Biomolecular Detection and Quantification最新文献

The estimated mean copy per partition (λ) is the essential information from a digital PCR (dPCR) experiment because λ can be used to calculate the target concentration in a sample. However, little information is available how to statistically compare dPCR runs of multiple runs or reduplicates. The comparison of λ values from several runs is a multiple comparison problem, which can be solved using the binary structure of dPCR data. We propose and evaluate two novel methods based on Generalized Linear Models (GLM) and Multiple Ratio Tests (MRT) for comparison of digital PCR experiments. We enriched our MRT framework with computation of simultaneous confidence intervals suitable for comparing multiple dPCR runs. The evaluation of both statistical methods support that MRT is faster and more robust for dPCR experiments performed in large scale. Our theoretical results were confirmed by the analysis of dPCR measurements of dilution series.

Both methods were implemented in the dpcR package (v. 0.2) for the open source R statistical computing environment.

Digital PCR has become the emerging technique for the sequence-specific detection and quantification of nucleic acids for various applications. During the past years, numerous reports on the development of new digital PCR methods have been published. Maturation of these developments into reliable analytical methods suitable for diagnostic or other routine testing purposes requires their validation for the intended use.

Here, the results of an in-house validation of a droplet digital PCR method are presented. This method is intended for the quantification of the absolute copy number concentration of a purified linearized plasmid in solution with a nucleic acid background. It has been investigated which factors within the measurement process have a significant effect on the measurement results, and the contribution to the overall measurement uncertainty has been estimated. A comprehensive overview is provided on all the aspects that should be investigated when performing an in-house method validation of a digital PCR method.

Measurement of RNA can be used to study and monitor a range of infectious and non-communicable diseases, with profiling of multiple gene expression mRNA transcripts being increasingly applied to cancer stratification and prognosis. An international comparison study (Consultative Committee for Amount of Substance (CCQM)-P103.1) was performed in order to evaluate the comparability of measurements of RNA copy number ratio for multiple gene targets between two samples. Six exogenous synthetic targets comprising of External RNA Control Consortium (ERCC) standards were measured alongside transcripts for three endogenous gene targets present in the background of human cell line RNA. The study was carried out under the auspices of the Nucleic Acids (formerly Bioanalysis) Working Group of the CCQM. It was coordinated by LGC (United Kingdom) with the support of National Institute of Standards and Technology (USA) and results were submitted from thirteen National Metrology Institutes and Designated Institutes. The majority of laboratories performed RNA measurements using RT-qPCR, with datasets also being submitted by two laboratories based on reverse transcription digital polymerase chain reaction and one laboratory using a next-generation sequencing method. In RT-qPCR analysis, the RNA copy number ratios between the two samples were quantified using either a standard curve or a relative quantification approach. In general, good agreement was observed between the reported results of ERCC RNA copy number ratio measurements. Measurements of the RNA copy number ratios for endogenous genes between the two samples were also consistent between the majority of laboratories. Some differences in the reported values and confidence intervals (‘measurement uncertainties’) were noted which may be attributable to choice of measurement method or quantification approach. This highlights the need for standardised practices for the calculation of fold change ratios and uncertainties in the area of gene expression profiling.

Background

microRNAs have recently been identified as powerful biomarkers of human disease. Reliable polymerase chain reaction (PCR)-based quantification of nucleic acids in clinical samples contaminated with polymerase inhibitor heparin requires deheparinization. However, the effects of deheparinization procedure on quantification of nucleic acids remain largely unknown. The aim of this study was to determine whether the deheparinization procedure completely eliminates the inhibition of amplification, while maintaining RNA integrity and technical variability of the measured microRNA levels.

Methods

Heparinized plasma from 9 patients undergoing coronary artery bypass grafting (CABG) and the heparin-free plasma from 58 rats were spiked with a synthetic RNA oligonucleotide and total RNA was extracted. The RNA solutions were then treated with heparinase I to remove contaminating heparin prior to reverse transcription. Levels of synthetic spike-in RNA oligonucleotide, as well as endogenous hsa-miR-1-3p and hsa-miR-208a-3p, were measured using quantitative reverse transcription PCR (RT-qPCR). The amplification efficiency and presence of inhibitors in individual samples were directly determined using calibration curves.

Results

In contrast to RNA samples from rat plasma, RNA samples derived from the CABG patient plasma contained inhibitors, which were completely eliminated by treatment with heparinase. The procedure caused a decrease in the amount of detected RNA; however, the technical variability of the measured targets did not change, allowing for the quantification of circulating endogenous hsa-miR-1-3p and hsa-miR-208a-3p in the plasma of CABG patients.

Conclusions

The heparinase treatment procedure enables utilization of RT-qPCR for reliable microRNA quantification in heparinized plasma.

RAS mutation analysis is an important companion diagnostic test. Treatment of colorectal cancer with anti-Epidermal Growth Factor Receptor (EGFR) therapy requires demonstration of RAS mutation status (both KRAS and NRAS), and it is good practice to include BRAF. In Non-Small Cell Lung Cancer (NSCLC) and melanoma, assessment of RAS mutation status can be helpful in triaging patient samples for more extensive testing. This mini-review will discuss the role of PCR methods in providing rapid diagnostic information for cancer patients.

NIST standard reference material (SRM) 2373 was developed to improve the measurements of the HER2 gene amplification in DNA samples. SRM 2373 consists of genomic DNA extracted from five breast cancer cell lines with different amounts of amplification of the HER2 gene. The five components are derived from the human cell lines SK-BR-3, MDA-MB-231, MDA-MB-361, MDA-MB-453, and BT-474. The certified values are the ratios of the HER2 gene copy numbers to the copy numbers of selected reference genes DCK, EIF5B, RPS27A, and PMM1. The ratios were measured using quantitative polymerase chain reaction and digital PCR, methods that gave similar ratios. The five components of SRM 2373 have certified HER2 amplification ratios that range from 1.3 to 17.7. The stability and homogeneity of the reference materials were shown by repeated measurements over a period of several years. SRM 2373 is a well characterized genomic DNA reference material that can be used to improve the confidence of the measurements of HER2 gene copy number.

The current, and welcome, focus on standardization of techniques and transparency of reporting in the biomedical, peer-reviewed literature is commendable. However, that focus has been intermittent as well as lacklustre and so failed to tackle the alarming lack of reliability and reproducibly of biomedical research. Authors have access to numerous recommendations, ranging from simple standards dealing with technical issues to those regulating clinical trials, suggesting that improved reporting guidelines are not the solution. The elemental solution is for editors to require meticulous implementation of their journals’ instructions for authors and reviewers and stipulate that no paper is published without a transparent, complete and accurate materials and methods section.

Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called ‘rain’. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis.

We developed an Excel based ‘experience matrix’ that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations.

The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Analysis of multiple analytes from biological samples can be challenging as different analytes require different preservation measures. Heat induced enzymatic inactivation is an efficient way to preserve proteins and their modifications in biological samples but RNA quality, as measured by RIN value, has been a concern in such samples. Here, we investigate the effect of heat stabilization compared with standard snap freezing on RNA quality using two RNA extraction protocols, QiaZol with and without urea pre-solubilization, and two RNA quality measurements: RIN value, as defined by the Agilent Bioanalyzer, and an alternative qPCR based method. DNA extraction from heat stabilized brain samples was also examined. The snap frozen samples had RIN values about 1 unit higher than heat stabilized samples for the direct QiaZol extraction but equal with stabilized samples using urea pre-solubilization. qPCR based RNA quality measurement showed no difference in quality between snap frozen and heat inactivated samples. The probable explanation for this discrepancy is that the RIN value is an indirect measure based on rRNA, while the qPCR score is based on actual measurement of mRNA quality. The DNA yield from heat stabilized brain tissue samples was significantly increased, compared to the snap frozen tissue, without any effects on purity or quality. Hence, heat stabilization of tissues opens up the possibility for a two step preservation protocol, where proteins and their modifications can be preserved in the first heat based step, while in a second step, using standard RNA preservation strategies, mRNA be preserved. This collection strategy will enable biobanking of samples where the ultimate analysis is not determined without loss of sample quality.