Biomolecular Detection and Quantification最新文献

Changes in mitochondrial DNA (mtDNA) content is a useful clinical biomarker for various diseases, however results are controversial as several analytical factors can affect measurement of mtDNA. MtDNA is often quantified by taking ratio between a target mitochondrial gene and a reference nuclear gene (mtDNA/nDNA) using quantitative real time PCR often on two separate experiments. It measures relative levels by using external calibrator which may not be comparable across laboratories. We have developed and optimized a droplet digital PCR (ddPCR) based method for quantification of absolute copy number of both mtDNA and nDNA gene in whole blood. Finally, the role of mtDNA in suspected cancer patients referred to a cancer diagnostic center was investigated.

Analytical factors which can result in false quantification of mtDNA have been optimized and both target and reference have been quantified simultaneously with intra- and inter-assay coefficient variances as 3.1% and 4.2% respectively. Quantification of mtDNA show that compared to controls, solid tumors (but not hematologic malignancies) and other diseases had significantly lower copy number of mtDNA. Higher mtDNA (highest quartile) was associated with a significantly lower risk of both solid tumors and other diseases, independent of age and sex. Receiver operating curve demonstrated that mtDNA levels could differentiate controls from patients with solid tumors and other diseases.

Quantification of mtDNA by a well optimized ddPCR method showed that its depletion may be a hallmark of general illness and can be used to stratify healthy individuals from patients diagnosed with cancer and other chronic diseases.

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means’ computational cost is a fraction of NMF’s. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

Reducing the time taken to run qPCR assays on today’s qPCR cyclers is rather straightforward and requires no specialised reagents or instruments. As the first article in a new series of short technical reports, I demonstrate that it is possible to reduce significantly both denaturation temperatures and cycling times, whilst retaining sensitivity and specificity of the original qPCR conditions.

Attendance at this year’s European Calcified Tissue Society’s (ECTS) Congress reveals that the methods used to obtain qPCR results continue to be significantly flawed and that and their reporting remain inadequate.

Quantitative Real-Time Polymerase Chain Reaction, better known as qPCR, is the most sensitive and specific technique we have for the detection of nucleic acids. Even though it has been around for more than 30 years and is preferred in research applications, it has yet to win broad acceptance in routine practice. This requires a means to unambiguously assess the performance of specific qPCR analyses. Here we present methods to determine the limit of detection (LoD) and the limit of quantification (LoQ) as applicable to qPCR. These are based on standard statistical methods as recommended by regulatory bodies adapted to qPCR and complemented with a novel approach to estimate the precision of LoD.

We measured the impact of the presence of total Escherichia coli (E. coli) cellular material on the performance of the Linear Regression of Efficiency (LRE) method of absolute quantitative PCR (LRE qPCR), which features the putatively universal CAL1 calibration reaction, which we propose as a synthetic biology standard. We firstly used a qPCR reaction in which a sequence present in the lone genomic BirA locus is amplified. Amplification efficiency for this reaction, a key metric for many quantitative qPCR methods, was inhibited by cellular material from bioreactor cultivation to a greater extent than material from shake flask cultivation. We then compared LRE qPCR to the Standard Curve method of absolute qPCR (SC qPCR). LRE qPCR method matched the performance of the SC qPCR when used to measure 417–4.17 × 10⁷ copies of the BirA target sequence present in a shake flask-derived cell sonicates sample, and for 97–9.7 × 10⁵ copies in the equivalent bioreactor-derived sample. A plasmid-encoded T7 bacteriophage sequence was next used to compare the methods. In the presence of cell sonicates from samples of up to OD₆₀₀ = 160, LRE qPCR outperformed SC qPCR in the range of 1.54 × 10⁸–1.54 × 10¹⁰ copies of the T7 target sequence and matched SC qPCR over 1.54 × 10⁴–1.54 × 10⁷ copies. These data suggest the CAL1 standard, combined with the LRE qPCR method, represents an attractive choice as a synthetic biology qPCR standard that performs well even when unpurified industrial samples are used as the source of template material.

Many journal editors are a failing to implement their own authors’ instructions, resulting in the publication of many articles that do not meet basic standards of transparency, employ unsuitable data analysis methods and report overly optimistic conclusions. This problem is particularly acute where quantitative measurements are made and results in the publication of papers that lack scientific rigor and contributes to the concerns with regard to the reproducibility of biomedical research. This hampers research areas such as biomarker identification, as reproducing all but the most striking changes is challenging and translation to patient care rare.

The process of adipogenesis is controlled in a highly orchestrated manner, including transcriptional and post-transcriptional events. In developing 3T3-L1 pre-adipocytes, this program can be interrupted by all-trans retinoic acid (ATRA). To examine this inhibiting impact by ATRA, we generated large-scale transcriptomic data on the microRNA and mRNA level. Non-coding RNAs such as microRNAs represent a field in RNA turnover, which is very important for understanding the regulation of mRNA gene expression. High throughput mRNA and microRNA expression profiling was performed using mRNA hybridisation microarray technology and multiplexed expression assay for microRNA quantification. After quantitative measurements we merged expression data sets, integrated the results and analysed the molecular regulation of in vitro adipogenesis. For this purpose, we applied local enrichment analysis on the integrative microRNA-mRNA network determined by a linear regression approach. This approach includes the target predictions of TargetScan Mouse 5.2 and 23 pre-selected, significantly regulated microRNAs as well as Affymetrix microarray mRNA data. We found that the cellular lipid metabolism is negatively affected by ATRA. Furthermore, we were able to show that microRNA 27a and/or microRNA 96 are important regulators of gap junction signalling, the rearrangement of the actin cytoskeleton as well as the citric acid cycle, which represent the most affected pathways with regard to inhibitory effects of ATRA in 3T3-L1 preadipocytes. In conclusion, the experimental workflow and the integrative microRNA–mRNA data analysis shown in this study represent a possibility for illustrating interactions in highly orchestrated biological processes. Further the applied global microRNA–mRNA interaction network may also be used for the pre-selection of potential new biomarkers with regard to obesity or for the identification of new pharmaceutical targets.

Chronic myeloid leukemia (CML) is the paradigm for targeted cancer therapy. RT-qPCR is the gold standard for monitoring response to tyrosine kinase-inhibitor (TKI) therapy based on the reduction of blood or bone marrow BCR-ABL1. Some patients with CML and very low or undetectable levels of BCR-ABL1 transcripts can stop TKI-therapy without CML recurrence. However, about 60 percent of patients discontinuing TKI-therapy have rapid leukaemia recurrence. This has increased the need for more sensitive and specific techniques to measure residual CML cells. The clinical challenge is to determine when it is safe to stop TKI-therapy. In this review we describe and critically evaluate the current state of CML clinical management, different technologies used to monitor measurable residual disease (MRD) focus on comparingRT-qPCR and new methods entering clinical practice. We discuss advantages and disadvantages of new methods.

Over the past decade numerous publications have demonstrated how digital PCR (dPCR) enables precise and sensitive quantification of nucleic acids in a wide range of applications in both healthcare and environmental analysis. This has occurred in parallel with the advances in partitioning fluidics that enable a reaction to be subdivided into an increasing number of partitions. As the majority of dPCR systems are based on detection in two discrete optical channels, most research to date has focused on quantification of one or two targets within a single reaction. Here we describe ‘higher order multiplexing’ that is the unique ability of dPCR to precisely measure more than two targets in the same reaction. Using examples, we describe the different types of duplex and multiplex reactions that can be achieved. We also describe essential experimental considerations to ensure accurate quantification of multiple targets.