NEUROPROTEOMICS IN NEUROTRAUMA
Andrew K Ottens,1,2,3 Firas H Kobeissy,1,2,4 Erin C Golden,1,2,3 Zhiqun Zhang,1,2,3 William E Haskins,1,2,3
Su-Shing Chen,1,5 Ronald L Hayes,2,3,4 Kevin K W Wang,1,2,3,4 Nancy D Denslow1,6
1Center of Neuroproteomics and Biomarkers Research, McKnight Brain Institute, University of Florida,
2Center for Traumatic Brain Injury Studies, McKnight Brain Institute, University of Florida, Gainesville Florida
3Department of Neuroscience, University of Florida, Gainesville Florida
4Department of Psychiatry, University of Florida, Gainesville Florida
5Computing and Information Science Engineering, University of Florida, Gainesville Florida
6Department of Physiological Sciences and Department of Biochemistry and Molecular Biology, University of
Correspondence to: Andrew K Ottens, PO Box 100244, Gainesville, FL 32610-0244. E-mail:
Table of Contents:
A. Traumatic Brain Injury
Protein Dynamics in TBI
Rationale for Neuroproteomics Analysis in TBI
Biological Samples in Neurotrauma
A. Sample Sources
Blood, Serum, and Plasma
Animal Model of TBI
III. Sample Preparation and Prefractionation
A. Protein Extraction
IV. Protein Separation
A. Gel Electrophoresis
V. Protein Analysis
A. Protein Identification
A. Data Analysis Tools
Data Mining – Brain Cognitive Maps
VII. Recent Applications of Neuroproteomics
VIII. Concluding Remarks
Running Title: Neuroproteomics in Neurotrauma
Neurotrauma in the form of traumatic brain injury (TBI) afflicts more Americans annually than Alzheimer’s
and Parkinson’s disease combined, yet few researchers have used neuroproteomics to investigate the
underlying complex molecular events that exacerbate TBI. Discussed in this review is the methodology needed
to explore the neurotrauma proteome – from the types of samples used to the mass spectrometry identification
and quantification techniques available. This neuroproteomics survey presents a framework for large-scale
protein research in neurotrauma, as applied for immediate TBI biomarker discovery and the far-reaching
systems biology understanding of how the brain responds to trauma. Ultimately, knowledge attained through
neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of neurotrauma on
Keywords: Neurotrauma, Neuroproteomics, Brain Injury, Protein Separations, Mass Spectrometry
The application of proteomics in neurotrauma is all but unexplored, seemingly contrary to the fact that 5.3
million Americans live with disabilities that resulted from traumatic brain injury (TBI) according to the
National Institute of Neurological Disorders and Stroke - NINDS (www.ninds.nig.gov). Neurotrauma research
has brought to light direct evidence for the involvement of protein processes that induce secondary injury after
brain trauma, which present diagnostic and therapeutic avenues unique to this disorder. Indeed, TBI differs
from most neurological maladies in that it has no direct genetic influence; rather, acute tissue injury induces
cascading protein activities that activate protein-protein and protein-transcription factor pathways that often
culminate in cell death. These same pathways can be targeted for clinical intervention, along with pro-survival
protein pathways, to induce neuronal recuperation to thus reduce disability and promoting functional recovery.
This review presents the challenge of neurotrauma from a protein perspective, and outlines the technologies
available for the neuroproteomic study of TBI.
A. Traumatic Brain Injury
The impact of TBI on society is apparent from the staggering statistics published by NINDS. Annually, 1
million TBI cases are reported in U.S. emergency rooms that result in 230,000 hospitalizations, 80,000
individuals with long-term disabilities, 2,000 permanent vegetative states, and 50,000 deaths. TBI incidents
occur in ordinary life from: motor vehicle accidents (50%), falls (21%), assault & violence (12%), sports &
recreation (10%), and other causes (7%) to produce an estimated $56 billion in medical expenses annually in the
United States. In fact, more Americans are affected by TBI annually than by Alzheimer’s and Parkinson’s
disease combined, yet there are fewer proteomic studies in neurotrauma than neurodegenerative disease.
Traumatic brain injury is characterized by a direct physical impact or trauma to the head followed by a
dynamic series of injury and repair events (Figure 1). TBI pathology begins at the impact zone, where
mechanical compression and penetration induce tissue deformation, tearing, and hemorrhage that invokes rapid
oncotic injury and necrotic cell death. In distant brain regions, the impact force causes contusions that injure
distal gray matter regions and the interconnecting long fiber tracts (axons) of the white mater. Secondary
molecular insults are triggered by the release of glutamate from damaged nerve terminals that activate
ionotrophic glutamate receptors (Choi, 1992) and lower brain pH to neurotoxic levels. Afterward, ion transport
is disrupted to raise intercellular sodium and calcium concentrations that trigger a second wave of cell death,
which culminates through an over-activation of proteases and associated apoptosis for weeks after injury
(Raghupathi, 2004). At about the same time, microglia cells induce an inflammation response that increases
intracranial pressure (ICP), astroglia proliferation, and reactive oxygen species (ROS) levels. In time (weeks to
months), brain tissue remodeling occurs through synaptic plasticity and possible stem cell differentiation. In
today’s clinical environment, all of these pathological events are monitored through a limited set of
physiological parameters such as brain pH, pO2, ICP, and temperature. Advances in brain imaging (CAT or
MRI scans) have provided non-invasive observation of damaged regions, though imaging is used only as an
Due to response-time constraints, little can be done clinically to mitigate the first phase of cell and nerve
fiber injury; however, secondary molecular injury is within the realm of therapeutic treatment. Proteases in
particular are responsible for secondary post-TBI cell injury that results in further cell death through oncotic and
apoptotic mechanisms (Raghupathi & Wang, 2000). At the onset, cytosolic calpain activation increases as
intracellular calcium rises; a self-perpetuating process culminates in an over-activation of calpain in response to
post-injury membrane depolarization (Hayes, 1999). The activation rate can also be modulated upon
translocation of the pro-calpain zymogen to the plasma membrane in proximity to phospholipids. Two general
calpains are known to be active after TBI – calpain 1 (µ-calpain) requires micromolar concentrations of calcium
for activation, and is thus, more ubiquitous post-TBI than calpain 2 (m-calpain), which requires millimolar
calcium, and is primarily found in glial cells (Hamakubo, 1986). Indeed, evidence exists for only calpain 1
translocation into nuclei after brain injury (Kubbutat, 1997), where it is involved in chromosome degradation
that leads to apoptosis.
Much remains to be answered on the role of post-injury calpain. The specific inhibitor calpastatin acts to
check calpain activation in the presence of rising calcium levels. Though this mechanism has been studied in
vitro for some time, the role of calpastatin regulation after TBI remains poorly defined (Takano, 1999).
Interestingly, calpastatin is a known substrate of caspase 3, another cysteine protease that is activated post-TBI,
which suggests that calpain proteolysis is somehow regulated through the latter activation of caspase 3. On the
other hand, calpain can proteolyze the protein BAX triggering an intrinsic cascade that activates caspase 3.
Such crosstalk between calpain and caspase 3 is an example of the complex cellular events that occur after
injury (Wingrave & Rami, 2003) when many other proteases, such as caspase 7, are also activated in vivo
The caspase class of cysteine proteases cleaves proteins specifically on the C-terminus of aspartic acid. Like
calpains, caspases exist as a pro-form that is ubiquitous within the cytosol, but differ from calpain by lacking a
calcium-binding domain. Therefore, the mechanism and timing of activation differ between the two protease
classes. Caspase activation occurs through cascading pathways that often lead to apoptosis (Troy, 2002;
Moskowitz, 2003). The intrinsic pathway is triggered by a variety of stimuli – calpain 1 for example. Such
environmental stimuli alter the regulation of proteins that promote either cell survival (Bcl-2, Bcl-XL) or cell
death (Bcl-XZ, BAD, BAX) (Cheng, 1997). The latter promotes pyknosis of the mitochondrial membrane and
the release of cytochrome C into the cytosol to combine with Apaf-1 and ATP to form the apoptosome that
catalyzes caspase 9 activation. Caspase 9, in turn, activates caspases 3, 6, and 7 – the executioner caspases – of
which caspase 3 is well-known to be, and 7 was just shown to be, active after TBI (Larner, 2005).
Alternatively, the extrinsic pathway is mitigated by binding of the death ligands Fas or TNF to their respective
cell receptors. That binding promotes the formation of the DISC complex that catalyzes the activation of
caspase 8, and like caspase 9 activates caspases 3, 6, and 7. Recently, a third pathway has been uncovered post-
TBI to involve endoplasmic reticulum (ER) stress (Larner, 2005). ER stress causes an unfolded protein
response (UPR) that results in the activation of caspase 12 that in turn can activate caspase 9 followed by the
executioner caspases. Further, like calpain, caspase activation is regulated by a class of endogenous inhibitors
(IAPs) (Troy, 2002).
A third cysteine protease class, cathepsins, are also active following TBI (Yamashima, 2000 & 2004), though
much less is know about their role in neurotrauma. Normally, cathepsins are isolated within lysosomes, where
they degrade unwanted protein without injuring the cell; however, following neurotrauma, activated calpains
can rupture lysosomes to spill pro-cathepsins into the cytosol (Yamashima, 1998 & 2003), where they
autolytically activate at a relatively slow rate. Cathepsin B in particular has been found to be activated in the
cytosol days after TBI (unpublished work). Once active, cathepsins can interact with the caspase cascade,
among other protein pathways, to produce delayed neuronal death (DND) (Tontchev & Ishisaka, 1999).
In all, proteases are one example of protein changes that occur after TBI. Each protease also acts upon a
multitude of protein substrates to affect normal cellular function and promote possible cell death or cell
survival. The complex networks of protein-protein and protein-mRNA pathways involve dynamic temporal and
spatial changes that can only conceivably be characterized with large-scale proteome analysis. In our
laboratory, we have employed antibody arrays, differential protein separations, and mass spectrometry
techniques to investigate calpain and caspase pathological processes in the brain as a response to TBI (Pineda,
2004). Yet, the exact model of cell death in neurodegenerative injury remains sketchy, plausibly because other,
as yet undefined, cell death schemes (e.g., autophagic) occur after neurotrauma (Jellinger, 2001; Kim, 2004).
B. Protein Dynamics in TBI
The neuroproteome is highly dynamic, and its study will require similarly dynamic quantitative models of
protein pathways and networks to capture an integrated cellular response to neurotrauma in a systems biological
approach (Grant, 2001a; Choudhary & Kim, 2004). Any expressed protein at a steady-state is in balance
between its synthesis rate and its metabolic rate, as illustrated by synthesis and degradation ratios and the half-
lives of many cellular proteins (Pratt, 2002; Cargile & Michnick, 2004). Proteomic changes can be homeostatic
attempts to preserve normal physiological function, or to alter protein expression and posttranslational
modification in response to injury processes. Either way, a cellular phenotype remains a dynamic process
influenced by environmental signals (Figure 3). Post-TBI transitions in the transcriptome and proteome involve
several modification steps that include the transcription cues and posttranslational processes (Lisacek & Scriver,
2004) that are reviewed below.
After TBI, the transcriptome (messenger-RNA transcripts) is subject to a number of modifications, including
alternative splicing, polyadenylation, and methylation cues (Dongre, 2001; Morrison 2002). Newly modified
mRNA transcripts give rise to different sets of proteins, which are subject to further modification (Dongre,
2001; Phizicky, 2003). Unlike the invariant genome, the complexity of the cellular proteome depicted in its
temporal/spatial dynamic nature is encoded by multiple isoforms from single genes. In all, an estimated ten
protein isoforms could be generated from a single gene (Liebler & Morrison, 2002; Kim, 2004). However, the
transcriptome is generally not the first stage of functional expression after TBI, and does not faithfully correlate
with protein expression levels (Blackstock, 1999; McDonald, 2000; Morrison, 2002; Denslow, 2003; Freeman,
2004). Rather, posttranslational activity is the first cellular response to injury that generates an immense
variability in protein function.
Proteins are subjected to ca. 400 different posttranslational modifications (PTMs) (Morrison & Patton,
2002), to include phosphorylation/dephosphorylation by kinases and phosphatases, proteolytic processing,
acetylation, glycosylation, farnesylation, S-nitrosylation, lipidation, among many others. Proteins can also be
crosslinked by transglutaminases or conjugated to small protein tags such as ubiquitin or SUMO. PTMs are
important processes by which proteins acquire new functions or states in response to a specific cellular
condition such as activation, turnover, down-regulation, conformation, and localization (Husi, 2001; Morrison,
2002), that also occur after neurotrauma.
PTM identification can play an integral role in the study of brain injury by reflecting on the protein’s
biological significance (Husi, 2001). An example is the demonstrated dephosphorylation of neurofilament-68
that (NF-68) follows TBI insult (Posmantur, 1998 & 2000). NF-68 dephosphorylation is highly associated with
NF loss through calpain-mediated proteolysis, and reflects the post-TBI pathophysiology of dendritic and
axonal damage. In another study, large format two-dimensional gel blots were employed to detect altered
phosphorylation states of multiple proteins at 24 hours post-TBI (Jenkins, 2002). Differentially expressed
proteins included protein kinase B (PKB) substrates; namely, glucose transporter proteins 3 and 4, and forkhead
transcription factors. PKB substrates are involved in neural functions such as glucose metabolism, protein
synthesis, and cell survival. Hence, such PTMs play an important role in post-TBI cell injury pathways, and
might prove significance for diagnostic biomarker development (Newcomb, 1997; Posmantur, 1998; Pineda,
C. Rationale for Neuroproteomics Analysis in TBI
Application of proteomic technology to neuroscience has been of growing interest, with the field of
neuroproteomics established in 2004 (Choudhary, 2004). Neuroscience can benefit tremendously from the
proteomic approach to unravel complex protein interactions; for example, those occurring at synapses and at
neurotransmitter receptors (Husi, 2001). However, application to TBI is all but unexplored. TBI, as with other
neurodegenerative disorders, results in neuronal cell death, except that the exact molecular process after TBI
insult is characteristically unique, and therefore requires focused study. Once understood, those mechanisms
can be targeted to generate diagnostics and appropriate therapies to promote cell survival and functional
III. Biological Samples in Neurotrauma
Protein efflux from the brain to biofluids presents multiple access points to the TBI neuroproteome (Figure
4). Analysis at each level has distinct advantages and challenges, depending on the objective. For example,
successful diagnostics would likely target the blood as a less-invasive and plentiful sample source, whereas
therapies ultimately target the brain with possible administration through the cerebrospinal fluid (CSF) or blood.
This section reviews the different biological sources of TBI samples, animal models used, and collection
procedures to be followed.
A. Sample Sources
1. Brain Tissue
Brain tissue is the most direct means to observe post-TBI proteomic changes; however, due to ethical
considerations, the only human brain tissue available for TBI studies is from deceased patients. Significant
tissue deterioration occurs rapidly in postmortem brain samples, particularly when left at room temperature
(Franzén, 2003) and when subject to delayed sample retrieval (Whitehouse, 1984). In light of this limitation,
animal models have been used extensively in TBI studies, where brain tissue can be collected in a controlled
laboratory environment and processed immediately to minimize degradation. Furthermore, animal samples can
be reproducibly harvested from defined anatomic regions from a large number of subjects. For example, we
often focus on the cortex and hippocampus, which are differently vulnerable to traumatic insult.
2. Cerebrospinal Fluid
CSF contains rich proteome information particularly relevant for brain diagnosis (Davidson, 2002), since any
observed proteomic changes directly reflects central nervous system (CNS) processes (Yuan, 2005). However,
CSF is a low-volume fluid (only 50-150 µl withdrawn from rats, and 25-30 µl from mice) with a protein
concentration < 1 µg/µl. These two factors limit the utility of CSF in animal models, which often necessitates
sample pooling. Human CSF has a larger volume (2-5 mL from neurotrauma patients) and can be collected at
multiple time-points by ventriculotomy from the same patient. On the other hand, adequate human CSF
controls are difficult to procure, and are often from patients with other CNS disorders, each with a perturbed
protein complement (Terry, 2003). Also, CSF controls are typically obtained from a different location via
spinal tap, which further influences protein content. Indeed, uniformity of human CSF is of concern because of
the large variability among patient parameters – sex, age, injury severity, etc. (Conti, 2004). Those issues can be
mitigated with a statistically significant number of samples.
3. Blood, Serum, and Plasma
In human and animal traumatic brain injury studies, blood is easily collected and further processed into
serum or plasma fractions for proteomic analysis. Like CSF, blood contains proteomic information relevant to
the brain, particularly after neurotrauma when the blood-brain barrier is breached (Raabe, 1999; Romner 2000).
However, it can be difficult to identify brain proteins in blood due to their low concentrations (as much as
10,000-fold less than in CSF), mixing with other organ proteomes, and the large dynamic range (1010) of blood-
borne house-keeping proteins.
B. Animal models of TBI
Animal models, particularly rodent models, are frequently used to study TBI (Raghupathi, 2000; Finnie,
2001). Three well-characterized protocols exist to induce TBI in animals: (1) controlled cortical impact (CCI)
(Dixon, 1991), a method that controls the degree of impact by compressed gas (Figure 5); (2) fluid percussion,
in which a contusion force is incurred by the movement of a fluid in a chamber; and (3) vertical weight drop, in
which a weight is dropped from a certain height within a cylinder. The type and degree of injury can be
controlled either unilaterally or bilaterally. After injury, the animal is sacrificed to retrieve tissues and biofluids
for proteomics analyses.
An additional benefit of TBI animal models is that they allow researchers to collect tissues and biofluids at
multiple time points post injury. As illustrated in Figure 1, neuroproteomic changes post-TBI are highly
dynamic, thus the temporal pattern of protein changes can be characterized with samples collected from minutes
to weeks after injury. For example, the post-TBI temporal dynamics of the proteases calpain-1, caspase-3, and
caspase-7 are illustrated in Figure 6. The dynamics of protease activation are then mirrored in their substrate
targets, reflecting a continuum of degradative events. Temporal dynamics can also be characterized with
human biofluids, though it is difficult to match time points between trauma patients.
C. Biological Variability
Experimental design in neuroproteomics must account for biological variability, whether using cell culture,
animal, or human samples. However, the field of proteomics presently lacks the structured procedures to
deduce sample quality, sampling size, or the means to determine confidence values, as is routinely and
inexorably necessary in genomics experiments (Boguski, 2003). Indeed, proteomics is more impacted by
biological diversity due to protein variety than is genomics, which is based on a fixed set of gene targets.
Biological variance is largely determined by sample heterogeneity. The effect of biological heterogeneity is
illustrated by Molloy et al. (2003) by the increased biological coefficient of variation (CV) going from 26% CV
for mammalian cell-line cultures (n=3) to 40-50% CV for primary animal cell cultures (n=3). Animal tissues
and biofluids would show even greater sample variance due to individual differences between animals, which
must be minimized through thorough control of the experimental treatment, animal lineage, age, sex,
environmental exposure, and diet, among other factors.
Biological variance is most dramatic in human studies, where patient heterogeneity is beyond the control of
clinicians or researchers. For example, Molloy et al. (2003) analyzed 30 proteins that changed expression after
drug treatment, and found that at best only three of 30 showed a coherent regulation change in just three of four
patients treated. To minimize effects of biological variability in clinical studies, samples must be collected
under controlled conditions on ice with few processing steps. In humans, CSF and blood should be drawn at
regular intervals, prior to food consumption (affects blood-protein profiles), and processed immediately, snap-
frozen, and stored at -80 oC. Detailed records of patient parameters are crucial. Any difference between
patients, from what they ate last to whether they smoke, can confound proteomic results, which analytical
methods cannot account for (Boguski, 2003). In clinical TBI studies there is also no control over the initial
injury, which further compounds biological differences between samples. In the end, only general, common-
sense guidelines exist – control and treated samples must be acquired and processed as similarly as possible,
and sample numbers should be significantly large as to attain statistical significance after accounting for the
technical variation of the method used; however, without a priori knowledge it is difficult to ascertain these
parameters prior to performing the experiment.
IV. Sample Preparation and Prefractionation
Prior to proteomics analysis, samples must first be processed to solubilize protein and to remove extraneous
materials. Once prepared, samples often are fractionated into subproteomes to perform more detailed analysis.
This section reviews typical sample processing procedures performed in neuroproteomic research.
A. Protein Extraction
Protein must be extracted from brain tissue before analysis. Extraction begins with the crushing or
homogenizing of the tissue on ice to dissociate cells. A solubilization buffer, usually with surfactants, is added
to lyse cell membranes and to extract the protein content. The buffer used depends on the analysis methodology
and the desired result. Protease and phosphatase inhibitors are often added to minimize any PTM-induced
sample degradation during preparation and analysis.
B. Sample Cleanup
Tissue samples must be depleted of common contaminants such as cellular debris (Jiang, 2004) through
centrifugation and filtration (Ueyama, 2003). At a sufficiently low speed, heavier cell debris will sediment to
clarify the protein solution (Nothwang & Fountoulakis, 2003). We further process tissue samples through 0.1
µm filters to remove any particles that would interfere with liquid chromatography.
High-abundance house-keeping proteins such as albumin (>50%) and immunoglobulins (IgG) (>15%)
comprise 95% of protein in plasma, serum, and CSF samples (Anderson, 2002; Ahmed, 2003; Zhang & Yuan,
2005), that often mask less-abundant proteins of interest (Mehta, 2003). A number of studies have been
conducted to determine the best means to deplete house-keeping proteins without removing other components
(Figure 7) – membrane filtration, precipitations, affinity resins, and chromatography (Ahmed, Mehta, Brzeski &
Steel, 2003; Ghosh 2004). Most promising are highly selective affinity methods, though they still tend to
remove other less-abundant or albumin-bound proteins (Mehta & Ahmed, 2003). Protein precipitation methods
have fared worse, and are challenged by poor selectively and reproducibility (Jiang, 2004). Newer technology
addresses these issues, and might provide a reliable depletion of abundant proteins without any significant loss
of other proteins (Gershon, 2005). Ideally, new depletion methods should be able to process large volumes of
biofluid to provide sufficient material for neuroproteomic analysis, in general 25-200 µg of brain protein, which
would require 0.5-40 mL of CSF (5% of 0.1-1.0 µg/µl protein in CSF) and even more serum.
Often, salts, ionic detergents, lipids, and nucleic acids interfere with proteomic analysis (Jiang, 2004). High
salt concentrations should be avoided, because they disrupt gel electrophoresis and mass spectrometry. Sodium
dodecyl sulfate (SDS) is commonly used to solubilize proteins from tissues and cell culture for gel
electrophoresis; however, SDS and other ionic detergents are generally incompatible or tolerated only in small
amounts by isoelectric focusing in two-dimensional gel electrophoresis (2D-PAGE) and mass spectrometry.
Instead, nonionic detergents (e.g., Triton X-100 and Nonidet P-40) could be used as extraction buffers at
sufficiently low concentrations (1% is common) (Oh-Ishi, 2002). To reduce salt and detergent concentrations to
non-interfering levels, buffer exchange devices (dialysis or filtration) (Peyrl, 2003; Jiang, 2004), or protein
precipitation (Brzeski, 2003; Jiang, 2004) could be used.
C. Subcellular Fractionation
Subproteomes of subcellular compartments can provide important information related to the dynamic
biological state of post-TBI cells. For example, after TBI cathepsins, normally found in lysosomes, leak into
the cytosol where they in-turn trigger apoptotic pathways. Another advantage of subcellular fractionation is that
the reduced protein content simplifies analysis. Subcellular fractionation is typically performed with a sucrose
gradient (Fleischer, 1974). Under refrigerated conditions, brain-tissue homogenate and cell-culture slurries are
solubilized in sucrose, and are subjected to differential centrifugation to separate organelles by mass (Jung,
2000a; Ankarcrona, 2002; Nothwang, 2003; Hogenboom & Kabbani, 2004). Nuclear, mitochondrial,
membrane, cytoskeletal, and cytosolic subproteomic fractions are commonly produced, and each independently
is analyzed with proteomic technologies. Subproteomic purity is of concern, given the tendency for organelle
coprecipitation (Jung, 2000b), which can be improved with some protein loss through refractionation and more
washing steps. Despite certain limitations, subcellular fractionation provides a means to characterize the
proteomic spatial localities that are important for pathway elucidation in neurotrauma studies.
V. Protein Separation
It is exceedingly difficult to characterize an entire proteome because of the thousands of proteins contained
in a single tissue lysate. The sheer number of proteins is compounded by an enormous dynamic range and a
multitude of posttranslational modifications. The key to mitigating proteome complexity is more separations.
Through the use of multidimensional techniques, in combination with subproteomic fractionation strategies,
neuroscientists are now able to characterize more of the proteome that directly pertains to their interest. This
section describes the multidimensional protein separations that have been applied in neuroproteomics with
examples from TBI studies.
A. Gel Electrophoresis
Gel electrophoresis continues to be a powerful tool for protein separations. Proteins coated in SDS migrate
based on their nominal mass through pores within a polyacrylamide gel subjected to an electric field. Protein
mass is reflected by the migration distance on the gel, visualized using a protein stain relative to an adjacent
protein marker lane (Figure 8). One-dimensional SDS-polyacrylamide gel electrophoresis (1D-PAGE) is often
used in TBI studies for immunoblotting, which is a highly selective technique that uses protein-specific
antibodies to detect nanograms of protein. However, immunoblotting is limited to known proteins with
available antibodies (Freeman, 2004), and is generally reserved for a focused study on a particular protein. 1D-
PAGE has been used in neurotrauma studies for protein separation prior to capillary liquid chromatography-
tandem mass spectrometry (LC-MSMS) (Husi, 2000; Haskins, 2005). That approach may be sufficient for
depleted CSF and serum studies, but lacks sufficient separation power (102 peak capacity) for whole brain
lysate analysis without any prior fractionation.
Multidimensional separations more effectively resolve complex biological mixtures than any single
separation. Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) was developed in the 1970s
(Klose & O’Farrell, 1975) as the first multidimensional protein separation method and remains the benchmark
(Figure 9). 2D-PAGE can resolve thousands of proteins (between 103-104 peak capacity) to enable larger-scale
identification or quantification through mass spectrometric techniques (Lilley, 2001; Beranova-Giorgianni,
Peyrl & Nothwang, 2003). In the past, the time and technical skill required to conduct reproducible 2D-gel
analysis undermined its attractiveness as a potential tool (Herbert, 2001). However, recent advances have
solved many of the previous problems and have helped to make 2D-PAGE an integral part of neuroproteomics
for use in cell culture (e.g., Seow, 2001), primary culture (e.g., Boyd-Kimball, 2005), animal (e.g., Jenkins,
2002; Wang & Siman, 2004) and human tissue and biofluid studies (e.g., Zhan and Terry, 2003; Challapalli,
In 2D-PAGE, proteins are first separated by pI through isoelectric focusing (IEF), and further resolved
through SDS-PAGE (Freeman, 2004). Immobilized pH gradient (IPG) strips have become the standard for IEF,
where weak acids and bases are copolymerized within a polyacrylamide gel. In an IPG strip, proteins migrate
in an applied electric field until they reach a pH zone where their net charge is neutral, revealing their pI value
(Freeman, 2004). IPG strips are commercially offered in broad- and narrow-pH ranges to allow customized
protein resolution (Fountoulakis, 2003). In the second separation of 2D-PAGE, the IPG strip is equilibrated
with SDS to provide a uniform protein charge, and is mounted onto PAGE for mass resolution (Freeman, 2004).
A variety of linear and gradient acrylamide concentrations are commercially available in multiple sizes and
formats (Fountoulakis, 2003). A great deal of information can be gained from 2D-PAGE, including protein
isoelectric point, relative molecular mass (Mr), relative quantity, and posttranslational modifications. The pI
and Mr values attained can also be used to identify the protein by search of available 2D brain protein map
databases (Fountoulakis, 2004).
Despite its acceptance as the benchmark in protein separations, it is impossible to visualize an entire
proteome by 2D-PAGE alone. 2D-PAGE has difficulties with resolving hydrophobic, alkaline, high or low
mass, and low-abundance proteins (Herbert, 2001; Beranova-Giorgianni, 2003). Sample prefractionation
(Fountoulakis, 2004) and different solubilization buffers mitigate some of these complications, even though the
separation of those proteins continues to be inefficient (Lilley, 2001; Oh-Ishi, 2002). Despite its limitations,
2D-PAGE offers enough advantages to remain a staple in proteomic analysis for a long while (Herbert, 2001);
however, other separation tools are being developed that offer incremental improvements.
B. Liquid Chromatography
Recently, alternative liquid-phase multidimensional separations have been introduced (Issaq, 2001;
Morrison, 2002; Wang, 2003). Liquid chromatography (LC) (Wang, 2003; Freeman, 2004) offers a wide
variety of separation principles, as listed in Table 1, that contain two categories: 1) profiling mode, which
includes reversed-phase (RPLC), ion-exchange (IEC), chromatofocusing (CF), and size-exclusion (SEC)
chromatographies, where all mixture components are resolved; and 2) targeting mode, which includes affinity
chromatography (AC) antibody-to-antigen, tagged protein-to-substrate, and other chemistries such as metal
(IMAC), lectins, aptamers, RNA, and DNA affinities (Turkova, 1999) to provide a selective separation of a
single protein or group of proteins.
Tandem LC methods (multidimensional) offer increased resolution up to the multiplicative resolving power
of each respective chromatography (Giddings, 1987). Various tandem combinations have been proposed for
protein separations: SEC-RPLC (Opiteck, 1998; Zhang, 2001), IEC-RPLC (Opiteck, 1997; Feng, 2001;
Wagner, 2002), IEF-RPLC (Wall, 2000), and CF-RPLC (Chong, 2001). Fractions are generally collected after
each dimension, with the final RPLC fractions (predominant second dimension) readily digested (e.g., trypsin)
for mass spectrometry analysis.
An alternative to protein separations is multidimensional peptide analysis, often referred to as shotgun
proteomics (McDonald, 2002) or multidimensional protein identification technology (MuDPIT) (Washburn,
2001), which provides an automated means for high-resolution separation after digestion. Ion-exchange
chromatography (SCX) under acidic conditions (Link, 2002) is used for the first dimension as a peptide
reservoir (Smith, 2002) followed by fractionation either offline (Raida, 1999) or online (Link, 1999) with
RPLC-MSMS. Offline SCX prior to RPLC-MSMS can handle significantly higher sample loads, which adds
flexibility in sample analysis (Gygi, 2002). In online methods, SCX and RPLC packing are placed in tandem
within the same column (Figure 10) to reduce any loss between separation steps. Salt steps are delivered to
elute SCX fractions directly to the RPLC bed. An organic gradient is passed through the column to separate
peptides online with a mass spectrometer. Over 5,000 peptides from a single sample have been resolved by this
method (Resing, 2004), although this experiment can take days to perform and even longer for data analysis by
considering that a typical tryptic digest of a complex biological sample can contain close to a million peptides
In our recent TBI research, we targeted differentially expressed proteins for biomarker discovery using a
combination of liquid chromatography and gel electrophresis. Rat samples for control and TBI conditions were
pooled (n=7) to reduce the impact of biological variability (data to be published). We performed
multidimensional protein separations with combined cation- and anion-exchange media in tandem with 1D-
PAGE separation (CAX-PAGE) prior to RPLC tandem mass spectrometry (MSMS) analysis (Ottens, 2005). In
evaluating this method, reproducible band patterns were observed between repetitive separations of a control
sample, with a coefficient of variance (CV) of 11% (n=3). CAX-PAGE provides a protein visualization map
(Figure 11) that is more effective at high-mass protein separation (> 100 kDa) and more amenable to
hydrophobic proteins than comparable 2D-PAGE. Those benefits could be important for neurotrauma research
(Freeman, 2004), because signal transduction, receptor, and cytoskeletal proteins are often of high-mass and
tend to contain highly hydrophobic moieties. Such proteins are particularly important as biomarkers and in
mechanistic studies of TBI, because they are some of the more dynamic proteins in the cell. Once separated by
CAX-PAGE, only differential proteins were analyzed to reduce the data workload when compared with shotgun
proteomics, and to allow quantification at the protein and peptide levels for internal validation of differential
expression. CAX-PAGE, unlike liquid-phase multi-dimensional separations, provides protein mass information
that can later be used to confirm protein identity, as done with 2D-PAGE. An additional advantage arises when
a protein is identified by mass spectrometry in a gel band at a reduced mass relative to that protein’s intact
mass. In our experience, this situation often indicates a proteolytic breakdown product (BDP). Once a putative
list of differentially expressed proteins or their proteolytic breakdown products is compiled, our strategy is to
use complimentary immunological assays (e.g., Western blots, and enzyme-linked immunosorbent assays,
ELISA) to confirm differential expression or proteolysis in individual rat samples, as in the increased
proteolysis after TBI of the cytoskeletal protein alphaII-spectrin by calpain (145 kDa BDP) and caspase-3 (120
kDa BDP) shown in Figure 12 (n=4). In addition to animal brain tissue, we have also applied this method to
cell culture, animal liver tissue, and human brain tissue. We expect the technique will also work with biological
fluids, though salts may need to be removed.
In all, a myriad of separation options provide the ability to resolve and analyze segments of the TBI
proteome; however, no single separation strategy is sufficient to resolve the entire neuroproteome. Thus,
multiple methods could be used to complement each other, and must be considered for each experiment in terms
of sample complexity, sample loss between steps, added time and complexity, and the desired outcome. In
general, greater fractionation followed by multidimensional separations will result in a greater analysis of
complex samples such as brain lysate.
VI. Protein Analysis
Neuroscience is slowly incorporating proteomics-based technology (Kim, 2004); however, access to top of
the line mass spectrometers and data analysis software is a limitation. Although few select neuroscience
researchers might collaborate with the top mass spectrometry research laboratories, most are resigned to
performing rudimentary analysis. Part of the problem is the significant capital required to purchase mass
spectrometry in-house for a focused study. The instrumentation dollars needed to by mass spectrometer
systems are generally unavailable to individual biological scientists, and what is available generally goes to core
facilities, where limited manpower and instrumentation time dictates the feasibility of performing involved LC-
MSMS experiments. Despite the limitations, neurotrauma research must take advantage of the powerful, yet
time-consuming, mass spectrometry techniques that are presently available.
A. Protein Identification
Protein identification in neurotrauma studies has primarily been performed with 2D-PAGE (Figure 13) brain
protein map databases (Buonocore, 1999; Jenkins, 2002; Conti, 2004) available for different species, notably rat
(Fountoulakis, 1999; Taoka 2000; Krapfenbauer, 2003), mouse (Tsugita, 2000; Beranova-Giorgianni, 2002),
and human (Langen & Lubec, 1999; Lubec, 2003; Yang, 2004). More recently, 1D- and 2D-PAGE has been
coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS)
for neurotrauma research (Figure 13) (Sachell, 2003; Conti, 2004). Though MALDI has been available for
around twenty years (Karas, 1987), it became widely employed in protein analysis only in the later half of the
1990’s (Nguyen, 1995; Fenselau & Jungblut, 1997; Nilsson, 1998; Yates, 1998a; Courchesne, 1999). It is one
of two steadfast ionization techniques that reliably transfer large biomolecules into the gas phase (Kaufmann,
1995). MALDI, compared with electrospray ionization (ESI), is a gentle ionization process that produces little
fragmentation and predominately singly charged ions for easier protein identification via peptide mass mapping.
This method of protein identification requires high mass resolution for accurate protein identification, which is
cost effectively performed with TOFMS instrumentation.
Peptide fragmentation patterns serve as an alternative to peptide mass mapping for protein identification.
With tandem mass spectrometers, peptides of interest are isolated and fragmented into often predictable pieces
that are used for sequence correlation. Various tandem mass spectrometers are available for proteomics
applications, including quadrupole / time-of-flight (Q-TOF), time-of-flight / time-of-flight (TOF-TOF), Fourier
transform - ion cyclotron resonance (FT-ICR), and the most ubiquitous (2D and 3D) quadrupole ion trap mass
spectrometer (QITMS). Tandem mass spectrometry is advantageous in that it provides the selectivity through
sequence correlation to identify a protein from a single peptide (Veenstra, 2004), though time-consuming
manual interpretation is necessary for verification of that single peptide. The ability to identify proteins by a
single peptide ultimately translates to improved sensitivity, which is of importance considering the multitude of
low abundance proteins present in the brain. To adequately adapt MSMS for high-throughput analysis, new
peptide matching algorithms are being developed to incorporate statistical values to improve assignment
confidence (Resing & Wiemer, 2004). Tandem mass spectrometry has predominately been performed with
ESI-coupled liquid chromatography (LC), though MALDI-based LC-MSMS is also possible (Schulz-Knappe,
2001; Smith, 2002). Generally, protein from gel bands or spots (1D and 2D gels, respectively) are excised, in-
gel digested, and loaded onto reversed phase liquid chromatography for peptide resolution online with MSMS
as outlined in Figure 14.
As general as this approach has become, it is still plagued by difficulties with sample complexity and
dynamic range (Dickey, 2003). The most elaborate separation schemes cannot handle the immense complexity
of biological samples, and identify only a subset of predominant proteins. That was the case in our first 1D-
PAGE / LC-MSMS TBI study, where definitive identification of putative biomarkers was challenged by gel-
band complexity (Haskins, 2005), and remains a challenge even with greater protein separation (Ottens, 2005).
In neurotrauma, sample complexity is particularly problematic with biofluid analysis, because proteins of
interest are in low-abundance amidst high-abundance CSF or serum background proteins (e.g., albumin,
immunoglobulins, transferin, etc.). Lower level proteins are uncovered, but in general their identification will
rely on minimal data, and therefore prone to confusion with false positives. More reliable identification
strategies, including partial sequence tags employed with the GutenTag algorithm (Tabb 2003) and accurate
mass tag identification (Conrads, 2000) are becoming available. At the same time, new instrumentation such as
quadrupole linear (2D) ion traps (QLT) (Schwartz, 2002), QLT – Fourier transform (QLT-FT) (Syka, 2004a)
and QLT – Orbitrap (Hu, 2005) hybrids, as well as new fragmentation methods, such as electron transfer
dissociation (Coon, 2004; Syka, 2004b) might address some current limitations.
B. Protein Quantification
Quantitative analysis in neuroproteomics has primarily been used for differential expression profiling by 2D-
PAGE for biomarker discovery (see review Fountoulakis, 2004). For example, control and TBI samples from
either animal models or clinical samples can be resolved by 2D-PAGE and visually compared with different
staining methods (see Rabilloud, 2000 for review of staining methods); however, the issue of gel-to-gel
variability, likely the largest source of error in quantitative analysis by 2D-PAGE (Choe, 2003), confounds
differential analysis (Corthalas & Voss, 2000; Nishihara, 2002) with an expected CV value of 20-30% (n=3)
(Molloy, 2003). The utility of 2D-PAGE for differential expression analysis has been enhanced by the
development of 2D difference gel electrophoresis (DIGE), where two samples distinguished by different
cyanine fluorescent labels are separated within the same gel (Unlu, 1997; Tonge, 2001). DIGE minimizes gel-
to-gel reproducibility concerns by resolving two samples within the same gel (Figure 15). Normalization with
a third cyanine dye also allows for comparison between gels, as demonstrated by Alban et al. (2003) comparing
cell lysates where normalized CV values of four gel spots ranged from 1.09 to 25.62 in comparison with non-
normalized CV values from 3.11 to 120.93 (n=8). Newer generation cysteine-linked cyanine dyes permit label
saturation to improve sensitivity when sample protein is limited (e.g., CSF from TBI animals) and to permit
accurate spot excision from cyanine dye images without counter staining (Shaw, 2003). Despite the benefits of
DIGE technology, neuroscience has been slow to adapt its use. Rather, gel-to-gel comparison without
normalization appears to be the norm in neuroscience studies (Butterfield, 2003 & 2004; Fountoulakis, 1999;
Lubec, 2003; Chen, 2003), which is also true of the limited examples in neurotrauma (Jenkins, 2002; Satchell,
2003). Though detailed reproducibility studies employing normalization between 2D-gels without cyanine dyes
have been conducted with human brain tissue (n = 3-4) (Zhan, 2003) and human CSF (n = 2-4) (Terry, 2003),
and demonstrate the feasibility of using 2D-gel quantitation for differential analysis in neuroproteomics without
the need for DIGE technology.
An alternative to gel-based differential comparison is the use of isotopic-coded affinity tags (ICAT) (Gygi,
1999; Flory, 2002). Here, two samples are differentiated by parallel processing with cysteine-binding affinity
tags that have an integral mass difference from either deuterium or carbon-13 labeling (Zhang, 2002; Hansen,
2003). A key step is the reduction in sample complexity by affinity purification of cysteine-containing
peptides; however, the cysteine-linker requirement can bias ICAT toward certain proteins containing non-
oxidized cysteine residues (Sethuraman, 2004), which is a potential problem in TBI studies after ROS
formation. Due to their respective biases, 2D-PAGE and ICAT are complementary. ICAT biases toward high-
mass proteins (containing more cysteine residues) (Patton, 2002) and is favorable for hydrophobic protein
identification, e.g., membrane proteins (Yu, 2002; Stevens, 2003); whereas 2D-PAGE resolves proteins below
120 kDa (Freeman, 2002) and is not limited by cysteine residues. Through a combination of DIGE and ICAT-
labeling strategies, Smolka et al. (2002) identified a larger array of proteins than possible with a single method.
Modified His-tags have also been used to quantify membrane proteins from mouse brain (Olsen, 2004). To
reduce the mass added to the peptide, an arginine was included close to the linker such that the bulk of the tag
could be cleaved away upon trypsinization, similarly to acid cleavable ICAT (cICAT) reagents (Li, 2003).
Additional tagging strategies have been suggested (Qiu, 2002; Thompson, 2003), including one where the tag is
immobilized on a solid support (Zhou, 2002), and one that selectively targets the phosphoproteome (Goshe,
2001). A significant limitation in isotopic tagging strategies is the inability to multiplex samples as limited by
only two mass resolvable tags for pair analysis. In ICAT and similar methods, each new sample will have to be
analyzed with a control sample. As well, the low duty-cycle of data dependent mass spectrometry often results
in missed data when repeating isotopic tagging experiments, as exemplified by Von Haller et al. (2003) where
the total number of peptides identified in a repeated analysis (n=2) decreased by 28.5% with only 52% of
proteins identified in the first iteration observed in the second iteration. On the positive side, isotopic tagging
methods can be used in neuroproteomics (Lill, 2003) with primary culture (e.g., Lovell, 2005), animal (e.g.,
Prokai, 2005) and human tissues and biofluids (e.g., Jin & Zhang, 2005), though they only provide relative
An absolute measure of protein concentration is useful when for example, developing assays where protein
concentrations might be low, such as in serum. Isotope dilution assays (IDAs) (Barr, 1996) use isotope-labeled
internal standards (IS) for absolute quantification by mass spectrometry in most biological mediums: blood
(Kippen & Stocklin, 1997; Satterfield, 2003), urine (Fierens, 2000 & 2003), tissue (Desiderio, 1998; Barnidge,
2003; Peng, 2004), and cell culture (Gerber, 2003). Optimally, a synthetic version of the targeted tryptic
peptide would be used with heavy isotopes of 2H, 13C, and 15N for differentiation by MSMS (Gerber, 2003).
Once the internal standard is synthesized, absolute quantification is derived from the endogenous peptide-to-IS
chromatographic peak ratio and the known IS concentration (Figure 16). IDA analysis requires an a priori
knowledge of the IS peptide that can be a significant limitation when one considers that an average protein may
have a hundred tryptic peptides to choose from. Few peptides are suitable as internal standards, and based on
our own experience this depends on length, hydrophobicity, proton affinity, and cleavage efficiency – all
characteristics of the amino acid sequence. Further, peptide separations remain essential for a wide dynamic
range (four orders of magnitude typical) despite IS specificity. In a recent application of IDA to neuroscience
(Peng, 2004), six of 375 identified proteins were quantified, with CV values ranged from 2.5% to 14.3% (n=2).
The limited use of IDA in this study exemplifies the limited scope of absolute quantification in highly complex
systems due to throughput limitations.
An alternative methodology using iTRAQTM reagents (Applied Biosystems) was recently devised for relative
and absolute quantification of up to four samples, which partly addresses the difficulty of multiplexing with
other isotopic tagging methods (Hitt, 2004; DeSouza, 2005). Free peptide amines are labeled using one of four
tags of the same nominal 145 Da mass (same tagged-peptide precursor mass); however, upon fragmentation
each dissociated tag differs from one another by 1 Dalton (Figure 17). Thus, a peptide can be quantified from
four samples simultaneously. Absolute quantification is accomplished by labeling a known amount of a
selected peptide for use as an IS, without having to synthesis an isotopic version of the peptide. Choe et al.
(2005) compared iTRAQ with 2D-PAGE, and discerned a slight improvement in reproducibility among
identified proteins, with the average CV values from five quantification experiments being 24% (n=6) for
iTRAQ and 31% (n=2) for 2D-PAGE. iTRAQ is well-suited for tandem-in-space mass spectrometers such as
triple quadrupole and Q-TOF instruments, where the front quadrupole can be parked at the precursor mass. The
more ubiquitous ion-trap instruments, however, might have difficulty to resolve the 1 Da product ion mass
Metabolic incorporation of isotopes (Oda, 1999; Lahm, 2000) facilitated by culturing cells in either normal
14N or isotopically enriched 15N media is well-suited for differential analysis in cell culture experiments, though
not directly applicable to animal or human neurotrauma studies. However, recently 15N atomic replacement in
animals had been conducted by feeding an isotopically enriched diet (Wu, 2004). Though, the brain had the
least efficient replacement (average of 74%), the experiment demonstrated the possibility of using atomic
replacement for TBI studies in animals. A more cost-effective strategy is to replace a single amino acid only
with an isotopically labeled form [e.g., 2H-3 leucine (Ong, 2002 & 2003a) or 13C-6 arginine (Ong, 2003b)] by
stable isotope labeling with amino acids in cell culture (SILAC), which is presently limited to cell culture, but
could be applied to animals in a similar manor as done with 15N diet replacement. As yet, none of these
techniques have been applied to neurotrauma studies in animals, and none would be practical for human studies.
Separations and MS data must be assembled and processed to provide biologically relevant information.
Due to the large amount and complexity of data, computer software is required for all but the smallest
proteomic experiments. In this section, we discuss the computer tools used to turn raw data into tangible results
in TBI studies.
A. Data Analysis Tools
In bottom-up proteomic methods, peptides and their fragment mass-to-charge values must be matched with
theoretical peptides from protein (Pearson, 2000) or gene (Kuster, 2001) sequence databases (Figure 18) (Liska,
2003). Protein data are collated in non-redundant publicly available archives, and can be subset into tissue- and
species-specific databases to reduce analysis time to provide fewer false-positives/negatives (Resing, 2004).
The most widely used database-searching algorithms are Sequest (Yates, 1998b) and Mascot (Perkins, 1999).
In both methods, tandem-MS product-ion spectra of protease-specific peptide precursor ions are correlated with
theoretical product-ion spectra derived from the database. Some ambiguity in the data is expected; false-
positives occur even with widely accepted scoring criteria (MacCoss, 2002; Peng, 2003). Comparing peptide
sequences in multiple database-searching programs (Mascot is more stringent than Sequest) and to de novo
sequencing programs can be effective to reduce false-positives, although computation time is significantly
increased (Taylor, 2001; Johnson, 2002). Nevertheless, capillary LC-MSMS is widely used to identify proteins
with only 1-3 peptide sequences per protein. Probability-based scoring algorithms (Eriksson, 2000; Keller,
2002; Fenyö, 2003) in conjunction with intensity information (Gay & Parker, 2002), accurate tandem-MS mass
measurements (Conrads, 2000), and partial de novo sequencing (Tabb, 2003) promise to dramatically reduce
The output from such programs is cumbersome to interpret beyond small-scale proteomic experiments
(Fenyö, 2002); thus, additional sorting and filtering tools are essential to manage the enormous data sets. Tools
such as DTASelect and Contrast (Tabb, 2002) assemble data into hierarchical lists that use multi-level filters.
Hyperlinks are integrated to allow an easy retrieval of pertinent information, an essential feature when
manipulating hundreds of proteins. DTASelect is particularly useful for combining several MS data files into a
single sorted results table. Contrast provides a means to compare across multiple samples for differential
analysis. We have employed both programs to isolate expression differences between naïve and TBI samples
(Figure 19). To make these tools more effective, they can be used within batch or perl scripts to perform
redundant data manipulation on large data sets.
As useful as those tools are, they provide only a limited synopsis of the entire bioanalytical analysis.
Proteomics in terms of high-throughput, large-scale, functional level analysis will require an assemblage of
sample preparation, separation, mass spectrometry, and sequence analysis data (Smith, 2000). Various methods
have been proposed to assemble data sets, though none has become wide-spread due to difficulties with data
conventions and limited resources (Hancock, 2002). Data output from available programs like DTASelect must
be linked with more detailed protein information such as protein function and protein-protein interactions. Data
standardization applied to biological problems, such as neurotrauma, is essential for true functional proteomics
(reviewed in Godovac-Zimmermann, 2001; Hunter, 2002; Lefkovits, 2003). Tools are being developed (Grant,
2001b; Peri, 2004) for generation of protein networks, though to-date reference functional databases are
inadequate to apply broadly.
B. Data Mining – Brain Cognitive Maps
Ultimately, brain cognitive maps will relate how proteins interact with one another, neuropeptides, mRNA,
and genes to create functional units after neurotrauma (Zhang, 1992; Shi, 2002; Fang, 2003). To form cognitive
maps, information must be compiled about (1) pathophysiological stasis associated with TBI; (2)
neuroproteome and transcriptome dynamics to include pathology-mediated differential protein expression,
protein synthesis and metabolism, alternative mRNA splicing and RNA editing, signal transduction, protein-
protein interactions, enzymatic activity, protein function, and posttranslational modification of proteins; and (3)
brain function – how different cell types in the brain work together in networks. It is clear that various regions
such as the hippocampus and cortex respond differently to TBI (Freya, 2004). Brain cognitive maps seek to
relate that variation with the underlying machinery of cells. Discovery algorithms (Chen, 1998; Chen & Kitano,
2000; Chen, 2000) will help analyze data for a systems biology clear perspective of the neuroproteome to
provide insight into injury and repair mechanisms for TBI therapy.
VIII. Recent Applications of Neuroproteomics
As the application of proteomic technology to neurotrauma is scarce, it is useful to observe the wider
application of neuroproteomics to get a sense of what is presently possible (Veenstra, 2002). Proteomic
research has recently developed due to advances in protein separation, identification, and quantification
technologies not available even 3-5 years ago. Nevertheless, whole-animal proteomes are extremely complex,
by definition being organ-, cell type-, cell state-, and time-specific, and remain beyond our present technology.
A more feasible approach is to focus on substructures, such as the brain and its components (McDonald, Husi &
Jung, 2000). Thus, neuroproteomic studies are more manageable and productive (Rohlff, 2000; Husi, 2001;
Denslow & Lubec, 2003; Wang, 2004).
Multiple nervous system disorders can benefit from proteomics research. Within the CNS, there is enough
diversity, complexity, and therapeutic connections (e.g., neurological, neurodegenerative, and psychiatric
disorders and neuro-oncology) to make the efforts worthwhile and sustainable. Such purpose has prompted the
formation of the Human Brain Proteomic Project (HBPP), where researchers across the globe can converse on
the common theme of neuroproteomics (Meyer, 2003). Once distinct proteins are identified as important for a
specific nervous system disorder, there are two directions being pursued: (1) “upstream” gene expression and
regulation for the protein of interest can be readily backtracked by use of existing molecular biology
technologies; and (2) “downstream” roles of the protein in the pathogenesis of the disease can be followed with
protein enzymatic assays, pharmacological inhibitor studies, transgenic/knock-out studies, and importantly, the
potential development of biomarkers specific to the disease of interest.
Alzheimer’s disease is an excellent example of neurodegenerative diseases where brain samples from human
patients (Schonberger & Tsuji, 2001) and animal models (tau transgenic mice) (Tilleman, 2002) have been
subjected to proteomic analysis. Similarly, differential protein expression in fetal brain with Down syndrome
has been studied (Cheon, 2001; Bajo, 2002). As suggested earlier, 2D-PAGE is often employed as the means
for protein separation to allow for subsequent differential expression analysis as well as protein identification by
MS (Fountoulakis, 1999; Hill, 2003). There is also increased interest in applying proteomic tools to
understanding the molecular mechanisms of many elusive psychiatric disorders such as Schizophrenia, bipolar
disorder, major depressive disorder, and substance abuse (Johnston-Wilson, 2000; Anni 2002; Rohlff &
Marengo, 2003). Cerebrospinal fluid is also proving to be a very useful source for proteins that might reflect
the status of the central nervous system in pathology. In fact, CSF-proteomic studies have been initiated for
Alzheimer’s disease (Davidsson, 2002; Castegna, 2003) as well as schizophrenia (Jiang, 2003).
Neuroproteomic studies can also be very useful in the drug discovery process. For example, it can help to
identify and quantify various neuropeptides and peptide-based neurotransmitters that might be important to
monitor CNS-acting drug effects (Haskins, 2001; Svensson, 2003). Methods for proteomic analysis of the
synaptic plasma membrane fraction have also been devised, because the synaptic membrane is enriched with
transmembrane neurotransmitter-receptors and transporters, and is highly important for various psychiatric,
neurodegenerative, and neurological disorders (Stevens, 2003).
Lastly, proteomic analysis can help us understand complex protein-protein association and molecular
pathways in the nervous system. Protein-pathway mapping has been successfully attempted with the
postsynaptic density-associated NMDA receptor complex with over 100 associated proteins identified (Grant,
2001b). A smaller study of synaptic multiprotein complexes associated with the 5-HT (2C) receptor has also
been attempted (Becamel, 2002) as well as a protein-pathway associated with neurogenesis (Jin, 2004). Similar
studies are required to elucidate those protein-pathways involved in cell death for medical intervention and
functional recovery after TBI.
IX. Concluding Remarks
Neuroproteomics and the field of neurotrauma are poised to coalesce in the near future with great potential
for clinical benefits. Momentum is provided by the rapid advancement in proteomic technology. Present
limitations due to extremes in protein numbers and dynamic range in the heterogeneous brain will undoubtedly
be resolved through future access to improved proteomic technology for higher throughput and more precise
broader scope neuroproteome characterization. Eventually, bioinformatics tools will correlate protein and PTM
results with functional, spatial, and genomic information to create a systems-biology approach to neuroscience
and neurotrauma, which will enable an understanding at the organ scale, with countless possibilities for human
health and social advancement.
We gratefully thank Dr. Joseph Pancrazio of the National Institute for Neurological Disorders and Stroke and
Dr. Mary Ellen Michel of the National Institute for Drug Abuse at the National Institutes of Health for their
input in preparing this manuscript.
Ahmed N, Barker G, Oliva K, Garfin D, Talmadge K, Georgiou H, Quinn M, Rice G. 2003. An approach to
remove albumin for the proteomic analysis of low abundance biomarkers in human serum. Proteomics
Alban A, David SO, Bjorkesten L, Andersson C, Sloge E, Lewis S, Currie I. 2003. A novel experimental design
for comparative two-dimensional gel analysis: Two-dimensional difference gel electrophoresis
incorporating a pooled internal standard. Proteomics 3:36-44.
Anderson NL, Anderson NG. 2002. The human plasma proteome. Mol Cell Proteomics 1:845-867.
Ankarcrona M, Hultenby K. 2002. Presenilin-1 is located in rat mitochondria. Biochem Biophys Res Commun
Anni H, Israel Y. 2002. Proteomics in alcohol research. Alcohol Res Health 26:219-232.
Bajo M, Fruehauf J, Kim SH, Fountoulakis M, Lubec G. 2002. Proteomic evaluation of intermediary
metabolism enzyme proteins in fetal Down's syndrome cerebral cortex. Proteomics 2:1539-1546.
Barnidge DR, Dratz EA, Martin T, Bonilla LE, Moran LB, Lindall A. 2003. Absolute quantification of the G
protein-coupled receptor rhodopsin by LC/MS/MS using proteolysis product peptides and synthetic peptide
standards. Anal Chem 75:445-451.
Barr JR, Maggio VL, Patterson DG Jr, Cooper GR, Henderson LO, Turner WE, Smith SJ, Hannon WH,
Needham LL, Sampson EJ. 1996. Isotope-dilution – mass spectrometric quantification of specific proteins:
Model application with apolipoprotein A-I. Clin Chem 42:1676-1682.
Becamel C, Alonso G, Galeotti N, Demey E, Jouin P, Ullmer C, Dumuis A, Bockaert J, Marin P. 2002.
Synaptic multiprotein complexes associated with 5-HT(2C) receptors: A proteomic approach. EMBO J
Beranova-Giorgianni S, Pabst MJ, Russel TM, Giorgianni F, Goldowitz D, Desiderio DM. 2002. Preliminary
analysis of the mouse cerebellum proteome. Brain Res Mol Brain Res 98:135-140.
Beranova-Giorgianni S. 2003. Proteome analysis by two-dimensional gel electrophoresis and mass
spectrometry: strengths and limitations. Trac-Trend Anal Chem 5: 273-281.
Blackstock WP, Weir MP. 1999. Proteomics: Quantitative and physical mapping of cellular proteins. Trends
Boguski MS, McIntosh MW. 2003. Biomedical informatics for proteomics. Nature 422:233-237.
Boyd-Kimball D, Sultana R, Poon HF, Mohmmad-Abdul H, Lynn BC, Klein JB, Bufferfield DA. 2005. γ-
Glutamylcysteine ethyl ester protection of proteins from Aβ(1-42)-mediated oxidative stress in neuronal cell
culture: A proteomics approach. J Neurosci Res 79:707-713.
Brzeski H, Katenhusen RA, Sullivan AG, Russell S, George A, Somiari RI, Shriver C. 2003. Albumin depletion
method for improved plasma glycoprotein analysis by two-dimensional gel electrophoresis. BioTechniques
Buonocore G, Liberatori S, Bini L, Mishra OP, Delivoria-Papadopoulos M, Pallini V, Bracci R. 1999. Hypoxic
response of synaptosomal proteins in Term Guinea pig fetuses. J Neurochem 73:2139-2148.
Butterfield DA, Boyd-Kimball D, Castegna A. 2003. Proteomics in Alzheimer's disease: Insights into potential
mechanisms of neurodegeneration. J Neurochem 86:1313-1327.
Butterfield DA. 2004. Proteomics: A new approach to investigate oxidative stress in Alzheimer’s disease brain.
Brain Res 1000:1-7.
Cargile BJ, Bundy JL, Grunden AM, Stephenson JL Jr. 2004. Synthesis/degradation ratio mass spectrometry for
measuring relative dynamic protein turnover. Anal Chem 76:86-97.
Castegna A, Thongboonkerd V, Klein JB, Lynn B, Markesbery WR, Butterfield DA. 2003. Proteomic
identification of nitrated proteins in Alzheimer’s disease brain. J Neurochem 85:1394-1401.
Challapalli KK, Zabel C, Schuchhardt J, Kaindl AM, Klose J, Herzel H. 2004. High reproducibility of large-gel
two-dimensional electrohphoresis. Electrophoresis 25:3040-3047.
Chen S. 2000. Knowledge representation for systems biology. First international conference on Systems
biology, Tokyo Japan, November 14-16, 107-112
Chen W, Ji J, Xu X, He S, Ru B. 2003. Proteomic comparison between human young and old brains by two-
dimensional gel electrophoresis and identification of proteins. Int J Devl Neuroscience 21:209-216.
Cheng CH, Kirsch DG, Clem RJ, Ravi R, Kastan MB, Bedi A, Ueno K, Hardwick JM. 1997. Conversion of bcl-
2 to a bax-like death effector by caspases. Science 278:1966-1968.
Cheon MS, Fountoulakis M, Dierssen M, Ferreres JC, Lubec G. 2001. Expression profiles of proteins in fetal
brain with Down syndrome. J Neural Transm Suppl 61:311-319.
Choe LH, Lee KH. 2003. Quantitative and qualitative measure of intralaboratory two-dimensional protein gel
reproducibility and the effects of sample preparation, sample load, and image analysis. Electrophoresis
Choe LH, Aggarwal K, Franck Z, Lee KH. 2005. A comparison of the consistency of proteome quantitation
using two-dimensional electrophoresis and shotgun isobaric tagging in Escherichia coli cells.
Chong BE, Yan F, Lubman DM, Miller FR. 2001. Chromatofocusing nonporous reversed-phase high-
performance liquid chromatography/electrospray ionization time-of-flight mass spectrometry of proteins
from human breast cancer whole cell lysates: a novel two-dimensional liquid chromatography/mass
spectrometry method. Rapid Commun Mass Spectrom 15:291-296.
Choudhary J, Grant SG. 2004. Proteomics in postgenomic neuroscience: The end of the beginning. Nat
Choi DW. 1992. Excitotoxic cell death. J Neurobiol 23:1261-1276.
Conrads TP, Anderson GA, Veenstra TD, Pasa-Tolic L, Smith RD. 2000. Utility of accurate mass tags for
proteome-wide protein identification. Anal Chem 72:3349-3354.
Conti A, Sanchez-Ruiz Y, Bachi A, Beretta L, Grandi E, Beltramo M, Alessio M. 2004. Proteome study of
human cerebrospinal fluid following traumatic brain injury indicates fibrin(ogen) degradation products as
trauma-associated markers. J Neurotrauma 21:854-863.
Coon JJ, Syka JEP, Schwartz JC, Shabanowitz J, Hunt DF. 2004. Anion dependence in the partitioning between
proton and electron transfer in ion/ion reactions. Int J Mass Spectrom 236:33-42.
Corthals GL, Wasinger VC, Hochstrasser DF, Sanches JC. 2000. The dynamic range of protein expression: A
challenge for proteomics research. Electrophoresis 21:1104-1115.
Courchesne PL, Patterson SD. 1999. Identification of proteins by matrix-assisted laser desorption/ionization
mass spectrometry using peptide and fragment ion masses. Methods Mol Biol 112:487-511.
Davidsson P, Westman-Brinkmalm A, Nilsson CL, Lindbjer M, Paulson L, Andreasen N, Sjogren M, Blennow
K. 2002. Proteome analysis of cerebrospinal fluid proteins in Alzheimer patients. Neuroreport 13:611-615.
Denslow ND, Michel ME, Temple MD, Hsu CY, Saatman K, Hayes RL. 2003. Application of Proteomics
Technology to the Field of Neurotrauma. J Neurotrauma 20:401-407.
Desiderio DM, Zhu X. 1998. Quantitative analysis of methionine enkephalin and β-endorphin in the pituitary by
liquid secondary ion mass spectrometry and tandem mass spectrometry. J Chromatogr A 794:85-96.
DeSouza L, Diehl G, Rodrigues MJ, Guo J, Romaschin AD, Colgan TJ, Siu KW. Search for cancer markers
from endometrial tissues using differentially labeled tags iTRQ and cICAT with multidimensional liquid
chromatography and tandem mass spectrometry. J Proteome Res 4:377-386.
Dickey C. 2003. The experts discuss protein separation techniques. Drug Discov Devel 6:51-53.
Dixon CE, Clifton GL, Lighthall JW, Yaghmai AA, Hayes RL. 1991. A controlled cortical impact model of
traumatic brain injury in the rat. J Neurosci Methods 39:253-262.
Dongre AR, Opiteck G, Cosand WL, Hefta SA. 2001. Proteomics in the post-genome age. Biopolymers 60:206-
Eriksson J, Chait BT, Fenyö D. 2000. A statistical basis for testing the significance of mass spectrometric
protein identification results. Anal Chem 72:999-1005.
Fang Z, Polacco M, Chen S, Schroeder D, Hancock D, Sanchez H, Coe E. 2003. cMap: The comparative
genetic map viewer. Bioinformatics 19:416-417.
Feng BB, Patel AH, Keller PM, Slemmon JR. 2001. Fast characterization of intact proteins using a high-
throughput eight-channel parallel liquid chromatography mass spectrometry system. Rapid Commun Mass
Fenselau C. 1997. MALDI MS and strategies for protein analysis. Anal Chem 69:661A-665A.
Fenyö D, Beavis RC. 2002. Informatics and data management in proteomics. Trends in Biotechnol 20:S35-S38.
Fenyö D, Beavis RC. 2003. A method for assessing the statistical significance of mass spectrometry-bases
protein identifications using general scoring schemes. Anal Chem 75:768-774.
Fierens C, Thienpont LMR, Stöckl D, Willekens E, de Leenheer AP. 2000. Quantitative analysis of urinary c-
peptide by liquid chromatography-tandem mass spectrometry with a stable isotopically labeled internal
standard. J Chromatogr A 896:275-278.
Fierens C, Stöckl D, Baetens D, de Leenheer AP, Thienpont LM. 2003. Standardization of c-peptide
measurements in urine by method comparison with isotope-dilution mass spectrometry. Clin Chem 49:992-
Finnie JW. 2001. Animal models of traumatic brain injury: A review. Austral Vet J 79:628-633
Fleischer S, Kervina M. 1974. Subcellular fractionation of rat liver. In: Fleischer S, Packer L, editors. Methods
in enzymology, volume 31. New York: Academic Press. p 6-41.
Flory MR, Griffin TJ, Martin D, Aebersold R. 2002. Advances in quantitative proteomics using stable isotope
tags. Trends Biotechnol 20:S23-S29.
Freeman WM, Hemby SE. 2004. Proteomics for protein expression profiling in neuroscience. Neurochem Res
Fountoulakis M, Schuller E, Hardmeier R, Berndt P, Lubec G. 1999. Rat brain proteincs: Two-dimensional
protein database and variations in the expression level. Electrophoresis 20:3572-3579.
Fountoulakis M, Juranville JF. 2003. Enrichment of low-abundance brain proteins by preparative
electrophoresis. Anal Biochem 313:267-282.
Fountoulakis M. 2004. Application of proteomics technologies in the investigation of the brain. Mass Spectrom
Franzén B, Yang Y, Sunnemark D, Wickman M, Ottervald J, Oppermann M, Sandberg K. 2003.
Dihydropyrimidinase related protein-2 as a biomarker for temperature and time dependent post mortem
changes in the mouse brain proteome. Proteomics 3:1920-1929.
Gay S, Binz PA, Hochstrasser DF, Appel RD. 2002. Peptide mass fingerprinting peak intensity prediction:
Extracting knowledge from spectra. Proteomics 2:1374-1391.
Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. 2003. Absolute quantification of proteins and
phosphoproteins from cell lysates by tandem MS. PNAS 100:6940-6945.
Gershon D. 2005. Mass spectrometry: gaining mass appeal in proteomics. Nat Meth 2:465-470.
Ghosh R. 2004. Separation of human albumin and IgG by a membrane-based integrated bioseparation technique
involving simultaneous precipitation, microfiltration and membrane adsorption. J Memb Sci 237:109-117.
Giddings JC. 1987. Concepts and comparisons in multidimensional separation. HRC CC J High Resolut
Chromatogr Chromatogr Commun 10:319-323.
Godovac-Zimmermann J, Brown LR. 2001. Perspectives for mass spectrometry and functional proteomics.
Mass Spectrom Rev 20:1-57.
Goshe MB, Conrads TP, Panisko EA, Angell NH, Veenstra TD, Smith RD. 2001. Phosphoprotein isotope-
coded affinity tag approach for isolating and quantitating phosphopeptides in proteome-wide analysis. Anal
Grant SG, Blackstock WP. 2001a. Proteomics in neuroscience: from protein to network. J Neurosci 21:8315-
Grant SG, Husi H. 2001b. Proteomics of multiprotein complexes: Answering fundamental questions in
neuroscience. Trends Biotechnol 19:S49-S54.
Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. 1999. Quantitative analysis of complex protein
mixtures using isotope-coded affinity tags. Nat Biotechnol 17:994-999.
Gygi SP, Rist B, Griffin TJ, Eng J, Aebersold R. 2002. Proteome analysis of low-abundance proteins using
multidimensional chromatography and isotope-coded affinity tags. J Proteome Res 1:47-54.
Hamakubo T, Kannagi R, Murachi T, Matus A. 1986. Distribution of calpains I and II in rat brain. J Neurosci
Hancock WS, Wu SL, Stanley RR, Gombocz EA. 2002. Publishing large proteome datasets: Scientific policy
meets emerging technologies. Trends Biotechnol 20:S39-S44.
Hansen KC, Schmitt-Ulms G, Chalkley RJ, Hirsch J, Baldwin MA, Burlingame AL. 2003. Mass spectrometric
analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and
multidimensional chromatography. Mol Cell Proteomics 2:299-314.
Haskins WE, Wang Z, Watson CJ, Rostand RR, Witowski SR, Powell DH, Kennedy RT. 2001. Capillary LC-
MS2 at the attomole level for monitoring and discovering endogenous peptides in microdialysis samples
collected in vivo. Anal Chem 73:5005-5014.
Haskins WE, Kobeissy FH, Wolper RA, Ottens AK, Kitlen JW, McClung SH, O’Steen BE, Chow MM, Pineda
JA, Denslow ND, Hayes RL, Wang KKW. 2005. Rapid discovery of putative protein biomarkers of
traumatic brain injury by SDS-PAGE-capillary liquid chromatography-tandem mass spectrometry. J
Hayes RL, Kampfl A, Posmantur RM. 1999. The contribution of calpain proteolysis to neuronal death
following traumatic brain injury. In: Wang KKW, Yuen P, editors. Calpain: Pharmacology and Toxicology
of Calcium-Dependent Protease. Philadelphia: Taylor & Francis p 191-209.
Herbert BR, Harry JL, Packer NH, Gooley AA, Pederson SK, Williams KL. 2001. What place for
polyacrylamide in proteomics? Trends Biotechnol 10: S3-9.
Hill A, Kim H. 2003. The UAB proteomics database. Bioinformatics 19:2149-2151.
Hitt E. 2004. On the iTRAQ. The Scientist 18:43.
Hogenboom S, Tuyp JJM, Espeel M, Koster J, Wanders RJA, Waterham HR. 2004. Human mevalonate
pyrophosphate decarboxylase is localized in the cytosol. Mol Genet Metab 81:216-224.
Hu Q, Noll RJ, Li H, Makarov A, Hardman M, Cooks GR. 2005. The orbitrap: a new mass spectrometer. J
Mass Spectrom 40:430-443.
Hunter TC, Andon NL, Koller A, Yates JR III. 2002. The functional proteomics toolbox: methods and
applications. J Chromatogr B 782:165-181.
Husi H, Ward MA, Choudhary JS, Blackstock WP, Grant SGN. 2000. Proteomic analysis of NMDA receptor-
adhesion protein signaling complexes. Nat Neurosci 7:661-669.
Husi H, Grant SGN. 2001. Proteomics of the nervous system. Trends Neurosci 24:259-266.
Ishisaka R, Utsumi T, Kanno T, Arita K, Katunuma N, Akiyama J, Utzumi K. 1999. Participation of a cathepsin
L-type protease in the activation of caspase-3. Cell Struct Funct 24:465-470.
Issaq HJ. 2001. The role of separation science in proteomics research. Electrophoresis 22:3629-3638.
Jellinger KA, Stadelmann C. 2001. Problems of cell death in neurodegeneration and Alzheimer's disease. J
Alzheimers Dis 3:31-40.
Jenkins LW, Peters GW, Dixon CE, Zhang X, Clark RSB, Skinner JC, Marion DW, Adelson PD, Kochanek
PM. 2002. Conventional and functional proteomics using large format two-dimensional gel electrophoresis
24 hours after controlled cortical impact in postnatal day 17 rats. J Neurotrauma 19:715-740.
Jiang L, Lindpaintner K, Li HF, Gu NF, Langen H, He L, Fountoulakis M. 2003. Proteomic analysis of the
cerebrospinal fluid of patients with schizophrenia. Amino Acids 25:49-57.
Jiang L, He L, Fountoulakis M. 2004. Comparison of protein precipitation methods for sample preparation prior
to proteomic analysis. J Chromatogr A 1023:317-320.
Jin J, Meredith GE, Chen L, Zhou Y, Xu J, Shie FS, Lockhart P, Zhang J. 2005. Quantitative proteomic analysis
of mitochondrial proteins: relevance to Lewy body formation and Parkinson’s disease. Brain Res Mol Brain
Jin K, Mao XO, Cottrell B, Schilling B, Xie L, Row RH, Sun Y, Peel A, Childs J, Gendeh G, Gibson BW,
Greenberg DA. 2004. Proteomic and immunochemical characterization of a role for stathmin in adult
neurogenesis. FASEB J 18:287-299.
Johnson RS, Taylor JA. 2002. Searching sequence databases via de novo peptide sequencing by tandem mass
spectrometry. Mol Biotechnol 22:301-315.
Johnston-Wilson NL, Sims CD, Hofmann JP, Anderson L, Shore AD, Torrey EF, Yolken RH. 2000. Disease-
specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive
disorder. The Stanley Neuropathology Consortium. Mol Psychiatry 5:142-149.
Jung E, Hoogland C, Chiappe D, Sanchez J, Hochstrasser DF. 2000a. The establishment of a human liver nuclei
two-dimensional electrophoresis reference map. Electrophoresis 21:3483-3487.
Jung E, Heller M, Sanchez JC, Hochstrasser DF. 2000b. Proteomics meets cell biology: The establishment of
subcellular proteomes. Electrophoresis 21:3369-3377.
Jungblut P, Thiede B. 1997. Protein identification from 2-DE gels by MALDI mass spectrometry. Mass
Spectrom Rev 16:145-162.
Kabbani N, Jeromin A, Levenson R. 2004. Dynamin-2 associated with the dopamine receptor signalplex and
regulates internalization of activated D2 receptors. Cell Signal 16: 497-503.
Karas M, Bachmann D, Bahr U, Hillenkamp F. 1987. Matrix-assisted ultraviolet-laser desorption of nonvolatile
compounds. Int J Mass Spectrom Ion Process 78:53-68.
Kaufmann R. 1995. Matrix-assisted laser desorption ionization (MALDI) mass spectrometry: A novel analytical
tool in molecular biology and biotechnology. J biotechol. 41:155-175.
Keller A, Nesvizhskii AI, Kolker E, Aebersold R. 2002. Empirical statistical model to estimate the accuracy of
peptide identifications made by MS/MS and database search. Anal Chem 74:5383-5392.
Kim SI, Voshol H, van Oostrum J, Hastings TG, Cascio M, Glucksman MJ. 2004. Neuroproteomics:
Expression profiling of the brain's proteomes in health and disease. Neurochem Res 29:1317-1331.
Kippen AD, Cerini F, Vadas L, Stöcklin R, Vu Lan, Offord RE, Rose K. 1997. Development of an isotope
dilution assay for precise determination of insulin, c-peptide, and proinsulin levels in non-diabetic and type
II diabetic individuals with comparison to immunoassay. J Bio Chem 272:12513-12522.
Kitano H. 2000. Perspectives on systems biology. New Gen Comput 18:199-216.
Klose J. 1975. Protein mapping by combined isoelectric focusing and electrohpresis of mouse tissues – Novel
approach to testing for induced point mutations in mammals. Humangenetik 26:231-243.
Krapfenbauer K, Fountoulakis M, Lubec G. 2003. A rat brain protein expression map including cytosolic and
enriched mitochondrial and microsomal fractions. Electrophoresis 24:1847-1870.
Kubbutat MH, Vousden KH. 1997. Proteolytic cleavage of human P53 by calpain: a potential regulator of
protein stability. Mol Cell Biol 17:460-468.
Kuster B, Mortensen P, Anderson JS, Mann M. 2001. Mass spectrometry allows direct identification of proteins
in large genomes. Proteomics 1:641-650.
Lahm HW, Langen H. 2000. Mass Spectrometry: A tool for the identification of proteins separated by gels.
Langen H, Berndt P, Röder D, Cairns N, Lubec G, Fountoulakis M. 1999. Two-dimensional map of human
brain proteins. Electrophoresis 20:907-916.
Larner SF, McKinsey DM, Hayes RL, Wang KKW. 2005. Caspase-7: Increased expression and activation after
traumatic brain injury in rats. J. Neurochem. 97:97-108.
Lefkovits I. 2003. Functional and structural proteomics: a critical appraisal. J Chromatogr B 787:1-10.
Li J, Steen H, Gygi SP. Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents. The yeast
salinity stress response. Mol Cell Proteomics 2:1198-1204.
Liebler DC. 2002. Proteomic approaches to characterize protein modifications: New tools to study the effects of
environmental exposures. Environ Health Perspect 110:3-9.
Lill J. 2003. Protemic tools for quantification by mass spectrometry. Mass Spectrom Rev 22:182-194.
Lilley KS, Razzaq A, Dupree P. 2001. Two-dimensional gel electrophoresis: recent advances in sample
preparation, detection, and quantification. Curr Opin Chem Biol 6:46-50.
Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik BM, Yates JR III. 1999. Direct analysis
of protein complexes using mass spectrometry. Nat Biotechnol 17:676-682.
Link AJ. 2002. Multidimensional peptide separations in proteomics. Trends Biotechnol 20:S8-S13.
Lisacek F, Chichester C, Gonnet P, Jaillet O, Kappus S, Nikitin F, Roland P, Rossier G, Truong L, Appel R.
2004. Shaping biological knowledge: Applications in proteomics. Comp Funct Genomics 5:190-195.
Liska AJ, Shevchenko A. 2003. Combining mass spectrometry with database interrogation strategies in
proteomics. Trends Anal Chem 22:291-298.
Lovell MA, Xiong S, Markesbery WR, Lynn BC. 2005. Quantitative proteomic analysis of mitochondria from
primary neuron cultures treated with amyloid beta peptide. Neurochem Res 30:113-122.
Lubec G, Labudova O, Cairns N, Fountoulakis M. 1999. Increased glyceraldehydes 3-phosphate dehydrogenase
levels in the brain of patients with Down’s syndrome. Neurosci lett 260:141-145.
Lubec G, Krapfenbauer K, Fountoulakis M. 2003. Proteomics in brain research: Potentials and limitations. Prog
MacCoss MJ, Wu CC, Yates JR III. 2002. Probability-based validation of protein identifications using a
modified SEQUEST algorithm. Anal Chem 74:5593-5599.
Marengo E, Robotti E, Gianotti V, Righetti PG, Cecconi D, Domenici E. 2003. A new integrated statistical
approach to the diagnostic use of two-dimensional maps. Electrophoresis 24:225-236.
McDonald WH, Yates JR III. 2000. Proteomic tools for cell biology. Traffic 1:747-754.
McDonald WH, Yates JR III. 2002. Shotgun proteomics and biomarker discovery. Dis Markers 18:99-105.
Mehta AI, Ross S, Lowenthal MS, Fusaro V, Fishman DA, Petricoin EF, Liotta LA. 2003. Biomarker
amplification by serum carrier protein binding. Dis Markers 19:1-10.
Meyer HE, Klose J, Hamacher M. 2003. HBPP and the pursuit of standardisation. Lancet Neurol 2:657-658.
Michnick SW. 2004. Proteomics in living cells. Drug Discov Today 9:262-267.
Morrison RS, Kinoshita Y, Johnson MD, Uo T, Ho JT, McBee JK, Conrads TP, Veenstra TD. 2002. Proteomic
analysis in the neurosciences. Mol Cell Proteomics 1:553-560.
Moskowitz MA, Le DA, Whalen MJ. 2003. Caspases and upstream mechanisms in central nervous system
ischemic injury. Int Congr Ser 1252:155-161.
Newcomb JK, Kampfl A, Posmantur RM, Zhao X, Pike BR, Liu SJ, Clifton GL, Hayes RL. 1997.
Immunohistochemical study of calpain-mediated breakdown products to alpha-spectrin following controlled
cortical impact injury in the rat. J. neurotrauma 14:369-383.
Nguyen DN, Becker GW, Riggin RM. 1995. Protein mass spectrometry: Applications to analytical
biotechnology. J Chromatogr A 705:21-45.
Nilsson CL, Karlsson G, Bergquist J, Westman A, Ekman R. 1998. Mass spectrometry of peptides in
neuroscience. Peptides 19:781-789.
Nishihara JC, Champion KM. 2002. Quantitative evaluation of proteins in one- and two-dimensional
polyacrylimide gels using a fluorescent stain. 23:2203-2215.
Nothwang HG, Becker M, Ociepka K, Friauf E. 2003. Protein analysis in the rat auditory brainstem by two-
dimensional electrophoresis and mass spectrometry. Mol Brain Res 116:59-69.
O’Farrell PH. 1975. High-resolution 2-dimentional electrophoresis of proteins. J Biol Chem 250:4007-4021.
Oda Y, Huang K, Cross FR, Cowburn D, Chait BT. 1999. Accurate quantification of protein expression and
site-specific phosphorylation. PNAS 96:6591-6596.
Oh-Ishi M, Maeda T. 2002. Separation techniques for high-molecular mass proteins. J Chromatogr B 771:49-
Olsen JV, Anderson JR, Nielsen PA, Nielsen ML, Figeys D, Mann M, Wisniewski JR. 2004. Hystag – a novel
proteomic quantification tool applied to differential display analysis of membrane proteins from distinct
areas of mouse brain. Mol Cell Proteomics 3:82-92.
Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. 2002. Stable isotope
labeling by amino acid in cell culture, SILAC, as a simple and accurate approach to expression proteomics.
Mol Cell Proteomics 1:376-386.
Ong SE, Foster LJ, Mann M. 2003a. Mass spectrometric-based approaches in quantitative proteomics. Methods
Ong SE, Kratchmarova I, Mann M. 2003b. Properties of 13C-substituted arginine in stable isotope labeling by
amino acid in cell culture (SILAC). J Proteome Res 2:173-181.
Opiteck GJ, Lewis KC, Jorgenson JW, Anderegg RJ. 1997. Comprehensive on-line LC/LC/MS of proteins.
Anal Chem 68:1518-1524.
Opiteck GJ, Ramirez SM, Jorgenson JW, Moseley MA III. 1998. Comprehensive two-dimensional high-
performance liquid chromatography for the isolation of overexpressed proteins and proteome mapping. Anal
Ottens AK, Kobeissy FH, Wolper RA, Haskins WE, Hayes RL, Denslow ND, Wang KKW. 2005. A
multidimensional differential proteomic platform using dual-phase ion-exchange chromatography –
polyacrylamide gel electrophoresis/reversed-phase liquid chromatography tandem mass spectrometry. Anal
Parker KC. 2002. Scoring methods in MALDI peptide mass fingerprinting: ChemScore, and the ChemApplex
program. J Am Soc Mass Spectrom 13:22-39.
Patton WF. 2002. Detection technologies in proteome analysis. J Chromatogr B 771:3-31.
Pearson WR. 2000. Flexible sequence similarity searching with the FASTA3 program package. Methods Mol
Peng JM, Elias JE, Thoreen CC, Licklider LJ, Gygi SP. 2003. Evaluation of multidimensional chromatography
coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: The yeast
proteome. J Proteome Res 2:43-50.
Peng J, Kim MJ, Cheng D, Duong DM, Gygi SP, Sheng M. 2004. Semiquantitative proteomic analysis of rat
forebrain postsynaptic density fractions by mass spectrometry. J Bio Chem 279:21003-21011.
Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TKB, Chandrika KN,
Deshpande N, Suresh S, Rashmi BP, Shanker K, Padma N, Niranjan V, Harsha HC, Talreja N,
Vrushabendra BM, Ramya MA, Yatish AJ, Joy M, Shivashankar HN, Kavitha MP, Menezes M, Choudhury
DR, Ghosh N, Saravana R, Chandran S, Mohan S, Jonnalagadda CK, Prasad CK, Kumar-Sinha C,
Deshpande KS, Pandey A. 2004. Human protein reference databse as discovery resource for proteomics.
Nucleic Acids Res 32:D497-D501.
Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. 1999. Probability-based protein identification by searching
sequence databases using mass spectrometry data. Electrophoresis 20:3551-3567.
Peyrl A, Krapfenbauer K, Slavc I, Strobel T, Lubec G. 2003. Proteomic characterization of the human cortical
neuronal cell line HCN-2. J Chem Neuroanat 26:171-178.
Phizicky E, Bastiaens PI, Zhu H, Snyder M, Fields S. 2003. Protein analysis on a proteomic scale. Nature
Pineda JA, Wang KKW, Hayes, RL. 2004. Biomarkers of proteolytic damage following traumatic brain injury.
Brain Pathol 14:202-209.
Posmantur RM, Zhao X, Kampfl A, Clifton GL, Hayes RL. 1998. Immunoblot analyses of the relative
contributions of cysteine and aspartic proteases to neurofilament breakdown products following
experimental brain injury in rats. Neurochem Res 23:1265-1276.
Posmantur RM, Newcomb JK, Kampfl A, Hayes RL. 2000. Light and confocal microscopic studies of
evolutionary changes in neurofilament proteins following cortical impact injury in the rat. Exp Neurol
Pratt JM, Petty J, Riba-Garcia I, Robertson DH, Gaskell SJ, Oliver SG, Beynon RJ. 2002. Dynamics of protein
turnover, a missing dimension in proteomics. Mol Cell Proteomics 1:579-591.
Prokai L, Zharikova AD, Stevens SM Jr. 2005. Effect of chronic morphone exposure on the synaptic plasma-
membrane subproteome of rats: a quantitative protein profiling study based on isotope-coded affinity tags
and liquid chromatography/mass spectrometry. J Mass spectrum 40:169-175.
Qiu Y, Sousa EA, Hewick RM, Wang JH. 2002. Acid-labile isotope-coded extractants: A class of reagents for
quantitative mass spectrometric analysis of complex protein mixtures. Anal Chem 74:4969-4979.
Raabe A, Grolms C, Sorge O, Zimmermann M, Seifert V. 1999. Serum S-100B protein in severe head injury.
Rabillard T. 2000. Detecting proteins separated by 2D gel electrophoresis. Anal Chem 72:48A-55A.
Raghupathi R, Graham DI, McIntosh TK. 2000. Apoptosis after traumatic brain injury. J Neurotrauma 17:927-
Raghupathi R. 2004. Cell death mechanisms following traumatic brain injury. Brain Pathol 14:215-222.
Raida M, Schulz-Knappe P, Heine G, Forssmann WG. 1999. Liquid chromatography and electrospray mass
spectrometry mapping of peptides from human plasma filtrate. J Am Soc Mass Spectrom 10:45-54.
Rami A. 2003. Ischemic neuronal death in the rat hippocampus: The calpain-calpastatin-caspase hypothesis.
Neurobio Dis 13:75-88.
Raymackers J, Daniels A, De Brabandere V, Missiaen C, Dauwe M, Verhaert P, Vanmechelen E, Meheus L.
2000. Identification of two-dimensionally separated human cerebrospinal fluid proteins by N-terminal
sequencing, matrix-assisted laser desorption/ionization--mass spectrometry, nanoliquid chromatography-
electrospray ionization-time of flight-mass spectrometry, and tandem mass spectrometry. Electrophoresis
Regnier F, Amini A, Charkraborty A, Geng M, Ji JY, Riggs L, Sioma C, Wang SH, Zhang X. 2001.
Multidimensional chromatography and the signature peptide approach to proteomics. LC-GC 19:200-213.
Resing KA, Meyer-Arendt K, Medoza AM, Aveline-Wolf LD, Jonscher KR, Pierce KG, Old WM, Cheung HT,
Russell S, Wattawa JL, Geohle GR, Knight RD, Ahn NG. 2004. Improving reproducibility and sensitivity in
identifying human proteins by shotgun proteomics. Anal Chem 76:3556-3568.
Rohlff C. 2000. Proteomics in molecular medicine: applications in central nervous systems disorders.
Rohlff C, Hollis K. 2003 Modern proteomic strategies in the study of complex neuropsychiatric disorders. Biol
Romner B. 2000. Traumatic brain damage: Serum S-100 protein measurements related to neuroradiological
findings. J Neurotrauma 17:641-647.
Ruiz-Vela A, Gonzalez de Buitrago G, Martinez AC. 1999. Implication of calpain in caspase activation during
B cell clonal deletion. EMBO J 18:4988-4998.
Satchell MA, Zhang X, Kochanek PM, Dixon CE, Jenkins LW, Melick J, Szabo C, Clark RSB. 2003. A dual
role for poly-ADP-ribosylation in spatial memory acquisition after traumatic brain injury in mice involving
NAD+ depletion and ribosylation of 14-3-3γ. J Neurochem 85:697-708.
Satterfield MB, Sniegoski LT, Welch MJ, Nelson BC. 2003. Comparison of isotope dilution mass spectrometry
methods for determination of total homocysteine in plasma and serum. Anal Chem 75:4631-4638.
Schonberger SJ, Edgar PF, Kydd R, Faull RL, Cooper GJ. 2001. Proteomic analysis of the brain in Alzheimer's
disease: Molecular phenotype of a complex disease process. Proteomics 1:1519-1528.
Schulz-Knappe P, Zucht HS, Heine G, Jürgens M, Hess R, Schrader M. 2001. Peptidomics: The comprehensive
analysis of peptides in complex biological mixtures. Comb Chem High Throughput Screen 4:207-217.
Schwartz JC, Senko MW, Syka JEP. 2002. A two-dimensional quadrupole ion trap mass spectrometer. J Am
Soc Mass Spectrom 13:659-669.
Scriver CR. 2004. After the genome - the phenome? J Inherit Metab Dis 27:305-317.
Seow TK, Korke R, Liang RC, Ong SE, Ou K, Wong K, Hu WS, Chung MC. 2001. Proteomic investigation of
metabiloic shift in mammalian cell culture. Biotechnol Prog 17:1137-1144.
Sethuraman M, McComb ME, Huang H, Huang S, Heibeck T, Costello CE, Cohen RA. 2004. Isotope-coded
affinity tag (ICAT) approach to redox proteomics: identification and quantitation of oxidant-sensitive
cysteine thiols in complex protein mixtures. J Proteome Res 3:1228-1233.
Shaw J, Rowlinson R, Nickson J, Stone T, Sweet A, Williams K, Tonge R. 2003. Evaluation of saturation
labeling two-dimensional difference gel electrophoresis fluorescent dyes. Proteomics 3:1181-1195.
Shi H, Rodriguez O, Shang Y, Chen S. 2002. Integrating adaptive and intelligent techniques into a web-based
environment for active learning. In: Leondes CT, editor. Intelligent Systems: Technology and Applications.
Boca Raton: CRC Press. p 229-260.
Siman R, McIntosh TK, Soltesz KM, Chen Z, Neumar RW, Roberts VL. 2004. Proteins released from
degenerating neurons are surrogate markers for acute brain damage. Neurobiol Dis 16:311-320.
Smith CM. 2000. Bioinformatics, genomics, and proteomics. Scientist 14:26-30.
Smith RD. 2002. Trends in mass spectrometry instrumentation for proteimics. Trends Biotechnol 20:S3-S7.
Smolka M, Zhou HL, Aebersold R. 2002. Quantitative protein profiling using two-dimensional gel
electrophoresis, isotope-coded affinity tag labeling, and mass spectrometry. Mol Cell Proteomics 1:19-29.
Steel LF, Trotter MG, Nakajima PB, Mattu TS, Gonye G, Block T. 2003. Efficient and specific removal of
albumin from human serum samples. Mol Cell Proteomics 2: 262-270.
Stevens SM, Zharikova AD, Prokai L. 2003. Proteomic analysis of the synaptic plasma membrane fraction
isolated from rat forebrain. Brain Res Mol Brain Res 117:116-128.
Stocklin R, Vu L, Vadas L, Cerini F, Kippen AD, Offord RE, Rose K. 1997. A stable isotope dilution assay for
the in vivo determination of insulin levels in humans by mass spectrometry. Diabetes 46:44-51.
Svensson M, Skold K, Svenningsson P, Andren PE. 2003. Peptidomics-based discovery of novel neuropeptides.
J Proteome Res 2:213-219.
Syka JEP, Marto JA, Bai DL, Horning S, Senko MW, Schwartz JC, Ueberheide B, Garcia B, Busby S,
Muratore T, Shabanowitz J, Hunt DF. 2004a. Novel linear quadrupole ion trap/FT mass spectrometer:
Performance characterization and use in the comparative analysis of histone H3 posttranslational
modifications. J Proteome Res 3:621-626.
Syka JEP, Coon JJ, Schroeder MJ, Shabanowitz J, Hunt DF. 2004b. Peptide and protein sequence analysis by
electron transfer dissociation mass spectrometry. PNAS 101:9528-9533.
Tabb DL, McDonald WH, Yates JR III. 2002. DTASelect and Contrast: Tools for assembling and comparing
protein identifications from shotgun proteomics. J Proteome Res 1:21-26.
Tabb DL, Saraf A, Yates JR III. 2003. GutenTag: High-throughput sequence tagging via an empirically derived
fragmentation model. Anal Chem 75:6415-6421.
Takano E, Maki M. 1999. Structure of calpastatin and its inhibitory control of calpain. In Wang KKW, Yuen P,
editors. Calpain: Pharmacology and Toxicology of Calcium-Dependent Protease. Philadelphia: Taylor &
Francis. p 25-49.
Taoka M, Wakamiya A, Nakayama H, Isobe T. 2000. Protein profiling of rat cerebella during development.
Taylor JA, Johnson RS. 2001. Implementation and uses of automated de novo peptide sequencing by tandem
mass spectrometry. Anal Chem 73:2594-2604.
Terry DE, Desiderio DM. 2003. Between-gel reproducibility of the human cerebrospinal fluid proteome.
Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Homon C. 2003. Tandem mass
tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal
Tilleman K, Van den Haute C, Geerts H, van Leuven F, Esmans EL, Moens L. 2002. Proteomics analysis of the
neurodegeneration in the brain of tau transgenic mice. Proteomics. 2:656-665.
Tonge R, Shaw J, Middleton B, Rowlinson R, Rayner S, Young J, Pognan F, Hawkins E, Currie I, Davison M.
2001. Validation and development of fluorescence two-dimensional differential gel electrophoresis
proteomics technology. Proteomics 1:377-396.
Tontchev AB, Yamashima T. 1999. Ischemic delayed neuronal death: Role of the cysteine proteases calpain and
cathepsins. Neuropathol 19:356-365.
Troy CM, Salvesen GS. 2002. Caspases on the brain. J Neurosci Res 69:145-150.
Tsugita A, Kawakami T, Uchida T, Sakai T, Kamo M, Matsui T, Watanabe Y, Morimasa T, Hosokawa K, Toda
T. 2000. Proteome analysis of mouse brain: Two-dimensional electrophoresis profiles of tissue proteins
during the course of aging. Electrophoresis 21:1853-1871.
Tsuji T, Shimohama S. 2001. Analysis of the proteomic profiling of brain tissue in Alzheimer's disease. Dis
Turkova J. 1999. Bioaffinity Chromatography. In: Aboul-Enein HY, editor. Analytical and preparative
separation methods of biomolecules. New York: Marcel Dekker. p 99-165.
Ueyama J, Kitaichi K, Iwase M, Takagi K, Hasegawa T. 2003. Application of ultrafiltration method to
measurement of catecholamines in plasma of human and rodents by high-performance liquid
chromatography. J Chromatogr B 798:35-41.
Unlu M, Morgan ME, Minden JS. 1997. Difference gel electrophoresis: A single gel method for detecting
changes in protein extracts. Electrophoresis 18:2071-2077.
Veenstra TD. 2002. Proteomic analysis in the neurosciences. Mol Cell Proteomics 1:553-560.
Veenstra TD, Conrads TP, Issaq HJ. 2004. Commentary: What to do with “one-hit wonders”? Electrophoresis
Voss T, Haberl P. 2000. Observations on the reproducibility and matching efficiency of two-dimensional
electrophoresis gels: Consequences for comprehensive data analysis. Electrophoresis 21:3345-3350.
Wagner K, Miliotis T, Marko-Varga G, Bischoff R, Unger KK. 2002. An automated on-line multidimensional
HPLC system for protein and peptide mapping with integrated sample preparation. Anal Chem 74:809-820.
Wall DB, Kachman MT, Gong SY, Hinderer R, Parus S, Misek DE, Hanash SM, Lubman DM. 2000.
Isoelectric focusing nonporous RP HPLC: A two-dimensional liquid-phase separation method for mapping
of cellular proteins with identification using MALDI-TOF mass spectrometry. Anal Chem 72:1099-1111.
Wang H, Hanash S. 2003. Multi-dimensional liquid phase based separations in proteomics. J Chromatogr B
Wang KKW. 2000. Calpain and caspase: Can you tell the difference? Trends Neurosci 23:20-26.
Wang KKW, Ottens A, Haskins W, Liu MC, Kobeissy F, Denslow N, Chen SS. Hayes RL. 2004. Proteomics
studies of traumatic brain injury. In Neuhold LA, editor. International review of neurobiology. London:
Elsevier. p 215-240.
Washburn MP, Wolters D, Yates JR III. 2001. Large-scale analysis of the yeast proteome by multidimensional
protein identification technology. Nat Biotechnol 19:242-247.
Whitehouse PJ, Lynch D, Kuhar MJ. 1984. Effects of postmortem delay and temperature on neurotransmitter
receptor binding in a rat model of the human autopsy process. J Neurochem 43:553-559.
Wiemer JC, Prokudin A. 2004. Bioinformatics in proteomics: application, terminology, and pitfalls. Pathol Res
Wingrave JM, Schaecher KE, Sribnick EA, Wilford GG, Ray SK, Hazen-Martin DJ, Hogan EL, Banik NL.
2003. Early induction of secondary injury factors causing activation of calpain and mitochondria-mediated
neuronal apoptosis following spinal cord injury in rats. J Neurosci Res 73:95-104.
Wu CC, MacCoss MJ, Howell KE, Matthews DE, Yates RJ III. 2004. Metabolic labeling of mammalian
organisms with stable isotopes for quantitative proteomics analysis. Anal Chem 76:4951-4959.
Yamashima T, Kohoda Y, Tsuchiya K, Ueno T, Yamashita J, Yoshioka T, Kominami E. 1998. Inhibition of
ischaemic hippocampal neuronal death in primates with cathepsin B inhibitor CA-074: A novel strategy for
neuroprotection based on ‘calpain-cathepsin hypothesis’. Euro J Neurosci 10:1723-1733.
Yamashima T. 2000. Implication of cysteine proteases calpain, cathepsin and caspase in ischemic neuronal
death of primates. Prog Neurobiol 63:273-295.
Yamashima T, Tochev AB, Tsukada T, Saido TC, Imajoh-Ohmi S, Momoi T, Kominami E. 2003. Sustained
calpain activation associated with lysosomal rupture executes necrosis of the postischemic CA1 neurons in
primates. Hippocampus 13:791-800.
Yamashima T. 2004. Ca2+ -dependent proteases in ischemic neuronal death: A conserved ‘calpain-cathepsin
cascade’ from nematodes to primates. Cell Calcium 36:285-293.
Yang JW, Czech T, Lubec G. 2004. Proteomic profiling of human hippocampus. Electrophoresis 25:1169-1174.
Yates JR III. 1998a. Mass spectrometry and the age of the proteome. J Mass Spectrom 33:1-19.
Yates JR III, Morgan SF, Gatlin CL, Griffin PR, Eng JK. 1998b. Method to compare collision-induced
dissociation spectra of peptides: Potential for library searching and subtractive analysis. Anal Chem
Yu LR, Johnson MD, Conrads TP, Smith RD, Morrison RS, Veenstra TD. 2002. Proteome analysis of
comptothecin-treated cortical neurons using isotope-coded affinity tags. Electrophoresis 23:1591-1598.
Yuan X, Desiderio DM. 2005. Proteomics analysis of human cerebrospinal fluid. J Chromatogr B 815:179-189.
Zhan X, Desiderio DM. 2003. Differences in the spatial and quantitative reproducibility between two second-
dimensional gel electrophoresis systems. Electrophoresis 24:1834-1846.
Zhang J, Goodlett DR, Peskind ER, Quinn JF, Zhou Y, Wang Q, Pan C, Yi E, Eng J, Aebersold RH, Montine
TJ. 2005. Quantitative proteomic analysis of age-related changes in human cerebrospinal fluid. Neurobio
Zhang R, Regnier FE. 2002. Minimizing resolution of isotopically coded peptides in comparative proteomics. J
Proteome Res 1:139-147.
Zhang WR, Chen S, Wang W, King RS. 1992. A cognitive map based approach to the coordination of
distributed cooperative agents. IEEE Trans SMC 22:103-114.
Zhang Z, Smith DL, Smith JB. 2001. Multiple separations facilitate identification of protein variants by mass
spectrometry. Proteomics 1:1001-1009.
Zhou H, Ranish JA, Watts JD, Aebersold R. 2002. Quantitative proteome analysis by solid-phase isotope
tagging and mass spectrometry. Nat Biotechnol 19:512-515.
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Table 1. Types and principles of protein separations
Separation Type Abbr Separation Principle
Charge / Complexation
PAGE Relative Molecular Mass
CZE Charge Migration
Lower capacity, high resolution
Good capacity, poor resolution
High selectivity, can be expensive
Similar capacity to IEC, good resolution
Good capacity, poor resolution
IEF Good resolution, buffer-sensitive
Good resolution, immobilized result
High resolution, high salt conditions