Automating Challenges Associated with Proteomics Workflows
Sample preparation workflows for mass spectrometric analysis that involve proteolysis are often labor intensive, time consuming, and user dependent. Typical proteomic workflows require enzymatic digestion, solid phase extraction, drying, and resuspension before the reversed phase liquid chromatography-mass spectrometry (LC-MS) analysis. Variable digestion times and efficiencies, sample loss during manual sample transfer, variable sample reconstitution, and non-reproducible pipetting techniques during resuspension all contribute to workflows that are intrinsically irreproducible.
However, while most researchers admit current practices are far from optimal, these methods continue to be standard operating procedure for conventional proteomics experiments.
By its very definition, automation exists to facilitate and accelerate routine tasks. The benefits reaped surpass effective time management, enabling implementation of processes at critical points. In the biopharmaceutical industry, the use of traditional sample preparation protocols prohibits immediate identification of protein artifact. Due to protein product complexities, cell culture conditions can significantly impact quality and potency of the generated product, especially with respect to glycosylation, post-transcriptional modifications, and impurity profiles. Since small deviations from optimal culture conditions can lead to protein degradation, a switch from overnight digestion to a one-to-four-minute digestion enables real-time monitoring and rapid identification of microheterogeneities, leading to cost and time savings.
Recently, the Perfinity integrated digestion platform (iDP) was introduced by Shimadzu Scientific Instruments, Columbia, Md., to address the challenges associated with protein sample preparation. This fully automated LC-based system uses standard Shimadzu HPLC components and Perfinity Biosciences' patented column technology and optimized buffers to standardize, simplify, and accelerate protein digestion and preparation workflows.
The iDP accelerates enzymatic digestion while increasing the reproducibility associated with proteomic sample preparation. A typical Perfinity iDP experiment is depicted in Figure 1. First, sample is diluted in loading buffer and placed into the autosampler. From the autosampler, the sample is injected onto the Perfinity immobilized enzyme reaction chamber (IMER) trypsin column. Proteins are subjected to an extremely efficient digestion (one to eight minutes). The rapid digestion typified in Figure 1 is facilitated by the high excess of trypsin relative to protein substrate contained within the Perfinity IMER column.
The timing and temperature of the digestion process is tightly controlled within the Perfinity iDP interface, resulting in the identical digestion product injection to injection. As the tryptic peptides leave the Perfinity IMER trypsin column, they are trapped on a c18-based desalting column. Following sample desalting, the digested peptide mixture is released and eluted directly onto a reversed phase c18 column inline with the mass spectrometer. The entire sample preparation process takes between six to 13 minutes, depending upon digestion time.
By converting user-dependent digestion protocols to a streamlined, automated platform, experimental error is minimized run-to-run and the coefficients of variation fall well below 10%.
The Perfinity iDP takes the concept of reproducible data one step beyond automation by standardizing the digestion process through use of Perfinity trypsin columns and optimized digestion buffers. As laboratories adopt Perfinity iDP, consistency in conditions and reagents is achieved to help achieve inter-laboratory reproducibility, detection method aside.
While Perfinity addresses the need for automation to a process that precedes the mass spectrometer, an equally important challenge in proteomics is the post-acquisition bioinformatics processes. The four orthogonal bioinformatics roadblocks in proteomics are as follows: handling large mass spectrometry data sets (terabytes/year); integrating distinct, yet unconnected informatics programs; sharing and reporting data across laboratories; and lastly, affording the high cost of maintaining a bioinformatics platform.
From a practical standpoint, researchers find themselves restricted by rigid established workflows due to the limitations of computing time and power. Resulting outputs can be far from optimal and difficult to replicate between individual users. New technologies, specifically cloud-based technologies, can resolve these challenges, enabling what was once only possible for a few to be accessible for the majority.
To this end, Shimadzu Scientific Instruments, in partnership with Integrated Analysis Inc., is providing a comprehensive cloud-based informatics service, Enterprise Informatics Service (EIS). The EIS is subdivided into three integrated modules: storage and distribution; workflow execution on a secured, private cloud; and data querying and reporting. Overall, the service allows users to quickly reduce a data set containing thousands of proteins down to a manageable list of candidate biomarkers.