Proteomic Profiling and Analytical Chemistry

The Crossroads

Pawel Ciborowski, Jerzy Silberring

• Publisher: Elsevier
• Year: 2016
• ISBN:
  9780444636904
• Edition: 2
• Topic: Physical Sciences
• Subtopic: Analytic Chemistry

Proteomic Profiling and Analytical Chemistry: The Crossroads, Second Edition helps scientists without a strong background in analytical chemistry to understand principles of the multistep proteomic experiment necessary for its successful completion. It also helps researchers who do have an analytical chemistry background to break into the proteomics field. Highlighting points of junction between proteomics and analytical chemistry, this resource links experimental design with analytical measurements, data analysis, and quality control. This targeted point of view will help both biologists and chemists to better understand all components of a complex proteomic study.

The book provides detailed coverage of experimental aspects such as sample preparation, protein extraction and precipitation, gel electrophoresis, microarrays, dynamics of fluorescent dyes, and more. The key feature of this book is a direct link between multistep proteomic strategy and quality control routinely applied in analytical chemistry. This second edition features a new chapter on SWATH-MS, substantial updates to all chapters, including proteomic database search and analytical quantification, expanded discussion of post-hoc statistical tests, and additional content on validation in proteomics.

• Covers the analytical consequences of protein and peptide modifications that may have a profound effect on how and what researchers actually measure
• Includes practical examples illustrating the importance of problems in quantitation and validation of biomarkers
• Helps in designing and executing proteomic experiments with sound analytics

1

Introduction

J. Silberring     AGH University of Science and Technology, Krakow, Poland     Polish Academy of Sciences, Zabrze, Poland

P. Ciborowski     University of Nebraska Medical Center, Omaha, NE, United States

Abstract

Three fundamental topics have been briefly introduced before further chapters are examined. First topic is why does analytics matter? Brief answer is that although analytical phase of every proteomic experiment is in its core, we should not forget of equally important steps of sample preparation and data analysis. Second topic brings our attention to the fact that proteomic studies are highly multidisciplinary and require an interdisciplinary team of scientists with various expertise. Third topic is what are the future developments for proteomics? Brief answer is that all steps of a proteomic study are equally important and that major challenge is to make connection of these steps as a “well-oiled logically working machine”.

Keywords

analytical methods; bioinformatics; biological system; data analysis; Proteomics

Chapter Outline

1.1 Why Do Analytics Matter? 1

1.2 Expectations: Who and What? 3

1.3 What Is Next and Where Are We Going? 4

1.1. Why Do Analytics Matter?

The sum of the optimal steps in the analytical and proteomic analysis (process) is not equal to the optimal process in its entirety! As much as it is a trivial statement, which most of us accept to be true, it has not been fully appreciated despite having a profound impact on the success of laborious, expensive and, in many instances, lengthy projects, as proteomic studies are multistep tasks involving a variety of methods, each governed by its own strengths and limitations. The concept of a proteomic study can be depicted in many ways. In Fig. 1.1, we intentionally highlighted analytical components/phases because the same rules of analytical chemistry/biochemistry apply to discovery as well as validation experiments. The experimental design will be governed by a set of different rules, which does not include instrumentation but has biology heavily involved.

Figure 1.1 Schematic representation of a proteomic study.

As can be observed, bioinformatics analyses are not depicted in this model, as it is focused on analytics. Bioinformatics will be governed by its own set of rules which are applied to the validation of algorithms. Nevertheless, when looking at constituents of a proteomic study, we realized that the scientist conducting such experiments must grasp the overview of not only how biological systems work, but also analytical boundaries for sample preparation, fractionation, and measurements, tools for database searches, statistics, and eventually bioinformatics tools for data analysis. Because of their complexity, proteomic studies should be conducted by a team of experts. As the proteomics field evolves, the collective experience from an increasing number of studies inevitably leads to widely accepted quality criteria. Although significant progress has been made, many questions about uniform quality control criteria remain to be answered. Such answers will result from systematic studies conducted across many laboratories, platforms, and biological systems (models). Therefore, in this book we attempt to highlight in a short, yet comprehensive manner, the impact of the basic principles of analytical chemistry/biochemistry on the final success of a proteomic experiment. We hope that this point of view will help both biologists and chemists to better understand all components of complex proteomic study.

1.2. Expectations: Who and What?

If two scientists, a biologist and chemist, sit at a table and discuss proteomic methodology, they will likely emphasize different aspects of the same study, which in each viewpoint is critical for a successful outcome. Moreover, they quite often speak in technical language that is not fully understood by the other. This is because chemists are focused on sensitivity and accuracy of analytical measurements, while biologists pay attention to explaining biological/pathological effects and are less concerned with exact quantitation of analytes. This resembles the famous poem by John G. Saxe, “The Blind Men and the Elephant,” in which everyone tries to identify the part they are touching (ie, biologist/chemist) but nobody can get a sense of the whole system (ie, proteomic study). Biologists are willing to accept a high range of responses, resulting in high standard deviations showing or indicating “trends” in data behavior that support their hypothesis. Chemists, on the other hand, expect data to be expressed by numerical values with high precision, accuracy, reproducibility, and low standard deviation. Indeed, as much as precision of analytical measurements is important, in many instances, such efforts will not improve the overall output discriminating between true and false. This is mostly because, very often, an exact correlation between quantitative change and biological effect is not defined. For example, how important is it to measure a difference between levels of protein expression above 10-fold change when the response of biological system is already saturated by the 5-fold change of this protein? A similar question may arise from enzymology, where the most important factor is enzymatic activity and not the protein expression measured by a typical proteomic approach, which will also measure inactive enzymes. If we bring statisticians and bioinformaticians to the same table as the biologist and chemist, which very often happens, the discussion becomes even more complicated. As illustrated in Fig. 1.2, our question is what do we see on the other side of our office walls when we look for the expertise of our fellow colleagues? It is critical for each of us to peer outside of the walls that confine us and behold the world of those who surround us.

Figure 1.2 What we see on the other side of the wall of our office when we look into the office space of our fellow colleagues with their expertise.

1.3. What Is Next and Where Are We Going?

Since proteomics moved from qualitative to quantitative profiling using liquid-phase-based methods of sample fractionation, it fully entered the domain of analytical chemistry. As much as it is beneficial for proteomics to have a wide range of well-established analytical methods, the complexity of proteomic profiling creates multiple technical issues. First, classical analytical chemistry focuses on high accuracy measurements of single or few compounds at the same time. It allows adjusting methods of sample preparation and analytical parameters with specific objective(s) scarifying measurements of other compounds, which are contaminants, rather than analytes. Importantly, analytical chemistry exploits specific characteristics of analyzed compounds and this concept fulfills its purpose. In contrast, proteomics attempts to measure hundreds and thousands of molecules at the same time which can have a wide range of chemical characteristics (eg, posttranslational modifications of proteins and peptides) and that have a wide dynamic range of concentrations, such as the circumstance with plasma or serum. In the illustration in Fig. 1.1, all steps of a proteomic study are shown as equally important. It would have been a trivial effort if we looked at each step separately. Caveats arise from the connection of these steps as a “well-oiled logically working machine.”

In summary, the main goal of this book is to highlight points of junction between proteomics and analytical chemistry, and to link experimental design with analytical measurements, data analysis, and quality control. We also provide a list of points to consider for those who are planning on entering the field of proteomics and have minimal experience.

2

Biomolecules

A. Burns University of Nebraska Medical Center, Omaha, NE, United States

P. Olszowy University of Nebraska Medical Center, Omaha, NE, United States Polpharma SA Pharmaceutical Works, Starogard Gdański, Poland

P. Ciborowski University of Nebraska Medical Center, Omaha, NE, United States

Abstract

This chapter provides short overview of major features of proteins and peptides which are important for b...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of Contributors
• Preface
• 1. Introduction
• 2. Biomolecules
• 3. General Strategies for Proteomic Sample Preparation
• 4. Protein Extraction and Precipitation
• 5. Online and Offline Sample Fractionation
• 6. Immunoaffinity Depletion of Highly Abundant Proteins for Proteomic Sample Preparation
• 7. Gel Electrophoresis
• 8. Quantitative Measurements in Proteomics: Mass Spectrometry
• 9. SWATH-MS: Data Acquisition and Analysis
• 10. Top-Down Proteomics
• 11. Proteomic Database Search and Analytical Quantification for Mass Spectrometry
• 12. Design and Statistical Analysis of Mass-Spectrometry-Based Quantitative Proteomics Data
• 13. Principles of Analytical Validation
• 14. Validation in Proteomics and Regulatory Affairs
• Index