Proteomics for Biological Discovery

Timothy D. Veenstra, John R. Yates, Timothy D. Veenstra, John R. Yates

• Editore: Wiley-Blackwell
• Anno: 2019
• ISBN:
  9781119081722
• Edizione: 2
• Argomento: Biowissenschaften
• Categoria: Biochemie

An update to the popular guide to proteomics technology applications in biomedical research

Building on the strength of the original edition, this book presents the state of the art in the field of proteomics and offers students and scientists new tools and techniques to advance their own research. Written by leading experts in the field, it provides readers with an understanding of new and emerging directions for proteomics research and applications.

Proteomics for Biological Discovery begins by discussing the emergence of proteomics technologies and summarizing the potential insights to be gained from proteome-level research. The tools of proteomics, from conventional to novel techniques, are thoroughly covered, from underlying concepts to limitations and future directions. Later chapters provide an overview of the current developments in post-translational modification studies, structural proteomics, biochemical proteomics, applied proteomics, and bioinformatics relevant to proteomics. Chapters cover: Quantitative Proteomics for Differential Protein Expression Profiling; Protein Microarrays; Protein Biomarker Discovery; Biomarker Discovery using Mass Spectrometry Imaging; Protein-Protein Interactions; Mass Spectrometry Of Intact Protein Complexes; Crosslinking Applications in Structural Proteomics; Functional Proteomics; High Resolution Interrogation of Biological Systems via Mass Cytometry; Characterization of Drug-Protein Interactions by Chemoproteomics; Phosphorylation; Large-Scale Phosphoproteomics; and Probing Glycoforms of Individual Proteins Using Antibody-Lectin Sandwich Arrays.

• Presents a comprehensive and coherent review of the major issues in proteomic technology development, bioinformatics, strategic approaches, and applications
• Chapters offer a rigorous overview with summary of limitations, emerging approaches, questions, and realistic future industry and basic science applications
• Features new coverage of mass spectrometry for high throughput proteomic measurements, and novel quantitation strategies such as spectral counting and stable isotope labeling
• Discusses higher level integrative aspects, including technical challenges and applications for drug discovery
• Offers new chapters on biomarker discovery, global phosphorylation analysis, proteomic profiling using antibodies, and single cell mass spectrometry

Proteomics for Biological Discovery is an excellent advanced resource for graduate students, postdoctoral fellows, and scientists across all the major fields of biomedical science.

1
Quantitative Proteomics for Differential Protein Expression Profiling

Christian K. Frese, Henk van den Toorn, Albert J.R. Heck, and Shabaz Mohammed

Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands

*Christian K. Frese and Henk van den Toorn contributed equally.

1.1 INTRODUCTION

Mass spectrometry (MS)‐based proteomics has become an integral analytical technology in life science research (Aebersold and Mann 2003; Chait 2011). Coupling liquid chromatography (LC) to MS facilitated the routine analysis of several thousands of proteins in parallel within a few hours and has mostly replaced two‐dimensional gel electrophoresis‐based approaches. Over the past decade, MS has matured from a technique that produces purely qualitative information into a versatile tool that provides accurate quantitative data on protein abundance. Quantitative information is a fundamental necessity to interrogate the highly dynamic global proteome of living organisms. Driven by recent advances in mass spectrometric instrumentation and data analysis software, today, MS‐based proteomics builds the analytical framework for various challenges in biomarker discovery, systems biology, and structural biology (Bensimon et al. 2012; Altelaar et al. 2013b).

1.2 QUANTIFICATION APPROACHES

Multiple quantification strategies for MS‐based proteomics have been reported. They can be categorized into two main regimes: absolute and relative quantification. In this chapter we will focus on the latter and discuss practical aspects, applicability, and problems of the currently most popular quantification strategies. For those interested in absolute quantification, a detailed insight is given in a review by Brun et al. (2009). The need for reliable peptide identification underlies all MS‐based quantification strategies. Protein identification in the commonly employed bottom‐up proteomics strategy involves enzymatic proteolysis followed by one‐dimensional or two‐dimensional peptide chromatographic separations. Trypsin and Lys‐C are the routinely used enzymes where the former generates peptide lengths between 10 and 20 amino acids for the majority of the proteolytic peptides. These “MS‐friendly” peptides are sequenced by tandem mass spectrometry (MS/MS) and identified by database search algorithms (Eng et al. 1994; Perkins et al. 1999). Peptide quantification is performed either at the MS or MSⁿ‐level, depending on the quantification strategy.

The use of isotopes to label specific molecules has greatly expanded the toolbox in biochemistry. Radioisotopes such as ³²P, ³³P, ³⁵S, or ¹²⁵I and stable isotopes such as ²H, ¹³C, and ¹⁵N are widely utilized for quantifying, tracking protein–protein interactions, determining enzyme kinetics or dissecting metabolic pathways. Besides proteins, nucleic acids, lipids, and carbohydrates have also been subjected to isotopic labeling. The main techniques to analyze biomolecules involving the use of nonradioactive, stable isotopes are nuclear magnetic resonance (NMR) and MS. Today, MS‐based proteomics aims to provide large‐scale quantitative information on protein abundances. The basic principle of stable isotope‐based labeling for peptide and protein quantification is that the physicochemical characteristics of the differentially labeled peptides are nearly identical (Gouw et al. 2010). These similarities include sample preparation procedures, LC‐separation performance, ionization efficiency and MS/MS fragmentation behavior. An additional assumption is that the different isotope variants do not influence any cellular process in the case of metabolic labeling.

Most labeling strategies introduce stable (nonradioactive) isotopes with distinct masses that allow each sample to be distinguishable at the MS stage. The stable isotopes can be introduced via chemical reactions of a labeling reagent with a distinct functional group, by metabolic processes or enzymatic activity (Figure 1.1). Differentially labeled samples are combined prior to MS analysis assuming an overall near‐complete labeling efficiency. The shift in absolute mass depends on the number of heavy stable isotopes incorporated into the peptide or protein. The actual signal shift between the isoforms (in the m/z range) of a differentially labeled peptide depends on the absolute mass shift and the peptide ion charge state. For each peptide, the area under the curve of the isotopic envelope is integrated over the LC elution time. Relative quantification is performed by calculating the ratio of the peak areas of the differentially labeled peptides. Intensity‐based label‐free quantification is based on the same principle; however, peptide abundances are retrieved from consecutive LC‐MS/MS analyses since each sample is analyzed individually.

Figure 1.1 Workflows in quantitative proteomics. Dashed lines highlight the steps where samples are treated separately, which introduces experimental variation. Horizontal lines illustrate at which step samples are combined. Starting with the step when samples are labeled, samples are highlighted in orange and green, respectively. In metabolic labeling, samples are combined at the earliest possible step which, in theory, minimizes variation introduced at each sample preparation step. In chemical labeling, samples are combined after labeling. Label‐free quantification requires every sample to be processed individually. Here, the samples are combined at the data analysis level.

Source: Adapted from Ong and Mann (2005).

1.2.1 Metabolic Labeling

In metabolic labeling, the stable isotopes are introduced into the proteins in living cells or organisms during protein synthesis. ...