Therapeutic potency of compound RMY-205 for pulmonary fibrosis induced by SARS-CoV-2 nucleocapsid protein.

Pulmonary fibrosis is a typical sequela of coronavirus disease 2019 (COVID-19), which is linked with a poor prognosis for COVID-19 patients. However, the underlying mechanism of pulmonary fibrosis induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. Here, we demonstrated that the nucleocapsid (N) protein of SARS-CoV-2 induced pulmonary fibrosis by activating pulmonary fibroblasts. N protein interacted with the transforming growth factor ß receptor I (TßRI), to disrupt the interaction of TßRI-FK506 Binding Protein12 (FKBP12), which led to activation of TßRI to phosphorylate Smad3 and boost expression of pro-fibrotic genes and secretion of cytokines to promote pulmonary fibrosis. Furthermore, we identified a compound, RMY-205, that bound to Smad3 to disrupt TßRI-induced Smad3 activation. The therapeutic potential of RMY-205 was strengthened in mouse models of N protein-induced pulmonary fibrosis. This study highlights a signaling pathway of pulmonary fibrosis induced by N protein and demonstrates a novel therapeutic strategy for treating pulmonary fibrosis by a compound targeting Smad3.

Progress on genomics and locus of important agronomic traits in Chenopodium quinoa.

Quinoa (Chenopodium quinoa, Willd.) as a new health food in the 20th century, its comprehensive nutritional composition, stress resistance and other characteristics have been paid much of attention, and enjoys the reputation of "nutritional gold", "vegetarian king" and "food in the future" in the world. In recent years, with the rapid development of genomics and high-throughput sequencing technology, the high-quality whole genome sequence of quinoa has been completed, and the omics analysis and functional research of a series of key genes have been gradually carried out. In this review, we summarize the research progress in quinoa genomics, gene family analysis of important transcription factors, genetic map construction, QTL mapping of important traits, and genes for important agronomic and yield traits. Moreover, according to the current status of quinoa breeding, this paper also put forward five key problems in quinoa breeding, and pointed out four important directions of genetic improvement and breeding of quinoa in the future, so as to provide reference for the realization of directional genetic improvement of quinoa in the future.

Superhydrophobic-Superhydrophilic Hybrid Surface with Highly Ordered Tip-Capped Nanopore Arrays for Surface-Enhanced Raman Scattering Spectroscopy.

The designed superhydrophobic-superhydrophilic hybrid surface (SSHS) with highly ordered tip-capped nanopore arrays can be used as an intelligent and fast platform to realize different analyte solutions with different concentrations to be detected at the same time by surface-enhanced Raman spectroscopy. This surface is fabricated in a large area by a facile and low-cost method of programmed multistep anodization of aluminum and pore widening process followed by selective chemical modification. The highly ordered tip-capped nanopore arrays can induce the highly sensitive and reproducible Raman signal, whose enhanced factor for rhodamine 6G (R6G) at 1358 cm-1 is 4.46 × 106. The superhydrophobic-superhydrophilic hybrid property can realize the homogeneous distribution of the concentrated analyte in a droplet at the fixed place, which can avoid the diffusion-limit problem and further enhance the Raman signal. Surface-enhanced Raman spectroscopy of dried droplets with different concentrations of R6G or thiram is tested on SSHS, which show good reproducibility. The detection limits of R6G and thiram on SSHS are 10-10 and 10-7 M in 50 µL droplets, respectively. Due to the industrial compatibility of the fabrication technique, this smart surface has the potential to evolve into a general platform to develop various advanced chemical and biological sensors.

Proximity ligation assays for precise quantification of femtomolar proteins in single cells using self-priming microfluidic dPCR chip.

The quantification of low concentration proteins can facilitate the discovery of some significant biomarkers, and provide us a more profound understanding of cell heterogeneity when applied to single cell analysis. However, most state-of- art single cell protein detection platforms are bulky, expensive and complicated. Here we report a simple and low cost microfluidic dPCR (digital polymerase chain reaction) chip-based proximity ligation assay (PLA) for the quantification of low concentration proteins. First, standard hCSTB (human cystatin B) protein was used to optimize the related experimental conditions. Comparing to ordinary PLA tests, the results showed that our method achieved femtomolar limit of detection (LOD) with a linear dynamic range over three to four orders of magnitude. Then human CD147 protein, a reported biomarker for hepatoma carcinoma, was detected in single HepG2 and L02 cells. The results showed that there were wide disparities in single cell CD147 abundance for both of the two cell lines. And the average CD147 protein content in single HepG2 cells displayed 2-fold increase in comparison to that in single L02 cells. Comparing to the research findings obtained at bulk level, our method can provide more useful information for diagnosis and targeted therapy of tumors.

Power-free polydimethylsiloxane femtoliter-sized arrays for bead-based digital immunoassays.

A novel microfluidic chip employing power-free polydimethylsiloxane (PDMS) femtoliter-sized arrays was developed for the detection of low concentrations of protein biomakers by isolating individual paramagnetic beads in single wells. Arrays of femtoliter-sized wells were fabricated with PDMS using well-developed molding techniques. Paramagnetic beads were functionalized with specific antibodies to capture the antigens. These antigens were labeled with enzymes via conventional multistep immunosandwich approach. After suspending in aqueous solutions of enzyme substrate, the solutions were delivered to the arrays using a conventional micropipette. The aqueous solutions were introduced into the microwells by capillarity and the beads were loaded into microwells by gravity. A fluorocarbon oil was then flowed into the chip to remove excess beads from the surface of the array and meanwhile isolated the femtoliter-sized wells. All processes were achieved by conventional micropipette, without external pumping systems and valves. Finally, the arrays were imaged using standard fluorescence imaging after incubation 30 min for digital counting enzyme molecules. It was demonstrated that the chip platform possessed the performance of digital counting with a linear dynamic range from 1 aM to 1 fM for the detection of biotinylated ß-galactosidase (BßG), achieving a limit of detection (LOD) of 930 zM. Using this chip, a digital immunoassay to detect Tumor Necrosis Factor α (TNF- α) was developed. Since the chip fabrication is low-cost and circumvents the surface modification, we expect it can become a new chip-based digital immunoassay platform for ultrasensitive diagnostic of biomarkers.

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.

High throughput single cell separation and identification using a self-priming isometric and Equant screw valve-based (SIES) microfluidic chip.

The emergence of various single cell separation and identification platforms has greatly promoted the development of single cell research. Among these platforms, microfluidic chip-based strategies occupy a significant position in single cell separation and identification. Here, we proposed a self-priming isometric and Equant screw valve-based microfluidic chip (SIES chip) for high throughput single cell isolation and identification. With several special designs, such as a peripheral water tank to balance negative pressure distribution in a marginal area of the chip, a screw valve to preserve the suction power during the step-by-step sample loading, and multistage branching "T" shape channels to separate cells evenly into the chambers, up to 2000 single cells can be well dispersed and analyzed at the same time using this chip. We applied this chip for the isolation and identification of single A549 cells targeting the activated leukocyte cell adhesion molecule (ALCAM) gene. The results showed that only a small proportion (approximately 5.1%) of A549 cells expressed ALCAM, which can potentially provide a reference for A549 cell reclassification. Besides being inexpensive, user-friendly and portable, our chip can be used in some resource-limited settings and may have a great potential in POC (Point-of-Care) applications.

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.