Give me a drop of your blood and I will reveal your plasma profile….

Proteomic analysis of human blood plasma has the potential to provide a powerful diagnostic tool for healthcare professionals. However, transfer of proteomics from the research lab to routine measurements in clinical and hospital settings requires rapid and robust methods.

While previous methods require days for sample processing, Geyer et al. (2016) present a streamlined workflow demonstrating the acquisition of the plasma proteome in a total time of just 3 hours, from blood sample to quantified proteome results (Figure 1).

Plasma proteome workflowFigure 1: Rapid plasma proteome workflow (Geyer et al. 2016). The sample preparation protocol capitalizes on innovations developed in the Mann Department (Kulak et al. 2014).

Much of the time savings from >48 hours to 3 hours is realized by decreasing sample processing time. For example, verifying reproducible digestions in just 1 hour instead of 24 hours, and elution in just 60𝛍L to shorten speedvac time. High throughput sample processing is possible, and “training does not even require a day, just 2 hours and you can do it” explains PhD student and lead author Philipp Geyer. Additionally, continues Geyer, “these same workflows can be applied to blood cells, urine, cerebrospinal fluid (CSF), cell lines, and all kinds of tissue, including brain tissue, muscle tissue, and skin. For all of these challenging matrices we can use the same digestion protocols and workflow with little adaptations”.

Additionally, while previous methods require a seemingly small blood sample volume of up to 1ml, the method reported by Geyer et al. requires only 5μl of blood. Therefore, as opposed to drawing blood with a syringe, patients can be sampled with a simple finger prick, providing both sampling efficiency as well as allowing a distinct advantage for sampling infants and the elderly. Geyer muses that “the dream is to implement proteomics in the clinic, go into your doctor, just take a finger prick and be able to provide individual proteome monitoring”. It can then be possible to foresee disease before it manifests with physical symptoms. “Currently, we simply don’t have this data, and we don’t have the data as a function of time”, explains Geyer, “with routine testing, focus can shift from not just single protein biomarkers, but proteomic profiles, and we can look for disease profiles”.

For example, proteins identified in the plasma proteome, such as lipoproteins and inflammatory markers, provide insight regarding an individual’s metabolic and cardiovascular health. While the Geyer, et al. method detects and quantifies biomarkers currently approved by the FDA, the deep proteome afforded by this method allows the further opportunity for the study and application of additional proteins of interest. Geyer explains, “the 50 FDA biomarkers are found within just the 180 most abundant proteins, the remaining 120 proteins may hold additional biomarkers and proteome profile characteristics not previously identified”.  

In addition to diagnostic and health monitoring benefiting to individual patients, this method provides the opportunity to analyze plasma proteomes at a population scale, providing large data sets amenable to data mining and the potential to reveal new insights in public health and basic research. Geyer reflects and shares his vision, “with this workflow you can really apply it to all proteomic areas, if you want to learn basic things about humans, about animals, investigate what happens as I exercise, if I don’t eat for two days, what if I am training for endurance, or for muscle mass. How an individual’s proteome changes under these different conditions are basic things that no one knows. Ultimately, you are learning something about people”.

Geyer, P.E., Kulak, N.A., Pichler, G., Holdt, L.M., Teupser, D., Mann, M., 2016. Plasma proteome profiling to assess human health and disease. Cell Systems, 2, 185-195.

Kulak, N.A., Pichler, G., Baron, I., Nagaraj, N., Mann, M., 2014. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nature Methods, 11, 319-324.


Contributed by: Jason McAlister, PhD

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