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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

Proteomics and drug discovery in new areas

     Beyond the described roles for proteomics in target discovery approaches, specialized applications of MS-based proteomics in surfaceome, secretome, and immunopeptidomics are important for drug discovery. Proteins involved in intercellular communication, such as cell surface-exposed receptors or ligands, represent targets for antibodies and small molecule inhibitors to manipulate cellular crosstalk or signaling cascades. Moreover, surface-exposed immunogenic host or microbial proteins function as antigens and therefore proteomics lends itself to the discovery of vaccine candidates.

     Proteomic strategies have been used in combination with the enrichment and identification of surface-exposed proteins e.g. based on glycosylation or biotin labeling. Moreover, secretome analysis by MS can identify proteins associated with cellular communication and suggest novel avenues for the design of host- and pathogen-directed therapeutic options. The interface between host cells and pathogens is a rapidly advancing research area with potential for drug discovery. For example, pathogen protein secretion upon host-stimulation or secreted proteins from tumor cells can be enriched and identified, serving as diagnostic and therapeutic biomarkers.

     The field of immunopeptidomics focuses on peptides derived from host and/or microbial proteins, which are presented by the human leukocyte antigens (HLA), also known as the major histocompatibility complex (MHC), and are decisive in controlling antigen-specific T cell responses. Presently, MS-based proteomics is the only unbiased platform capable of large-scale investigation of the collection of HLA bound peptides. This technology drives the field of antigen discovery by immunopeptidomics and presents a powerful avenue for personalized immunotherapy, for example, against cancer.

Suggested reading:

Wollscheid, B. et al. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat Biotechnol 27, 378-86 (2009).

Frei, A.P. et al. Direct identification of ligand-receptor interactions on living cells and tissues. Nat Biotechnol 30, 997-1001 (2012).

Eichelbaum, K., Winter, M., Berriel Diaz, M., Herzig, S. & Krijgsveld, J. Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat Biotechnol 30, 984-90 (2012).

Meissner, F., Scheltema, R.A., Mollenkopf, H.J. & Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475-8 (2013).

Geddes, J.M. et al. Secretome profiling of Cryptococcus neoformans reveals regulation of a subset of virulence-associated proteins and potential biomarkers by protein kinase A. BMC Microbiol 15, 206 (2015).

Dieterich, D.C., Link, A.J., Graumann, J., Tirrell, D.A. & Schuman, E.M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci U S A 103, 9482-7 (2006).

Mahdavi, A. et al. Identification of secreted bacterial proteins by noncanonical amino acid tagging. Proc Natl Acad Sci U S A 111, 433-8 (2014).

Caron, E. et al. Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry. Mol Cell Proteomics 14, 3105-17 (2015).

Bassani-Sternberg, M. & Coukos, G. Mass spectrometry-based antigen discovery for cancer immunotherapy. Curr Opin Immunol 41, 9-17 (2016).

Shotgun proteomics workflow: LC-MS and computational proteomics

    Following sample preparation, peptides are commonly chromatographically separated in an aqueous to organic solvent gradient based on peptide interaction with the hydrophobic stationary phase. Small bead size (< 2 μM) and inner column diameter (75 μM), in combination with a longer, heated column (~ 50 cm), improve sensitivity and efficiency of peptide separation. Using high pressure-high performance liquid chromatography (HP-HPLC), ionization of the eluting peptides by electrospray results in the transfer of ions to the mass spectrometer. Initially, the MS1 survey scan records all peptide masses that elute from the chromatography at a given time. This is followed by the selection and fragmentation of peptides in an MS2 or MS/MS scan, for their identification based on the measured peptide fragment masses. In comparison to shotgun proteomics, directed tools for protein identification include ‘targeted’ and ‘top-down’ proteomic approaches.

Shotgun Proteomics Targeted Proteomics Top-Down Proteomics
Principle Digested proteins Digested proteins Intact proteins
Applications Biological, Clinical Biomarkers Quality control of biologics
Scalability Moderate High Low
Dynamic range Broad Broad Narrow
Data acquisition DDA+ DIA++ DIA DDA
Quantification*

·  Chemical multiplexing

·  Label-free

 

Yes

 

Yes

 

No

 

Yes

 

No

 

Yes

 

No

 

Yes

*General applications

+DDA: Data-dependent acquisition

++DIA: Data-independent acquisition

     The processing of modern MS data requires sophisticated bioinformatic platforms to systematically identify and quantify peptides and proteins from the spectral data. Beyond standard search strategies for identification by scoring of peptide and fragment masses in an organism-specific database, identification of modified sites such as those generated from post-translational modifications (PTMs) or activity-based proteomic probe (ABPP) experiments can also be performed. Bioinformatic platforms for the processing of proteomic datasets include MaxQuant and the Trans Proteomic Pipeline. Each of these tools are applicable to a range of proteomic workflows including label-free quantification (LFQ) for the relative quantification of protein abundances without prior consideration of labeling strategies. In addition, software packages, such as Perseus and dedicated R packages, are important for data analysis, visualization, and interpretation. Specifically, they are capable of mining large and complex data sets using machine learning tools (e.g. Big Data Analytics), integrating different proteomics approaches or OMICs technologies. In addition, they define leads based on rigorous statistics accounting for multiple-hypotheses testing and perform enrichment and pathway analyses.

Suggested Reading:

Sandra, K. et al. Highly efficient peptide separations in proteomics Part 1. Unidimensional high performance liquid chromatography. J Chromatogr B Analyt Technol Biomed Life Sci 866, 48-63 (2008).

Thakur, S.S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol Cell Proteomics 10, M110 003699 (2011).

Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods 9, 555-66 (2012).

Tran, J.C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254-8 (2011).

Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26, 1367-72 (2008).

Keller, A., Eng, J., Zhang, N., Li, X.J. & Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 1, 2005 0017 (2005).

Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods (2016).

R.D.C., T. R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. (2008).

Shotgun proteomics: biochemistry and sample preparation

     Shotgun proteomics is a discovery-based platform, which has evolved to yield comprehensive proteomes. Commencing with sample preparation, proteins are isolated from a variety of biological materials, including cells, tissues, or body fluids, using customizable extraction buffers. Extraction protocols may require extensive tissue disruption or the simple solubilization and denaturation of proteins by using a chaotropic agent (e.g. urea) or by boiling in a detergent (e.g. sodium dodecyl sulfate, SDS). The extracted proteins are then subjected to digestion by sequence-specific proteases, such as trypsin, for the generation of peptides, followed by purification.

     Shotgun proteomics workflows include the investigation of protein-protein interactions (PPIs), enrichment of post-translational modifications (PTMs), and fractionation using chromatography-based separation techniques. For PPIs, ‘bait’ proteins are attached to a matrix and incubated with a sample containing ‘prey’ protein(s) to capture interacting partners. Considerations pertaining to detergent selection may influence the retainment of strong versus weak interactions and the utilization of chemical cross-linkers may permit capturing of transient interactions. Conversely, the investigation of PTMs captures peptides on an antibody-based matrix, matrices with affinities to distinct peptide properties such as titanium dioxide (TiO2) beads, or an immobilized metal affinity chromatography (IMAC) for the biochemical enrichment of the modified peptides. Lastly, peptides can be fractionated based on their biophysical and chemical properties by methods including cation exchange (SCX), size-exclusion (SEC), and high pH chromatography. The application of fractionation techniques often leads to increased proteomic depth at the expense of increased measurement time.

     For the absolute and relative quantification of proteins or peptides, different strategies include metabolic and chemical labeling, as well as label free methods (discussed in the next post). For example, metabolic labeling, including stable isotope labeling of amino acids in cell culture (SILAC) and 15N involves the incorporation of isotopically stable and non-radioactive forms of amino acids into proteins at the cellular or organizational levels. Alternatively, chemical labeling such as tandem mass tags (TMT), isobaric tags for relative and absolute quantification (iTRAQ), and dimethyl are based on the chemical derivatization of peptides.

Suggested reading:

Mann, M., Kulak, N.A., Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol Cell 49, 583-90 (2013).

Liu, F. & Heck, A.J. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry. Curr Opin Struct Biol 35, 100-8 (2015).

Thingholm, T.E. & Jensen, O.N. Enrichment and characterization of phosphopeptides by immobilized metal affinity chromatography (IMAC) and mass spectrometry. Methods Mol Biol 527, 47-56, xi (2009).

Doll, S. & Burlingame, A.L. Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem Biol 10, 63-71 (2015).

Humphrey, S.J., Azimifar, S.B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat Biotechnol 33, 990-5 (2015).

Batth, T.S., Francavilla, C. & Olsen, J.V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J Proteome Res 13, 6176-86 (2014).

Mohammed, S. & Heck, A., Jr. Strong cation exchange (SCX) based analytical methods for the targeted analysis of protein post-translational modifications. Curr Opin Biotechnol 22, 9-16 (2011).

Ong, S.E., Foster, L.J. & Mann, M. Mass spectrometric-based approaches in quantitative proteomics. Methods 29, 124-30 (2003).

Gouw, J.W., Krijgsveld, J. & Heck, A.J. Quantitative proteomics by metabolic labeling of model organisms. Mol Cell Proteomics 9, 11-24 (2010).

Li, Z. et al. Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res 11, 1582-90 (2012).