Digital PCR for Single-Cell Analysis.

Single-cell analysis provides an overwhelming strategy for revealing cellular heterogeneity and new perspectives for understanding the biological function and disease mechanism. Moreover, it promotes the basic and clinical research in many fields at a single-cell resolution. A digital polymerase chain reaction (dPCR) is an absolute quantitative analysis technology with high sensitivity and precision for DNA/RNA or protein. With the development of microfluidic technology, digital PCR has been used to achieve absolute quantification of single-cell gene expression and single-cell proteins. For single-cell specific-gene or -protein detection, digital PCR has shown great advantages. So, this review will introduce the significance and process of single-cell analysis, including single-cell isolation, single-cell lysis, and single-cell detection methods, mainly focusing on the microfluidic single-cell digital PCR technology and its biological application at a single-cell level. The challenges and opportunities for the development of single-cell digital PCR are also discussed.

A Self-Priming Digital Polymerase Chain Reaction Chip for Multiplex Genetic Analysis.

Digital PCR (polymerase chain reaction) is a powerful and attractive tool for the quantification of nucleic acids. However, the multiplex detection capabilities of this system are limited or require expensive instrumentation and reagents, all of which can hinder multiplex detection goals. Here, we propose strategies toward solving these issues regarding digital PCR. We designed and tested a self-priming digital PCR chip containing 6-plex detection capabilities using monochrome fluorescence, which has six detection areas and four-layer structures. This strategy achieved multiplex digital detection by the use of self-priming to preintroduce the specific reaction mix to a certain detection area. This avoids competition when multiple primer pairs coexist, allowing for multiplexing in a shorter time while using less reagents and low-cost instruments. This also prevents the digital PCR chip from experiencing long sample introduction time and evaporation. For further validation, this multiplex digital PCR chip was used to detect five types of EGFR (epidermal growth factor receptor) gene mutations in 15 blood samples from lung cancer patients. We conclude that this technique can precisely quantify EGFR mutations in high-performance diagnostics. This multiplex digital detection chip is a simple and inexpensive test intended for liquid biopsies. It can be applied and used in prenatal diagnostics, the monitoring of residual disease, rapid pathogen detection, and many other procedures.

A new method using machine learning for automated image analysis applied to chip-based digital assays.

Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enzyme-linked immunosorbent assay (digital ELISA) and digital proximity ligation assay (digital PLA) need high-throughput quantification of the captured fluorescence image data. However, traditional methods that are mainly based on image segmentation using either a fixed threshold or an automated hard threshold failed to extract valid signals over a broad range of image characteristics. In this study, we introduce a new method for automated image analysis to extract signals applied to chip-based digital assays. This approach precisely locates each micro-compartment based on the structure design of the chip, thereby eliminating the interference of non-signal noise in the image. Utilizing the principle that the human eyes can distinguish between the positive micro-compartments and the negative micro-compartments, we take the parameters of each micro-compartment together with its surrounding micro-compartments as the training dataset of the Random Forest classifier to classify the micro-compartments and extract valid signals, thus solving the problem caused by the differences among images. Furthermore, we adopted the iteration methodology that adds the output of a model's prediction to the input of the next model's training dataset, until the output of a model's prediction reaches the accuracy we expected, which improves the work efficiency during data training greatly. We demonstrate the method on the dPCR dataset and it performs well without any manual adjustment of settings. The results show that our proposed method can recognize the positive signals from the fluorescence images with an accuracy of 97.78%. With minor modification, bio-instrument companies or researchers can integrate this method into their digital assay devices' software conveniently.

A highly integrated real-time digital PCR device for accurate DNA quantitative analysis.

Misclassification of positive partitions in microfluidic digital polymerase chain reaction (dPCR) can cause the false positives and false negatives, which significantly alter the resulting estimate of target DNA molecules. To address this issue, establishing real-time fluorescence interrogation of each partition in microfluidic arrays is an effective way in which false positive and false negative partitions can be eliminated. However, currently available devices for real-time fluorescence interrogation are either not competent for microfluidic digital array, or they are bulky, expensive and entail peripheral equipment due to low integration. Therefore, in this study, a Raspberry Pi based, low-cost and highly integrated device is presented to achieve real-time fluorescence detection for microfluidic digital array, termed real-time dPCR device. In the device, uniform thermocycler, streamlined real-time fluorescence imaging setup, and compact data processing system are all integrated to undergo on-chip dPCR amplification, real-time fluorescence detection, and data analysis. Using this real-time dPCR device, the accuracy of DNA absolute quantification by dPCR is improved, since the misclassification of positive partitions is efficiently reduced based on the characteristic real-time fluorescence curves of positive partitions in a self-priming microfluidic chip. Compared with end-point dPCR on our device and commercialized QuantStudio™ 3D dPCR system, the real-time dPCR on our device exhibits a higher accuracy for DNA quantification. In addition, this real-time dPCR device is much smaller and cheaper than the commercialized Digital PCR system, but not sacrificing the capability of error correction for absolute quantitation analysis. Conclusively, this highly integrated real-time dPCR device is very beneficial for DNA quantitative analysis where the determination accuracy is pivotal.

Smartphone-based mobile digital PCR device for DNA quantitative analysis with high accuracy.

Digital polymerase chain reaction (dPCR) circumventing the external calibration and potentially providing absolute quantification of nucleic acids has become an increasingly popular manifestation of PCR in biological researches. However, currently reported or commercial dPCR devices are not suitable for applications in laboratories or zones with limited infrastructures, due to low function integration, cost-inefficiency, or weak mobility. Herein, in order to enable accurate DNA quantitative analysis in such situations, we have developed a smartphone-based mobile dPCR device integrated with thermal cycling control, on-chip dPCR, data acquisition, and result analysis. All the function units are automatically controlled using a customized Android software. The device is approximately 90 mm × 90 mm × 100 mm in size and about 500 g in weight, only costing about 320 dollars except the smartphone. Coupled with the self-priming dPCR chip previously developed by our lab, the device is able to accurately quantify down to 10 copies of the human 18 S ribosomal DNA fragment inserted in a plasmid. Comparing to the commercial QuantStudio™ 3D dPCR platform, our device achieves a comparable analytical accuracy. Besides, our device is capable of detecting single molecule of cancer biomarker gene CD147 in a low number of HepG2 cells. Therefore, our dPCR device as a low-cost, potable, and robust tool for highly accurate DNA quantitative analysis has a great potential in Point-of-care (POC) applications.

TaZAT8, a C2H2-ZFP type transcription factor gene in wheat, plays critical roles in mediating tolerance to Pi deprivation through regulating P acquisition, ROS homeostasis and root system establishment.

Transcription factors (TFs) play critical roles in mediating defense of plants to abiotic stresses through regulating downstream defensive genes. In this study, a wheat C2H2-ZFP (zinc finger protein) type TF gene designated as TaZAT8 was functionally characterized in mediating tolerance to the inorganic phosphate (Pi)-starvation stress. TaZAT8 bears conserved motifs harboring in the C2H2-ZFP type counterparts across vascular plant species. The expression of TaZAT8 was shown to be induced in roots upon Pi deprivation, with a Pi concentration- and temporal-dependent manner. Overexpression of TaZAT8 in tobacco conferred plants improved tolerance to Pi deprivation; the transgenic lines exhibited enlarged phenotype and elevated biomass and phosphorus (P) accumulation relative to wild-type (WT) after Pi-starvation treatment. NtPT1 and NtPT2, the tobacco phosphate transporter (PT) genes, showed increased transcripts in the Pi-deprived transgenic lines, indicative of their transcriptional regulation by TaZAT8. Overexpression analysis of these PT genes validated their function in mediating Pi acquisition under the Pi deprivation conditions. Additionally, the TaZAT8-overexpressing lines also behaved enhanced antioxidant enzyme (AE) activities and enlarged root system architecture (RSA) with respect to WT. Evaluation of the transcript abundance of tobacco genes encoding AE and PIN proteins, including NtMnSOD1, NtSOD1, NtPOD1;2, NtPOD1;5, NtPOD1;6, and NtPOD1;9, and NtPIN1 and NtPIN4 are upregulated in the TaZAT8-overexpressing lines. Overexpression of NtPIN1 and NtPIN4 conferred plants to enlarged RSA and elevated biomass under the Pi-starvation stress conditions. Our investigation provides insights into plant adaptation to the Pi-starvation stress mediated by distinct ZFP TFs through modulation of Pi acquisition and cellular ROS detoxicity.