases in cortical GFAP expression were apparent as the disease progressed. No gene expression changes were observed in the cerebellum, which is relatively spared of AD neuropathology.</p> <p>Conclusions</p> <p>This first study to quantify ZnT3 mRNA levels in human pm brain tissue from individuals with AD and controls has revealed a significant loss of ZnT3 expression in cortical regions, suggesting that neuronal cells in particular show reduced expression of ZnT3 mRNA in the disease. This suggests that altered neuronal Zn²⁺handling may be an early event in AD pathogenesis.</p

RDML: structured language and reporting guidelines for real-time quantitative PCR data

The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium (http://www.rdml.org) to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results