An automated high-throughput workflow for multi-omics analysis of biological samples by employing sequential extraction of DNA, RNA and proteins

Background The AllPrep DNA/RNA/Protein kit enables the simultaneous extraction of DNA, RNA and protein from a single biological sample, facilitating downstream analysis. This strategy is crucial when samples are limited and eliminates the need to split material for separate extractions, which is currently the default approach. Despite its widespread use, the compatibility of this workflow with unbiased, global proteomic analyses remains uncharacterized. Methods We systematically compared precipitation-based and non-precipitation-based protein cleanup methods to assess the suitability of this strategy for high-throughput proteomic applications. Results Precipitation-based protein cleanup typically performed in the AllPrep workflow was compared to non-precipitation-based methods including single-pot, solid-phase–enhanced sample preparation (SP3) protocol. The SP3-based workflow was comparable to precipitation-based methods in terms of protein and peptide identifications along with the missed cleavages and peptide length distribution. Further optimization of the conditions enabling protein binding to SP3 beads showed minimal impact on performance of the SP3 workflow. Importantly, this workflow was automated using the KingFisher Apex liquid handling platform to obtain robust and reproducible performance. To demonstrate the utility of this workflow, we applied it to investigate proteomic alterations in a multiple myeloma cell line following treatment with the immunomodulatory drug, pomalidomide. When coupled with diaPASEF analysis, this approach enabled in-depth proteome coverage, identifying over 6,300 proteins and confirmed decreased expression of the CRBN neosubstrates IKZF1, IKZF3 and RAB28 following pomalidomide treatment. Conclusion Overall, the optimized workflow offers a robust, scalable and automated approach for integrated multi-omics studies, enabling efficient and reproducible proteomic profiling from limited sample inputs.