Low-cost RNA extraction method for highly scalable transcriptome studies

Rapid, effective and low-cost purification of dideoxy-sequencing reactions by home-made magnetic beads suspension and magnetic separator

Removal of excess dideoxy terminators from the sequencing mix after the enzymatic reaction is a key process affecting the dideoxy/Sanger sequencing quality. Ethanol precipitation may be the most popular clean-up method because of its low cost; however, it takes a long centrifugation time and frequently results in low quality sequence data. Commercially available clean-up kits provide high quality sequence data, while they generally have high cost. Here, we describe rapid, effective and low-cost dideoxy terminator clean-up method using a home-made magnetic beads suspension, MagNA, and magnetic separator. We found that MagNA enables rapid and efficient clean-up at ~1/100 of the cost of commercially available kits. The magnetic separator made using low-cost neodymium magnets worked well for the MagNA separation, representing a rapid, efficient and cost-effective dideoxy terminator clean-up system.

Developing novel liquid biopsy by selective capture of viral RNA on magnetic beads to detect COVID-19

Objectives

Early, specific, and sensitive detection methods of COVID-19 are essential for force stopping its worldwide infection. Although CT images of the lung and/or viral RNA extraction followed by real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) are widely used; they have some limitations. Here, we developed a highly sensitive magnetic bead-based viral RNA extraction assay followed by rRT-PCR.

Materials and Methods

Case group included oropharyngeal/nasopharyngeal and blood samples from 30 patients diagnosed positive by PCR test for COVID-19 and control group included 30 same samples from COVID-19 negative PCR test individuals. RNA was extracted, using viral RNA extraction kit as well as using our hand-made capture bead-based technique. A one-step cDNA synthesis and Real Time PCR was conducted. A two-step comparison of the different viral RNA extraction methods for oropharyngeal/nasopharyngeal and blood samples was performed. Student t-test was applied with a P<0.05 considered statistically significant.

Results

In the case group, all 30 mucosal samples extracted either with viral RNA extraction kit or with beads-based assay were COVID-19 positive although in the latter category, Cqs were much lower. Although 43% of plasma samples extracted by bead-based method were found to be positive but no plasma samples extracted with column-based kit were detected positive by Real Time PCR.

Conclusion

Bead-based RNA extraction method can reduce RNA loss by its single-tube performance and enhance the test sensitivity. It is also more sensitive to lower viral loads as shown in the detection of blood samples and the lower Cqs of mucosal samples.

Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

Low-Cost, Open-Source Device for High-Performance Fluorescence Detection of Isothermal Nucleic Acid Amplification Reactions

The ability to detect SARS-CoV-2 is critical to implementing evidence-based strategies to address the COVID-19 global pandemic. Expanding SARS-CoV-2 diagnostic ability beyond well-equipped laboratories widens the opportunity for surveillance and control efforts. However, such advances are predicated on the availability of rapid, scalable, accessible, yet high-performance diagnostic platforms. Methods to detect viral RNA using reverse transcription loop-mediated isothermal amplification (RT-LAMP) show promise as rapid and field-deployable tests; however, the per-unit costs of the required diagnostic hardware can be a barrier for scaled deployment. Here, we describe a diagnostic hardware configuration for LAMP technology, named the FABL-8, that can be built for approximately US$380 per machine and provide results in under 30 min. Benchmarking showed that FABL-8 has a similar performance to a high-end commercial instrument for detecting fluorescence-based LAMP reactions. Performance testing of the instrument with RNA extracted from a SARS-CoV-2 virus dilution series revealed an analytical detection sensitivity of 50 virus copies per microliter-a detection threshold suitable to detect patient viral load in the first few days following symptom onset. In addition to the detection of SARS-CoV-2, we show that the system can be used to detect the presence of two bacterial pathogens, demonstrating the versatility of the platform for the detection of other pathogens. This cost-effective and scalable hardware alternative allows democratization of the instrumentation required for high-performance molecular diagnostics, such that it could be available to laboratories anywhere-supporting infectious diseases surveillance and research activities in resource-limited settings.

iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things-based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi-Fi-enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice (Oryza sativa L.) varieties and near-isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow-rooted rice accessions was severely affected by drought stress compared with that of deep-rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system is a reliable platform for phenotyping plants under drought stress.

The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions

Root system architecture affects plant drought resistance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, particularly for below-ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice (Oryza sativa) accessions with highly diverse below-ground traits grown in an upland field. We found that four principal components explained the phenotypic variation and that accessions could be classified into four subpopulations (indica, aus, japonica and admixed) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific subpopulations were enriched with stress response-related genes, suggesting that subpopulations have distinct stress response mechanisms. Root growth was negatively correlated with auxin-inducible genes, suggesting an association between auxin signaling and upland field conditions. A negative correlation between crown root diameter and stress response-related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co-expression network analysis implemented with DNA affinity purification followed by sequencing analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under upland field conditions.

The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions

Root system architecture affects plant drought resistance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, particularly for below-ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice (Oryza sativa) accessions with highly diverse below-ground traits grown in an upland field. We found that four principal components explained the phenotypic variation and that accessions could be classified into four subpopulations (indica, aus, japonica and admixed) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific subpopulations were enriched with stress response-related genes, suggesting that subpopulations have distinct stress response mechanisms. Root growth was negatively correlated with auxin-inducible genes, suggesting an association between auxin signaling and upland field conditions. A negative correlation between crown root diameter and stress response-related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co-expression network analysis implemented with DNA affinity purification followed by sequencing analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under upland field conditions.