Sequential Extraction Saves Precious Multiomics Samples

In MS and NGS, where signal interpretation depends heavily on the quality and integrity of input material, starting from a unified source strengthens the validity of cross-omics comparisons. Extracting protein, DNA, and RNA from the same sample helps ensure that each dataset reflects the same kinds of cells and conditions at a given time, supporting more reliable and comprehensive comparisons.

In addition, sequential extraction of all analytes from the same sample is particularly important when working with very low sample volumes/precious samples, where splitting of the sample is not an option, and/or if working with heterogeneous tissue samples. 

Isolating sufficient quantities of protein, DNA, and RNA from diverse tissue types is often challenging due to their distinct biochemical and structural properties. For example, brain tissue is highly enriched in lipids, which interferes with protein and nucleic acid solubilization and can reduce effective analyte recovery. Heart tissue contains extensive extracellular matrix and relatively low nuclear density, limiting nucleic acid yield per unit mass. Liver tissue exhibits high endogenous nuclease and protease activity, leading to rapid degradation of RNA, DNA, and proteins if not processed efficiently. Spleen tissue contains abundant RNases and DNases that can compromise nucleic acid integrity.

Despite these inherent challenges, the Sequential Protein/DNA/RNA Extraction Kit enables robust multi-analyte isolation from as little as 1 mg of each tissue type. Across all tissue types, DNA and RNA integrity remained high, confirming suitability for sensitive downstream analyses. Protein yields were consistent with the known protein content of the tested organs, with brain producing the lowest yield and liver the highest. Similarly, DNA yield reflected expected tissue biology, including the high cell density of the spleen and the polyploidy of the liver. RNA yield aligned with the high metabolic activity of the liver and the lymphocyte-rich nature of the spleen. Across several diverse tissue types, the Sequential Protein/DNA/RNA Extraction Kit supported high-quality, consistent yield in DNA, RNA, and protein extraction.

In addition, the Sequential Protein/DNA/RNA Extraction Kit enables efficient recovery of protein, DNA, and RNA from as few as 1,000 cells and as little as 5 µg of tissue. This performance is driven by optimized bead and buffer chemistry within the workflow, delivering a wide dynamic range and making the kit particularly well-suited for very small and precious samples. 

In a traditional multiomics study, a researcher running protein, DNA, and RNA analyses from the same sample would typically run three separate protocols, each with its own hands-on time, reagent consumption, and failure risk. Consolidating that into a single sequential workflow compresses preparation time and reduces the technical variation introduced across those parallel processes.

In addition, the workflow is based on magnetic bead chemistry, making it inherently compatible with automation, such as on KingFisher™ systems, and readily integrable with robotic liquid handlers. Automation of the sequential workflow reduces hands-on time while improving reproducibility and enabling higher throughput.

That said, several bottlenecks remain for routine large-scale multiomics studies. Data integration and standardization across platforms are still major challenges, especially because proteomics and sequencing data differ in structure, scale, and dynamic range. In addition, bioinformatics tools and data analysis approaches still need to evolve to fully make use of multiomics datasets. Addressing these challenges will be key to turning improvements in sample preparation into truly scalable, end-to-end multiomics workflows.

One of the most fundamental challenges is sample heterogeneity, when proteomics and genomics workflows are performed on separate samples, biological variability can mask true correlations between molecular layers. Pre-analytical variability, including differences in extraction methods and sample handling, can further introduce bias that complicates downstream interpretation. A key step toward overcoming these challenges is to control variability as early as possible in the workflow. This is where approaches like sequential extraction become particularly valuable.

For researchers looking to integrate MS and NGS data, the main advice is to think holistically about the workflow rather than treating each omics layer in isolation. Prioritize standardized, reproducible sample preparation, ensure compatibility with downstream analytical platforms, and adopt bioinformatics strategies that account for the intrinsic differences between data types.

Ultimately, robust multiomics insights depend as much on upstream sample integrity and consistency as they do on downstream computational integration.