NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

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

Cover of Neuroproteomics

Neuroproteomics.

Show details

Chapter 3Multidimensional Techniques in Protein Separations for Neuroproteomics

and .

3.1. SUMMARY

Proteomic analysis of brain tissue is becoming an integral component of neuroscience research. A proteomic analysis typically involves protein separation, protein identification, and protein characterization. Global characterization of proteins is providing new insights into biological structures such as synapses, axons, and dendrites. Additionally, proteomics has been applied in the investigation of various neurological disorders and has resulted in the detection and identification of a large number of disease-related proteins. The field of neuroproteomics faces special challenges given the complex cellular and subcellular architecture of the brain including sample preparation and limited sample amounts. This chapter presents an overview of current separation-based proteomic technologies, and the advantages and disadvantages of these technologies, and reviews the recent use of these approaches in neuroscience research.

3.2. INTRODUCTION

The increasing use of proteomics has created a basis for the development of new strategies for the rapid identification of protein profiles in living organisms. It has also become evident that proteomics has potential applications other than protein and peptide identification. Neuroproteomic studies have provided information about protein function, subcellular localization, activity, interaction partners, biochemical pathways, and molecular networks. Despite the advance of proteomic technologies, the brain poses particular challenges to studying protein function. There is a huge level of heterogeneity, with complex neuronal morphologies and the existence of unique subcellular structures, such as dendrites, dendritic spines, and axons. There are billions of neurons in the brain. Thus, intercellular and intracellular signal transduction plays a major role in brain function. It has been estimated that approximately one fifth of human genes encode proteins involved in signal transduction (1). It is likely that a thorough understanding of neuronal function will require identification and characterization of the complex neuronal proteome, and that future proteomic approaches will complement pharmacogenomics and may result in the development of therapeutic agents for neurological disorders.

Recent advances in proteomic technologies offer significant potential not only for gaining a better understanding of brain function, but also for achieving more effective treatments for neurological disorders such as Alzheimer’s disease. A study by Kislinger et al. used multidimensional protein identification technology (MudPIT) and comparative proteomics to analyze organ- and organelle-specific protein expression in the mouse brain. The authors identified 4388 proteins in the brain. Of the 4388 proteins identified, 1336 localized to the cytosol, 1075 to mitochondria, 907 to nuclei, and 1040 to membrane fractions (2). Protein abundance patterns in the brain can also be localized by using imaging mass spectrometry or other high-throughput proteomic strategies (3–6) (see Chapters 4–7). The proteomic profiling of different regions or subcellular compartments of healthy and diseased brains promises new insights into the molecular basis of brain function and the pathogenesis of brain-related diseases (36).

Protein-protein interactions are the basis on which the cellular structure and function are built, and interaction partners are an immediate lead into the biological function that can be exploited for therapeutic purposes. One way to increase the understanding of the mechanisms of brain-related proteins is to identify interacting partners and to establish the location of individual proteins in a cellular pathway. Methods to explore interaction partners of a protein are affinity chromatography in conjunction with mass spectrometry and Western blotting (78), and the use of surface plasmon resonance (SPR) combined with protein identification by mass spectrometry (SPR-MS) (9). An early proteomic analysis of brain multiprotein complexes was the purification and identification of the molecular constituents of the N-methyl-D -aspartate (NMDA) receptor-adhesion protein signaling complexes (10). The NMDAR multiprotein complex (NRC) was shown to comprise 77 proteins organized into receptor, adaptor, signaling, cytoskeletal, and novel proteins, of which 30 are implicated from binding studies and another 19 participate in N-methyl-D-aspartate receptor (NMDAR) signaling (10). Future interaction neuroproteomic studies may reveal how protein-protein interactions contribute to the proliferation of neurodegenerative diseases and how they affect signal transduction pathways.

Proteomics as a global analysis of proteins has opened a wide range of new opportunities to study distinct subcellular structures in the brain. One such structure is the synapse. Chemical synapses are specialized asymmetric cell-cell contacts between neurons. Synaptic transmission involves an intricate network of synaptic proteins that form the molecular machinery responsible for transmitter release, activation of transmitter receptors, and signal transduction cascades. Recently, a number of proteomic studies have been performed on synaptic subdomains, including postsynaptic density (PSD) and synapse protein complexes (11–14). A proteomic analysis of the PSD has been instrumental in the formulation of hypotheses that may explain the molecular basis of neuropathogenic events. A recent study examined the alteration of the hip-pocampal PSD in response to morphine using the isotope coded affinity tag (ICAT)-based relative quantitation approach (13). A total of 102 proteins were identified. Future proteomic studies should yield insights into the structure of the synapse.

The analysis of proteins in the brain is extremely challenging due to the complex nature, heterogeneity, and wide dynamic concentration range (1516). To perform a reliable analysis there are a variety of multidimensional approaches available (1718). There is no universal procedure, but rather components selected to answer the questions important to the researcher. Mass spectrometric detection is an integral part of the multidimensional approach (see Chapters 5 and 6) (19). Prior to embarking on the experiment the researcher must decide on the details such as the origin of the samples, the time point or points needed, and the dynamic range required. Identifying as many proteins as possible in a sample is a common goal (2021). However, in neuroscience many studies are designed from a hypothesis-driven approach focusing on identification of functional protein complexes (22). It is important to remember that any given sample will reflect only a snapshot of the ever-changing biological system.

The good news is these challenges can be addressed by the variety of techniques available. For that reason, multiple stages of separations are usually employed to answer important questions surrounding protein identification, system function, and pathway construction. These stages are orthogonal and can provide clean-up, fractionation, and specificity. In its simplest form, the multidimensional workflow can be defined as target collection, sample preparation (one step or several), separations (one step or several), and detection. The actual workflow will be much more complex. The number of separation stages is determined by the complexity of the sample and the goals of the research. A recent publication by Hoffman et al. compared various multidimensional separation strategies (23). They report that a 1D experiment will yield about 100 protein identifications. A 2D experiment (1-DE gel, 20–40 fractions analyzed by high performance liquid chromatography [HPLC]) and a 3D (isoelectric focusing [IEF], 1-DE gel, 100–150 fractions on HPLC) experiment will yield 800–1000 and more than 2000 proteins, respectively. Lastly, a 4D experiment (depletion, IEF, 1-DE gel, 100–150 fractions on HPLC) yields more than 2800 protein identifications. This chapter focuses on the separation choices available to the neuroscience researcher.

3.3. SAMPLE PREPARATIONS

Sample preparation is very important to a successful separation. Sample preparation methods can range from simple solubilizations to complex extractions. Isolating the protein component of the target tissue, complex, or organelle involves releasing the protein from the cells, breaking protein-lipid interactions, and solubilizing the proteins in a suitable buffer for protein fractionation and analysis (24). Often nucleic acids, lipids, polysaccharides, and a variety of cellular debris are removed prior to proteomic analyses. Cañas et al. reviewed the techniques, strategies, and pitfalls of various sample preparation techniques (25). Unique sample preparations are necessary for all samples from composite sections of brain tissue, specific brain areas, areas of cellular similarity, and subcellular components (organelles, cytoplasm, membranes, target cellular compartments, and protein complexes such as synapto-somes) (112627). Some sample preparation techniques are listed in Table 3.1 and include laser capture microdissection (28–32), centrifugation (33–35), and protein precipitation (36–38).

TABLE 3.1. Techniques for Sample Preparation.

TABLE 3.1

Techniques for Sample Preparation.

Laser capture microdissection (LCM) is an indispensable tool in neuroscience. LCM is a method for isolating highly pure cell populations from a heterogeneous tissue sample under direct microscopic visualization (2831). LCM technology can selectively dissect the cells of interest or can isolate specific cells by cutting away unwanted cells for histologically pure cell populations. LCM has been proven to be capable of isolating specific cells directly from tissue slides with a resolution as small as 3–5 mm in diameter (282931). Espina et al. reviewed current LCM technology, with an emphasis on troubleshooting advice derived from LCM users (29).

Centrifugation and protein precipitation are the most commonly used first steps in sample preparation. Protein precipitation involves the removal of high abundance proteins or sample fractionation (38). One of the limitations of protein precipitation for removal of the high abundance proteins is the simultaneous removal of target proteins, which are bound with the high abundant proteins being removed (such as serum albumin). Centrifugation is also one of the most widely used methods for organelle isolation. Samples are spun at high speeds and the resulting force causes the separation of various cellular components based on their specific density (39). Several other techniques that exploit various physical properties (e.g., electrical charge for free flow electrophoresis) have been applied to study complex organelles. However, the advantage of centrifugation is that it is easily set up and ideally combined with analytical proteomic techniques.

3.4. INTACT PROTEIN OFF-LINE MULTIDIMENSIONAL APPROACHES

Off-line separations are frequently used as the first stage of a multidimensional separation scheme following the physical sample preparation steps. If the sample is extremely complex or if a higher level of specificity is needed, a second off-line step can be added to the experimental design. The researcher is reminded that each added dimension gives more specificity, but there is a potential for protein loss and time will be added to the experiment. The goals of the experiment will define the appropriate number of dimensions needed.

The purpose of the first stage of a multidimensional experiment is to simplify a complex mixture. Brain samples are some of the most complex and are rarely used for direct analysis without prior fractionation. Several fractionation choices of intact proteins are shown in Table 3.2. The off-line approaches include two-dimensional gel electrophoresis (2-DE), one-dimensional gel electrophoresis (1-DE), difference gel electrophoresis (DIGE), off-gel electrophoresis (OGE), and a number of affinity separations that have varying degrees of selectivity.

TABLE 3.2. Off-Line Protein Fractionation Techniques.

TABLE 3.2

Off-Line Protein Fractionation Techniques.

2-DE remains the most common method used to simplify a complex protein mixture (4041) (see Chapter 4 for a detailed discussion of 2D-DIGE-based proteomics). A report on the study of the mouse brain proteome using 2-DE was recently published (42). 2D-DIGE has the advantage of simultaneously providing separations and visualization that can be used in detecting protein expression differences. These gels separate the proteins using two orthogonal physical properties of the proteins. The first dimension separates based on the pI of the protein and different pH ranges can be used. The most general is a broad pH range (pH 3–11). These gels have the advantage of separating a wide range of different proteins. Smaller pH ranges (pH 4–7, pH 6–9, and pH 6–11, among many other possibilities) can be used to provide better resolution of a subset of the proteins in the sample. Larger format gels also provide increased resolution (43). The higher the resolution, the easier it is to visualize differences between the gels in terms of proteins present and establish quantitative expression levels. The trade-off on the narrow pH range strips is that fewer proteins are isolated compared to the wide pH range strip. This could mean that multiple strips must be run, which greatly increases the sample needed and the time required to complete the experiments.

In the second dimension, the proteins are separated based on molecular weight. A Polyacrylamide gel with sodium dodecyl sulfate controls the separation (SDS-PAGE). The combination of 1-DE and 2-DE gels with mass spectrometry has improved the accuracy of protein identification and molecular weight determination. Limitations of the 2-DE technique include the concentration dynamic range, suitable molecular weight range, suitable pI range, and protein hydrophobicity (4445). 2-DE is mostly applicable to the separation of soluble proteins. Not all membrane and other hydrophobic proteins will migrate properly into or separate on a 2-DE gel. There are multiple staining techniques that can be used for visualization, each having different lower detection limits. Proteins with pI’s below 4 and above 10 or with molecular weights less than ~10 kDa or above ~100 kDa are not effectively isolated in 2-DE. Running a 2-DE gel is time consuming and there are a number of steps that follow the separation. After a 2-DE separation, the spots of interest are excised, de-stained, and digested. The time is well spent because the resulting samples are sufficiently purified for direct application to a matrix-assisted laser desorption ionization (MALDI)/MS or liquid chromatography (HPLC)/MS analysis. The primary use of 2-DE gel analysis is the fractionation of many proteins from a heterogeneous mixture. Lubec et al. identified 110 proteins from the microsomal and cytosolic fractions from the human frontal cortex. They also categorized the identified proteins by function (46). It is rare for an experiment to call for all of the spots to be analyzed. Intact proteins can be removed from the gel for further analysis by several methods, including electroblotting onto nitrocellulose or polyvinylidene fluoride (PVDF) membranes (47).

A simpler approach to separation is a 1-DE gel experiment that uses either isoelectric focusing or the SDS-PAGE separation. The gel bands can be fractionated again (48) or excised, de-stained, and in-gel digested as in 2-DE. Because the fractions are complex protein mixtures in 1-DE, additional dimensions are generally performed such as an ion-exchange/HPLC chromatographic method. Stevens et al. identified 112 proteins with high confidence from the rat forebrain by a multidimensional approach which included three stages of sample preparation (1-DE gel, strong cation exchange [SCX], and HPLC) (34). Similar to an intact protein separation, Wu et al. used strong anion exchange HPLC as the first dimension. They subsequently collected the strong anion exchange HPLC fractions and separated them on a 1-DE SDS-PAGE gel. The differentially expressed protein bands were excised and digested. Lastly, the resulting peptides were analyzed by both MALDI and electrospray ionization (ESI) mass spectrometry. This method was applied to the study of the Alzheimer’s disease relevant protease, beta amyloid cleaving enzyme. Wu et al. identified four proteins that seem to be part of the protease functional mechanism (49).

DIGE offers a solution to the reproducibility and differential expression issues of 2-DE (5051). In this method two protein samples are labeled with cyanine dyes (Cy3 or Cy5). The samples are then mixed, separated, and visualized on a single gel (see Chapter 4). The proteins from each sample migrate to the same position for precise identification and the fluorescent signals give a more accurate relative quantification and wider concentration dynamic range for differential comparisons. The DIGE method is still limited to soluble proteins of appropriate pI and molecular weight. Kakisaka et al. used a 4-D experiment which included protein depletion followed by anion exchange fractionation prior to running the DIGE (a quantitative 2-DE gel) and HPLC/MS experiment to identify cancer biomarkers (52). In unfractionated, undepleted plasma, 290 spots were observed in the DIGE experiment. In depleted plasma with five fractions separated by anion exchange, a total of 1200 spots were observed.

Recently, off-gel fractionation (OGE) procedures for the separation of intact proteins by pI has been introduced (53–55). The separation is analogous to an isoelectric focusing 1-DE gel, with the difference that the separated proteins are present in the solution phase at the end of the experiment for direct analysis by HPLC/MS. This is important to the researcher because it provides greater flexibility in the following separation stage choices. Similar to a 1-DE gel experiment, the OGE separation of proteins is not high resolution. As a result, the fractions are amenable to various multidimensional separation strategies including (i) off-line reversed phase chromatography with fraction collection (Figure 3.1) for MALDI detection; (ii) on-line reversed phase chromatography (Figure 3.2) with electrospray MS; (iii) digestion followed by on-line SCX-reversed phase HPLC (RPLC)-MS; (iv) digestion followed by peptide off-gel fractionation; or (v) HPLC-MS. Another positive feature is that the process does not involve the use of ampholytes, which interfere with direct analysis by mass spectrometry. One major limitation of the technique is that visualization is not available. Thus, OGE is not useful for looking at differential protein expression. Also, like 1-DE, the OGE procedure is limited to soluble proteins with pI’s <9 and molecular weights between 10 kDa and 150 kDa.

FIGURE 3.1. Two-dimensional liquid chromatography off-line workflow.

FIGURE 3.1

Two-dimensional liquid chromatography off-line workflow. The first dimension (left) typically includes protein fractionation by ionic strength using a strong cation exchange (SCX) column. This stage fractionates proteins using a continuous salt gradient, (more...)

FIGURE 3.2. Two-dimensional liquid chromatography on-line workflow.

FIGURE 3.2

Two-dimensional liquid chromatography on-line workflow. A fundamental difference in concept is applied to the on-line method. In this case instead of manually “loading” the first fractionation into the loading pump, the analytes are sent (more...)

There are a wide variety of affinity-based separations. In all cases, the key is the reversibility of the binding reaction. The most specific utilize an antibody or other compound which binds a specific target protein or complex (6). For affinity-based separations, the binding substance is immobilized on a surface, the sample is run through the column, and the target protein or complex is retained, washed, and then eluted (56). These affinity methods require knowledge of the target protein and the appropriate affinity material (antibody, RNA or DNA fragment, aptamers, etc.). Burre et al. successfully isolated synaptic vesicles through bead-bound SV2 antibody and identified the associated soluble and membrane proteins (57).

Another, although less specific, fractionation method is the multiple affinity removal system (Figure 3.3). In the case of multiple affinity removal systems, high abundance proteins are retained and the less abundant proteins are collected in the flow-through. There are several sources of columns which remove any where from 6 to the top 100 proteins (58,59). The depletion methods remove the high abundance proteins from plasma or cerebrospinal fluid (CSF) but are not applicable to the analysis of tissues. Running a depletion step effectively increases the number of proteins that can be visualized (Figure 3.4). Figure 3.4A shows a 2-DE gel without depletion. Notice that few protein spots can be seen other than the high abundance proteins. Compare this to Figure 3.4B which shows the enhanced number of detected proteins. A common application for this approach is protein profiling for maximum protein identification or for biomarker discovery. In the depletion method only two fractions are collected (Figure 3.3). The flow-through fraction contains the low abundance proteins while the bound fraction contains the high abundance proteins. The bound fraction is eluted and the column can be used multiple times. The fraction can also be used for downstream analysis. The flow-through fraction, which contains a high number of proteins, is ready for subsequent analysis either as intact proteins or digested peptides. For highly complex samples, a 2-DE gel is frequently performed on the flow-through. A recent study by Burgess et al. used a multidimensional approach to identify proteins released into the CSF following brain injury (60). In this four-stage approach, the CSF was first depleted of high abundance proteins by a multiple affinity column separation. The flow-through from the affinity column separation was subjected to off-gel electrophoresis, followed by 1-DE SDS-PAGE. The protein bands were excised from the gel, digested, separated by HPLC, and identified by MS/MS. Two hundred ninety-nine proteins were identified of which 172 proteins were not previously known to exist in CSF Most of the proteins identified were intracellular, suggesting that they were associated with damaged cells.

FIGURE 3.3. Separation of high abundance and low abundance proteins using a depletion column.

FIGURE 3.3

Separation of high abundance and low abundance proteins using a depletion column. Very complex fluids such as serum and cerebrospinal fluid are extremely rich in small numbers of proteins that account for most of the protein contents in the sample, leaving (more...)

FIGURE 3.4. (a) Serum samples previous to high abundant depletion.

FIGURE 3.4

(a) Serum samples previous to high abundant depletion. (b) Same sample after the high abundant proteins have been depleted.

There are other affinity separations that have more specificity than the high abundance removal system but less specificity than target affinity separations. These columns fractionate a particular class of proteins rather than one specific protein or protein complex (61). These classes include membrane proteins, glycoproteins, phosphoproteins, and others (8). Considerable efforts have gone into developing methods to fractionate membrane and other hydrophobic proteins. Heparin affinity columns show affinity for several classes of proteins including membrane proteins (62). Benzyldimethyl-n-hexadecylammonium chloride-(16 BAC)/SDS-PAGE gel is a cationic detergent used in the first dimension of a 2-DE separation to help solubilize membrane and other hydrophobic proteins. A study by Bierczynska-Krzysik et al. used a modified 16-BAC method and subsequent SDS-PAGE separation to identify 106 proteins from 187 spots (63). This study focused on the identification of insoluble and transmembrane brain proteins. Another technique useful for separating membrane proteins is blue native-PAGE (BN-PAGE). BN-PAGE is a gel-based separation that uses Coomassie blue dye anionic binding to help solubilize the membrane proteins. An advantage of this procedure is that in many cases intact protein complexes may be isolated (64,65).

Glycoproteins are of increasing interest because of the growing use of protein drugs, many of which are glycosylated. Heterogeneity of the glycoproteins complicates the analysis. When working with mixtures of proteins, some of which are glycosylated and others that are not, there is a need to selectively extract these modified proteins. Lectin columns are routinely used for this purpose, and more recently a highly specific affinity method has been described (66,67). The isolated glycoproteins are digested and the glycopeptides are analyzed to identify the site and structure of the glycan (68). Subsequently, the glycopeptides can be digested with exoglycosidase to release the oligosaccharides which are then profiled using a graphitized carbon column and mass spectrometry (69).

Phosphoproteins are of major importance in biological systems as they signal gene expression, cell adhesion, cell cycle, proliferation, and differentiation, to name a few. Many proteins undergo phosphorylation but only at very low levels compared to the non-phosphorylated proteome. In complex mixtures the phosphoproteins are represented at trace levels. Methods for isolating phosphoproteins include chromatofocusing, ion exchange chromatography, immobilized metal affinity chromatography (IMAC), metal oxide affinity chromatography (MOAC), and highly specific antibody immunoaffinity techniques (70,71).

3.5. INTACT PROTEIN ON-LINE MULTIDIMENSIONAL APPROACHES

There are relatively few on-line techniques for the identification of proteins. As a general rule off-line fractionation followed by MS analysis or digestion with further dimensions of chromatography are used. Complications that affect the detection of intact proteins and corresponding accuracy of the identifications include the inherent heterogeneity of biological molecules and the purity of the protein entering the detector. Common detection techniques are UV, laser-induced fluorescence, and MS.

A number of techniques are used for on-line separation of proteins including ion exchange chromatography/reversed phase HPLC (IEC/HPLC), size exclusion/ reversed phase HPLC (SEC/HPLC), size exclusion/capillary electrophoresis (SEC/ CE) (Figures 3.1 and 3.2), reversed phase HPLC with CE or capillary zone electrophoresis (CZE), and others (72). To be an effective on-line technique, the solvents and injection volumes must be compatible and the first dimension run must be slower and have lower peak resolution than the second dimension separation. When this cannot be accomplished it is generally possible to set up an off-line process instead. An additional consideration is that in all cases the on-line technique involves specialized (and often complex) chromatographic setups for both the instrumentation and the separation procedures.

Ion exchange chromatography (IEC)/HPLC is probably the best known application (73). It has been applied to both protein and peptide analyses. Ion exchange and reversed phase chromatography are highly compatible and provide separations based on orthogonal physical parameters. The solvents, concentrations, and injection volumes are easily matched, and commercially available systems make this a viable choice. The IEC run is measured in minutes, sometimes longer if the sample is complex, while the HPLC is fast, measured in seconds. Strong cation exchange (SCX) and strong anion exchange columns have been used in the first dimension in the analysis of intact proteins. Detection is commonly reported using UV and MS.

Capillary electrophoresis is a well-known separation technique for proteins as well as peptides (74,75). The multidimensional technique is somewhat more difficult to perform because the peak volume in HPLC is not compatible with the injection volume in CE. A method of introducing the sample from the HPLC with a stacking injection on the CE can be used to link the two dimensions. Size exclusion as a first dimension with capillary electrophoresis as the second dimension has also been demonstrated. This coupling has the same logistical issues with injection volume and peak volumes as discussed above. In addition, SEC solvent salt concentration must be low to be compatible with the CE. Specialized gating systems work for both 2-D schemes, but they require considerable expertise to construct and operate. Size-exclusion chromatography followed by RP-HPLC (SEC-HPLC) can be used in an on-line experiment and the two dimensions have compatible flow rates and matrices (76). The technique does not handle complex mixtures of proteins very well, is relatively labor intensive, requires custom instrument design which is not commercially available, and involves a multistep process including heart cutting and stop-flow sequences. (Heart cutting refers to isolating unresolved solutes from one separation and taking the mixture to another column where they will be resolved). Additionally, the SEC eluent is not compatible with either UV or MS detection without desalting. Happily, the combination with HPLC provides this necessary desalting step on-line. Advantages of the technique include reproducibility, stability, and improved resolution gained by coupling with the HPLC column (77).

3.6. PEPTIDE MULTIDIMENSIONAL APPROACHES AND QUANTITATIVE ANALYSES

Multidimensional peptide analysis is by far the most common approach used in the identification of proteins. Differential expression is one of the driving forces for many hypothesis-driven proteomic studies. Recent advances in isotopic labeling of peptides provide accurate relative and absolute quantitative results. By applying a multidimensional separation with chemically modified proteins a new era in differential expression studies has arrived (78).

Both on-line and off-line multidimensional schemes have been used successfully. Most of the off-line fractionation techniques for intact proteins take the collected proteins and digest them into peptides for subsequent separations. In 1-DE and 2-DE gel experiments, digestion effectively releases the protein from the gel in the form of peptides. Table 3.3 lists some of the techniques that are used after a digestion step. The advantages of on-line separations are the ability to automate the analysis and to reduce sample loss through liquid handling. The advantages of off-line separation techniques include the removal of column/column incompatibility and the flexibility of the mass spectrometry ionization choice (MALDI or electrospray). Widely used methods include 2-D strong cation exchange-reversed phase HPLC separation (SCX/HPLC or MudPIT), HPLC coupled to capillary electrophoresis (HPLC/CE), and a number of affinity-based separations such as selective phosphopeptide isolation using immobilized metal affinity chromatography (IMAC), and immunoaffinity removal of glycopeptides. Quantitative analyses include isotope coded affinity tag (ICAT), absolute quantitative analysis (AQUA), and isotope tagging reagents for absolute quantification (iTRAQ).

TABLE 3.3. Multidimensional Peptide Analyses and Quantitative Methods.

TABLE 3.3

Multidimensional Peptide Analyses and Quantitative Methods.

The SCX/HPLC technique has been widely used with highly complex peptide mixtures. The advantages of this technique include minimal sample handling, ease of use, and automation. The disadvantage is that identifications may be missed due to the co-elution of multiple peptides. The first dimension (SCX) can be run off-line with the collected fractions run on HPLC/MS or MALDI/MS. A comparison of the on-line/off-line performance is given by Nagele et al. (79). Their results show more proteins identified in the off-line approach (144 off-line, 101 on-line). The cost of the added information is time and labor. The on-line technique is referred to as MudPIT (multidimensional protein identification technology) because rather than selected spots from a gel, the entire complex mixture of peptides is analyzed (80–83). This “shotgun” approach is useful when the goal of the researcher is to identify as many proteins in the sample as possible with as little sample handling and prefractionation as possible (84,85).

The first dimension of the SCX/HPLC method relies on binding the positively charged amines of the peptide with an anionic group bound to the column packing. Peptides carry a positive charge in acidic solutions and this assists in retention. However, the solution should not have a pH less than 3. To elute the peptides, increasing concentrations of ionic strength buffers are injected in a stepwise manner onto the column to disrupt the peptide/sorbent bond. The buffer cations compete for binding sites on the column and as a result loosely bound peptides (singly charged) are eluted first. By increasing the ionic strength of each injection, peptides of increasingly strong positive charge (multiply charged) are eluted.

In the original MudPIT design the SCX phase and the reverse phase can be packed into the same column. A modification to the design has the SCX column and the Reversed phrase column separated by a switching valve (86). In the two-column approach, a small Reversed phrase column is placed between the SCX and HPLC columns via a switching valve to trap peptides as they elute from the SCX column (87). The run times of both designs are similar and both designs can be easily automated. The advantage of the two-column approach is that the SCX fractions are desalted prior to the Reversed phrase analysis. The presence of salts can interfere with the performance of the electro-spray ionization source. After trapping and washing, the small column is placed in line with the reverse phase analytical column. Peptides are eluted from these columns with a gradient of increasing organic mobile phase. The entire experiment involves sequential buffer injections with each injection triggering a full HPLC run. The number of salt injections can be large (from 10–25) and the HPLC run times can be in excess of an hour. In all cases, the on-line analyses will be performed at low flow rates on nanobore columns with electrospray MS/MS.

The advantages of the SCX/HPLC experiment are that the sample preparation is minimized and many proteins can be identified in one experiment. There are no time-consuming gel electrophoresis fractionation steps prior to digestion. Most importantly, the entire SCX/HPLC experiment can be automated. There are several disadvantages to this technique. First, the elution of the peptides from the SCX column is not precise. That is, the same peptide may elute across several of the SCX injections and appear in several of the HPLC runs. This reduces the amount of that peptide in any one HPLC run and thus decreases the sensitivity attainable. Also, most peptides are singly charged, even in acidic solutions, so the first few buffer injections contain the majority of peptides. This results in samples that are too complex. Breakthrough on the trapping column can occur if the sample is too concentrated for the capacity of the column. Di- and tri-peptides are usually not trapped.

When complex mixtures of proteins are digested, the complexity grows by more than 10-fold because each protein yields many peptides. Given this complexity it is not unusual to use an approach with more than two dimensions. A comparison of SCX/HPLC and off-gel/HPLC of complex protein digests provides a good example of the choices on which procedure and how many stages of separation to apply. In a 2-D experiment on Escherichia coli lysate, 900 proteins were identified with high confidence in 24 OGE fractions, and 500 proteins were identified with high confidence in 36 SCX fractions. A comparison between two 3-D experiments on plasma yields similar results. The workflow for the two 3-D experiments on plasma is as follows: depletion (off-line), digestion, SCX (off-line), and HPLC/MS or depletion (off-line), digestion, OGE (off-line), and HPLC/MS. Seventy-nine proteins were identified in 36 SCX salt fractions and 183 proteins were identified in 24 OGE fractions (88).

Reverse phase HPLC can be used in combination with capillary electrophoresis (CE) or capillary zone electrophoresis (CZE) for separation of intact proteins or protein digests. The separation is based on charge or size, respectively. This process works well as either an on-line or off-line technique (89–92). The on-line approach has significant challenges because the optimum HPLC operation and CE operation do not couple in a straightforward manner. The researcher must consider the buffer effects, sample concentrations, and the coordination between the chromatographic run time and the electrophoretic migration time. Fast CZE is ideally suited for the second dimension following an HPLC separation since the entire CZE analysis can be completed in seconds. CE injection volumes are in the nanoliter range; therefore, sampling from the high flow-rate HPLC requires specialized equipment. For most of the analyses the peptides are labeled and detected by fluorescence. This is a high sensitivity detection method and ideally suited for CE and CZE. Although there are significant challenges to setting up the on-line approach, it has the advantages of automation, high peak capacity, and speed. In the off-line approach, fractions are collected at the HPLC and then run in a 1-D fashion on the CE. While this creates a manual bottleneck, it is a simpler design to implement.

Affinity chromatography provides far more specificity in one dimension of the separation. In some cases the affinity is for a particular class of compounds (i.e., phosphopeptides), and in others it is specific to one individual protein. In hypothesis-driven design, there is often the need to isolate a target protein, often at trace levels, from a much more complex mixture. There are a wide variety of methods to preferentially isolate low abundance modified peptides from complex digest mixtures. Phosphoproteins are extremely important in brain research due to their involvement in signaling pathways and other biological activities. To complicate matters, the phosphate group is labile under most MS detection methods. Phosphopeptides can be isolated by immobilized metal affinity chromatography (IMAC) or immunoaffinity columns (22). The immobilized metal affinity columns have Fe(III), Ga(III), or Ti02 metals bound to the sorbent. The columns can be used to isolate phosphopeptides or phosphoproteins. This on-line technique is compatible with HPLC/MS. Detection limits for proteins are in the low picomolar range while peptides can be detected in the femtomolar range (93). One limitation of the technique is its lack of specificity because histidine-containing peptides also bind to the IMAC columns. Immunoaffinity columns for phosphopeptide isolation provide greater selectivity but are less robust and require highly specialized conditions for optimal performance. Several antibodies are used including anti-phosphoserine, -tyrosine, and -threonine.

Glycosylated proteins are implicated in many biological processes. The importance of the heterogeneity of the glycans has been found to be significant. Lectin affinity columns have been used to capture glycoproteins and are also very effective in enriching glycopeptides (94). The fractions can be collected for off-line analysis by MALDI or coupled on-line to HPLC with UV or MS detection.

An important area of proteomics involves the need for quantification (95). Most proteomics technologies to date can measure relative quantification. The search for biomarkers or the extent of post-translational modification necessitates the ability to accurately express the changes in the amount of protein in the context of a second cellular state or control sample. Table 3.3 lists a few of the quantitative approaches that may be used in proteomic studies. While not technically an added dimension of separation, quantification represents an important added dimension of information that can be included in to a multidimensional experiment; it also may obviate the need to use 2-D gels to look for differentially expressed proteins.

ICAT is one of the first of these quantitative approaches (96). This technique compares the relative quantity of proteins in two different samples for expression analyses. In the method, each protein sample is labeled with isotopically different affinity tags (C13- and D-linked biotin), which bind specifically to cysteine residues. Once the labeling is complete, the two samples are combined and digested with trypsin. The mixture is run through an avidin column and the cysteine-containing peptides are retained. The retained peptides are eluted and analyzed by reversed phase HPLC/MS. The major advantage to this technique is the ability to get accurate information on the relative quantity of the same peptides from each sample. A second advantage is that the peptide mixture is greatly simplified. The simplified peptide mixture improves the probability that spectra from low abundance peptides can be detected because there are fewer co-eluting species. In essence, the sample is enriched. However, there are a couple of limitations to the technique. Most importantly, proteins without cysteine are not captured. When a protein has few cysteine residues, identification is heavily dependent on locating those cysteines. It is likely that some of these proteins may be missed or erroneously identified. Lastly, the procedure is complicated and labor intensive. The technique requires three manual offline steps (i.e., digestion, biotin labeling, avidin chromatography) followed by ion exchange and HPLC/MS.

A technique termed AQUA, “absolute quantification,” uses a synthetic copy of a target peptide that has been isotopically labeled for use as an internal standard. The internal standard is mixed into the protein mixture during digestion and a selected monitoring MS experiment is performed to measure the absolute quantity of that peptide, and hence the protein. The procedure is used for differential expression analysis of known target proteins (97–99). The benefits of this method are the absolute quantitative results and the ability to examine the extent of post-translational modifications. Cheng et al. used a multidimensional approach to identify and quantify differentially expressed proteins associated with the postsynaptic density of rat forebrain and cerebellum (100). In the first dimension ICAT labeling was used to identify differentially expressed proteins. This experiment resulted in the identification of 296 proteins, of which 43 showed a statistically significant expression difference between the two brain regions. Appropriate peptides from 32 of those proteins were synthesized and used as internal standards for absolute quantification using the AQUA method.

A third labeling technique, iTRAQ, uses isobaric tags (methylpiperazine acetic acid N-hydroxysuccinimide ester), which react with primary amino groups (N-terminus and lysine residues). As a result all proteins are labeled. Furthermore, up to four samples can be labeled providing multiple sample comparisons in a single run. The analysis is performed by HPLC/MS/MS and the relative abundances can be calculated (101). One advantage of this technique is that no extra chromatographic step for isolation of the derivatized peptides is necessary (as in ICAT). Also, with all proteins having multiple labeled sites, there is greater confidence in the protein identifications because multiple peptides may be found for each protein. Lastly, with more peptides for each protein the relative area results can be evaluated for consistency. Ogata et al. used iTRAQ to quantitatively examine protein differences between male and female CSF (102).

3.7. CONCLUSIONS AND FUTURE DIRECTIONS

Currently there are two main research classes of proteomics applied in neuroscience: profiling and functional proteomics. Profiling encompasses maximum protein identifications and differential expression either with total protein identification or biomarker identification. Kobeissy et al. used a multidimensional approach to identify biomarkers of traumatic brain injury for potential diagnosis and treatment (103). Functional proteomics focuses on understanding the structure of protein clusters of known function. The identified proteins can then be classified as participating in that function by association (104–107) (see also Chapter 9). Some of the identified proteins in the complex may already be classified in that function. This confluence of information helps confirm existing hypotheses and builds a more detailed understanding of biological interactions.

Both of these research goals, as well as others such as genomic/proteomic correlations, occupy important areas of active research (108109). Thus, all of the multidimensional proteomic technologies are useful and valid provided they are used in a way that maximizes the advantages and minimizes the limitations. To be successful the experiment requires intelligent design, which includes a complete workflow definition including sample choice, sample preparation, separations, detection, protein identification, quantification, and biological significance.

Reproducibility of results remains a concern, in part due to factors related to the sample preparation and analysis as well as human factors such as disease progression, differences in individuals, age, sex, and many other environmental and physical factors. The large amount of data generated also present a challenge, but numerous search engines are available (110–112). A number of new software programs are available that aid in the statistical evaluation of data (113114) and pathway analysis (115116).

Regardless of the current limitations, protein identification, protein expression analysis, and biomarker discovery are now firmly entrenched in neuroscience. Sample preparation remains a major area where improvements must be made to deal with the complexities of gathering reproducible samples from brain tissues. The need for consistency and precision is the cornerstone of making accurate conclusions. It is well recognized that reducing the complexity of a sample through fractionation is a key component of the experimental design. Fractionation yields more protein identifications, better expression comparisons, and greater access to lower abundance proteins. On-line multidimensional separations continue to improve in terms of speed, sensitivity, reproducibility, and automation.

There are many potential applications for proteomics in neuroscience. Such applications include the determination of the brain proteome, the identification of disease-related proteins implicated in neurological disorders, comparative protein expression profiling, post-translational modification (PTM) profiling, and mapping protein-protein interactions. All of these applications have strengths and limitations. One of the major challenges ahead for the neuroscience researchers is to determine the most appropriate applications of the various proteomic platforms for their research goals.

High-throughput proteomic technologies specifically for the identification and characterization of protein modifications have to be created. Post-translational modifications are features of proteins that affect activity and localization. Modified proteins activate, target, and have important roles in determining turnover and enzyme activity. Current high-throughput technologies such as automatic peptide mass fingerprinting data ignore unknown or modified peptides. This may result in the loss of many interesting proteins. High-throughput proteomic technologies with greater sensitivity also need to be developed to identify low abundance proteins as protein amplification methods are not available. This is very important for the applicability of proteomics in clinical settings. Proteomics technologies are advancing rapidly and addressing fundamental questions in neuroscience involving brain function and behavior.

ABBREVIATIONS

2-dimensional gel electrophoresis (2-DE)

Absolute quantitative analysis (AQUA)

Benzyldimethyl-n-hexadecylammonium chloride (16 BAC)

Blue native-polyacrylamide gel electrophoresis (BN-PAGE)

Capillary electrophoresis (CE)

Capillary zone electrophoresis (CZE)

Cerebrospinal fluid (CSF)

Difference in gel electrophoresis (DIGE)

Electro-spray ionization (ESI)

High performance liquid chromatography (HPLC)

Immobilized metal affinity columns (IMAC)

Ion exchange chromatography (IEC)

Isotope coded affinity tag (ICAT)

Isotope tagging reagents for absolute quantification (iTRAQ)

Laser capture microdissection (LCM)

Mass spectrometry (MS)

Matrix assisted laser desorption ionization (MALDI)

Metal oxide affinity chromatography (MOAC)

Multidimensional protein identification technology (MudPIT)

N-methyl-D-aspartate (NMDA)

N-methyl-D-aspartate receptor (NMDAR)

Off-gel electrophoresis (OGE)

Postsynaptic density (PSD)

Post-translational modification (PTM)

Reversed phase (RP)

Reversed phase liquid chromatography (RPLC)

Size exclusion chromatography (SEC)

Sodium dodecyl sulfate Polyacrylamide gel electrophoresis (SDS-PAGE)

Strong cation exchange (SCX)

Tandem mass spectrometry (MS/MS)

REFERENCES

1.
Blume-Jensen P., Hunter T. Oncogenic kinase signalling. Nature. 2001;411:355–65. [PubMed: 11357143]
2.
Kislinger T., Cox B., Kannan A., et al. Global survey of organ and organelle protein expression in mouse: Combined proteomic and transcriptomic profiling. Cell. 2006;125:173–86. [PubMed: 16615898]
3.
Johnson M. D., Floyd J. L., Caprioli R. M. Proteomics in diagnostic neuropathology. Journal of Neuropathology and Experimental Neurology. 2006;65:837–45. [PubMed: 16957577]
4.
Laurent C., Levinson D. R., Schwartz S.A., et al. Direct profiling of the cerebellum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry: A methodological study in postnatal and adult mouse. Journal of Neuroscience Research. 2005;81:613–21. [PubMed: 16035104]
5.
Petyuk V. A., Qian W. J., Chin M. H., et al. Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chroma-tography-mass spectrometry. Genome Research. 2007;17:328–36. [PMC free article: PMC1800924] [PubMed: 17255552]
6.
Schwartz S.A., Weil R. J., Thompson R. C., et al. Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry. Cancer Research. 2005;65:7674–81. [PubMed: 16140934]
7.
Joseph T., Lee T. L., Ning C., et al. Identification of mature nocistatin and nociceptin in human brain and cerebrospinal fluid by mass spectrometry combined with affinity chromatography and HPLC. Peptides. 2006;27:122–30. [PubMed: 16043263]
8.
Lee W. C., Lee K. H. Applications of affinity chromatography in proteomics. Analytical Biochemistry. 2004;324:1–10. [PubMed: 14654038]
9.
Buijs J., Franklin G. C. SPR-MS in functional proteomics. Brief Funct Genomic Proteomic. 2005;4:39–47. [PubMed: 15975263]
10.
Husi H., Ward M. A., Choudhary J. S., Blackstock W. P., Grant S. G. Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nature Neuroscience. 2000;3:661–69. [PubMed: 10862698]
11.
Abul-Husn N. S., Devi L. A. Neuroproteomics of the synapse and drug addiction. Journal of Pharmacology and Experimental Therapeutics. 2006;318:461–68. [PubMed: 16644901]
12.
Li K. W., Hornshaw M. P., Van der Schors R. C., et al. Proteomics analysis of rat brain postsynaptic density—Implications of the diverse protein functional groups for the integration of synaptic physiology. Journal of Biological Chemistry. 2004;279:987–1002. [PubMed: 14532281]
13.
Moron J. A., Abul-Husn N. S., Rozenfeld R., et al. Morphine administration alters the profile of hippocampal postsynaptic density-associated proteins—A proteomics study focusing on endocytic proteins. Molecular and Cellular Proteomics. 2007;6:29–42. [PubMed: 17028301]
14.
Piccoli G., Verpelli C., Tonna N., et al. Proteomic analysis of activity-dependent synaptic plasticity in hippocampal neurons. Journal of Proteome Research. 2007;6:3203–15. [PubMed: 17622166]
15.
Choudhary J., Grant S. G. N. Proteomics in postgenomic neuroscience: The end of the beginning. Nature Neuroscience. 2004;7:440–45. [PubMed: 15114355]
16.
Marcus K., Schmidt O., Schaefer H., et al. Proteomics—Application to the brain. International Review of Neurobiology. 2004;61:285–311. [PubMed: 15482819]
17.
Garbis S., Lubec G., Fountoulakis M. Limitations of current proteomics technologies. Journal of Chromatography A. 2005;1077:1–18. [PubMed: 15988981]
18.
Hu L. H., Ye M. D., Jiang X. G., Feng S., Zou H. F. Advances in hyphenated analytical techniques for shotgun proteome and peptidome analysis—A review. Analytica Chimica Acta. 2007;598:193–204. [PubMed: 17719892]
19.
Aebersold R., Mann M. Mass spectrometry-based proteomics. Nature. 2003;422:198–207. [PubMed: 12634793]
20.
Le Bihan T., Duewel H. S., Figeys D. On-line strong cation exchange mu-HPLC-ESI-MS/MS for protein identification and process optimization. Journal of the American Society for Mass Spectrometry. 2003;14:719–27. [PubMed: 12837593]
21.
Liu X. Y., Valentine S. J., Plasencia M. D., et al. Mapping the human plasma proteome by SCX-LC-IMS-MS. Journal of the American Society for Mass Spectrometry. 2007;18:1249–64. [PMC free article: PMC2195767] [PubMed: 17553692]
22.
Williams K., Wu T., Colangelo C., Nairn A. C. Recent advances in neuroproteomics and potential application to studies of drug addiction. Neuropharmacology. 2004;47:148–66. [PubMed: 15464134]
23.
Hoffman S. A., Joo W. A., Echan L. A., Speicher D. W. Higher dimensional (Hi-D) separation strategies dramatically improve the potential for cancer biomarker detection in serum and plasma. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;849:43–52. [PubMed: 17140865]
24.
Bodzon-Kulakowska A., Bierczynska-Krzysik A., Dylag T., et al. Methods for samples preparation in proteomic research. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;849:1–31. [PubMed: 17113834]
25.
Carlas B., Pineiro C., Calvo E., Lopez-Ferrer D., Gallardo J. M. Trends in sample preparation for classical and second generation proteomics. Journal of Chromatography A. 2007;1153:235–58. [PubMed: 17276441]
26.
Moron J. A., Devi L. A. Use of proteomics for the identification of novel drug targets in brain diseases. Journal of Neurochemistry. 2007;102:306–15. [PubMed: 17419802]
27.
Tannu N. S., Hemby S. E. Methods for proteomics in neuroscience. In: Hemby S. E., Bahn S., editors. Progress in brain research. Amsterdam: Elsevier BV; 2006. pp. 41–82. [PubMed: 17027691]
28.
Banks R. E., Dunn M. J., Forbes M. A., et al. The potential use of laser capture microdissection to selectively obtain distinct populations of cells for proteomic analysis—Preliminary findings. Electrophoresis. 1999;20:689–700. [PubMed: 10344234]
29.
Espina V., Wulfkuhle J. D., Calver V. S., et al. Laser-capture microdissection. Nature Protocols. 2006;1:586–603. [PubMed: 17406286]
30.
Jain K. K. Application of laser capture microdissection to proteomics. Laser Capture Microscopy and Microdissection. 2002;356:157–67. [PubMed: 12418196]
31.
Mouledous L., Hunt S., Harcourt R., et al. Navigated laser capture microdissection as an alternative to direct histological staining for proteomic analysis of brain samples. Proteomics. 2003;3:610–15. [PubMed: 12748941]
32.
Wilson K. E., Marouga R., Prime J. E., et al. Comparative proteomic analysis using samples obtained with laser microdissection and saturation dye labelling. Proteomics. 2005;5:3851–58. [PubMed: 16145713]
33.
Pan S., Shi M., Jin J. H., et al. Proteomics identification of proteins in human cortex using multidimensional separations and MALDI tandem mass spectrometer. Molecular and Cellular Proteomics. 2007;6:1818–23. [PubMed: 17644759]
34.
Stevens S. M., Zharikova A. D., Prokai L. Proteomic analysis of the synaptic plasma membrane fraction isolated from rat forebrain. Molecular Brain Research. 2003;117:116–28. [PubMed: 14559145]
35.
Taylor S. W., Fahy E., Ghosh S. S. Global organellar proteomics. Trends in Biotechnology. 2003;21:82–88. [PubMed: 12573857]
36.
Chang M. S., Ji Q., Zhang J., El-Shourbagy T. A. Historical review of sample preparation for chromatographic bioanalysis: Pros and cons. Drug Development Research. 2007;68:107–33.
37.
Chen J. Z., Anderson M., Misek D. E., Simeone D. M., Lubman D. M. Characterization of apolipoprotein and apolipoprotein precursors in pancreatic cancer serum samples via two-dimensional liquid chromatography and mass spectrometry. Journal of Chromatography A. 2007;1162:117–25. [PMC free article: PMC2692198] [PubMed: 17434173]
38.
Jiang L., He L., Fountoulakis M. Comparison of protein precipitation methods for sample preparation prior to proteomic analysis. Journal of Chromatography A. 2004;1023:317–20. [PubMed: 14753699]
39.
Huber L. A., Pfaller K., Vietor I. Organelle proteomics—Implications for subcellular fractionation in proteomics. Circulation Research. 2003;92:962–68. [PubMed: 12750306]
40.
Gorg A., Weiss W., Dunn M. J. Current two-dimensional electrophoresis technology for proteomics. Proteomics. 2004;4:3665–85. [PubMed: 15543535]
41.
Rabilloud T. Two-dimensional gel electrophoresis in proteomics: Old, old fashioned, but it still climbs up the mountains. Proteomics. 2002;2:3–10. [PubMed: 11788986]
42.
Wang J., Gu Y., Wang L. H., et al. HUPOBPP pilot study: A proteomics analysis of the mouse brain of different developmental stages. Proteomics. 2007;7:4008–15. [PubMed: 17922513]
43.
Levenson R. M., Anderson G. M., Cohn J. A., Blackshear P. J. Giant two-dimensional gel electrophoresis: Methodological update and comparison with intermediate-format gel systems. Electrophoresis. 2005;11:269–79. [PubMed: 1693119]
44.
Gygi S. P., Corthals G. L., Zhang Y., Rochon Y., Aebersold R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proceedings of the National Academy of Sciences of the United States of America. 2000;97:9390–95. [PMC free article: PMC16874] [PubMed: 10920198]
45.
Whitelegge J. Tandem mass spectrometry of integral membrane proteins for top-down proteomics. Trac-Trends in Analytical Chemistry. 2005;24:576–82.
46.
Lubec G., Krapfenbauer K., Fountoulakis M. Proteomics in brain research: Potentials and limitations. Progress in Neurobiology. 2003;69:193–211. [PubMed: 12758110]
47.
Cooper J. W., Gao J., Lee C. S. Electronic gel protein transfer and identification using matrix-assisted laser desorption/ionization-mass spectrometry. Electrophoresis. 2004;25:1379–85. [PubMed: 15174060]
48.
Gorg A., Boguth G., Kopf A., et al. Sample prefractionation with Sephadex isoelectric focusing prior to narrow pH range two-dimensional gels. Proteomics. 2002;2:1652–57. [PubMed: 12469334]
49.
Wu S., Tang X. T., Siems W. E., Bruce J. E. A hybrid LC-Gel-MS method for proteomics research and its application to protease functional pathway mapping. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2005;822:98–111. [PubMed: 15994140]
50.
Righetti P. G., Castagna A., Antonucci E., et al. Critical survey of quantitative proteomics in two-dimensional electrophoretic approaches. Journal of Chromatography A. 2004;1051:3–17. [PubMed: 15532550]
51.
Unlu M., Morgan M. E., Minden J. S. Difference gel electrophoresis: A single gel method for detecting changes in protein extracts. Electrophoresis. 1997;18:2071–77. [PubMed: 9420172]
52.
Kakisaka T., Kondo T., Okano T., et al. Plasma proteomics of pancreatic cancer patients by multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis (2D-DIGE): Up-regulation of leucine-rich alpha-2-glycoprotein in pancreatic cancer. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;852:257–67. [PubMed: 17303479]
53.
Heller M., Michel P. E., Morier P., et al. Two-stage Off-Gel (TM) isoelectric focusing: Protein followed by peptide fractionation and application to proteome analysis of human plasma. Electrophoresis. 2005;26:1174–88. [PubMed: 15704250]
54.
Michel P. E., Crettaz D., Morier P., et al. Proteome analysis of human plasma and amniotic fluid by Off-Gel (TM) isoelectric focusing followed by nano-LC-MS/MS. Electrophoresis. 2006;27:1169–81. [PubMed: 16470776]
55.
Ros A., Faupel M., Mees H., et al. Protein purification by Off-Gel electrophoresis. Proteomics. 2002;2:151–56. [PubMed: 11840561]
56.
Rigaut G., Shevchenko A., Rutz B., et al. A generic protein purification method for protein complex characterization and proteome exploration. Nature Biotechnology. 1999;17:1030–32. [PubMed: 10504710]
57.
Burre J., Zimmermann H., Volknandt W. Immunoisolation and subfraction-ation of synaptic vesicle proteins. Analytical Biochemistry. 2007;362:172–81. [PubMed: 17266918]
58.
Maccarrone G., Milfay D., Birg I., et al. Mining the human cerebrospinal fluid proteome by immunodepletion and shotgun mass spectrometry. Electrophoresis. 2004;25:2402–12. [PubMed: 15274023]
59.
Pieper R., Gatlin C. L., Makusky A. J., et al. The human serum proteome: Display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins. Proteomics. 2003;3:1345–64. [PubMed: 12872236]
60.
Burgess J. A., Lescuyer P., Hainard A., et al. Identification of brain cell death associated proteins in human post-mortem cerebrospinal fluid. Journal of Proteome Research. 2006;5:1674–81. [PubMed: 16823975]
61.
Lescuyer P., Hochstrasser D. E., Sanchez J. C. Comprehensive proteome analysis by chromatographic protein prefractionation. Electrophoresis. 2004;25:1125–35. [PubMed: 15095456]
62.
Iida T., Kamo M., Uozumi N., Inui T., Imai K. Further application of a two-step heparin affinity chromatography method using divalent cations as eluents: Purification and identification of membrane-bound heparin binding proteins from the mitochondrial fraction of HL-60 cells. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2005;823:209–12. [PubMed: 16019269]
63.
Bierczynska-Krzysik A., Kang S. U., Silberrring J., Lubec G. Mass spec-trometrical identification of brain proteins including highly insoluble and transmembrane proteins. Neurochemistry International. 2006;49:245–55. [PubMed: 16616972]
64.
Hooker B. S., Bigelow D. J., Lin C. T. Methods for mapping of interaction networks involving membrane proteins. Biochemical and Biophysical Research Communications. 2007;363:457–61. [PubMed: 17897627]
65.
Sanders P. R., Cantin G. T., Greenbaum D. C., et al. Identification of protein complexes in detergent-resistant membranes of Plasmodium falciparum schizonts. Molecular and Biochemical Parasitology. 2007;154:148–57. [PubMed: 17553576]
66.
Luque-Garcia J. L., Neubert T. A. Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry. Journal of Chromatography A. 2007;1153:259–76. [PubMed: 17166507]
67.
Monzo A., Bonn G. K., Guttman A. Lectin-immobilization strategies for affinity purification and separation of glycoconjugates. Trac-Trends in Analytical Chemistry. 2007;26:423–32.
68.
Wuhrer M., Catalina M. I., Deelder A. M., Hokke C. H. Glycoproteomics based on tandem mass spectrometry of glycopeptides. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;849:115–28. [PubMed: 17049937]
69.
Itoh S., Kawasaki N., Hashii N., et al. N-linked oligosaccharide analysis of rat brain Thy-1 by liquid chromatography with graphitized carbon column/ion trap-Fourier transform ion cyclotron resonance mass spectrometry in positive and negative ion modes. Journal of Chromatography A. 2006;1103:296–306. [PubMed: 16364349]
70.
Li Y., Liu Y., Tang J., Lin H., Yao N., Shen X., Deng C., Yang P., Zhang X. Fe3O4@ A12O3 magnetic core-shell microspheres for rapid and highly specific capture of phosphopeptides with mass spectrometry analysis. J.Chromatogr. A. 2007;1172:57–71. [PubMed: 17936290]
71.
Schmidt S. R., Schweikart E., Andersson M. E. Current methods for phosphoprotein isolation and enrichment. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;849:154–62. [PubMed: 17035107]
72.
Issaq H.J. The role of separation science in proteomics research. Electrophoresis. 2001;22:3629–38. [PubMed: 11699900]
73.
Wagner K., Racaityte K., Unger K. K., et al. Protein mapping by two-dimensional high performance liquid chromatography. Journal of Chromatography A. 2000;893:293–305. [PubMed: 11073299]
74.
Larmann J. P., Lemmo A. V., Moore A. W., Jorgenson J. W. 2-Dimensional separations of peptides and proteins by comprehensive liquid-chromatography capillary-electrophoresis. Electrophoresis. 1993;14:439–47. [PubMed: 8354227]
75.
Stroink T., Ortiz M. C., Bult A., et al. On-line multidimensional liquid chromatography and capillary electrophoresis systems for peptides and proteins. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2005;817:49–66. [PubMed: 15680788]
76.
Opiteck G.J., Ramirez S.M., Jorgenson J.W., Moseley M.A. Comprehensive two-dimensional high-performance liquid chromatography for the isolation of overexpressed proteins and proteome mapping. Analytical Biochemistry. 1998;258:349–61. [PubMed: 9570851]
77.
Carol J., Gorseling M. C. J. K., Jong C. E d., et al. Determination of denaturated proteins and biotoxins by on-line size-exclusion chromatography-digestion-liquid chromatography-electrospray mass spectrometry. Analytical Biochemistry. 2005;246:150–57. [PubMed: 16185643]
78.
Ye M. L., Jiang X. G., Feng S., Tian R. J., Zou H. E. Advances in chromatographic techniques and methods in shotgun proteome analysis. Trac-Trends in Analytical Chemistry. 2007;26:80–84.
79.
Nagele E., Vollmer M., Horth P., Vad C. 2D-HPLC/MS techniques for the identification of proteins in highly complex mixtures. Expert Review of Proteomics. 2004;1:37–46. [PubMed: 15966797]
80.
Delahunty C., Yates J. R. Protein identification using 2D-LC-MS/MS. Methods. 2005;35:248–55. [PubMed: 15722221]
81.
McDonald W. H., Ohi R., Miyamoto D. T., Mitchison T. J., Yates J. R. Comparison of three directly coupled HPLC MS/MS strategies for identification of proteins from complex mixtures: Single-dimension LC-MS/MS, 2-phase MudPIT, and 3-phase MudPIT. International Journal of Mass Spectrometry. 2002;219:245–51.
82.
Washburn M. P., John R., Yates I. New methods of proteome analysis: Multidimensional chromatography and mass spectrometry. Trends in Biotechnology. 2000;18 Suppl 1:27–30.
83.
Wolters D. A., Washburn M. P., Yates J. R. An automated multidimensional protein identification technology for shotgun proteomics. Analytical Chemistry. 2001;73:5683–90. [PubMed: 11774908]
84.
Swanson S. K., Washburn M. P. The continuing evolution of shotgun proteomics. Drug Discovery Today. 2005;10:719–25. [PubMed: 15896685]
85.
Washburn M. P., Wolters D., Yates J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotechnology. 2001;19:242–47. [PubMed: 11231557]
86.
Millea K. A., Kass I. J., Cohen S. A., et al. Evaluation of multidimensional (ion-exchange/reversed-phase) protein separations using linear and step gradients in the first dimension. Journal of Chromatography A. 2005;1079:287–98. [PubMed: 16038315]
87.
Wang Y., Zhang J., Liu C. L., Gu X., Zhang X. M. Nano-flow multidimensional liquid chromatography with electrospray ionization time-of-flight mass spectrometry for proteome analysis of hepatocellular carcinoma. Analytica Chimica Acta. 2005;530:227–35.
88.
Miller C. A., Hoerth P. Off-gel electrophoresis vs. SCX fractionation for the HPLC/MS analysis of human serum. Paper presented at the Human Proteome Organization. 2006
89.
Issaq H. J., Chan K. C., Janini G. M., Muschik G. M. A simple two-dimensional high performance liquid chromatography high performance capillary electrophoresis set-up for the separation of complex mixtures. Electrophoresis. 1999;20:1533–37. [PubMed: 10424477]
90.
Issaq H. J., Chan K. C., Liu C. S., Li Q. B. Multidimensional high performance liquid chromatography—Capillary electrophoresis separation of a protein digest: An update. Electrophoresis. 2001;22:1133–35. [PubMed: 11358138]
91.
Moore A. W., Jorgenson J. W. Rapid comprehensive 2-dimensional separations of peptides via RPLC optically gated capillary zone electrophoresis. Analytical Chemistry. 1995;67:3448–55. [PubMed: 8686893]
92.
Moore A. W., Jorgenson J. W. Comprehensive 3-dimnensional separation of peptides using size-exclusion chromatography reversed-phase liquid-chromatography optically gated capillary zone electrophoresis. Analytical Chemistry. 1995;67:3456–63. [PubMed: 8686894]
93.
Shi Y., Xiang R., Horvath C., Wilkins J. A. The role of liquid chromatography in proteomics. Journal of Chromatography A. 2004;1053:27–36. [PubMed: 15543969]
94.
Xiong L., Regnier E. E. Use of a lectin affinity selector in the search for unusual glycosylation in proteomics. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2002;782:405–18. [PubMed: 12458022]
95.
Fenselau C. A review of quantitative methods for proteomic studies. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;855:14–20. [PubMed: 17161666]
96.
Gygi S. P., Rist B., Gerber S. A., et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnology. 1999;17:994–99. [PubMed: 10504701]
97.
Gerber S. A., Rush J., Stemman O., Kirschner M. W., Gygi S. P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proceedings of the National Academy of Sciences of the United States of America. 2003;100:6940–45. [PMC free article: PMC165809] [PubMed: 12771378]
98.
Kirkpatrick D. S., Gerber S. A., Gygi S. P. The absolute quantification strategy: A general procedure for the quantification of proteins and post-translational modifications. Methods. 2005;35:265–73. [PubMed: 15722223]
99.
Mayya V., Rezual K., Wu L.E., Fong M. B., Han D. K. Absolute quantification of multisite phosphorylation by selective reaction monitoring mass spectrometry—Determination of inhibitory phosphorylation status of cyclin-dependent kinases. Molecular and Cellular Proteomics. 2006;5:1146–57. [PubMed: 16546994]
100.
Cheng D. M., Hoogenraad C. C., Rush J., et al. Relative and absolute quantification of postsynaptic density proteome isolated from rat forebrain and cerebellum. Molecular and Cellular Proteomics. 2006;5:1158–70. [PubMed: 16507876]
101.
Ross P. L., Huang Y. L. N., Marchese J. N., et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Molecular and Cellular Proteomics. 2004;3:1154–69. [PubMed: 15385600]
102.
Ogata Y., Charlesworth M. C., Higgins L., et al. Differential protein expression in male and female human lumbar cerebrospinal fluid using iTRAQ reagents after abundant protein depletion. Proteomics. 2007;7:3726–34. [PubMed: 17853512]
103.
Kobeissy E. H., Ottens A. K., Zhang Z. Q., et al. Novel differential neuroproteomics analysis of traumatic brain injury in rats. Molecular and Cellular Proteomics. 2006;5:1887–98. [PubMed: 16801361]
104.
Grant S. G. N., Blackstock W. R. Proteomics in neuroscience: From protein to network. Journal of Neuroscience. 2001;21:8315–18. [PubMed: 11606617]
105.
Kim S. I., Voshol H., van Oostrum J., et al. Neuroproteomics: Expression profiling of the brain’s proteomes in health and disease. Neurochemical Research. 2004;29:1317–31. [PubMed: 15176488]
106.
Vercauteren E. G. G., Bergeron J. J. M., Vandesande E., Arckens L., Quirion R. Proteomic approaches in brain research and neuropharmacology. European Journal of Pharmacology. 2004;500:385–98. [PubMed: 15464047]
107.
Virginia Espina J. D. W., Valerie S., Calvert, Amy VanMeter, Weidong Zhou, George Coukos, David H., Geho, Emanuel E., Petricoin, Liotta Lance A. Laser-capture microdissection. Nature Protocols. 2006;1:586–603. [PubMed: 17406286]
108.
Grant S. G. N. Systems biology in neuroscience: bridging genes to cognition. Current Opinion in Neurobiology. 2003;13:577–82. [PubMed: 14630221]
109.
Tyers M., Mann M. From genomics to proteomics. Nature. 2003;422:193–97. [PubMed: 12634792]
110.
Kapp E. A., Schutz F., Connolly L. M., et al. An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis. Proteomics. 2005;5:3475–90. [PubMed: 16047398]
111.
Moulder R., Filen J. J., Salmi J., et al. A comparative evaluation of software for the analysis of liquid chromatography-tandem mass spectrometry data from isotope coded affinity tag experiments. Proteomics. 2005;5:2748–60. [PubMed: 15952233]
112.
Zhang W. Z., Chait B. T. Profound: An expert system for protein identification using mass spectrometric peptide mapping information. Analytical Chemistry. 2000;72:2482–89. [PubMed: 10857624]
113.
Hartler J., Thallinger G. G., Stocker G., et al. MASPECTRAS: A platform for management and analysis of proteomics HPLC-MS/MS data. BMC Bioinformatics. 2007;8:197. [PMC free article: PMC1906842] [PubMed: 17567892]
114.
Sriyam S., Sinchaikul S., Tantipaiboonwong P., et al. Enhanced detectability in proteome studies. Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences. 2007;849:91–104. [PubMed: 17140866]
115.
Husi H., Grant S.G. Construction of a protein-protein interaction database (PPID) for synaptic biology. In: Kötter R., editor. Neuroscience databases: A pratical guide. Boston/Dordrecht/London: Kluwer Academic Publishers; 2002. pp. 51–62.
116.
Kitano H. Computational systems biology. Nature. 2002;420:206–10. [PubMed: 12432404]
Copyright © 2010 by Taylor and Francis Group, LLC.
Bookshelf ID: NBK56015PMID: 21882446
PubReader format: click here to try

Views

  • PubReader
  • Print View
  • Cite this Page

Other titles in this collection

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to pubmed

Related citations in PubMed

See reviews...See all...

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...