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

·  Chemical multiplexing

·  Label-free

















*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).

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