http://www.gene-quantification.net/livak-2001.pdf

Analysis of relative gene expression data using real-time quantitative pcr and the 2(-delta delta c(t)) method

Two different methods of presenting quantitative gene expression exist: absolute and relative quantification. Absolute quantification calculates the copy number of the gene usually by relating the PCR signal to a standard curve. Relative gene expression presents the data of the gene of interest relative to some calibrator or internal control gene. A widely used method to present relative gene expression is the comparative C(T) method also referred to as the 2 (-DeltaDeltaC(T)) method. This protocol provides an overview of the comparative C(T) method for quantitative gene expression studies. Also presented here are various examples to present quantitative gene expression data using this method.

/pdf/analyzing-real-time-pcr-data-by-the-comparative-c-t-method-tnacbujy1o.pdf

Analyzing real-time PCR data by the comparative C(T) method.

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.

/pdf/accurate-normalization-of-real-time-quantitative-rt-pcr-data-34rwzsvnu5.pdf

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

Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.

Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

/pdf/the-miqe-guidelines-minimum-information-for-publication-of-27m8n04lrd.pdf

The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

MicroRNA (miRNA ) alterations are involved in the initiation and progression of human cancer. The causes of the widespread differential expression of miRNA genes in malignant compared with normal cells can be explained by the location of these genes in cancer-associated genomic regions, by epigenetic mechanisms and by alterations in the miRNA processing machinery. MiRNA-expression profiling of human tumours has identified signatures associated with diagnosis, staging, progression, prognosis and response to treatment. In addition, profiling has been exploited to identify miRNA genes that might represent downstream targets of activated oncogenic pathways, or that target protein- coding genes involved in cancer.

MicroRNA signatures in human cancers

Enrichment of the erythrocyte miR-451a in brain extracellular vesicles following impairment of the blood-brain barrier.

Part B--microRNAs: microRNA methods.

The present invention is directed to methods, reagents, kits and compositions for detecting microRNA (miRNA) precursors in a biological sample. The method uses gene-specific primers and reverse transcriptase to convert the primary miRNA precursors (pri-miRNA) and pre-miRNA precursors (pre-miRNAs) to cDNA. The method also uses amplification reactions using gene specific forward and reverse primers that are targeted to the hairpin sequence of pri- and pre-microRNA precursors to detect the expression levels of both the pri- and the pre-micoRNAs.

Thomas D. Schmittgen

Papers

Enrichment of the erythrocyte miR-451a in brain extracellular vesicles following impairment of the blood-brain barrier.

Part B--microRNAs: microRNA methods.

Methods for quantifying microRNA precursors

Method for improved integrity of RNA isolated from Matrigel cultures.

Correction: Detection of microRNA Expression in Human Peripheral Blood Microvesicles