Thomas D. Schmittgen

Abstract: The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data.

Abstract: 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.

Abstract: Those who have worked in the field of quantitative polymerase chain reaction (PCR) since the early 1990s have accepted many of the tedious aspects of the early assays as routine. Difficulties associated with early quantitative PCR techniques included: (i) ensuring that the PCR was within the linear range of amplification (that portion where the PCR signal is directly proportional to the input copy number) and (ii) finding a suitable method to detect the product once linear amplification was achieved. To ensure linearity, researchers would amplify serial dilutions of cDNA (1) or alter the amplification cycle for each gene (2). Others would add a competitor template (3), perform limiting dilution assays (4), or use a PCR mimic with a similar primer sequence that would be coamplified along with the product (5). Detection of the amplicon generally resorted to running gels and DNA detection using a stain, radioactivity, or probe. For those who have converted to using real-time quantitative PCR, the old days are but a memory. In real-time PCR, worrying about linear amplification, running gels, working with radioactivity, and gathering numbers are a thing of the past. The ability to generate data within a 2to 3-h period is now a reality. Other advantages of real-time PCR include enhanced sensitivity, high throughput, use of a closed-tube system, reduced variation, the ability to simultaneously multiplex reactions, and lack of post-PCR manipulations. The technology to detect PCR products in real time, i.e., during the reaction, has been available for more than 5 years, but has seen a dramatic increase in use over the past 2 years. A Medline search using the key words TaqMan or real-time and PCR yielded 19 citations in 1996, 28 citations in 1997, and 52, 157, and 409 citations in 1998, 1999, and 2000, respectively. At the time of this writing, there were 551 citations in 2001 (Fig. 1). Various manufacturers are touting realtime PCR instrumentation and affiliated technology

Abstract: Background: MicroRNAs (miRNA) are small non-coding RNAs that regulate translation of mRNA and protein. Loss or enhanced expression of miRNAs is associated with several diseases, including cancer. However, the identification of circulating miRNA in healthy donors is not well characterized. Microvesicles, also known as exosomes or microparticles, circulate in the peripheral blood and can stimulate cellular signaling. In this study, we hypothesized that under normal healthy conditions, microvesicles contain miRNAs, contributing to biological homeostasis. Methodology/Principal Findings: Microvesicles were isolated from the plasma of normal healthy individuals. RNA was isolated from both the microvesicles and matched mononuclear cells and profiled for 420 known mature miRNAs by realtime PCR. Hierarchical clustering of the data sets indicated significant differences in miRNA expression between peripheral blood mononuclear cells (PBMC) and plasma microvesicles. We observed 71 miRNAs co-expressed between microvesicles and PBMC. Notably, we found 33 and 4 significantly differentially expressed miRNAs in the plasma microvesicles and mononuclear cells, respectively. Prediction of the gene targets and associated biological pathways regulated by the detected miRNAs was performed. The majority of the miRNAs expressed in the microvesicles from the blood were predicted to regulate cellular differentiation of blood cells and metabolic pathways. Interestingly, a select few miRNAs were also predicted to be important modulators of immune function. Conclusions: This study is the first to identify and define miRNA expression in circulating plasma microvesicles of normal subjects. The data generated from this study provides a basis for future studies to determine the predictive role of peripheral blood miRNA signatures in human disease and will enable the definition of the biological processes regulated by these miRNA.

Abstract: The effects of serum on the expression of four commonly used housekeeping genes were examined in serum-stimulated fibroblasts in order to validate the internal control genes for a quantitative RT-PCR assay. NIH 3T3 fibroblasts transfected with an inducible chimeric gene were serum-starved for 24 h and then induced with 15% serum for 8 h. Serum did not alter the amount of total RNA that was expressed in the cells, however, the amount of mRNA significantly increased over time with serum-stimulation. Both messenger and total RNA from each of the time points were reverse transcribed under two different conditions; one in which the reactions were normalized to contain equal amounts of RNA and another series of reactions that were not normalized to RNA content. The resulting cDNA was amplified by real-time, quantitative PCR using gene-specific primers for beta-actin, beta-2 microglobulin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and 18S ribosomal RNA. The expression of beta-actin and GAPDH increased up to nine- and three-fold, respectively, under all conditions of reverse transcription (P 0.05). The expression of beta-2 microglobulin increased up to two-fold when assayed from cDNA synthesized from non-normalized mRNA, but was unaffected by serum when the reverse transcriptions were normalized to mRNA. beta-2 Microglobulin expression was found to be directly proportional to the amount of mRNA that was present in non-normalized reverse transcription reactions. Thus, beta-2 microglobulin and 18S rRNA are suitable internal control genes in quantitative serum-stimulation studies, while beta-actin and GAPDH are not. The internal control gene needs to be properly validated when designing quantitative gene expression studies.

Abstract: The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.