For approximately 10 years the field of proteomics has held promise to assist researchers in crossing the “valley of death” in translational medicine—that is, to assist in translating basic research findings from the bench to the bedside. While steady progress has been made in improving proteomic techniques and instrumentation, the field has largely failed so far to deliver blockbuster results in the form of novel disease biomarkers.
One approach we have used to attempt to rectify this phenomenon and assure reliable results from experiments performed in our laboratory is to go back to the basics of our training in analytical chemistry. Analytical chemistry teaches us that the most important features of a technique are accuracy, specificity, and reproducibility.
We would argue that some early failures in proteomics are at least partially due to incomplete attention to detail with respect to the analytical quality of the approaches deployed. In order to apply any novel methodology to a complex biological or translational medicine question, the methodology must first be vetted such that all analytical variables are sufficiently identified, measured, and minimized.
Proteomics is most commonly practiced in the manner known as bottom-up, which involves taking a complex mixture of proteins and proteolytically digesting it with a protease, such as trypsin, into fragments ~8 to 35 amino acids in length. This step makes the mixtures even more complex by increasing the total number of unique molecules to be analyzed, but is advantageous because the biophysical properties of peptides make them much easier to separate (using liquid chromatography), and quantify and identify (using mass spectrometry) than the protein parent molecules.
With the advent of modern direct-flow nanoscale liquid chromatography systems and high-resolution mass spectrometers, the technical variability between measurements within a study has been drastically reduced; it is possible for thousands of peptides to be measured across 10s to 100s of samples with average coefficients of variation less than 15%.
The more common challenge to reproducible proteomics measurements, both within and between studies, is now sample preparation. Metabolic labeling techniques (e.g., SILAC, N15 labeling) attempt to bypass variability introduced during sample preparation because the conditions to be compared are mixed, then processed together through all preparation steps.
The obvious disadvantage of such labeling strategies is that the number of biological conditions is limited by the number of labels available for use, and some systems simply do not lend themselves well to labeling approaches (such as primary tissues or cells and animal models).
Label-free quantitation uses the area-under-the-curve of chromatographic peptide elution profiles to perform robust quantitation and can be utilized for both relative and absolute quantitation across sample cohorts without limitation to experimental design.
Although techniques in sample preparation vary widely depending on the application, we will seek to offer practical considerations, which have served to improve sample preparation reproducibility in our laboratory, and which are particularly important for label-free quantitative proteomics experiments.
It is largely accepted that a large factor, if not the most dominant causative factor in variation in proteomics results between laboratories is differences in sample preparation. Therefore, before we explore specific techniques, which can improve reproducibility in sample preparation in proteomics research, it is worthwhile to spend a few words on general practices that can be utilized to assess results reproducibility:
1) Be a minimalist. Utilize the simplest procedure possible, and seek to remove all unnecessary steps in a procedure. Even seemingly simple steps like a volume transfer can increase analytical variability.
2) Standardize where possible. Standardization of steps (such as digestion) within a protocol makes it easier to troubleshoot new protocols or methods, easier for others to replicate your results, and saves time because scientists within a lab can share reagent stocks. Establish and follow standard operating procedures (SOPs) once a protocol is mature.
3) Employ quality control (QC) metrics. QC metrics throughout a protocol allow for easier troubleshooting if unexpected results are observed. Track and utilize all available data, such as protein assay (Bradford) results, sample volumes, and sample storage time and conditions, to monitor the procedures. Accumulate the results from routine QC measurements in a common repository accessible to all scientists performing the assays, in order to enable easy identification of sample outliers.
Although it may seem that standardization and research are somewhat mutually exclusive activities, some standardization of common practices makes research activities higher throughput and, more importantly, improves reproducibility within and between proteomics studies.