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Journal of Proteomics & Bioinformatics
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Gel-based Proteomic Characterization of Soluble and Insoluble Fraction Proteins in Rat Spinal Cord

Shuguang Yang1#, Qinxue Ding1, 2#, Yaojun Guo3, Congjian Zhao1, Yufeng Jia1, Haiping Que1, Hongxia Wang4, Kaihua Wei4, Dacheng He2, Shuqian Jing1, 5, Shaojun Liu1*
1State Key Laboratory of Proteomics and Department of Neurobiology, Department of Neurobiology, Institute of Basic Medical Sciences, Beijing 100850, P.R. China
2Institute of Cell Biology, Beijing Normal University, Beijing 100875, P.R. China
3Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, P.R. China
4China National Center of Biomedical Analysis, Beijing 100850, P.R. China
5Department of Clinical Immunology, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, USA
#These authors contributed equally to this work
Corresponding Author : Professor Shaojun Liu
State Key Laboratory of Proteomics and Department of Neurobiology,
Neurobiology Department,
Institute of Basic Medical Sciences,
Beijing, 100850, P.R.China,
Tel: +86-10-66931304;
Fax: 86-10-68213039;
Received January 22, 2010; Accepted February 23, 2010; Published February 23, 2010
Citation: Yang S, Ding Q, Guo Y, Zhao C, Jia Y, et al. (2010) Gel-based Proteomic Characterization of Soluble and Insoluble Fraction Proteins in Rat Spinal Cord. J Proteomics Bioinform 3:074-081. doi:10.4172/jpb.1000124
Copyright: © 2010 Yang S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Fractionation efficiency and protein characterization of neural soluble and insoluble proteins by sequential extraction was scrutinized by gel-based proteomic analysis. Spinal cord proteins of adult rats were first extracted with aqueous buffer (fraction A), followed by standard (fraction B) or modified (fraction C) enhanced solutions. Of the top 30 most abundant proteins in fractions A, B and C, the percentage of cytoplasmic proteins was 74% (28/38) , 37% (14/38) and 42% (15/36), respectively; membrane organellar proteins accounted for 8% (3/38), 45% (17/38), and 44% (16/36); membrane proteins accounted for 13% (5/38), 18% (7/38) and 14% (5/36). The number of hydrophobic domains was 5, 15 and 9. Shared proteins in three fractions were only 13%. When additional less abundant 30 spots enriched the insoluble fraction C were characterized, membrane proteins accounted for 31%, among which 83% were peripheral membrane proteins and 17% were integral membrane proteins. Functional analysis also revealed some difference between different fractions although all fractionated proteins are involved in energy metabolism, redox regulation, signal transduction and cellular architecture.

Fractionation efficiency; Membrane protein; Quantitative analysis; Sequential extraction; Spinal cord
CNS: Central Nervous System; 2-DE: Two- Dimensional Electrophoresis; GRAVY: Grand Average of Hydropathicity; HR: Hydrophobic Region; NOG: N-octyl-Dglucopyranoside; PMF: Peptide Mass Fingerprint; SB3-10: Ndecyl-N, N-dimethyl-3- ammonio-1-propanesulfonate; SP: Signal Peptide; TMH: Transmembrane Helix
Membrane proteins play crucial roles in biology from cell interaction and recognition to ion transport and signal transduction. In the central nervous system (CNS), membrane proteins are of particular relevance to their pathophysiological disciplines involved with specific functions, such as neural degeneration and regeneration. Due to high hydrophobicity and low abundance,membrane proteins remain a great challenge in proteomic study (Ahram and Springer, 2004; Tan et al., 2008). Considering the complexity and extensive protein-protein and proteinlipid interactions in the CNS, membrane proteins are extraordi extraordinarily stable and become more resistant to disruption and isolation,leaving many unknown yet potentially very interesting proteins to be characterized.
Sequential extraction has been applied in many studies as a successful approach to membrane protein enrichment in Escherichia coli (Molloy et al., 1998), eukaryotic cells (Lehner et al.,2003; Abdolzade-Bavil et al., 2004) and animal tissues (Yao and Li, 2003; Cheng et al., 2005; Mangum et al., 2005). Given its important contribution to proteomics, it is worthy to systematically evaluate the fractionation efficiency of the technique itself. In fact, some basic questions have no satisfactory answers yet. For example, what's the percentage of cytoplasmic/secreted proteins in the soluble fraction? what's the percentage of membrane/ membrane organellar proteins in the insoluble fraction? How many proteins are overlapped in both fractions? Of those membrane proteins, how many are peripheral and how many are transmembrane proteins? Functional categorization of proteins in each fraction is also interesting to address.
In the present study, we used sequential extraction coupled with solubility enhanced solution to fractionate soluble and insoluble proteins from rat spinal cord. Protein profiling was accomplished with 2-DE and mass spectrometry. Fractionation efficiency was evaluated by two strategies. The top 30 most abundant protein spots in each fraction were firstly characterized and compared; as an indispensable strategic complement, additional less abundant 30 spots enriched in the insoluble fraction were characterized. Our quantitative data confirmed that sequential extraction combined with optimized lysis system is an efficient approach to fractionating soluble and insoluble proteins. When integrated with such strategies, 2-DE-based platform remains an effective technique to resolve insoluble proteins, including hydrophobic neural integral membrane proteins.
Materials and Methods
Three adult male Wistar rats (200-250g, 2.5 months of age) were killed and fresh spinal cord tissues were collected for proteome analysis immediately. Every effort was made to minimize the number of animals used and their suffering. The animal subjects review board of our institute approved all the experiment procedures, which were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals revised 1996.
Sample preparation
Sequential extraction of soluble and insoluble proteins was performed as described by Molloy et al. (1998) with moderate modifications. Briefly, 3 fresh spinal cords (from the medulla oblongata through the horsetail, about 0.35g each) were quickly taken out after decapitation and homogenized with Heidolph DIAX900 polytron (Heidolph, Germany) (speed 3, 10 strokes) in 5 volume of ice-cold buffer, which contained 40 mmol/L Tris (Sigma, St. Louis, MO, USA), 1% w/v dithiothreitol (DTT; Promega, Madison, WI, USA) and 0.5% w/v carrier ampholytes (pH3-10; Amersham Biosciences, San Francisco, CA, USA). The homogenate was sonicated in UP200s sonicator (Dr.hielscher, Germany) to facilitate protein solubility (30 seconds, under ice water bath) then centrifuged at 40,000 g for 30 min at 15°C. Supernatant (fraction A) was recovered for later use. Insoluble pellets were rinsed with 1mL Tris (Sigma) buffer four times to maximally avoid soluble protein contamination. Rinsed pellets were reconstituted and sonicated in standard enhanced solution, which consisted of 5 mol/L urea (Sigma), 2 mol/L thiourea (Sigma), 2% w/v 3-[(3-cholamidopropyl) dimethylammonio]-1-propane sulfonate (CHAPS; Sigma), 2% w/v n-decyl-N,N-dimethyl-3-ammonio-1-propanesulfonate (SB3-10; Sigma), 1% w/v dithiothreitol (DTT; Promega), 0.5% w/v carrier ampholyte(pH3-10; Amersham Biosciences) and 40 mM Tris (Sigma) (fraction B). In parallel, same amount of pellets were suspended and sonicated in same volume of modified enhanced solution (fraction C, whose recipe was the same as fraction B except addition of 40 mmol/L N-Octyl -Dglucopyranoside (NOG; Sigma)). The final pellets (less than 1% w/w of total tissue) were boiled in about 100 µL of SDS buffer (1% w/v SDS (Sigma), 0.375 mol/L Tris (Sigma), 1% w/ v dithiothreitol (DTT; Promega), and 25% v/v glycerol (fraction D). A cocktail of protease inhibitors (0.7 µg/mL pepstatin A (Sigma), 0.5 µg/mL leupeptin (Sigma), 0.3 mg/mL EDTANa2 (Sigma) and 100 g/mL PMSF (Sigma)) was added in all sample solutions except fraction D. Protein concentration in each fraction was determined by the Bradford method on a Unicam UV300 spectrometer (Unicam, Cambridge, UK) except fraction D, for which A280 method was used to estimate the protein concentration.
2-DE and image analysis
Each fraction proteins were separated in duplicate by 2-DE as described elsewhere (Ding et al., 2006). Briefly, 100 or 1000 microgram protein mixture from fractions A, B or C were loaded on a ceramic immobilized pH gradient (IPG) gel strip holder with in-gel rehydration mode for analytical and micropreparative purposes respectively. Using the IPGphor Isoelectric focusing(IEF) system (Pharmacia Biotech, San Francisco, CA, USA), the IPG gels (pH3-10L, 18 cm; Amersham Biosciences) were rehydrated for 12 h under low voltage of 30 V at 20ºC. IEF was performed with the following presetting: 200 V for 1h, 500 V for 1h, 1000 V for 1h, 8000 V (gradient) for 0.5 h, and finally 8000 V for a total of 50 kVh. Then vertical SDS-PAGE was performed using the Ettan DALT II system (Amersham Pharmacia Biotech AB, San Francisco, CA, USA) with laboratory- made homogeneous acrylamide gel(13%T, 3%C, 245X180X1mm3). About 25 microgram D fraction proteins were loaded for 2-DE analysis, which had to be diluted enough before loading for IEF with rehydration buffer (8M urea (Sigma), 2%w/v 3-[(3-cholamidopropyl) dimethylammonio]-1-propane sulfonate (CHAPS; Sigma), 0.5% v/v IGP buffer(pH3-10L; Amersham Biosciences), 1% w/v dithiothreitol (DTT; Promega)) with final sodium dodecyl sulfate (SDS; Sigma) concentration below 0.25%. Gels were stained with Coomassie Brilliant Blue R250 (higher loading amount; Sigma) or silver nitrate (lower loading amount; Sigma). Image analysis was performed by the ImageMaster® Platinum 5.0 software (Amersham Biosciences), from which not only the relative abundance of each protein spot can be obtained (based on staining intensity), also obtained are matched spots in different gels (based on spot location). According to imaging data, the top 30 most abundant proteins were selected for validating fractionation efficiency. Additional 30 enriched spots in fraction C were further characterized. The volume increase of all spots enrolled in this group should reach statistical significance during image analysis when compared to Fraction A.
Mass spectrometric analysis
Protein spots were excised from gels and digested with in-gel digestion mode (Scheler et al., 1998; Gharahdaghi et al., 1999; Ding et al., 2006). Peptide mass spectra were recorded in reflect mode with delayed extraction on a Bruker Daltonics autoflex MALDI-TOF-MS instrument (Bruker Daltonics, Bremen, Germany).Positively charged ions were analyzed and 100 singleshot spectra were accumulated to improve the signal-to-noise ratio. Internal calibration was performed with trypsin autolysis peptide masses at m/z 842.51 (M+) and m/z2211.10 (M+). Nano ES tandem mass spectrometry was performed on a Micromass Q-TOF2 mass spectrometer (Manchester, Britain) as described in reference (Kristensen et al., 2000).
Protein identification (Database search)
Protein identification by peptide mass fingerprinting (PMF) was completed using the ProFound search algorithm at Searching parameters were preset as follows: NCBI nr database (2007/11); maximum of one missed cleavage; partial oxidation with methione residue and carbamidomethylation with cysteine residue; maximum mass tolerance of 0.3 Da; taxonomic category rattus norvegicus unless otherwise indicated. Contaminated masses from matrix, keratins and trypsin were manually deleted before database searching (Ding et al., 2003). Relative mass intensity of peptide fragments was taken into consideration during database searching. Minimum of 4 matched peptide masses was required for unambiguous protein identification when PMF was used alone. For tandem mass spectrometric analysis, raw MS/ MS data were directly transferred to the Mascot engine and searched against NCBI nr database. In both cases, the quality of the search result, as indicated by Z score for PMF and ion score for MS/MS, reached statistical significance (P<0.05).
Western blot
Thirty microgram of protein mixture in different fractions was separated by 10% polyacrylamide gel and electrotransferred to nitrocellulose membrane in a Trans-Blot® Semi-dry Electrophoretic Transfer Cell (Bio-Rad, Hercules, CA, USA). Non-specific binding sites were blocked in TBS-T (25mM Tris (Sigma), 150mM NaCl, 0.05 w/w Tween20, pH7.5) containing 5% nonfat milk overnight at 4ºC; membranes were incubated with the primary antibody of vimentin (Santa Cruz Biotechnology, USA)(1:300) for 1hr at room temperature and followed by antirabbit IgG horseradish peroxidase conjugate (1:2000). The immunocomplexes were visualized by chemiluminescence using the ECL kit (Amersham Pharmacia Biotech AB). The film signals were digitally scanned and then quantified using the ImageMaster® Platinum 5.0 software(Amersham Biosciences).
Grand average of hydropathicity (GRAVY) calculation and signal peptide (SP), transmenbrane helix (TMH) and hydrophobic region (HR) prediction
Protein GRAVY(Kyte and Doolittle, 1982) was calculated using the Protparam tool at protparam.html. SP was predicted with SignalP 3.0 server at TMH was predicted with TMHMM server at TMHMM-2.0. The number of predicted hydrophobic regions inside protein sequences was based on the combination analysis of SP, TMH and HMMTOP (
Functional and subcellular localization sorting
Function and component sorting of proteins were primarily based on the annotations in Swiss-Prot/UniProt knowlegedatabase ( In case of "one protein multiple locations" (for example, HSP8 can be translocated between cytoplasm and nucleus), the denominator used for component percentage calculation is the counts of all locations rather than the number of proteins.
Spinal cord proteins were sequentially extracted by aqueous Tris buffer (fraction A), standard (fraction B) or modified (fraction C) enhanced solution and finally by SDS solution (fraction D). Concentration determination showed that more proteins were harvested in the soluble fraction than in each insoluble fraction (Table 1). Between the two insoluble fractions, it seems that more proteins were extracted in fraction C than in fraction B (P<0.05, t-test, Table 1). In consistence to this observation, the staining intensity of many proteins in Figure 1c was higher than that of matched spots in Figure 1b (Table 2). Representative 2-DE maps of the four fractions are shown in Figure 1. Approximately 1400, 680, 700 and 50 protein spots were visualized by silver staining from fractions A, B, C, and D, respectively. More basic proteins seem to be collected in fractions B and C than in fraction A, which are located on the right region in the 2-DE map. The percentage of proteins with pI >7 in fractions A, B and C was 43.3%, 46.7 and 50% (p<0.05, compared to Fraction A, t-test, Table 3). Since membrane proteins are more likely basic proteins (Wilkins et al., 1998; Schwartz et al., 2001), this fact encouraged us that more membrane proteins may have been harvested in the enhanced solutions. Due to very low abundance, proteins from fraction D had very poor mass spectrometric signals, making their identification rather uncertain (data not shown). Further analysis with this fraction was not included in current study.
To quantitatively evaluate the fractionation efficiency, the top 30 most strongly stained spots in each fraction were firstly analyzed (protein identification see Supplementary Table 4). These proteins were listed in the descending order of abundance (Tables 4, Supplementary Table 1 and 2). Venn diagram analysis showed that 22% (20/90) were exclusively displayed in the soluble fraction A while 39% (35/90) were exclusively displayed in the enhanced fractions B and C. Proteins overlapped in all three fractions were only 13% (12/90) (Figure 2).
The average GRAVY of abundant proteins in fractions A, B and C is -0.317, -0.255 and -0.253, respectively. One protein (3%) in fraction A has a positive GRAVY value while six (20%) and five (17%) was found to be such proteins in fractions B and C (Tables 4, Supplementary table 1 and 2). As for the number of hydrophobic regions in each fraction, there were five in fraction A and fifteen and nine in fractions B and C. Among those hydrophobic regions, two were signal peptides in fraction A and seven and five in fractions B and C. No transmembrane helix was found in most abundant proteins either from soluble or insoluble fractions (Tables 4, Supplementary table 1 and 2; Figure 3).
Protein localization sorting in different fractions is demonstrated in Figure 4. Cytoplasmic proteins accounted for 74% (28/38), 37% (14/38) and 42% (15/36) in fractions A, B and C, respectively. Membrane organellar proteins were 8% (3/38), 45% (17/38) and 44% (16/36). Membrane proteins were 13% (5/38), 18% (7/38) and 14% (5/36). Secreted proteins only appeared in the soluble fraction A which accounted for 5% (2/38). Pooled analysis of most abundant proteins in three fractions revealed that cytoplasmic, membrane organellar, membrane and secreted proteins accounted for 51% (57/112), 32% (36/112), 15% (17/ 112) and 2%(2/112). Among the organellar proteins, mitochondrial, nuclear and endoplasmic reticulum proteins accounted for 72% (26/36), 20% (7/36), 8% (3/36), respectively (Figure 5).
As a critical complement to most abundant protein analysis, we characterized another 30 less abundant protein spots which were significantly enriched in the enhanced solutions (Supplementarytable 3). The average GRAVY of proteins in this group was -0.266, three proteins (10%) possessed positive GRAVYs.There were twenty one hydrophobic domains in total, among which seven were signal peptides and particularly, two were transmembrane helixes (Figure 3). Component analysis demonstrated that cytoplasmic, membrane organellar and membrane proteins accounted for 10% (4/39), 59% (23/39) and 31% (12/ 39), respectively (Figure 4). Among various membrane organellar proteins, mitochondrial, endoplasmic reticulum and nuclear proteins accounted for 56% (13/23), 22% (5/23) and 22% (5/23), respectively. Among those membrane proteins, peripheral-related proteins accounted for 83% (10/12) and integral mem-brane proteins accounted for 17% (2/12). Besides, two unknown proteins with novel amino acid sequences (7%) were also found in this enriched group.
Functional categorization of identified proteins in soluble and insoluble fractions was shown in Figure 6. Involved proteins were mainly clustered into energy metabolism, signal transduction, redox regulation, and cytoarchitecture. For energy metabolism- involved proteins/enzymes, it is noted that glycolytic enzymes dominated in the soluble fraction (Table 4) whereas tricarboxylic acid cycle-related enzymes were major in the insoluble fractions (Supplementary table 1 and 2). It is also noted that, signal transduction and redox regulation-related proteins were more likely seen in the soluble fraction whereas ion channels and receptors were only found in the insoluble fractions.
Finally, we used western blot to validate our 2-DE/mass spectrometry- based results. Figure 7 is a quantitative result of western blot of vimentin, a nuclear protein enriched in the insoluble fractions. In consistence with 2-DE-based analysis, this protein was virtually invisible in soluble fraction A, but showed medium intensity in fraction B and was most intensive in fraction C.
Gel-based (2-dimensional gel electrophoresis and mass spectrometry) and gel-free (liquid chromatography and mass spectrometry) approaches currently are the two most popular strategies for profiling protein expression. It is fair to say that gelfree strategy seems more powerful when very hydrophobic mema15brane proteins are to be addressed. Integral membrane proteins bearing more than 4 transmembrane helixes are hard to resolve by gel-based technique (Bunai and Yamane, 2005). However, 2- DE-based technique possesses its own attractive merits, such as high- throughput, high resolution, providing pI and molecular weight as well as other valuable information. Particularly, when integrated with fractionation, lysis buffer optimization and new electrophoretic system, quite a few membrane proteins, including integral membrane proteins, have been successfully characterized by this technique (Molloy et al., 1998; Lehner et al., 2003; Yao and Li, 2003; Abdolzade-Bavil et al., 2004; Cheng et al., 2005; Mangum et al., 2005).
In this gel-based proteomic study, we quantified the sequential fractionation efficiency of soluble and insoluble neural proteins dissolved in different buffers. Our study, for the first time to our knowledge, provides key data which definitely help to answer some basic questions raised in the introduction. In order to simplify research approach and avoid characterizing all hundred of thousands of spots on the gels (which is ideal but really a daunting work), we took two complementary strategies to achieve our goal. Firstly, same number of most abundant proteins were analyzed and compared in different fractions; secondly, additional couples of enriched yet less abundant proteins were characterized. Our data unambiguously confirmed that sequential fractionation combined with optimized lysis buffer definitely facilitates 2-DE-based approach to addressing membrane proteomics.
High fractionation efficiency of soluble and insoluble proteins achieved in this study depends on the combination of two key factors: sequential extraction and optimized lysis buffer. We developed here a modified enhanced solution based on the recipe of Molloy et al. (1998), in which we introduced an additional key component of NOG, expecting to better solubilize membrane related proteins. NOG is a nonionic surfactant which was first used to solubilize membrane protein of insulin receptors (Gould et al., 1981). Compared with other surfactants such as CHAPS, NOG can solubilize more protein than lipids, making it more suitable for neural protein extraction. As expected, the staining intensity of many proteins in Figure 1c (standard enhanced solution) was higher than that of matched spots in Figure 1b(modified enhanced solution) (Table 2), confirming that NOG did improve membrane/membrane organelle-related protein solubility. Western blot provided additional evidence to this conclusion.
In an earlier 2-DE-based analysis, only 4% or 15% of identified proteins were membrane proteins when sequential extraction or detergent-based strategy was used alone (Lehner et al., 2003). In another one-step-for-all extraction study, 30% of identified proteins were membrane and membrane organelle-related proteins (Fountoulakis et al., 2005). Compared to the fact that as high as 90% of proteins (enriched in the insoluble fraction C) belonged to this category in our study, the advantage of current strategy is really impressive. In fact, when focusing on proteins enriched in the insoluble fraction, one can find that membrane proteins alone accounted for 31%, which seems to be even competitive to gel-free (LC-based) strategy (28% membrane proteins) (Chen et al., 2006).
It is also necessary to compare our experimental data with the theoretical component distribution. Gene ontology classification predicts 43% membrane, 14% extracellular and 43% intracellular proteins in entire mouse proteome (Hood et al., 2005). By contrast, the component distribution was 19%, 2% and 79% in our study (based on all indentified proteins). Obviously, membrane and extracellular proteins are underestimated but intracellular proteins are overvalued. Similar phenomenon also appeared in a gel-free based technique (28%, 4% and 68%, (Chen et al., 2006)). The discrepancy between theoretical and experimentaldata may be explained from the following aspects. Firstly, membrane proteins are inherently hydrophobic and usually lowly abundant. Secondly, a great part of secreted proteins are also very low abundant (i.g., cytokines and hormones), which tend to be ignored (Anderson et al., 2004). Thirdly, a lot of intracellular proteins are highly abundant and have multiple isoforms (glycolytic and mitochondrial enzymes are typical examples), likely leading to overvalue bias of this fraction. Last but not least, our current analysis is based on partial proteins. Relevant data are subject to change if all proteins are analyzed.
GRAVY is a widely-used parameter for evaluating protein hydropathy (hydrophilic of hydrophobic). A protein with a positive GRAVY is highly hydrophobic while one with a negative value is more hydrophilic. However, membrane proteins are not necessarily GRAVY positive, even transmembrane proteins. Fountoulakis and Gasser, (2003) reported that only 10% of the proteins in the membrane fraction of E coli envelopes had positive values (Fountoulakis and Gasser, 2003). In our study, two integral membrane proteins of endoplasmic reticulum protein 29 and TNF-alpha-converting enzyme are both GRAVY negative. It seems that the GRAVY value of protein domain rather than overall sequence may be more meaningful because all signal peptides, transmenbrane helixes as well as other hydrophobic regions (predicted by HMMTOP) involved in this study bear positive GRAVYs with no exception (data not shown).
In summary, we successfully fractionated, quantified and characterized soluble and insoluble spinal cord proteins. Our quantification data strengthen gel-based proteomic application potentials in membrane proteomics. Additionally, a complete protein filing of enhanced solution fractions should reveal more interesting neural proteins which may play important functional roles in the CNS.
The authors are grateful to Ms. Janice Willson for editing this manuscript. This work was supported by grants from the China National Key Basic Research and Development Projects Foundation (Grant No.2001CB510206) and the Beijing Municipal Nature Foundation (Grant No. 7012032).
  1. Abdolzade-Bavil A, Hayes S, Goretzki L, Kroger M, Anders J, et al. (2004) Convenient and versatile subcellular extraction procedure, that facilitates classical protein expression profiling and functional protein analysis. Proteomics 4: 1397-1405.»  CrossRef  »  PubMed  »  Google Scholar

  2. Ahram M, Springer DL (2004) Large-scale proteomic analysis of membrane proteins. Expert Rev Proteomics 1: 293-302.»  CrossRef  »  PubMed  »  Google Scholar

  3. Anderson NL, Polanski M, Pieper R, Gatlin T, Tirumalai RS, et al. (2004) The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics 3: 311-326.»  CrossRef  »  PubMed  »  Google Scholar

  4. Bunai K, Yamane K (2005) Effectiveness and limitation of two-dimensional gel electrophoresis in bacterial membrane protein proteomics and perspectives. J Chromatogr B Analyt Technol Biomed Life Sci 815: 227-236.»  CrossRef  »  PubMed  »  Google Scholar

  5. Chen EI, Hewel J, Felding-Habermann B, Yates JR 3rd (2006) Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol Cell Proteomics 5: 53-56.»  CrossRef  »  PubMed  »  Google Scholar

  6. Cheng GF, Lin JJ, Feng XG, Fu ZQ, Jin YM, et al. (2005) Proteomic analysis of differentially expressed proteins between the male and female worm of Schistosoma japonicum after pairing. Proteomics 5: 511-521.»  CrossRef  »  PubMed  »  Google Scholar

  7. Ding Q, Wu Z, Guo Y, Zhao C, Jia Y, et al. (2006) Proteome analysis of upregulated proteins in the rat spinal cord induced by transection injury. Proteomics 6: 505-518.»  CrossRef  »  PubMed  »  Google Scholar

  8. Ding Q, Xiao L, Xiong S, Jia Y, Que H, Guo Y, et al. (2003) Unmatched masses in peptide mass fingerprints caused by cross-contamination: an updated statistical result. Proteomics 3: 1313-1317.»  CrossRef  »  PubMed  »  Google Scholar

  9. Fountoulakis M, Gasser R (2003) Proteomic analysis of the cell envelope fraction of Escherichia coli. Amino acids 24: 19-41.»  CrossRef  »  PubMed  »  Google Scholar

  10. Fountoulakis M, Tsangaris GT, Maris A, Lubec G (2005) The rat brain hippocampus proteome. J Chromatogr B Analyt Technol Biomed Life Sci 819: 115- 129.»  CrossRef  »  PubMed  »  Google Scholar

  11. Gharahdaghi F, Weinberg CR, Meagher DA, Imai BS, Mische SM (1999) Mass spectrometric identification of proteins from silver-stained polyacrylamide gel: a method for the removal of silver ions to enhance sensitivity. Electrophoresis 20: 601-605.»  CrossRef  »  PubMed  »  Google Scholar

  12. Gould RJ, Ginsberg BH, Spector AA (1981) Effects of octyl beta-glucoside on insulin binding to solubilized membrane receptors. Biochemistry 20: 6776-6781.»  CrossRef  »  PubMed  »  Google Scholar

  13. Hood BL, Lucas DA, Kim G, Chan KC, Blonder J, et al. (2005) Quantitative analysis of the low molecular weight serum proteome using 18O stable isotope labeling in a lung tumor xenograft mouse model. J Am Soc Mass Spectrom 16: 1221-1230.»  CrossRef  »  PubMed  »  Google Scholar

  14. Kristensen DB, Imamura K, Miyamoto Y, Yoshizato K (2000) Mass spectrometric approaches for the characterization of proteins on a hybrid quadrupole time-of-flight (Q-TOF) mass spectrometer. Electrophoresis 21: 430-439.»  CrossRef  »  PubMed  »  Google Scholar

  15. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157: 105-132.»  CrossRef  »  PubMed  »  Google Scholar

  16. Lehner I, Niehof M, Borlak J (2003) An optimized method for the isolation and identification of membrane proteins. Electrophoresis 24: 1795-1808.»  CrossRef  »  PubMed  »  Google Scholar

  17. Mangum JE, Farlie PG, Hubbard MJ (2005) Proteomic profiling of facial development in chick embryos. Proteomics 5: 2542-2550.»  CrossRef  »  PubMed  »  Google Scholar

  18. Molloy MP, Herbert BR, Walsh BJ, Tyler MI, Traini M, et al. (1998) Extraction of membrane proteins by differential solubilization for separation using twodimensional gel electrophoresis. Electrophoresis 19: 837-844.»  CrossRef  »  PubMed  »  Google Scholar

  19. Scheler C, Lamer S, Pan Z, Li XP, Salnikow J, et al. (1998) Peptide mass fingerprint sequence coverage from differently stained proteins on two-dimensional electrophoresis patterns by matrix assisted laser desorption/ionization-mass spectrometry (MALDI-MS). Electrophoresis 19: 918-927.»  CrossRef  »  PubMed  »  Google Scholar

  20. Schwartz R, Ting CS, King J (2001) Whole proteome pI values correlate with subcellular localizations of proteins for organisms within the three domains of life. Genome Res 11: 703-709.»  CrossRef  »  PubMed  »  Google Scholar

  21. Tan S, Tan HT, Chung MC (2008) Membrane proteins and membrane proteomics. Proteomics 8: 3924-3932.»  CrossRef »  Google Scholar

  22. Wilkins MR, Gasteiger E, Sanchez JC, Bairoch A, Hochstrasser DF (1998) Two-dimensional gel electrophoresis for proteome projects: the effects of protein hydrophobicity and copy number. Electrophoresis 19: 1501-1505.»  CrossRef  »  PubMed  »  Google Scholar

  23. Yao R, Li J (2003) Towards global analysis of mosquito chorion proteins through sequential extraction, two-dimensional electrophoresis and mass spectrometry. Proteomics 3: 2036-2043.»  CrossRef  »  PubMed  »  Google Scholar.
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Informatics Journals

Stephanie Skinner

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

1-702-714-7001Extn: 9039

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Jim Willison

1-702-714-7001 Extn: 9042

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Nimmi Anna

1-702-714-7001 Extn: 9038

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Nathan T

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

1-702-714-7001 Extn: 9042

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