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Low-cost RNA extraction method for highly scalable transcriptome studies.

ABSTRACT: RNA extraction has been improved by integration of a variety of materials in the protocol, such as phenol, guanidine thiocyanate, and silica, according to the case-specific demands. However, few methods have been designed for high-throughput RNA preparation for large-scale transcriptome studies. In this study, we established a high-throughput guanidinium thiocyanate and isopropyl alcohol based RNA extraction method (HighGI). HighGI is based on simple and phenol-free homemade buffers and the cost is substantially lower than a column-based commercial kit. We demonstrated that the quality and quantity of RNA extracted with HighGI were comparable to those extracted with a conventional phenol/chloroform-based method and a column-based commercial kit. HighGI retained small RNAs less than 200 bp, which are lost with a commercial column-based kit. We also demonstrated that HighGI is readily applicable to semi-automated RNA extraction. HighGI enables high-throughput RNA extraction for large-scale RNA preparation with high yield and quality.

SUBMITTER: Yoshino K

PROVIDER: S-EPMC7495201 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Low-cost RNA extraction method for highly scalable transcriptome studies.

Yoshino Kanami K Nishijima Ryo R Kawakatsu Taiji T

Breeding science 20200703 4

RNA extraction has been improved by integration of a variety of materials in the protocol, such as phenol, guanidine thiocyanate, and silica, according to the case-specific demands. However, few methods have been designed for high-throughput RNA preparation for large-scale transcriptome studies. In this study, we established a high-throughput guanidinium thiocyanate and isopropyl alcohol based RNA extraction method (HighGI). HighGI is based on simple and phenol-free homemade buffers and the cost ...[more]

PMID: 32968351

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Project description:BACKGROUND: There has been increased interest in the study of molecular survival mechanisms expressed by foodborne pathogens present on food surfaces. Determining genomic responses of these pathogens to antimicrobials is of particular interest since this helps to understand antimicrobial effects at the molecular level. Assessment of bacterial gene expression by transcriptomic analysis in response to these antimicrobials would aid prediction of the phenotypic behavior of the bacteria in the presence of antimicrobials. However, before transcriptional profiling approaches can be implemented routinely, it is important to develop an optimal method to consistently recover pathogens from the food surface and ensure optimal quality RNA so that the corresponding gene expression analysis represents the current response of the organism. Another consideration is to confirm that there is no interference from the "background" food or meat matrix that could mask the bacterial response. FINDINGS: Our study involved developing a food model system using chicken breast meat inoculated with mid-log Salmonella cells. First, we tested the optimum number of Salmonella cells required on the poultry meat in order to extract high quality RNA. This was analyzed by inoculating 10-fold dilutions of Salmonella on the chicken samples followed by RNA extraction. Secondly, we tested the effect of two different bacterial cell recovery solutions namely 0.1% peptone water and RNAprotect (Qiagen Inc.) on the RNA yield and purity. In addition, we compared the efficiency of sonication and bead beater methods to break the cells for RNA extraction. To check chicken nucleic acid interference on downstream Salmonella microarray experiments both chicken and Salmonella cDNA labeled with different fluorescent dyes were mixed together and hybridized on a single Salmonella array. Results of this experiment did not show any cross-hybridization signal from the chicken nucleic acids. In addition, we demonstrated the application of this method in a meat model transcriptional profiling experiment by studying the transcriptomic response of Salmonella inoculated on chicken meat and exposed to d-limonene. We successfully applied our method in this experiment to recover the bacterial cells from the meat matrix and to extract the RNA. We obtained high yield and pure RNA. Subsequently, the RNA was used for downstream transcriptional profiling studies using microarrays and over 600 differentially regulated genes were identified. CONCLUSIONS: Our result showed that 8 log cfu/g of Salmonella is ideal to obtain optimal RNA amount and purity. Our results demonstrated that RNAprotect yielded higher RNA amounts (approximately 10 to 30 fold) when compared to 0.1% peptone water. The differences between the RNAprotect and 0.1% peptone samples were significant at a p-value of 0.03 for the bead beater method and 0.0005 for the sonication method, respectively. The microarray experiment demonstrated that the chicken samples do not interfere with the hybridization of Salmonella cDNA on the array slide. Hence, the background chicken RNA will not interfere with the microarray analysis when poultry meat models are used. Finally, we successfully demonstrated the application of the poultry meat model proposed in this study by conducting transcriptional profiling analysis of Salmonella inoculated on the poultry. Results of this study proved that this method has the potential to be employed in other meat model studies.

Project description:BACKGROUND:The advent of Next Generation Sequencing has allowed transcriptomes to be profiled with unprecedented accuracy, but the high costs of full-length mRNA sequencing have posed a limit on the accessibility and scalability of the technology. To address this, we developed 3'Pool-seq: a simple, cost-effective, and scalable RNA-seq method that focuses sequencing to the 3'-end of mRNA. We drew from aspects of SMART-seq, Drop-seq, and TruSeq to implement an easy workflow, and optimized parameters such as input RNA concentrations, tagmentation conditions, and read depth specifically for bulk-RNA. RESULTS:Thorough optimization resulted in a protocol that takes less than 12 h to perform, does not require custom sequencing primers or instrumentation, and cuts over 90% of the costs associated with TruSeq, while still achieving accurate gene expression quantification (Pearson's correlation coefficient with ERCC theoretical concentration r = 0.96) and differential gene detection (ROC analysis of 3'Pool-seq compared to TruSeq AUC = 0.921). The 3'Pool-seq dual indexing scheme was further adapted for a 96-well plate format, and ERCC spike-ins were used to correct for potential row or column pooling effects. Transcriptional profiling of troglitazone and pioglitazone treatments at multiple doses and time points in HepG2 cells was then used to show how 3'Pool-seq could distinguish the two molecules based on their molecular signatures. CONCLUSIONS:3'Pool-seq can accurately detect gene expression at a level that is on par with TruSeq, at one tenth of the total cost. Furthermore, its unprecedented TruSeq/Nextera hybrid indexing scheme and streamlined workflow can be applied in several different formats, including 96-well plates, which allows users to thoroughly evaluate biological systems under several conditions and timepoints. Care must be taken regarding experimental design and plate layout such that potential pooling effects can be accounted for and corrected. Lastly, further studies using multiple sets of ERCC spike-ins may be used to simulate differential gene expression in a system with known ground-state values.

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Low-cost RNA extraction method for highly scalable transcriptome studies.

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Low-cost RNA extraction method for highly scalable transcriptome studies.

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