7 Automated sample prep – bringing proteomics closer to the clinic

Automated sample prep – bringing proteomics closer to the clinic

Successful implementation of proteomics in the clinical environment has still not materialized, and lags far behind genomics, even after decades of advances in protein sample preparation. The primary cause for this underwhelming performance lies in the diverse physiochemical properties of proteins and the complexity of the sample prep workflow itself. So what are the bottlenecks that prevent proteomics from entering clinical use?

Proteins – both the disease and the cure

Proteins are the root cause of disease and represent a pathway to cures, whether we are looking at cancer or coronavirus. Indeed, the vast majority of drugs hit protein targets. We have long anticipated that proteomics would deliver novel biomarkers to be used in the diagnosis, prognosis and therapeutic monitoring of disease. However, for this to happen, simple and efficient protein recovery and sample preparation must be reproducibly achieved from a broad range of sample types without significantly altering sample processing.

Why hasn’t proteomics delivered yet?

To date, proteomics has not hit its stride for two reasons. Firstly, expectations were set unreasonably high, hot on the optimistic coat tails of the genomics revolution. Secondly, techniques have only recently been developed that allow reproducible sample processing for a large variety of samples without changes to the protocol. The obvious complexity and diverse physiochemical nature of the proteome has all but tripped us up, raising a frustrating list of ‘must haves’ for proteomics to be useful in clinical research and, eventually, in the clinic. At a bare minimum, these include:

• the ability to analyze enough (possibly hundreds or thousands or more) samples to achieve statistically significant results in patient cohorts

• simplification of the workflow to remove the need for personnel with specific technical skills in proteomics

• achieving acceptable turnaround times from receiving samples to the generation of a complete proteome profile analysis

• a cost-effective workflow

Let us examine each of these in turn, as potential steps on our path to automated protein sample preparation.

Increasing protein sample analysis throughput

When considering sample prep, there are broadly two types of proteomics experiments that researchers might want to perform: discovery or targeted proteomics. Both types can have significantly different sample prep requirements, and any solutions that can mitigate the need for differences in sample prep would be helpful.

For discovery proteomics, experiments are designed to identify as many proteins as possible across a broad dynamic range. For example, the complete measured range for the plasma proteome – from upper limits for albumin to lowest values for thyroid-stimulating hormone (TSH) – represents more than 10 logs of molar abundance.1 Discovery proteomics can be used to form an inventory of all detectable proteins in a given sample or to detect differences in the abundance of specific proteins amongst multiple samples. When it comes to sample prep, this intrinsic complexity means that we might require the concurrent depletion of highly abundant proteins and enrichment of less abundant proteins, plus fractionation – for example by SDS-PAGE – or liquid chromatography

prior to mass spectrometry. If quantitation is needed, this will add yet another layer of difficulty.

Once proteins of interest have been identified and demonstrated to be biomarkers for a given disease state, targeted proteomics experiments can be considered. In contrast to discovery proteomics, targeted experiments might seek to quantify specific proteins with high precision, sensitivity, specificity and throughput. These proteins may become biomarkers for specific diseases. A targeted approach might decrease the complexity of sample preparation and will likely be far easier to implement when it comes to increasing throughput. Targeted proteomics is therefore the method of choice to quantify specific proteins and metabolites in complex samples in pharmaceutical and diagnostic applications.2,3

Whether considering discovery or targeted proteomics, sample preparation for bottom-up proteomics consists of several critical steps:

1) extraction and solubilization of protein

2) protein denaturation 3) removal of detergent and desalting

4) enzymatic digestion

5) separation of peptides

6) analysis by mass spectrometry, electrophoresis or immunoassay

Detergents such as SDS are routinely used for solubilization and denaturation of proteins. However, these reagents can interfere with downstream protease digestion and MS analysis – even at low concentrations – and are notoriously hard to remove, creating a major barrier to increasing throughput. Throughput is further hampered if different sample types require different protocols.

Any attempt to automate protein sample prep and still retain as complete as possible an inventory of the proteome – including membrane proteins – is likely to require some sort of solid support onto which the proteins can be captured so they can be rinsed free from substances that are incompatible with downstream processing. Such a protocol will need to remove all types of potential contaminants, including detergents (eg. up to 15 % SDS), salts, glycerol, PEG, Laemmli loading buffer and bile salts, among other contaminants.

Simplifying the protein sample prep workflow

When it comes to preparing protein samples for downstream analysis, enzymatic digestion protocols can be extremely effective when working with protein suspensions.4 This approach typically requires use of a lysis buffer with a high concentration of detergent, such as 5 % SDS. However, SDS is incompatible with downstream analytical instrumentation and assays, such as LC-MS/MS or ELISA. Wash or clean-up steps prior to analysis are therefore essential yet time consuming and possibly prone to human error when done manually.

Simplification of protein sample prep workflows requires a robust protocol that can be automated for binding, washing and digestion steps. The ideal protocol should be identical for the different sample types of interest, or at least have as few variations as possible, because differences in the sample preparation are a major source of experimental variation.5

Complex protein samples can be viscous, so automation is also likely to necessitate positive pressure or centrifugation – vacuum may not be enough. Positive pressure has been

shown to be more reproducible than vacuum.6 In addition, positive pressure units can generally operate at higher pressures than a vacuum is able to provide and can therefore more easily guarantee sufficient and constant pressure on all the columns in a parallel set-up of a matrix of samples, such as in a 96-well plate. This results in a steady pressure being applied over a specified time with a defined pressure-assisted sample processing (PASP) protocol.

Thus, if we want to be realistic about simplifying the protein sample prep workflow by automation, we will need to consider processing our captured protein samples using a positive pressure unit with a compatible liquid handling protocol for cleaning up and digesting samples prior to analysis.

Decreasing sample turnaround time

To optimize sample turnaround times, one can either decrease the time it takes for an individual sample to be processed, process as many samples as possible in parallel, or both. If the upstream capture, clean-up and digestion is automated, the process is probably as optimized as it can be on the level of treatment of the individual sample, so then one can look towards parallel processing of multiple samples in order to decrease sample turnaround times overall.

The ability to perform accurate, automated total protein sample prep in a 96-well plate format would be a game-changer when it comes to experimental design in proteomics. When done manually, total protein sample prep is tedious, as samples are handled and processed one by one, whereas automation allows the scalable analysis of proteomes, with multiple samples at multiple time points, giving researchers the potential to do infinitely more dynamic and complex sets of experiments.

Achieving an acceptable turnaround time from receiving samples to the generation of a complete proteome profile analysis will also depend on the technology used downstream. This may involve one or more of the following: mass spectrometry or LC-MS/MS, electrophoresis or, if specific proteins have already been identified, ELISA or other immunoassays.

Ensuring cost effectiveness of the workflow

Automation is often thought of as an expensive luxury in the lab. However, we cannot forget that the cost of repeating research can be massive, far dwarfing the cost of automation. Cost effectiveness will depend on the aim of a particular proteomics research project, as well as on the existing infrastructure. The streamlining of the protein sample prep workflow as described here will certainly help towards cost effectiveness, potentially saving time and money with fewer errors and less manual labor. In turn, a new, automated way of working will shift the bottlenecks and the questions that can be answered, allow new projects to be conceived and, ultimately, quicken scientific progress.

References

1) Hortin, GL & Sviridov, D. The dynamic range problem in the analysis of the plasma proteome. Journal of

Proteomics, 2010, 73(3), 629-636. 2) Thakur, S. Proteomics and Its

Application in Pandemic Diseases.

Journal of Proteome Research, 2020, 19(11), 4215-4218. 3) Faria, SS et al. A Timely Shift from

Shotgun to Targeted Proteomics and

How It Can Be Groundbreaking for

Cancer Research. Frontiers in

Oncology, 2017, 7, 13. 4) Klont, F et al. Assessment of

Sample Preparation Bias in Mass

Spectrometry-Based Proteomics.

Analytical Chemistry, 2018, 90(8), 5405-5413. 5) Piehowski, PD et al. Sources of technical variability in quantitative

LC-MS proteomics: human brain tissue sample analysis. Journal of

Proteome Research, 2013, 12(5), 2128-2137. 6) www.americanlaboratory.com/914-

Application-Notes/172423-Evaluationof-an-Automated-Solid-Phase-

Extraction-Method-Using-Positive-

Pressure