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Alzate O, editor. Neuroproteomics. Boca Raton (FL): CRC Press/Taylor & Francis; 2010.

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Chapter 1Neuroproteomics



For the past several years, a large group of collaborators has been working together toward understanding key biological problems related to brain function, brain structure, and the complexity of the nervous system. Problems such as the structure and the function of pre- and post-synaptic densities, the sets of proteins that are regulated by mental processes such as learning and memory formation, the protein networks affected by apoE genotypes in Alzheimer’s disease patients, and the structure of synaptic protein complexes in animal models of epilepsy, just to mention a few, have been under scrutiny in these studies. To tackle biological problems using neuroproteomics, we have learned that multiple experimental approaches need to be implemented. Techniques such as 2D-DIGE, mass spectrometry (MS), MS-based tissue imaging, protein arrays, surface plasmon resonance (SPR), protein interaction network analysis, multidimensional liquid chromatography, and many others are now in daily use. This book is intended to provide the reader with an introduction to some of the techniques that are most commonly used in neuroproteomics, and includes some examples of how such techniques are used to understand biological processes. A general overview of these techniques and their scope is discussed in this chapter.

Modern biotechnology is enjoying an explosion of new systematic approaches for the study of biomolecules and their interactions. The advent of genomics, proteomics, metabolomics, peptidomics, lipidomics, glycomics, and all the other “omics” is providing a vast amount of information that is enriching our knowledge of biological systems. In this book, a group of experts in neuroproteomics and its applications present the concept that understanding the dynamics of the proteome of a complex biological system requires the integration of many different experimental approaches.


To define neuroproteomics we must start by understanding the term proteomics. Although there are many definitions of proteomics, what we mean here by proteomics is the study of a proteome (1), and a proteome is the complete set of proteins of an organ or an organism at a given time and under specific physiological conditions. A proteome is complex and refers to much more than the mere identification of the proteins in the set. In any given proteome, proteins may interact with a certain number of other proteins (or other molecules), determining how the protein functions as part of the whole system. In addition, protein structure and/or function can be altered by changes in the environment, including factors such as temperature, ionic strength, pH, levels of oxidants or anti-oxidants, etc. (2). The study of the proteome should provide information about all of these factors. Proteomics may start by elucidating the “proteome” at a specific time, but it should also determine the “dynamics” of this proteome under all the possible factors that affect the organ or organism. Thus proteomics, by its very nature, is faced with a huge task that requires the collaboration of multiple disciplines including physics, chemistry, biology, and bioinformatics.

Neuroproteomics is the sub-field of proteomics dedicated to answering these same questions about the organs, tissues, and cells that make up the nervous system (3–12). In neuroproteomics, our goals are (i) to identify all the proteins of a given tissue, cell type, or organelle under specific conditions at a specific time; (ii) to identify the post-translational modifications in all the proteins at that time and under these conditions; (iii) to determine how this proteome changes as a function of time (age), environmental changes, genetic factors, and with disease; and (iv) to determine how these changes affect the organism as a whole. At the present time, with existing technologies, the current knowledge of protein structure and function, our current knowledge about all possible protein-protein and protein-other molecule interactions, and the financial resources available, the full achievement of all of these goals is not possible. It is possible to find a “partial set” of proteins from a given tissue, or to determine how a subset of these proteins changes under the influence of some external factors such as a certain drug, etc. In fact, the complete definition of a proteome as presented above has not yet been determined for any organ or any tissue—not even for a single cell. This is what makes proteomics such an interesting field—despite so much that has been accomplished we realize that there is so much more to do.

In neuroproteomics the different pieces of the nervous system are “fragmented” so that the dynamics of each given sub-proteome can be better understood. Just to mention some examples (and this is not intended to be an exhaustive list), neuroproteomics works at solving the proteome of single neurons or astrocytes grown in cell cultures or from primary brain cells isolated from tissues under several conditions (13–16); at identifying a set of proteins characterizing a brain tumor (17–20); or at determining the set of proteins making up post-synaptic or pre-synaptic densities (21–27). It is also common to try to solve a specific sub-proteome such as the heat-shock response proteome (28–32), or the proteome responding to oxidative stress (33–37). From these examples, it can be seen that specific groups of proteins are targeted for analysis in a way that eventually will lead to solving a single proteome, and possibly being able to determine the dynamics of this proteome. The final goal will be to be able to predict “how” the proteome will evolve when influenced by specific conditions and to use this information to design methods that will modulate the evolution of the proteome. This is the ultimate dream for rational drug design, and molecular manipulation. The accumulation of huge amounts of data all around the world requires the advent of better information systems that will permit this “global” understanding of the dynamics of systems proteomics.

To accomplish the goals described above, proteomics requires the conjunction of many disciplines and techniques. In this book, we describe some of these techniques and give examples of several applications. A short description of the current approaches used in neuroproteomics follows next. For a more detailed description of some these techniques and their applications to proteomics, the reader is referred to specific chapters in this volume and to the literature cited therein.


1.3.1. Mass Spectrometry

Mass spectrometry (MS) is the workhorse of proteomics. This technique offers tremendous power in terms of sensitivity to detect either digested peptides or intact proteins (38,39). Current developments allow targeting specific protein modifications such as phosphorylation, oxidation, ubiquitination, and others (7,40,41), albeit with varying degrees of difficulty and success. Unlike other approaches, such as x-ray diffraction that require intact proteins either in crystal form or in solution, respectively, MS requires that peptides or proteins be studied as ions in the gas phase.

As described in Chapter 5, matrix-assisted laser desorption/ionization (MALDI) and electro-spray ionization (ESI) are the most common protein and peptide ionization techniques used in MS-based proteomics. The availability of instrumentation and computer programs, together with the availability of protein databases that can be used for automated comparisons with the experimentally derived-MS and MS/MS (gasphase sequencing) data, makes this the leading technique for protein identification and characterization. In principle, hundreds to thousands of proteins can be identified in a single sample using liquid chromatography (LC) separation coupled on-line with these ionization techniques (LC-MALDI and/or LC/MS/MS). The major obstacles that we face in applying MS to neuroproteomics are limitations in sample availability, which plagues most neuroproteomics experiments.

A recent application of MS to neuroproteomics research is the molecular imaging of brain tissues by MALDI, as described in Chapter 7. This approach, although it has advanced tremendously under the leadership of Dr. Richard Caprioli, is still in its infancy and its applications to neuroproteomics will be mainly for functional analysis of subsets of proteins and for understanding the onset and progression of neurological diseases. A great advantage of MALDI-based tissue imaging is the possibility of creating three-dimensional molecular images of the brain, or brain areas, and then using these images to determine the dynamic evolution of sub-proteomes.

Mass spectrometry is also widely used to characterize protein post-translational modifications (see Chapter 6). Trying to discover protein phosphorylation (42–44), ubiqutination (24), palmitoylation, (45,46), oxidation (see Chapter 10) (33,47–51), and other post-translational modifications (PTMs), and their roles in neurophysiology is a challenging aspect of neuroproteomics. Chapter 6 describes mass spectrometry-based approaches to characterize these and other PTMs relevant to brain function.

1.3.2. Two-Dimensional Polyacrylamide Gel Electrophoresis

Two-dimensional acrylamide-based gel electrophoresis is a powerful technique that represents a tremendous resource for proteomics studies (see Chapters 3 and 4). Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) allows the separation of hundreds to thousands of proteins in a single experiment (40,52,53). Separated proteins may be identified by mass spectrometry, or some potential modifications may be analyzed “in” the gel. For example, some phosphorylated proteins can be identified by staining with phospho-specific stains (see Chapter 4) (32) or phospho-Ser/Thr-specific antibodies; oxidized proteins may be identified by Western blotting (see Chapter 10) (33,37,54,55). 2D-PAGE is an extremely useful technique that offers substantial advantages for quick and accurate protein separation and analysis, as well as providing an extremely useful way of “visualizing” a complex sample. Both 1D- and 2D-PAGE can be combined with LC/MS-based separation of gel extracts ("geLC") to provide an additional dimension of separation for complex mixtures.

1.3.3. Two-Dimensional Difference Gel Electrophoresis

Differential-display proteomics (comparative proteomics) can be applied using multidimensional liquid chromatography or 2D-gel-based electrophoresis. Differential-display proteomics is very powerful because it offers the advantage of comparing several samples in a single experiment. The samples to be compared may include a control (normal) versus an experimental (disease) sample (see Chapter 4) (6,53,56–59). The experimental sample is selected such that it reflects the effects of a specific condition including the effects of age, a drug, a gene, pH, oxidative stress, heat, etc. The most common approach for differential display is 2D-difference gel electrophoresis (2D-DIGE). This technique permits comparing thousands of fluorescently labeled proteins in a single experiment (see Chapter 4). With this technique, it is possible to determine changes in protein expression, as well as potential post-translational modifications (32,60,61). In addition, different types of fluorophores can be used to probe specific properties of a protein. These may include cysteine, tyrosine, lysine, and histidine modifications and, virtually, any modification for which a specific fluorophore can be found. 2D-DIGE can be used to study post-translational modifications including ubiquitination, phosphorylation, oxidation, and palmitoylation, among other modifications. As with any other technique, 2D-DIGE has limitations and advantages, and it is commonly understood in proteomics laboratories that a single technique will not suffice to answer all possible questions about a particular proteome. Proteins isolated and analyzed by 2D-DIGE can be identified and characterized by MS, or combined with immunoblotting for the analysis of specific sub-proteomes (3,21,62).

1.3.4. Liquid Chromatography

A workhorse technique that has experienced tremendous advances, liquid chromatography (LC) offers the advantage of separating proteins in a liquid phase (see Chapter 3) (6,63–65). Advances in all the technical aspects associated with LC make this a good complement for any proteomics laboratory. Proteins isolated by LC may be identified and characterized by MS, or they can be run on a 1D- or 2D-PAGE gel for further comparisons (6,63,65,66). In neuroproteomics, the major challenge for LC or LC/MS (see Chapters 3 and 5) is the scarce amount of samples for analysis. Large columns, with large volumes, are therefore not recommended; instead, nanoscale separations, micro-fluidic devices and affinity chromatography with specific antibodies or other molecules to enrich the target molecules may be the methods of choice (67,68). A major disadvantage of these nanoscale separations is the restrictions placed on flow rates as well as the challenges of reducing dead volumes. For on-line LC/MS, there are also restrictions on the selection of buffers and detergents. LC may be combined with differential display proteomics or extended to multidimensional approaches to provide a wider range of proteomics applications (see Chapter 3).

1.3.5. Protein Arrays

Protein arrays are designed to identify protein interactions using a solid surface to capture the proteins of interest, or to characterize a property of a protein of interest (16,41,53,69). This technique can be combined with other approaches such as 2D-DIGE and MS to identify specific proteins that interact with antibodies, peptides, or other suitable molecules properly attached to solid surfaces. In neuroproteomics, we use protein arrays to explore the changes in protein concentration, protein modifications, and protein-protein interactions in nervous systems such as neurons, axons, post-synaptic densities, etc. In this technique, protein lysates are incubated with arrays of antibodies against the protein of interest. (For a detailed discussion of protein arrays, protocols, and application see ref. 69.) The advantage of the arrays is that specific groups of proteins can be targeted for analysis—for instance, mitochondrial proteins, synaptic proteins, or membrane proteins. The molecules immobilized to the solid surface to produce the arrays can be specific for phosphorylated or oxidized proteins. In principle, this approach is comparable to multiplexed Western blots except instead of associating proteins one by one, molecular associations are made against a large number of proteins in a single experiment (69).

1.3.6. Immunoblot

Western blots (WBs) constitute a unique approach that allows identification of discrete proteins using targeted antibodies against specific proteins (70,71). Widely used in biological research, WB allows characterization of discrete changes in protein expression, and in combination with 2D gels, also allows one to detect changes in protein isoforms, or post-translational modifications (32,37,52,72). The current use of multiplexed WB using pre-labeled fluorescent antibodies permits quantitative characterization of multiple protein changes in a single experiment. Extensive use of WB can be used to determine protein changes in specific systems such as post-synaptic densities (73), mitochondria, etc. This approach is equivalent to a single protein array, or to a low-resolution, non-quantitative molecular image as obtained by MALDI-based tissue imaging (see Chapter 7). Several factors limit the application of immunoblotting for proteomics. These include the low specificity of many antibodies, and the lack of availability of pure antibodies. Moreover, antibodies simply do not exist for some proteins. Currently an antibody proteome project is under way that will provide a valuable tool for neuroproteomics research ( (74,75).

1.3.7. Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) offers a fast and reliable method for the determination of the molecular weight of a protein, and its hydrodynamic and thermodynamic properties (76,77). AUC is based on the thermodynamic analysis of sedimentation equilibrium, and may be used to determine sample purity, integrity of the structure, and degree of aggregation. The molecular weight determined by AUC is that of the native state of the protein, as opposed to the unfolded state as determined by gel electrophoresis, or in the gas state as determined by MS. This technique can be used for the study of small molecules (several hundreds of daltons of molecular weight such as small peptides) to multi-million-Da assemblies such as viruses or multicomplex proteins, and organelles (76–80). AUC can be applied to small samples in small volumes as are commonly found in neuroproteomics studies. Sedimentation equilibrium can be used to determine the molecular weights of protein complexes as they exist in solution, including the determination of aggregation states, and to study protein-protein interactions and protein interactions with small molecules (80–82).

1.3.8. Surface Plasmon Resonance

Surface plasmon resonance (SPR) is a powerful technique to determine protein-protein interactions in solution (83–87). This technique offers the incomparable ability to determine association (Ka) and dissociation (Kd) constants between two ligands (84,85). A combination of SPR and mass spectrometry allows the identification of protein interaction networks because the proteins bound to a ligand or a group of ligands can be identified by MS (86,87). This technique offers precise quantitative parameters that cannot be determined with protein arrays alone.

1.3.9. Circular Dichroism

This technique is largely ignored in the proteomics field, mostly because high-throughput approaches have not been developed. Circular dichroism (CD) spectroscopy offers the advantage of providing a fast and reliable screening for protein secondary and tertiary conformations in solution (88). Many proteins associated with neurological diseases including the amyloid ß peptides, α-synuclein, and prion proteins, for instance, form oligomeric conformations, which may be associated with onset and progression of some diseases (see Chapter 9) (89–93). CD is a reliable method for determining the folded conformations of these proteins. Implementing high-throughput CD will provide a method to categorize these proteins and their folded state. For now, neuroproteomics must use CD on discrete samples.


Neuroproteomics faces many challenges (7,8,38,66,94). For many years neuroscientists have been trying to answer questions such as “What is conscience?” “What are dreams made of?” “What are the physical substrates of memory and learning?” “What are the differences between short-term and long-term memories?” and so on. We are confident that neuroproteomics will offer a tool for neuroscientists working on these and many other brain- and mind-associated questions. It is not our expectation that proteomics alone will answer these questions, but instead it will be a powerful tool that will help in the search for the right answers (7,8,66). At the present time, being able to associate protein expression and protein modification with electrophysiological data and with some of the superior functions of the brain will be a good start. This is an area on which many research groups are working, and will be a fantastic beginning of a bright future for neuroproteomics.

Among the many challenges, the one that we face every day and that needs to be solved “up-front” so that data collection can be successful is sample preparation. The common presence of non-proteinaceous components such as nucleic acids, lipids, carbohydrates, and other biomolecules can affect the outcome of a proteomics analysis (see Chapters 3 and 4 for discussions on sample preparation). Many of these “contaminants” may actually be part of a functional proteome. Some of these biomolecules may be part of modulatory mechanisms for enzymatic reactions, including DNA-, RNA-, lipid-, glycolipid-, and carbohydrate-protein interactions. Therefore, elucidating what is a contaminant and what is part of modulatory mechanisms in the cell or organelle is a challenge that needs to be addressed.

Another problem that needs to be addressed is the availability of enough sample for proteomics analysis. For example, when working with post-synaptic densities (PSDs) it is possible to obtain low (tens of micrograms) amounts of proteins. In many cases, these protein lysates have to be cleaned for reliable analysis. During this cleaning process, the amount of protein may decrease between 10% and 40%. Assuming that the PSD contains several hundreds of proteins at any given time, this means that the sample contains on average only a few tens of nanograms of most proteins. Even this “best case” scenario represents the expression levels of the high abundance proteins. Unfortunately, protein identification and characterization by mass spectrometry require at least 20 to 30 ng in most cases. This means that many proteins—and proteins that are usually less abundant—will not be detected, and possibly may not be identified, or may be identified but with insufficient peptide coverage for modification site analysis. In most situations these problems need to be addressed on a case-by-case basis, and individual solutions may be found depending on the availability of animal models or tissues to solve particular situations. We should keep in mind that our goal is not to create catalogs of proteins; we are trying to tackle biological questions relevant to the nervous system. It is also important to keep in mind that in neuroproteomics we are interested in finding functional molecular pathways and not just simply trying to create a catalog of proteins in a tissue or an organelle. The major goal is to determine the dynamics of the proteome because “snapshots” alone do not provide enough valuable information about the changes occurring within the system and the other systems associated with it.

Post-translational modifications (PTMs) are another big challenge that neuroscientists need to address in their quest to understand the molecular mechanisms associated with brain functions. Some PTMs have been extensively studied, as is the case with phosphorylation, oxidation, and ubiquitination. The role of these PTMs in some cases has been addressed with optimistic results. Other PTMs such as sumoylation, palmitoylation, and methylation have been less studied but are equally important. However, there are a lot more PTMs than those mentioned above, and the complete picture of their roles in brain functions needs to be addressed (52,95–100). The techniques described in this book (see Chapters 3, 4, 6, and 7) provide some experimental approaches to address some of the questions associated with functional PTMs, but this is just the beginning. A lot more work needs to be done.

The field of neuroproteomics requires a lot more work and a lot of dedication to uncover the protein interaction networks (PINs) (see Chapters 8 and 9) (26,101–108). Currently, PINs are evaluated using genomics data, or by using discrete groups of proteins. However, a global view of the PINs associated with normal- and diseased-brain functions is still not available. Along with the identification of the PINs, it is very important to develop methodologies for their validation, and it is even more important to develop tools that will allow researchers to predict the function and the evolution of these networks under normal and disease conditions, under the effect of aging, or due to environmental factors.

A major push for neuroproteomics has been the as-yet-unfulfilled dream of finding “protein"-based biomarkers for neurodegenerative diseases. Thus far, proteomics has been unable to deliver on this dream. Why? There are many reasons why we still do not have hundreds of true protein biomarkers. We know a lot about individual proteins, but all of the aspects concerning a proteome (as described above) are not yet known even for a single proteome, and we know even less about the dynamics of proteomes. It will be possible to find true and very useful biomarkers, but this will have to wait a little longer while new technologies and—more importantly—new concepts are developed for determining a whole proteome and how it changes with time and environmental factors.


The book contains two major parts: the first part describes basic concepts for those principles used in neuroproteomics (Chapters 2 to 8) (Figure 1.1), while the second part is dedicated to illustrating a few examples showing how scientists are using basic principles and techniques to understand molecular mechanisms of neurobiological processes (Chapters 9 to 15) (Figure 1.1). These examples have been selected while keeping in mind the ideas described above, i.e., that neuroproteomics is a set of tools to be used for understanding fundamental neurobiological questions, and not simply to create a catalog of nervous system-associated proteins.

FIGURE 1.1. Neuroproteomics requires many tools of modern biotechnology.


Neuroproteomics requires many tools of modern biotechnology. As indicated in the upper part of this figure, we are concerned with sample preparation, protein fractionation, protein identification, and protein characterization. Then, we use a combination (more...)

As described earlier, one of the major challenges for neuroproteomics is sample preparation. Chapters 2, 3, and 4 discuss experimental considerations, methods, and concepts associated with sample preparation. Chapter 2 describes the experimental considerations for brain tissue collection and storage. Postmortem tissue is of major interest for research targeting molecular mechanisms of neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases. Proper collection, storage, and manipulation of postmortem tissue are very important for reproducible and reliable proteomics analysis. The Bryan Brain Bank at the Department of Medicine and Neurology at Duke University has been banking brain tissues for several years. This bank specializes in brain specimens for neurodegenerative diseases, especially for Alzheimer’s disease. Chapter 3 describes modern approaches for multidimensional separations for neuroproteomics. These approaches are mainly based on liquid chromatography and are appropriate for every proteomics method. Chapter 4 is devoted to differential gel electrophoresis (DIGE)-based proteomics. Methods required to prepare proteins for DIGE are described in this chapter, along with experimental considerations for a successful DIGE-based experiment. A discussion of DIGE applications to neuroproteomics is presented in Chapter 4.

The technique most widely used for proteomics is mass spectrometry (MS); three chapters are dedicated to mass spectrometry. Chapter 5 introduces the major concepts of MS for proteomics analysis, including sources, detectors, and analyzers. Chapter 6 presents major applications of mass spectrometry for the analysis of post-translational modifications of proteins. A new application of MS is MALDI-based tissue imaging. This technique has the potential to become one of the more useful techniques for neuroproteomics. Chapter 7 describes the concepts, techniques, and major applications of MALDI-based tissue imaging. Chapter 8 introduces the concept of protein interaction networks, a new and rapidly evolving field based on the theory of graphs that connects the elements of any group of proteins to other proteins in the system, and how the inter- and intra-connectivity determines the overall behavior of the system (101,104–107).

The second part of the book starts with an example of the identification of a protein interaction network in neurodegeneration (Chapter 9). Chapter 10 analyzes a large amount of data for studies of oxidized proteins in neurodegenerative diseases. This chapter describes the use of 2D gels, mass spectrometry, and 2D Western blots to tackle analysis of protein post-translational modifications. Chapters 11 and 12 present the use of neuroproteomics to study specific aspects of molecular mechanisms in the visual system. Chapter 13 discusses networks of genes associated with the auditory system, and it is accompanied by a neuroproteomics approach (Chapter 14) analyzing specific protein pathways associated with singing regulation in songbirds. The last chapter, Chapter 15, presents several neuroproteomics approaches to understand molecular mechanisms associated with nerve regeneration.


This volume is intended as a general introduction to the concepts, techniques, and applications of neuroproteomics. As presented in Figure 1.1, for neuroproteomics studies we have to utilize multiple techniques for sample preparation, protein fractionation, and protein identification. The combination of these approaches results in the creation of extensive catalogs of proteins associated with a tissue or a system; however, these catalogs are useless unless we can identify the function for each protein, the network to which each protein belongs, and the dynamics of the network under experimental conditions. Facing these challenges requires the combination of many approaches, some of which have matured enough to be used in proteomics, and some which are new and still under development. Combining mass spectrometry with surface plasmon resonance, gene silencing, 2D-DIGE, and protein arrays will help to discern these protein interaction networks and their dynamics.

It should be mentioned here that it is not possible to cover all aspects of neuroproteomics in a single volume. The field is growing so fast that it is not even possible to review all the available literature. There are thousands of scientists on all continents contributing to the many different fields of neuroproteomics, including both technical developments and specific applications. In the present volume, we tried to cover us much as possible of the current literature available, and give specific examples that demonstrate exactly what neuroproteomics is, and how and why it is used. I would like to apologize to all of those researchers whose publications were not cited, which is not a disregard for their work, but rather the result of how fast this field is growing, and lack of space in this single volume. Also, we expect that the future will bring applications of techniques such as x-ray crystallography, NMR, and EPR (electron paramagnetic resonance) to neuroproteomics.

Finally I want to thank all the authors who have contributed to this volume. It has been a difficult task that started more than five years ago, when we started designing experiments, preparing samples, collecting data, and trying to validate as much as possible the results obtained by proteomics approaches. Since then, the field has grown considerably and today it is almost impossible to know everything being done in neuroproteomics and its applications. This makes the efforts of all of the authors more valuable as they have tried to give an up-to-date view of particular aspects of this rapidly developing field.


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