Real-time PCR machine system modeling and a systematic approach for the robust design of a real-time PCR-on-a-chip system

A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR

This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.

Optimisation of broiler chicken responses from 0 to 7 d of age to dietary leucine, isoleucine and valine using Taguchi and mathematical methods

Three experiments were conducted to evaluate the applicability of the Taguchi method (TM) and optimisation algorithms to optimise the branch chain amino acids (BCAA) requirements in 0 to 7 d broiler chicks. In the first experiment, the standardised digestible (SID) amino acids and apparent metabolisable energy (AME) values of maize, wheat and soya bean meal were evaluated. In the second experiment, three factors including leucine (Leu), isoleucine (Ile) and valine (Val), each at 4 levels, were selected, and an orthogonal array layout of L16 (4(3)) using TM was performed. After data collection, optimisation of average daily gain (ADG) and feed conversion ratio (FCR) were obtained using TM. The multiobjective genetic algorithm (MOGA) and random search algorithm (RSA) were also applied to predict the optimal combination of BCAA for broiler performance. In the third experiment, a growth study was conducted to evaluate the applicability of obtained optimum BCAA requirements data by TM, MOGA and RSA, and results were compared with those of birds fed with a diet formulated according to Ross 308 recommendations. In the second experiment, the TM resulted in 13.45 g/kg SID Leu, 8.5 g/kg SID Ile and 10.45 g/kg SID Val as optimum level for maximum ADG (21.57 g/bird/d) and minimum FCR (1.11 g feed/g gain) in 0- to 7-d-old broiler chickens. MOGA predicted the following combinations: SID Leu = 14.8, SID Ile = 9.1 and SID Val = 10.3 for maximum ADG (22.05) and minimum FCR (1.11). The optimisation using RSA predicted Leu = 16.0, Ile = 9.5 and Val = 10.2 for maximum ADG (22.67), and Leu = 15.5, Ile = 9.0 and Val = 10.4 to achieve minimum FCR (1.08). The validation experiment confirmed that TM, MOGA and RSA yielded optimum determination of dietary amino acid requirements and improved ADG and FCR as compared to Aviagen recommendations. However, based on the live animal validation trial, MOGA and RSA overpredicted the optimum requirement as compared to TM. In general, the results of these studies showed that the TM may be used to optimise nutrient requirements for poultry.

Impact of smoothing on parameter estimation in quantitative DNA amplification experiments

BACKGROUND

Quantification cycle (Cq) and amplification efficiency (AE) are parameters mathematically extracted from raw data to characterize quantitative PCR (qPCR) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data.

METHODS

We obtained published high-replicate qPCR data sets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and AE.

RESULTS

Our results indicate that selected smoothing algorithms affect estimates of Cq and AE considerably. The commonly used moving average filter performed worst in all qPCR scenarios. The Savitzky-Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in qPCR AE, whereas other smoothers, such as running mean, introduced an AE-dependent bias.

CONCLUSIONS

The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time PCR experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic qPCR applications. The findings of our study were implemented in the R packages chipPCR and qpcR as a basis for the implementation of an analytical strategy.

Differential recovery of phylogenetically disparate microbes from spacecraft-qualified metal surfaces

Universal and species-specific quantitative polymerase chain reaction-based methods were employed to compare the effectiveness of four distinct materials used to collect biological samples from metal surfaces. Known cell densities of a model microbial community (MMC) were deposited onto metal surfaces and subsequently collected with cotton and nylon-flocked swabs for small surface areas and biological sampling kits (BiSKits) and polyester wipes for large surface areas. Ribosomal RNA gene-based quantitative PCR (qPCR) analyses revealed that cotton swabs were superior to nylon-flocked swabs for recovering nucleic acids (i.e., DNA) from small surface areas. Similarly, BiSKits outperformed polyester wipes for sampling large surface areas. Species-specific qPCR results show a differential recovery of rRNA genes of the various MMC constituents, seemingly dependent on the type of sampling device employed. Both cotton swabs and BiSKits recovered the rDNA of all nine of the MMC constituent microbes assayed, whereas nylon-flocked swabs and polyester wipes recovered the rDNA of only six and four of these MMC strains, respectively. The findings of this study demonstrate the importance and proficiency of molecular techniques in gauging the effectiveness and efficiency of various modes of biological sample collection from metal surfaces.