Biological data derived from tissues is inherently heterogeneous. Identical amounts of tissue will not always contain the same amounts of biological analytes, nor will the state of each cell contained in the tissue be identical in replicate extractions, even when using samples from the same organ. This presents a challenge in multiomic workflows where researchers need confidence that each independent study of specific biological substrates (DNA, RNA, proteins, etc.) are derived from a common source and that the resulting data from these studies correspond to each other with high assurance.1
In nucleic acid extractions, generally, DNA or RNA is independently extracted from each other, often by using targeted nucleases during the nucleic acid isolation step to remove the non-target analyte from the extraction. In both DNA and RNA analysis the non-target nucleic acid can alter results in quantification, inhibit/interfere in downstream analysis, and competitively bind to extraction media causing higher variance in yield of the target analyte. This feature of nucleic acid extractions requires researchers to use separate starting samples when trying to analyse both DNA and RNA from the same source. Frequently in comparative studies, these tissue samples may be taken from the same organ or will be made by splitting the sample from the beginning of the experiment to be extracted through independent DNA or RNA extraction workflows. Other methods include dividing the sample lysate instead of the tissue. However, both methods present a challenge when researchers work with very small tissue samples or with very conserved amounts of tissue from biopsies where multiple replicates would be better used in building data robustness instead of creating a second analyte extraction.2,3
The challenge is further compounded in tissues with intrinsically low DNA or RNA content, where larger amounts of input material are required to obtain sufficient yields, restricting the feasibility of parallel workflows. Sequential extraction methods have also been described but often lead to lower yields and quality of the latter extracted molecule.2
Physical lysis of tissue samples facilitated using homogenization allows a researcher to have high confidence in repeatability of nucleic acid extractions from any starting matrix, especially when the starting sample is limited. Intracellular analytes are released from cells through programable methods which remove operator bias created from manual methods and allows for maximum recovery of target analytes due to complete lysis of the cellular matrix. Additionally, homogenization is rapidly scalable. Methods optimized for a single extraction can readily be implemented on hundreds of samples with the use of a high throughput homogenizer like the Bead Ruptor™ 96+ well plate homogenizer. Combining this robust and reliable sample preparation technique with a unified DNA/RNA extraction pathway, a researcher can maximize yield or create more replicates from limited tissue samples that they have acquired.
In this application note, we demonstrate a robust nucleic acid purification workflow that takes advantage of a combined DNA/RNA isolation pathway that allows for both DNA and RNA to be extracted from the same sample. We utilize five common mice tissues in varying masses as starting matrices to analyze both resultant DNA and RNA. By incorporating a well plate homogenizer and a magnetic bead extraction kit, the displayed workflow is conducive to a high throughput environment where multiple tissue types of varying size can simultaneously be integrated into a single method resulting in high quality, high quantity nucleic acids.
Table 3. DNA yield and purity metrics from murine tissues using 5 mg and 10 mg input amounts. Average DNA concentration (μg) and absorbance ratios (260/280, 260/230) were measured using a Nanodrop™ spectrophotometer (n = 3).
1 Biedka, S., Alkam, D., Washam, C. L., Yablonska, S., Storey, A., Byrum, S. D., & Minden, J. S. (2024). One-pot method for preparing DNA, RNA, and protein for multiomics analysis. Communications Biology, 7(1). https://doi.org/10.1038/s42003-024-05993-1