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Rapid DNA and RNA isolation from few or single cells using low-cost NAxtra magnetic nanoparticles

GSE296244 Homo sapiens Expression profiling by high throughput sequencing 7 samples Submitted 2025/07/09 Platform GPL24676
Summary
A novel, cost-effective nucleic acid (NA) isolation method for purifying total NA, DNA, or RNA from both two- and three-dimensional cell cultures has been developed at the Norwegian University of Science and Technology utilizing NAxtra magnetic nanoparticles. This method achieves comparable yields to existing isolation kits while offering significant improvements in cost and processing speed. However, the original protocol was not optimized for small-cell numbers or single-cell applications. Given the growing interest in single-cell and rare-cell population studies, there is a critical need for more sensitive isolation techniques. In this study, we have enhanced the sensitivity of the NAxtra-based isolation method to facilitate mid- to high-throughput purification from as few as 10,000 cells down to single cells. Automated processing using KingFisher systems enables the rapid handling of 96 samples within 12-18 minutes. Our findings indicate that this method not only matches but can exceed the performance of existing alternatives in (RT)-qPCR detection while being significantly more economical and efficient. Additionally, it enables the extraction of high-quality RNA suitable for transcriptomics analyses from limited cell quantities, including single cells, as indicated by comparison with the Evercode Whole Transcritome v2 kit. This advancement holds substantial promise for improving the accessibility and efficiency of nucleic acid research, particularly in studies involving scarce cellular materials.
Published in
Rapid DNA and RNA isolation from few or single cells using low-cost NAxtra magnetic nanoparticles
Starheim EJ, Manaf A, Hashim A et al. · Scientific reports 2025 · PMID 40596037 · doi:10.1038/s41598-025-05770-y
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Also filed as BioProject PRJNA1258265 and SRA study SRP582847. Searching any of these in the dataset finder brings you back here.

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