Back

A highly effective and time-efficient method for extracting RNA from Cyanobacteria

Majhi, B. K.

2025-03-17 molecular biology
10.1101/2025.03.16.643587 bioRxiv
Show abstract

Cyanobacteria (blue-green algae) have been widely used as model organisms in photobiochemical research, and they have recently been exploited as hosts in numerous pilot studies to produce valuable biochemicals via genetic and metabolic modifications. The optimal production of these chemicals within the cell is dependent on several factors, including the expression of desired foreign genes. The successful expression of foreign genes within the cell translates into mRNA and corresponding proteins, which ultimately produce the desired end products. Several techniques, including RNA-sequencing (RNA-Seq), quantitative reverse transcription polymerase chain reaction (RT-qPCR), microarrays, northern blotting, and droplet digital polymerase chain reaction (ddPCR), have recently been developed to study gene expression levels in cells using ribonucleic acid (RNA) as the raw material. The quality of RNA is crucial for obtaining accurate and error-free results. Cyanobacteria have thick cellular membranes and a large spectrum of secondary metabolites, which require additional processes to break the cells and isolate RNA from cellular extracts, making extraction of high-quality RNA difficult and expensive. Using Synechocystis sp. PCC 6803 as a model, this study developed a highly effective and time-efficient method for extracting total RNA from cyanobacteria without the use of hazardous chemicals such as phenol and chloroform and with a minimal investment. This protocol uses standard centrifugation techniques and laboratory chemicals such as citric acid, EDTA, SDS, NaCl, and tri-sodium citrate dihydrate to extract RNA from cyanobacterial cells. The results of the quantification, purity, and integrity checks show that the quality and concentration of extracted RNA are superior to the phenol-chloroform extraction methods. Furthermore, RT-qPCR results demonstrate that the extracted RNA is of good quality and suitable for downstream applications. HighlightsO_LICost-effective and time-efficient RNA extraction method. C_LIO_LIRNA extraction from cyanobacteria without the involvement of hazardous chemicals. C_LIO_LIExtraction of high-quality RNA for downstream applications. C_LI

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.