Open Access

Quantifying raft proteins in neonatal mouse brain by 'tube-gel' protein digestion label-free shotgun proteomics

  • Hongwei Yu1Email author,
  • Bassam Wakim2,
  • Man Li1, 6,
  • Brian Halligan3,
  • G Stephen Tint4, 7 and
  • Shailendra B Patel1, 5
Contributed equally
Proteome Science20075:17

DOI: 10.1186/1477-5956-5-17

Received: 24 May 2007

Accepted: 24 September 2007

Published: 24 September 2007

Abstract

Background

The low concentration and highly hydrophobic nature of proteins in lipid raft samples present significant challenges for the sensitive and accurate proteomic analyses of lipid raft proteins. Elimination of highly enriched lipids and interfering substances from raft samples is generally required before mass spectrometric analyses can be performed, but these procedures often lead to excessive protein loss and increased sample variability. For accurate analyses of the raft proteome, simplified protocols are needed to avoid excessive sample handling and purification steps.

Results

We have devised a simple protocol using a 'tube-gel' protein digestion that, when combined with mass spectrometry, can be used to obtain comprehensive and reproducible identification and quantitation of the lipid raft proteome prepared from neonatal mouse brain. Lipid rafts (detergent-resistant membranes using Triton X-100 extraction) prepared from neonatal mouse brain were directly incorporated into a polyacrylamide tube-gel matrix without prior protein separation. After in-gel digestion of proteins, nanospray LC-MS/MS was used to analyze the extracted peptides, and the resulting spectra were searched to identify the proteins present in the sample. Using the standard 'label-free' proteomics approach, the total number of MS/MS spectra for the identified proteins was used to provide a measure of relative protein abundances. This approach was successfully applied to lipid rafts prepared from neonatal mouse brain. A total of 216 proteins were identified: 127 proteins (58.8%) were predicted to be membrane proteins, or membrane-associated proteins and 175 proteins (~80%) showed less than a 2-fold variation in the relative abundance in replicate samples.

Conclusion

The tube-gel protein digestion protocol coupled with nanospray LC-MS/MS (TubeGeLC-MS/MS) offers a simple and reproducible method for identifying and quantifying the changes of relative abundances in lipid raft proteins from neonatal mouse brain and could become a useful approach for studying lipid raft proteins from various tissues.

Background

Lipid rafts are cholesterol- and sphingolipid-enriched specialized structures present in biological membranes [15] that can be isolated by various techniques. A common method for the isolation of the rafts is to prepare detergent-resistant membranes (DRMs) by extraction with the nonionic detergent Triton X-100 at cold temperature. Recent interest in lipid rafts arises from observations that some membrane proteins appear to partition preferentially into raft domains, and may require this environment for their biological activity [4, 5]. Many previous studies have utilized two-dimensional gel electrophoresis (2DE) for proteomic profiling, but this method is limited by its lower sensitivity and it is often inefficient when analyzing raft proteins. Mass spectrometry (MS) has become a powerful tool for the analysis of complex protein mixtures. Proteomics profiling of either protein mixtures fractionated by 1DE or unfractionated protein mixtures by protease digestion and LC-MS/MS analysis has become increasingly popular. Peptides are identified by searching the resulting MS/MS spectra against protein sequence databases and protein presence is inferred from peptide presence. This general approach is referred to as 'top-down' or 'shotgun' proteomics. Several studies utilizing 1D gel filtration or in-solution protein digestion, combined with stable isotope labeling or label-free LC-MS/MS, have successfully profiled the protein composition and abundance in lipid rafts prepared from different biological sources [614]. However, quantitation of changes in the raft protein abundance under various experimental circumstances remains a major challenge. A number of technical factors are critical for analytical reliability, such as sample quality, reproducibility of the raft preparations, quality of the chromatography system, and the performance of the mass spectrophotometer. The most pressing problems for lipid raft proteomic investigations are those involving sample preparation and handling. Lipid raft samples prepared by different methods are composed of highly enriched lipids and low concentrations of hydrophobic proteins. Raft preparations also contain many non-proteinaceous substances including exogenous reagents, such as salts, buffers and detergents employed for sample preparation. These highly enriched lipids and non-protein components, or contaminants can often interfere with proteome analysis and their removal is a critical step before any proteome analysis can be performed. Although the low protein concentrations in raft samples do not present a limitation for analysis, methods used for removing lipids and other interfering substances from raft samples can lead to excessive protein loss. Thus, the process of lipid raft preparation suitable for mass spectrometry is a major factor in the variability of data obtained by these powerful proteomic techniques.

For accurate analyses of the raft proteome, a robust protocol avoids excessive purification steps, each of which lead to additional protein losses, is desirable. To avoid protein loss during sample preparation for mass spectrometry, a 'tube-gel' protein digestion protocol was adopted in which the lipid raft samples were directly incorporated into a polyacrylamide tube-gel without electrophoresis [15]. Detergents, lipids and other possible LC-MS/MS interfering materials in the raft samples are eliminated from the gel matrix while proteins are retained in the gel matrix. After the in-gel digestion of proteins, automated nanospray liquid chromatography tandem mass spectrometry (nanospray LC-MS/MS) is used to analyze the extracted peptides for protein identification. This protocol was used to analyze the protein profile of lipid rafts prepared from neonatal mouse brain. Neonatal mouse brain was chosen because there have been few proteomic studies of lipid rafts from neonatal brain [1621]. Neonatal brain disorders are an important cause of mortality and morbidity contributing to the development of autism, cholesterol biosynthesis disorders, and a myriad of learning and developmental neurological and cognitive disabilities [2227]. Developmental membrane defects have been postulated as one of the pathophysiological processes in these neonatal brain disorders. Additionally, the higher sterol content in brain tissue presents an additional challenge in preparing lipid raft samples for nanospray LC-MS/MS analysis.

Starting with limited amounts of frozen brain tissue, a total of 216 raft proteins were identified. Among the identified proteins, 127 (58.8%) were predicted to be plasma membrane (PM) or PM associated proteins including a number of authentic raft and/or GPI and lipid anchoring proteins, receptors, channel proteins, synaptic proteins, kinases, heterotrimeric G protein subunits, and some novel membrane proteins important for neurodevelopment. The major brain raft proteins, reported in previous investigations [8, 18, 19, 28], were also identified as high abundance raft proteins in the present study. An advantage of this method is that it allows for raft proteins to be digested directly, dramatically reducing variations due to sample preparation prior to mass spectrometry. In this study, the standard 'label-free' proteomics approach in which total MS/MS spectral count is utilized to quantify the relative abundance of the identified proteins was used [29]. The results showed that the variations of relative abundance in ~80% of the identified proteins in replicate samples were less than 2-fold, suggesting that the method is highly reproducible. This approach offers a simple and reproducible protocol for identifying and quantifying changes in the relative abundance of the lipid raft proteins from neonatal mouse brain and could become a useful method for studying lipid raft proteins from various tissues.

Results and Discussion

Characterization of lipid rafts by sucrose density gradient ultracentrifugation

Biochemical isolation of lipid raft membranes by gradient ultracentrifugation, as well as their subsequent analysis, is a useful and simple method to determine if membrane components are located in raft microdomains. The distributions of total protein, sucrose density, and contents of sterols, sphingomyelin (SM) and ceramide (Cer), as well as the lighting-scattering properties at 620 nm, for each of the sucrose gradient fractions are summarized in Figure 1. Buoyant low density fractions 2–4 (DRMs/rafts) had the greatest light-scattering properties at 620 nm, consistent with a high content of lipids, but the non-raft fractions 8–11 had little or no absorbance. Conversely, most of the recovered proteins were present in the non-raft fractions and the total protein in the lipid raft fractions was too low to allow for accurate measurement by conventional methods. The lipid raft fractions 2~4 were highly enriched in sterols (a mixture composed of ~60% cholesterol, ~40% desmosterol, and trace amounts of other sterol precursors such as 7-dehydrodesmosterol and lathosterol), SM, and Cer compared to plasma membranes. Further characterization of the known raft and the non-raft marker proteins in the sucrose gradients was performed by immunoblotting (Figure 2). Known raft proteins, such as caveolin-1 (cav-1), flotillin-1 (flot-1), contactin-1 (Cntn-1), annexin -VI (Anx-VI, Anx6A), GTP-binding protein αq (Gαq), and NAP-22, were present in the low-density fractions (fractions 2~4). Various accepted non-raft markers, such as β-COP (a Golgi marker), transferrin receptor (TfR) (a non-raft membrane marker protein), α-tubulin (a cytoskeletal protein), calnexin (an ER resident membrane protein), and ATP synthase (a mitochondrial protein), were only present in the high-density fractions. Collectively, these results reflect the typical biochemical profiles of lipid rafts from brain tissue [8, 30, 31].
Figure 1

Biochemical characterization of sucrose density gradient fractions of neonatal mouse brain. Panel A shows the distributions of protein and sucrose density in membrane fractions from sucrose gradients of neonatal brain. Panel B shows the light-scattering properties of each fraction by absorbance at 620 nm. The buoyant low density fractions 2~4 showed the greatest light-scattering properties at 620 nm, consistent with a high content of lipids. Panel C shows the distribution of sterols (cholesterol, desmosterol, lathosterol, and 7-dehydrodesmosterol (7DHD)) in each fraction from sucrose gradients of neonatal mouse brain and Panel D shows total sphingomyelin (SM) and ceramide (Cer).

Figure 2

Localization of known raft and non-raft marker proteins in sucrose gradients. Post-nuclear homogenates (PNH) from neonatal brain tissues were extracted using 1% of Triton X-100 (TX) and fractionated in 5–40% discontinuous sucrose-density gradient as described in Methods. Twelve fractions of each 1.0 ml were collected from the top to bottom. Twenty μg of PNH protein (H) and equal 30 μl of each fraction of gradient were subjected to immunoblotting with antibodies against indicated proteins.

Analysis of core raft proteome in neonatal mouse brain by TubeGeLC-MS/MS

The core protein composition of lipid rafts from neonatal mouse brain was determined by using a tube-gel protein digestion coupled with nanospray LC-MS/MS (TubeGeLC-MS/MS) analyses. The major benefit of this modification is that the raft proteins (usually in limiting quantities) are digested in a tube-gel matrix without fractionation and purification. Thus, sample losses are minimized compared to in-gel digestion based on SDS-PAGE. Moreover, inclusion of detergents (Triton X-100 and SDS. See Methods) can facilitate the effective solubilization and denaturation of hydrophobic lipid raft membrane proteins [32, 33]. After proteins are incorporated into the tube-gel, the detergents, lipids and other interfering substances can be efficiently eliminated by extensive washing with acetonitrile prior to protein enzymatic digestion and subsequent nanospray LC-MS/MS analysis, without any significant loss of the proteins that are trapped in the gel matrix [15]. This tube-gel approach has been successfully employed for high throughout mass spectrometric analysis of membrane proteins [15].

Two biological replicates (raft preparations from two neonatal brains), each with two MS technical replicates, were analyzed by TubeGeLC-MS/MS method. The peptides and corresponding proteins that were commonly identified in two biological samples (total 4 replicates) were considered as confident protein identifications. All identified proteins were then searched using UniProtKB/Swiss-Prot Release 52.3, TMHMM 2.0, and PubMed, to obtain information about their subcellular localization. The presence of predicted or verified transmembrane domains, glycosylphosphatidylinositol (GPI)-anchors and the lipid consensus sequences for myristoylation, pamitolyation, geranylgeranylation, farnesylation, and prenylation was used to classify proteins as either a membrane protein, or a membrane-associated protein [31]. Identified proteins were also analyzed by UniProtKB for the predicted presence of these motifs in order to provide an additional criterion for the evaluation. The overall experimental results for lipid raft core proteome of neonatal mouse brain thus characterized are shown in Figure 3. The complete lists of identified proteins, categorized as plasma membrane (PM) or PM-associated proteins and non-PM proteins, are shown in Tables 1 and 2, respectively.
Table 1

Plasma membrane associated proteins identified in the lipid rafts of neonatal mouse brain

Acc. No.

Protein name

Pep a

% cov b

MW (KDa)

PTM c

Spectral count d

SD e

P12960

Contactin-1 (Neural cell surface protein F3)

26

35.03

113.4

GPI

174.0

4.0

Q91XV3

Neuronal axonal membrane protein NAP-22

8

66.52

22.1

Lipid anchor

125.0

0.6

P59216

G-protein G(o), alpha subunit 1

7

25.28

40.1

Lipid anchor

101.0

2.0

P14824

Annexin A6

21

41.43

75.8

 

99.0

22.0

Q8BLK3

Limbic system-associated membrane protein

5

17.94

38.0

GPI

66.0

1.0

Q80Z24

Neuronal growth regulator 1 (Neurotractin)

4

14.99

37.9

GPI

56.5

2.5

Q61330

Contactin-2

13

20.69

113.2

GPI

44.5

5.5

P38401

G-protein G(i), alpha-1 subunit

5

19.03

40.4

Lipid anchor

42.5

10.5

O08917

Flotillin-1

8

29.04

47.5

 

40.0

0.0

Q8BFZ9

SPFH domain-containing protein 2

3

11.30

38.9

 

38.0

1.2

Q9Z2S9

Flotillin-2 (Reggie-1) (REG-1)

6

22.22

41.7

 

36.5

1.5

P13595

Neural cell adhesion molecule 1, 180 kDa isoform

6

9.43

119.4

 

32.5

10.5

P38402

G-protein G(i), alpha-2 subunit

2

8.78

40.5

Lipid anchor

30.0

4.0

Q9Z1G4

Vacuolar proton pump subunit 1

7

9.43

96.5

 

28.5

0.5

Q62188

Dihydropyrimidinase-related protein 3 (DRP-3)

7

20.39

61.9

 

21.5

0.5

P27600

G-protein alpha-12 subunit (G alpha-12)

2

8.49

44.0

Lipid anchor

20.5

5.5

O70443

G-protein G(z), alpha subunit (G(x) alpha chain)

4

17.85

40.9

 

19.5

1.5

Q99PJ0

Neurotrimin

3

11.95

38.0

GPI

17.0

1.0

Q9WTR5

Cadherin-13

4

9.26

78.3

GPI

17.0

2.0

P21278

G-protein alpha-11 subunit

3

9.78

42.0

 

15.0

0.6

P51150

Ras-related protein Rab-7

3

19.90

23.5

Lipid anchor

14.5

0.5

Q8VDN2

Sodium/potassium-transporting ATPase alpha-1 chain

5

8.61

113.0

 

13.5

0.5

P32736

Opioid-binding protein/cell adhesion molecule

3

13.08

38.1

GPI

13.0

2.0

P21279

G-protein G(q), alpha subunit

3

13.64

41.5

Lipid anchor

13.0

0.0

P01831

Thy-1 membrane glycoprotein precursor (Thy-1)

3

26.71

18.1

GPI

12.5

0.5

P59729

Ras and Rab interactor 3

2

3.14

107.3

 

12.0

2.0

Q6TMK6

G-protein G(I)/G(S)/G(T) beta subunit

2

8.81

37.4

 

12.0

4.0

P06837

Neuromodulin (Axonal membrane protein GAP-43

2

18.14

23.6

Lipid anchor

11.5

0.5

Q6PIE5

Sodium/potassium-transporting ATPase alpha-2 chain

3

10.71

112.2

 

11.0

1.0

P62821

Ras-related protein Rab-1A

2

16.26

22.7

Lipid anchor

11.0

1.0

P53994

Ras-related protein Rab-2A

3

20.38

23.5

Lipid anchor

10.5

0.5

P38403

G-protein G(k), alpha subunit

3

13.64

40.6

Lipid anchor

10.0

2.0

Q8BKV1

Glypican-2 precursor

2

5.88

63.3

GPI

10.0

2.0

Q68FD5

Clathrin heavy chain

5

4.06

191.6

 

10.0

3.0

Q91X78

SPFH domain-containing protein 1

6

24.48

38.9

 

9.5

2.5

P39688

Proto-oncogene tyrosine-protein kinase Fyn

2

3.57

59.9

Lipid anchor

9.5

1.5

Q61735

Integrin-associated protein (IAP)

2

4.64

33.1

 

9.5

0.5

P97792

Coxsackievirus and adenovirus receptor homolog

2

6.04

39.9

Lipid anchor

9.5

1.5

Q9WUC3

Lymphocyte antigen Ly-6H precursor

2

12.32

14.67

GPI

9.5

1.5

Q6PIC6

Sodium/potassium-transporting ATPase alpha-3 chain

3

4.45

111.7

 

9.0

1.0

Q8BMT4

Leucine-rich repeat-containing protein 33

2

2.75

77.1

 

9.0

1.0

P48036

Annexin A5

5

21.07

35.8

 

9.0

1.0

P17182

Alpha-enolase

3

11.57

47.1

 

9.0

0.6

O08532

L-type calcium channel subunit delta

2

2.75

124.6

 

8.5

2.5

P63044

Vesicle-associated membrane protein 2 (VAMP-2)

3

35.09

12.7

 

8.0

0.0

P54227

Stathmin (Phosphoprotein p19)

2

14.97

17.3

 

8.0

1.0

P61027

Ras-related protein Rab-10

3

16.58

22.5

Lipid anchor

8.0

1.0

Q9QZF2

Glypican-1 precursor

3

8.99

61.4

GPI

8.0

2.0

Q3U1F9

Transmembrane phosphoprotein Cbp

2

7.48

46.5

Lipid anchor

7.0

0.6

Q69Z26

Contactin-4

3

2.15

117.5

GPI

7.0

1.0

Q9R0N7

Synaptotagmin-7 (Synaptotagmin VII) (SytVII)

2

7.71

45.5

 

6.5

0.5

Q9Z0P4

Paralemmin

3

10.73

41.6

Lipid anchor

6.5

1.5

O35454

Chloride channel protein 6 (ClC-6)

4

6.56

97.0

 

6.5

1.5

P51863

Vacuolar ATP synthase subunit d

2

6.86

40.3

 

6.0

0.6

P62814

Vacuolar ATP synthase subunit B, brain isoform

3

8.04

56.6

 

6.0

1.0

Q9ER00

Syntaxin-12

2

11.72

31.19

 

6.0

2.0

Q7SIG6

Development and differentiation-enhancing factor 2

2

2.84

106.8

 

6.0

1.0

Q6PHN9

Ras-related protein Rab-35

2

5.50

23.0

Lipid anchor

5.5

0.5

P63213

G-protein G(I)/G(S)/G(O) gamma-2 subunit

2

8.78

37.4

 

5.5

0.5

P14094

Sodium/potassium-transporting ATPase beta-1 chain

2

8.25

35.2

 

5.0

2.0

Q6PCX7

Repulsive guidance molecule A

2

6.62

50.0

GPI

5.0

2.0

P70296

Phosphatidylethanolamine-binding protein (PEBP)

3

28.65

20.7

 

5.0

3.0

P59108

Copine-2 (Copine II)

3

6.76

61.0

 

5.0

1.7

Q9WV55

VAMP-associated protein A

2

4.98

27.3

 

4.5

0.5

P46096

Synaptotagmin-1

2

5.71

47.4

Lipid anchor

4.5

1.5

Q9D1G1

Ras-related protein Rab-1B

2

16.50

22.2

Lipid anchor

4.5

0.5

P07356

Annexin A2 (Annexin II)

3

13.95

38.5

 

4.5

1.5

Q99KR6

RING finger protein 34

2

4.53

42.0

 

4.0

1.0

Q91V41

Ras-related protein Rab-14

2

12.62

23.9

Lipid anchor

4.0

1.2

P10852

4F2 cell-surface antigen heavy chain

2

5.14

58.3

 

4.0

0.6

P35279

Ras-related protein Rab-6A (Rab-6)

2

5.34

23.6

Lipid anchor

3.5

1.5

Q9CQD1

Ras-related protein Rab-5A

2

10.28

23.6

Lipid anchor

3.5

0.5

O35963

Ras-related protein Rab-33B

2

4.82

25.8

Lipid anchor

3.5

0.5

Q8K386

Ras-related protein Rab-15

2

5.21

24.3

Lipid anchor

3.5

1.5

P60764

Ras-related C3 botulinum toxin substrate 3

2

7.33

21.4

Lipid anchor

3.5

0.5

Q9QXL2

Kinesin family member 21A

2

1.14

186.53

 

3.5

0.5

Q8R4A8

G-protein G(s), alpha subunit

3

13.64

45.7

Lipid anchor

3.5

1.5

Q9JMB8

Contactin-6

3

3.21

113.8

GPI

3.5

2.5

Q65CL1

Alpha-3 catenin (Alpha T-catenin)

1

1.79

99.8

 

3.5

1.5

Q60547

Synaptonemal complex protein 3

3

6.01

27.1

 

3.0

0.6

Q9DAS9

G-protein G(I)/G(S)/G(O) gamma-12 subunit

4

22.86

78.7

Lipid anchor

3.0

1.0

P97449

Aminopeptidase N (Membrane protein p161)

1

1.87

109.7

 

3.0

1.0

Q9JHS3

Late endosomal/lysosomal Mp1-interacting protein

2

14.52

13.48

 

3.0

1.0

P31324

Prkar2b

1

4.11

46.04

 

3.0

1.5

P61264

Syntaxin-1B2 (Syntaxin 1B)

2

8.01

33.3

 

2.5

1.5

P35278

Ras-related protein Rab-5C

2

6.51

23.4

Lipid anchor

2.5

0.5

P63011

Ras-related protein Rab-3A

2

8.68

25.0

Lipid anchor

2.5

0.5

P97855

Ras-GTPase-activating protein binding protein 1

1

3.02

51.8

 

2.5

0.5

Q9CYH2

Protein C10orf58 homolog

2

5.99

24.4

 

2.5

0.5

P11505

Plasma membrane calcium-transporting ATPase 1

1

1.43

138.7

 

2.5

0.5

P05480

Neuronal proto-oncogene tyrosine-protein kinase Src

2

5.38

60.6

Lipid anchor

2.5

0.5

Q07310

Neurexin-3-alpha

1

4.11

174.0

 

2.5

0.5

O35136

Neural cell adhesion molecule 2

1

1.56

93.2

GPI

2.5

1.5

O89051

Integral membrane protein 2B

2

6.04

30.3

 

2.5

0.5

O08842

GDNF family receptor alpha-2

1

3.68

51.6

GPI

2.5

0.5

O08545

Ephrin-A3 precursor

2

8.60

21.2

GPI

2.5

0.5

Q9R1T7

Inducible T-cell co-stimulator (CD278 antigen)

2

8.54

225.30

 

2.5

0.5

P60879

Synaptosomal-associated protein 25

2

6.83

23.3

Lipid anchor

2.0

1.0

P80236

Ras-related C3 botulinum toxin substrate 1

3

18.42

8.8

 

2.0

1.0

P68404

Protein kinase C beta type

1

2.38

76.9

 

2.0

0.6

Q04690

Neurofibromin

2

3.81

319.6

 

2.0

0.6

Q60437

Insulin receptor substrate p53

2

6.15

57.64

 

2.0

0.6

Q61411

GTPase HRas

2

12.77

21.3

Lipid anchor

2.0

0.6

P51655

Glypican-4 precursor (K-glypican)

2

4.86

62.6

GPI

2.0

1.0

P23818

Glutamate receptor 1 (GluR-1)

1

2.21

101.57

 

2.0

0.6

Q8VBX4

C-type lectin domain family 4 member K

1

4.24

37.6

 

2.0

0.6

Q8JZW4

Copine-5 (Copine V)

1

2.53

65.6

 

2.0

1.0

Q8BLR2

Copine-4 (Copine IV)

1

2.70

62.4

 

2.0

0.6

Q02013

Aquaporin-1

1

7.49

28.66

 

2.0

1.2

Q07076

Annexin A7

1

3.46

49.9

 

2.0

1.7

P97429

Annexin A4 (Annexin IV)

3

10.09

35.9

 

2.0

0.6

Q9DBE8

Alpha-1,3-mannosyltransferase ALG2

1

3.86

47.4

 

2.0

1.0

P84078

ADP-ribosylation factor 1

2

6.15

20.6

 

2.0

1.0

Q6QIY3

Sensory neuron sodium channel

1

3.09

220.6

 

2.0

0.6

P49817

Caveolin-1

1

7.91

20.54

 

2.0

0.6

P18708

Vesicle-fusing ATPase

2

2.69

82.54

 

1.5

0.5

O70439

Syntaxin-7

2

5.41

29.8

 

1.5

0.5

P16546

Spectrin alpha chain, brain

1

1.24

274.7

 

1.5

0.5

Q9QZB0

Regulator of G-protein signaling 17

1

6.22

24.3

 

1.5

0.5

Q9JIR4

Regulating synaptic membrane exocytosis protein 1

2

6.07

179.7

 

1.5

0.5

Q05909

Receptor-type tyrosine-protein phosphatase gamma

2

9.72

161.2

 

1.5

0.5

P46638

Ras-related protein Rab-11B

2

9.72

24.5

Lipid anchor

1.5

0.5

P05696

Protein kinase C alpha type(PKC-alpha)

1

2.09

76.8

 

1.5

0.5

O35764

Neuronal pentraxin receptor

1

2.84

52.37

 

1.5

0.5

Q8CGK7

G-protein G(olf), alpha subunit

6

20.74

44.3

 

1.5

0.5

P50153

G-protein G(I)/G(S)/G(O) gamma-4 subunit

2

9.35

84.1

Lipid anchor

1.5

0.5

Q99KJ8

Dynactin subunit 2

1

4.75

44.0

 

1.5

0.5

a Pep: peptide counts; b % cov: protein coverage%; c PTM: posttranslational lipid modification, GPI and lipid anchor: myristoylation, pamitolyation, geranylgeranylation, farnesylation, and prenylation; d Spectral count: total MS/MS spectral counts. Number represents mean value of 4 replicates; c standard deviation of spectral counts in 4 replicates.

Table 2

Non-PM proteins identified in the lipid rafts of neonatal mouse brain

Acc. No.

Protein name

Pep a

% Cov b

MW (KDa)

Loc. c

Spectral count d

SD e

P69893

Tubulin beta-1 chain

12

38.60

49.67

cyto

262.0

14.0

P68361

Tubulin alpha-1 chain

9

30.44

50.15

cyto

175.0

20.0

Q71FK5

Actin, cytoplasmic 1 (Beta-actin)

5

21.39

41.74

cyto

102.5

4.5

Q03265

ATP synthase alpha chain

10

26.63

59.75

mc

63.0

4.0

P56480

ATP synthase beta chain

9

20.83

56.30

mc

51.5

5.5

P04104

Keratin, type II cytoskeletal 1

3

5.59

65.1

cyto

37.0

4.0

P62629

Elongation factor 1-alpha 1

3

9.33

50.11

cyto

36.0

5.0

P19378

Heat shock cognate 71 kDa protein

4

10.39

70.8

cyto

33.0

1.0

Q922U2

Keratin, type II cytoskeletal 5

2

4.15

61.8

cyto

26.5

6.5

P14873

Microtubule-associated protein 1B (MAP 1B)

7

4.34

270.41

cyto

25.5

3.5

Q6IFZ6

Keratin, type II cytoskeletal 1b

2

4.20

61.4

cyto

25.5

3.5

P97427

Dihydropyrimidinase-related protein 1

4

8.93

62.17

cyto

25.0

0.0

Q62188

Dihydropyrimidinase-related protein 3

7

20.39

61.94

cyto

21.5

0.5

P14733

Lamin-B1

5

12.80

66.66

nuc

19.0

3.0

P68372

Tubulin beta-2C chain

2

6.76

49.83

cyto

18.5

1.5

Q6IG00

Keratin, type II cytoskeletal 4

2

1.68

57.7

cyto

18.0

1.0

Q60932

Voltage-dependent anion-selective channel protein 1

7

38.31

32.35

mc

16.5

3.5

P48962

ADP/ATP translocase 1

3

11.15

32.77

mc

15.0

2.0

Q922F4

Tubulin beta-6 chain

2

3.81

50.09

cyto

14.5

0.5

Q04447

Creatine kinase B-type

3

12.63

42.71

cyto

13.5

2.5

P67778

Prohibitin

4

21.03

29.82

mc

12.5

1.5

Q10758

Keratin, type II cytoskeletal 8

2

3.33

53.9

cyto

12.5

0.5

Q61696

Heat shock 70 kDa protein 1A

2

5.78

70.08

cyto

12.0

1.0

O08553

Dihydropyrimidinase-related protein 2

5

13.84

62.17

cyto

11.5

2.5

P46633

Heat shock protein HSP 90-alpha (HSP 86)

2

3.56

84.72

cyto

10.5

2.5

P63101

14-3-3 protein zeta/delta

2

12.30

27.77

cyto

10.5

0.5

Q60930

Voltage-dependent anion-selective channel protein 2

3

14.29

31.73

mc

10.0

0.0

Q9ERD7

Tubulin beta-3 chain

2

9.35

50.42

cyto

10.0

1.0

P50672

Cytochrome c oxidase subunit 2

2

11.50

25.82

mc

9.0

0.0

Q9DCT2

NADH-ubiquinone oxidoreductase 30 kDa subunit

2

6.35

34.00

mc

8.5

0.5

P18760

Cofilin-1 (Cofilin, non-muscle isoform)

2

15.24

18.43

nuc

8.5

1.5

P07823

78 kDa glucose-regulated protein

5

11.18

72.38

er

8.5

1.5

P11497

Acetyl-CoA carboxylase 1

1

4.24

37.62

cyto

8.0

1.0

Q60931

Voltage-dependent anion-selective channel protein 3

3

13.83

30.75

mc

7.5

0.5

P09445

Elongation factor 2

2

2.92

95.27

cyto

7.5

0.5

P62977

Ubiquitin

1

21.33

8.57

cyto

7.0

1.0

P19783

Cytochrome c oxidase subunit IV isoform 1

1

7.14

19.53

mc

7.0

2.0

P14152

Malate dehydrogenase

1

3.61

36.35

mc

6.5

0.5

P11499

Heat shock protein HSP 90-beta

2

3.88

83.20

cyto

6.5

0.5

P12787

Cytochrome c oxidase polypeptide Va

1

10.42

16.03

mc

6.5

0.5

P31253

Ubiquitin-activating enzyme E1 X

2

7.35

50.99

cyto

6.0

3.0

P35564

Calnexin

2

4.92

67.28

er

6.0

1.0

Q91VD9

NADH-ubiquinone oxidoreductase 75 kDa subunit

2

3.99

79.75

mc

5.5

2.5

P56135

ATP synthase f chain, mitochondrial

2

26.74

10.21

mc

5.5

1.5

P51881

ADP/ATP translocase 2

2

8.11

32.80

mc

5.5

0.5

Q8R429

SR Ca(2+)-ATPase 1

2

3.12

109.43

er

5.0

1.0

Q9DB20

ATP synthase O subunit

2

9.91

23.36

mc

5.0

1.0

Q8R429

Calcium pump 1 (SERCA1)

2

3.12

109.43

er

5.0

1.0

Q91V61

Sideroflexin-3

1

4.06

35.41

mc

4.5

0.5

P03995

Glial fibrillary acidic protein, astrocyte (GFAP)

1

2.56

49.92

cyto

4.5

1.5

Q9CQV8

14-3-3 protein beta/alpha

1

5.43

21.22

cyto

4.5

2.5

P68368

Tubulin alpha-4 chain

2

3.11

50.14

cyto

3.5

0.5

P62962

Profilin-1 (Profilin I)

2

21.74

14.83

cyto

3.5

0.5

Q8QZT1

Acetyl-CoA acetyltransferase

2

7.09

44.82

mc

3.5

0.5

P62962

Profilin-1

2

21.74

14.82

cyto

3.5

0.5

Q02053

Ubiquitin-activating enzyme E1 1

3

4.82

117.81

cyto

3.0

1.0

P42932

T-complex protein 1 subunit theta

3

6.59

59.43

cyto

3.0

2.0

O35129

Prohibitin-2

4

21.03

29.82

mc

3.0

1.0

P31324

Prkar2b

1

4.11

46.04

cyto

3.0

0.0

P20357

Microtubule-associated protein 2 (MAP 2)

2

1.20

198.98

cyto

3.0

0.0

P34926

Microtubule-associated protein 1A (MAP 1A)

2

2.36

299.53

cyto

3.0

0.0

P52480

Pyruvate kinase isozyme M2

4

12.48

57.76

mc

3.0

2.0

P63209

S-phase kinase-associated protein 1A

1

9.32

18.53

cyto

3.0

1.0

P60879

Synaptonemal complex protein 3

3

6.01

27.1

nuc

3.0

1.0

O88809

Neuronal migration protein doublecortin

1

3.56

40.61

cyto

2.5

0.5

P17156

Heat shock-related 70 kDa protein 2

2

3.01

69.74

cyto

2.5

1.5

Q9EQF6

Dihydropyrimidinase-related protein 5

1

3.20

61.52

cyto

2.5

0.5

Q8BH59

Calcium-binding mitochondrial carrier protein Aralar1

2

4.59

74.57

mc

2.5

0.5

P48670

Vimentin

1

4.25

51.85

cyto

2.5

1.5

P80315

T-complex protein 1 subunit delta

1

2.98

57.94

cyto

2.0

0.0

P11984

T-complex protein 1 subunit alpha A

1

4.14

60.34

cyto

2.0

0.0

Q9JKK8

Serine-protein kinase ATR

1

2.36

84.26

nuc

2.0

0.0

Q04899

Serine/threonine-protein kinase PCTAIRE-3

1

3.78

51.85

nuc

2.0

1.0

Q99PT1

Rho GDP-dissociation inhibitor 1 (Rho GDI 1)

1

7.88

23.41

er

2.0

0.0

Q61879

Myosin-10

1

3.09

49.59

cyto

2.0

0.0

P24638

Lysosomal acid phosphatase

1

2.13

48.51

lysosome

2.0

1.0

O70251

Elongation factor 1-beta

2

12.56

24.56

cyto

2.0

0.0

Q9CPQ8

ATP synthase g chain, mitochondrial

1

18.63

11.43

mc

2.0

0.0

O35627

Orphan nuclear receptor NR1I3

1

2.52

40.89

nuc

2.0

1.0

P53026

60S ribosomal protein L10a

1

6.98

24.78

nuc

2.0

1.0

P97524

Very-long-chain acyl-CoA synthetase

1

2.58

70.69

er

1.5

0.5

Q01853

Transitional endoplasmic reticulum ATPase

2

3.11

89.18

cyto

1.5

0.5

Q99JR1

Sideroflexin-1

1

5.63

35.52

mc

1.5

0.5

Q62627

PRKC apoptosis WT1 regulator protein

1

4.53

35.87

nuc

1.5

0.5

Q9DCS9

NADH-ubiquinone oxidoreductase PDSW subunit

1

10.92

20.89

mc

1.5

0.5

P08249

Malate dehydrogenase

2

9.79

35.60

mc

1.5

0.5

Q8BGU5

Cyclin fold protein 1

1

5.00

39.39

nuc

1.5

0.5

Q8CEE6

PAS-kinase (PASKIN)

1

1.16

151.27

cyto

1.5

0.5

P35980

60S ribosomal protein L18

1

6.99

21.5

nuc

1.5

0.5

a Pep: peptide counts; b % cov: protein coverage%; c Loc.: subcellular localization. mc. mitochondria, cyto. cytoplasm, er. endoplasmic reticulum; e Spectral count: total MS/MS spectral counts. Number represents mean value of 4 replicates; c standard deviation of spectral counts in 4 replicates.

Figure 3

Cellular localization of identified proteins in lipid rafts from neonatal mouse brain. Cellular localization was annotated based on Gene Ontology (GO) terms and the PubMed literature database. The number of proteins and their percentage of the total identified proteins associated with each cellular location are indicated.

The identified core proteome in the lipid rafts of neonatal mouse brain covered a wide range of sizes (8.0~319.6 kDa). Up to 216 non-redundant proteins were identified from 100 μl of a lipid raft fraction by TubeGeLC-MS/MS, 75% of these proteins were identified by at least two peptide matches and 25% of those identified were based upon a single peptide match. Although protein identifications based upon a single peptide match may be problematic, this does not necessarily imply a potential false identification [34]. For example, caveolin-1 (cav-1) was detected in raft samples from neonatal mouse brain by immunoblots, but was represented in each of the 4 replicates identified by mass spectrometry by a single peptide match. Using the Gene Ontology (GO) classifications and PubMed database searches, 127 (58.8%) of the proteins identified were PM or PM-associated proteins, with 18 (14.2%) having a GPI-anchoring site and 34 (26.8%) with other lipid-anchoring sites as described above. Many of the PM proteins identified were reported previously as being lipid raft proteins by conventional biochemical procedures. Typical raft marker proteins, such as caveolin-1, flotillin-1 and -2, Fyn and Src, were identified in the lipid raft preparations from neonatal mouse brain. Functional categories revealed that the identified PM proteins cover a broad range of neural functions involving neurodevelopment. Several proteins are known to function as part of the neurotransmitter release and re-uptake machinery; 3 syntaxin (Stx) proteins, Stx1A, Stx1B, Stx7; synaptosomal-associated protein 25 (Snap25); synaptotagmin (Syt) proteins, Syt1 and Sty7; vesicle-associated membrane proteins (Vamp), Vamp1 and Vamp2; regulating synaptic membrane exocytosis protein 1 (RIM1); and the glutamate receptor (GluR1) were all present in the raft fractions. Relatively large numbers of guanine nucleotide-binding protein (G protein) isoforms and Ras subfamily of GTPases were identified; G(s)α, G(i)α1, G(i)α2, G(o)α, G(k)α3, G(olf)α, G(q)α, G(z)α, Gα11, Gα12, G(I)/G(S)/G(T)β1, G(I)/G(S)/G(O)γ2, G(I)/G(S)/G(O)γ4, G(I)/G(S)/G(O)γ12 and Rab1A, Rab1B, Rab2A, Rab3A, Rab5A, Rab5C, Rab10, Rab11B, Rab14, Rab15, Rab33B, Rab35, p21Rac1, p21Rac3, Rab GDIα, RIN3, and G3BP. These proteins have been implicated in a variety of developmental processes in neonatal brain, including signal transduction, neurotransmitter release, and membrane trafficking [2]. Another important group of proteins in the neonatal brain-raft proteome comprises the cell adhesion/recognition molecules for cell-cell communication. Twenty-four such proteins were identified; contactin1 (Cntn1), Cntn2, Cntn4, and Cntn6, neurotrimin, Thy1, neurotractin (Kilon protein), Nap22, Gap43, paralemmin, desmocollin-2, neurexin-3α, Ncam2, Ncam180, dynactin, glypican (Gpc)1, Gpc 2, Gpc4, limbic system-associated membrane protein (LSAMP), transmembrane phosphoprotein Cbp, neurofibromin, opioid-binding cell adhesion molecule (Obcam), Alpha-3 catenin, and cadherin-13. These cell surface communication proteins are known to participate in the formation of neuronal networks in the brain during development, specifically axon growth, synapse formation, and fasciculation [3538]. Several transporters and non-receptor type channel proteins were also identified; Na(+)/K(+) ATPase (ATP1A1, ATP1A2 and ATP1A3), small conductance calcium-activated potassium channel protein 3 (Kcnn3), plasma membrane calcium-transporting ATPase 1, Slc3a2, GDNF family receptor alpha-2, integrin-associated protein, integral membrane protein 2B, sensory neuron sodium channel, L-type calcium channel subunit delta, aquaporin-1, and chloride channel protein 6 (Clc-6). Calcium and phospholipid binding proteins cupine (Cnpe)2, Cnpe4, Cnpe591, annexin (Anx)2A, Anx4A, Anx5A, Anx6A, and Anx7A were also identified. A number of proteins of unknown function were also identified, such as receptor-type tyrosine-protein phosphatase gamma, protein C10orf58 homolog, Coxsackie's virus and adenovirus receptor homologs. As expected, since these raft proteins are from neonatal mouse brain, myelin proteins (present in adult brain tissue) such as myelin basic protein (MBP), myelin proteolipid protein (PLP), oligodendrocyte-myelin glycoprotein (Omg), and 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase), were not represented. Functional annotation and grouping of the major neonate-brain raft proteome will provide a basis for determining the potential targets of lipid raft disorganization in mouse models of neonatal brain disorders.

As reported in most raft proteomic studies [8, 10, 11, 14, 19, 3943], non-PM proteins were also found in the raft samples in the present study (Fig. 2 and Table 2). Eighty-nine of the 216 (41.2%) identified proteins from neonatal mouse brain rafts were predicted to be non-PM proteins by their GO terms. They are comprised of 47 cytoplasmic proteins including 20 cellular structural proteins (such as tubulins, actins, keratins, and microtubule-associated proteins), 25 mitochondrial proteins, 10 nuclear proteins, 6 ER proteins, and 1 lysosomal protein. Proteins from other subcellular compartments such as endosome and Golgi apparatus were poorly represented. The presence of subcellular membrane and cytoplasmic proteins in lipid raft fractions have been discussed in several proteomic studies [1, 8, 11, 4346]. One possibility is the contamination of non-plasma membrane proteins during gradient purification. The position of membrane particles in the density gradient ultracentrifugation is determined mainly by the ratio of its lipid and protein contents; different ratios of lipids to proteins for the various intracellular membrane particles could lead them to have different buoyant properties in density gradients. In this context, any method used for preparing cell membrane 'lipid rafts' is likely to generate a fraction containing membranes from a number of sub-cellular membranes, but not necessarily one enriched specifically in plasma membrane lipid rafts [8]. Certain subcellular proteins highly enriched in raft samples may be structurally involved and play critical roles in cell membrane lipid raft organization. For example, the cellular structural proteins such as tubulins, actins, keratins, and microtubular proteins, are highly enriched in lipid raft samples including brain-rafts as shown in this study and many other reports [7, 8, 13, 28]. These cytoskeletal proteins not only contribute to the structural organization of cytoplasm but also play important roles in regulating the topography of the plasma membrane and trafficking and in modulating the localization of lipid raft proteins in eukaryotic cells [47, 48]. Additionally, many proteins could have multiple cellular localizations regulated by multiple mechanisms. For example, cytoplasmic microtubule-associated proteins and 14-3-3 proteins, histones, and mitochondrial ATP synthases and voltage-dependent anion-selective channel 1 (VDAC1), have also been identified in cell plasma membranes [44, 4952]. Thus, enrichment of certain non-PM proteins in lipid rafts (DRMs) may represent a true observation of protein localization in different biological conditions and not necessarily be due to cross-contamination acquired during purification.

Compilation of proteins into abundance lists

All proteins identified as PM protein or non-PM proteins in lipid rafts of neonatal mouse brain by TubeGeLC/MS/MS are compiled in Tables 1 and 2, respectively, and were sorted by their relative abundance calculated from the MS/MS spectral counts. Mass spectrometry of proteins and peptides is not quantitative, therefore, it is difficult to assess the abundance of a particular protein from the MS data per se. However, recent studies with label-free LC-MS/MS shotgun proteomics [29, 5357] revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count. The use of sampling statistics is a promising method for measuring the relative protein abundance and detecting differentially expressed proteins. In general, the greater the amount of protein, the greater the MS signal intensity, number of spectral counts, sequenced peptides/sequence coverage, total ion current (TIC), and total Xcorr or scores that combine these values. Label-free proteomics has emerged as an alternative to stable isotope labeling for protein quantitation. The MS/MS spectral count, which compares the number of MS/MS spectra assigned to each protein, was selected for relative protein abundance in this study. Although this method has a tendency to overestimate the abundance of large proteins because they yield more peptides and therefore more spectral counts than the smaller proteins, the results indicate that this may not be a fundamental problem [29]. In the current study, contactin-1 (113.4 kDa) had a MS/MS spectral count of 174, but the sodium/potassium-transporting ATPase alpha-3 chain, a protein of almost identical size (111.7 kDa), had a spectral count of 9 (Table 1). It is reasonable to assume that the former protein is much more abundant than the latter. When plotting MS/MS spectral count versus protein size for all proteins identified (data not shown), both the maximum spectral count distribution was highest for proteins with a size distribution of 20~50 kDa. Therefore, the bias that may be potentially caused by size towards larger proteins may not be overly large, when using MS/MS spectral counts as a measure of abundance [29]. About 50% of proteins were identified with fewer than 5 total spectral counts, presumably due to their relatively low abundances. A total of 11 identified proteins with > 40 spectral counts were arbitrarily categorized as the most abundant proteins in the lipid rafts from neonatal mouse brain. These include contactin-1, NAP-22, Gα(o), annexin-A6, Lsamp, neurotractin, contactin-2, Gα(i), and flotillin-1, as well as intracellular structural and mitochondrial proteins such as tubulins, actins, and ATP synthases. Proteins with total spectral counts from 5 to 40 were arbitrarily categorized as medium abundance proteins. The relative abundances agree well with published data [7, 18, 19, 30, 39, 40, 42, 43, 45, 49, 5871] and support our contention that the TubeGeLC-MS/MS approach provides a fair representation of the protein composition of the lipid rafts from neonatal mouse brain. The spectral count data for each identified protein provides proteome-wide semi-quantitative information on the relative abundance of lipid raft proteins.

Comparison of protein identifications between GeLC-MS/MS and TubeGeLC-MS/MS

In-gel digestion can be efficiently employed after protein mixtures are resolved by SDS-PAGE or directly polymerized into a 'tube-gel' without electrophoresis [7, 15]. Both of these in gel-based protein digestion protocols give clean LC-MS/MS baselines as interfering substances, such as detergents, salts and lipids, can be effectively removed during washing steps. To compare the GeLC-MS/MS versus the TubeGeLC-MS/MS, four separate experiments were conducted using 100 μl of sucrose-gradient isolated rafts that were subjected to a 1D SDS-PAGE combined with nanospray LC-MS/MS spectrometry (GeLC-MS/MS) modified by an established protocol [72], as described in Methods. The results for the peptides and corresponding proteins that were identified in a minimum of 2 of 4 independent experiments were used for comparative analyses. The comparison showed that about 200 proteins could also be identified by GeLC-MS/MS approach, with similar protein identifications, especially for high and medium abundance proteins, as compared to TubeGeLC-MS/MS (data not shown). However, the reproducibility of protein identified by GeLC-MS/MS was less than TubeGeLC-MS/MS (see below).

Reproducibility of raft proteome characterization by TubeGeLC-MS/MS

To test the reproducibility of proteins identified, 3 raft samples from 3 separate neonatal brains prepared by identical methods at the same time were processed by both TubeGeLC-MS/MS and GeLC-MS/MS protocols, and the resulting protein identifications for within technique variations compared. There was a 68.8 ± 6.5% (SD) concordance in the proteins identified by TubeGeLC-MS/MS protocol among the 3 raft samples. As expected, the high abundance proteins showed a higher reproducibility of identification. The non-concordant proteins of ~30% may reflect some false identification because 55% of the non-concordant proteins had single or two peptide identifications. In addition, lipid raft isolations per se have a degree of variability. The results from the GeLC-MS/MS protocol yielded 45 ± 11% of concordance for within technique protein identifications.

A MS/MS spectral-count method was employed as a semi-quantitative measure for comparing proteins in different samples. Variability in protein abundance, calculated as MS/MS spectral counts, between the brain raft samples from two separate animals was evaluated and compared between the two approaches. The ratio of the spectral count per protein between these two samples was presented as fold-change and plotted against the average of the spectral count of the two samples. With the TubeGeLC-MS/MS method the fold-change was less than 2 for ~80% of the identified proteins; the higher the abundance, the lower of fold changes as shown in Figure 4A. However, greater variations for low abundance proteins were evident, indicating that the sensitivity of quantifying changes for low abundance proteins was generally lower. The fold-change results of the same samples analyzed by the GeLC/MS/MS protocol are shown in Figure 4B; greater variations were evident for both high and low abundant proteins. These results suggested that there was larger experimental variation associated with 1D gel protein separation and extraction from the gel slices prior protein digestion and mass spectrometry using the GeLC/MS/MS method. One of the explanations is that lipid associated proteins and other hydrophobic proteins may not fully enter the gel lanes in the GeLC/MS/MS protocol, causing variations in quantitative analyses. Employing the TubeGeLC-MS/MS approach, despite the experimental variation in isolating the lipid rafts, the protein composition from replicate samples was less variable, indicating that this simple change in sample handling results in more reproducible results.
Figure 4

Variations of quantification by spectral counting. The variations in spectral counts for each protein were compared between the TubeGeLC/MS/MS and the GeLC/MS/MS protocols. The ratio of the spectral count per protein between two samples is presented as fold-change and plotted against the average of the spectral count of two samples. Panel A shows the results from the TubeGeLC/MS/MS method and panel B from the GeLC/MS/MS protocol.

Conclusion

We have successfully combined a 'tube-gel' protein digestion protocol with nanospray LC-MS/MS analysis to carry out a high throughput proteomic mapping of lipid raft proteins isolated from neonatal mouse brain. Characterization of analytically difficult lipid raft proteins was simplified by this method. The MS/MS spectral count information from mass spectrometric analyses allowed for the label-free quantitation of relative protein abundances of more than 200 raft proteins from a single sample. The major advantage of this protocol is that the raft proteins are directly digested in a gel matrix without fractionation and purification, thus dramatically minimizing variation in protein yields due to losses during sample manipulation prior to mass spectrometry. With careful isolation of rafts, this protocol should allow for a reproducible quantitation of relative protein abundance in lipid rafts. This methodology should allow the investigation of the role of these specialized membranes under various biological conditions.

Methods

Reagents and antibodies

Sources for antibodies were as follows: caveolin-1 (Cav-1), contactin-1 (Cntn-1), annexin-VI (Anx VI, Anx6A), GTP-binding protein αq (Gαq), NAP-22, calnexin, α-tubulin from Santa Cruz Biotechnology, CA USA; flotillin-1 (flot-1): BD Transduction Laboratories, CA USA; mouse monoclonal antibody against β-COP: Sigma-Aldrich, MO USA; and mouse anti-human transferrin receptor antibody: Zymed Laboratories, CA USA. Trypsin Gold (MS grade) was obtained from Promega, WI, USA. All other reagents were from ThermoFisher Scientific, MA, USA.

Preparation of raft-enriched detergent-resistant membranes from neonatal mouse brain

All animal experiments were performed with the approval of the Institutional Animal Care and Research Advisory Committee at the Clement Zablocki Veterans Medical Center. Neonatal mice (postnatal day 1, C57Bl/6J, Jackson Laboratories) were sacrificed by decapitation. Details of protocols used to prepare the raft-enriched detergent-resistant membranes have been described previously [30, 73]. Briefly, frozen brains from neonatal mice, 50~60 mg of wet brain tissue, were homogenized in an ice-cold lysis buffer containing 5% glycerol in buffer A (50 mM Tris-HCl, pH 8.0, 10 mM MgCl2, 0.15 M NaCl, 20 mM NaF, 1 mM Na3VO4, 5 mM β-mercaptoethanol, 10 μg/ml aprotinin, 10 μg/ml leupeptin, 1 mM PMSF), using a tissue homogenizer (PRO Scientific Inc., Oxford, CT USA) by three pulses of 10 seconds each, followed by 20 strokes of a Dounce homogenizer, pestle A. Tissue debris and nuclei were removed by centrifugation at 1,000 g for 5 minutes and the pellet was re-extracted. The protein concentration of the post-nuclear homogenates (PNH) was measured using Protein Reagent (Bio-Rad, CA USA), adjusted to 2 mg/ml and 2 ml of the homogenates extracted with 1% Triton X-100 (TX) on ice for 30 min. The samples were mixed with an equal volume of ice-cold 80% (w/v) sucrose in buffer A, and then overlaid with 2.0 ml each of 35, 30, 25, and 5% (w/v) sucrose (all in buffer A). The sucrose gradient was centrifuged at 36,000 rpm in a Sorval 90 ultracentrifuge using a TH-641 rotor for 15 hr at 4°C. After ultracentrifugation, TX-resistant lipid rafts appeared as an insoluble white light-scattering band at the interface between the 5% and 25% sucrose layer. Twelve 1.0 ml fractions were collected from the top to bottom, with fractions 2–4 containing the rafts (density range of 1.055~1.115 g/ml). Non-raft fractions 8–11 were collected in the density range 1.130~1.180 g/ml.

Tube-gel protein digestion

A Tube-Gel digestion method has been successfully used for high throughput analysis of membrane proteins and proven to be compatible with detergents in protein samples [15]. In these experiments, fraction 3 of the sucrose gradient was used as the lipid raft fraction. The raft fraction was directly incorporated into a polyacrylamide gel matrix as follows: 100 μl of the raft solution, 25 μl of acrylamide solution (40%, 29:1), 1.0 μl of 10% SDS, 0.5 μl of 10% ammonium persulfate, and 0.1 μl of TEMED were mixed in a 0.5 ml Eppendorf tube. The co-polymerization reaction was carried out for 30 min at room temperature. Post-polymerization, no liquid was extruded from the tube-gel, indicating that all of the materials were trapped in the gel matrix. The gel block was removed, cut into small pieces, and washed five times with 50% acetonitrile (v/v) in 25 mM ammonium bicarbonate for 15 min, using sonication and agitation. The gel pieces were dried using a SpeedVac, subjected to in-gel digestion using 100 μl of 10 ng/μl trypsin dissolved in 25 mM AMBIC and incubated at 37°C overnight. Peptides were then extracted from the gel using 500 μl of 0.1% formic acid in MS-grade water followed by 2 extractions with the same volume of 0.1% formic acid in 70% acetonitrile. Corresponding fractions were combined and dried using a SpeedVac. The dried samples were resuspended in 6 M guanidine-hydrochloride and 5 mM potassium phosphate, pH 6.0, purified using C-18 zip-tips from Millipore Corp., and subjected to nanospray LC-MS/MS analysis. This protocol is referred to as TubeGeLC-MS/MS.

Alternatively, lipid raft proteins were digested using an established protocol with some minor modification by 1-D electrophoresis coupled with nanospray LC-MS/MS (GeLC-MS/MS) [72]. Rather than conventional SDS-PAGE separation and multiple LC-MS/MS analyses, proteins in 100 μl of raft fractions, were first separated on 6% SDS-PAGE gels, long enough for the protein mixtures to penetrate the separation gel and then stained with silver. The stained areas of the gel containing the complex mixture of proteins were excised, digested with trypsin and applied to the nanospray LC-MS/MS to analyze raft proteome as described above.

Nanospray LC-MS/MS spectrometry and data Analysis

Automated nanospray liquid chromatography tandem mass spectrometry (nanospray LC-MS/MS) was performed using an LTQ-LC/MS from ThermoFisher Scientific. Peptide mixtures were separated using a C18 reverse phase column (0.75-Å internal diameter at a flow rate of 1 μl/min) in line with the mass spectrometer. The mobile phases consisted of 0.1% formic acid containing 5% acetonitrile (A) and 0.1% formic acid in 95% acetonitrile (B), respectively. A 260-min linear gradient was typically used.

The MS data obtained were searched using the SEQUEST algorithm against the UniProt Rodent database v49.1. The search was limited only to tryptic peptides, and identifications were filtered from the search results using the Epitomize program [74]. Epitomize reads all the SEQUEST.out files in a directory, filters the files based on user-defined levels of Xcorr, and outputs the proteins identified. The Xcorr versus charge state filter used was set to Xcorr values of 1.8, 2.3 and 3.0 for charge states +1, +2 and +3, respectively. These filter values are similar to others previously reported for SEQUEST analyses [75]. Protein hits that passed the filter were annotated using the generic Gene Ontology (GO) slim. All proteins were identified by two or more peptides, and those identified with single peptide were included in the analysis if identified in two or more scans. Finally, the peptides listed were manually verified for correct identification by comparing the experimental spectra with the theoretical band ion spectra. Quantitative analyses were done using the open-source software program ZoomQuant, which provides a validation and a quantization platform for protein mass spectrometry [74, 76].

Biochemical analysis of lipids in gradient fractions

Sterol composition in each of the 12 fractions was quantitatively determined by gas chromatography/mass spectroscopy (GC/MS). An aliquot of ethanol containing the internal standard 5α-cholestane (25 μg) was added to each sample tube, and samples were hydrolyzed at 50°C in ethanol containing 1 M NaOH for 1 hour. Sterols were extracted in hexane (final volume 30 ml), dried under nitrogen, and derivatized with HMDS-TMCS. GC-MS analysis was performed using a Focus DSQ system (ThermoFisher Scientific). The trimethylsilyl-derived sterols were separated on a TR-35MS capillary column (35 m × 0.25 mm internal diameter × 0.25 μm film) with helium as the carrier gas at the rate of 1.8 ml/min. The temperature program was 150°C for 1 minute, followed by increases of 20°C/min up to 310°C, which was then held for 6 minutes. The injector was operated in the splitless mode at 250°C. Standard curves were generated by MS analysis of various amounts of each sterol. The contents of sphingomyelin (SM), ceramide (Cer) in each of the 12 fractions was quantitatively determined by LC/ESI/MS/MS on a Thermo Finnigan TSQ 7000 triple quadrupole mass spectrometer, operating in a Multiple Reaction Monitoring (MRM) positive ionization mode, as described previously [77].

SDS/PAGE and immunoblots

A 30 μl aliquot of each fraction from the sucrose gradient was analyzed by SDS/PAGE on 10 or 12% (w/v) acrylamide gels. Separated proteins were transferred to nitrocellulose membranes for immunoblotting analyses. Membranes were blocked in 5% (w/v) non-fat milk in TBS-Tween [0.05% (w/v) Tween 20 in 10 mM Tris/100 mM NaCl, pH 7.5], and then incubated with the primary antibodies of choice. Membranes were subsequently incubated with HRP-conjugated second antibodies, and specific interactions were revealed using the ECL® (Enhanced Chemiluminescence) detection system (Amersham, CA, USA).

Notes

Declarations

Acknowledgements

We thank Yanhong Cai for her technical services and assistance with the animal husbandry. The authors thank the members of the Protein Core of Medical College of Wisconsin for the proteomic Service. We also thank Dr. Michael Olivier for critically reading this manuscript. This research was supported by Biomedical Research Grant RG-11311-M from the American Lung Association (HY) and by PHS grant HL68660 from the National Heart, Blood and Lung Institute, NIH (SBP). This work also was supported in part by the NHLBI Proteomics Center contract NIH-N01 HV-28182.

Authors’ Affiliations

(1)
Division of Endocrinology, Metabolism and Nutrition, Medical College of Wisconsin
(2)
Department of Biochemistry, Medical College of Wisconsin
(3)
National Center for Proteomics Research, Biotechnology and Bioinformatics Center, Medical College of Wisconsin
(4)
Research Service, Department of Veterans Affairs New Jersey Health Care System
(5)
Department of Veterans Affairs, Clement J. Zablocki Medical Center
(6)
Qilu Hospital, Shandong University
(7)
Department of Medicine, UMDNJ-New Jersey Medical School

References

  1. Brown DA: Lipid rafts, detergent-resistant membranes, and raft targeting signals. Physiology (Bethesda) 2006, 21: 430–439.View ArticleGoogle Scholar
  2. Allen JA, Halverson-Tamboli RA, Rasenick MM: Lipid raft microdomains and neurotransmitter signalling. Nat Rev Neurosci 2007, 8: 128–140. 10.1038/nrn2059PubMedView ArticleGoogle Scholar
  3. Pike LJ: Lipid rafts: heterogeneity on the high seas. Biochem J 2004, 378: 281–292. 10.1042/BJ20031672PubMed CentralPubMedView ArticleGoogle Scholar
  4. Simons K, Ehehalt R: Cholesterol, lipid rafts, and disease. J Clin Invest 2002, 110: 597–603. 10.1172/JCI200216390PubMed CentralPubMedView ArticleGoogle Scholar
  5. Simons K, Vaz WL: Model systems, lipid rafts, and cell membranes. Annu Rev Biophys Biomol Struct 2004, 33: 269–295. 10.1146/annurev.biophys.32.110601.141803PubMedView ArticleGoogle Scholar
  6. Blonder J, Hale ML, Lucas DA, Schaefer CF, Yu LR, Conrads TP, Issaq HJ, Stiles BG, Veenstra TD: Proteomic analysis of detergent-resistant membrane rafts. Electrophoresis 2004, 25: 1307–1318. 10.1002/elps.200405891PubMedView ArticleGoogle Scholar
  7. Martosella J, Zolotarjova N, Liu H, Moyer SC, Perkins PD, Boyes BE: High recovery HPLC separation of lipid rafts for membrane proteome analysis. J Proteome Res 2006, 5: 1301–1312. 10.1021/pr060051gPubMedView ArticleGoogle Scholar
  8. Magee AI, Parmryd I: Detergent-resistant membranes and the protein composition of lipid rafts. Genome Biol 2003, 4: 234. 10.1186/gb-2003-4-11-234PubMed CentralPubMedView ArticleGoogle Scholar
  9. MacLellan DL, Steen H, Adam RM, Garlick M, Zurakowski D, Gygi SP, Freeman MR, Solomon KR: A quantitative proteomic analysis of growth factor-induced compositional changes in lipid rafts of human smooth muscle cells. Proteomics 2005, 5: 4733–4742. 10.1002/pmic.200500044PubMedView ArticleGoogle Scholar
  10. Elortza F, Nuhse TS, Foster LJ, Stensballe A, Peck SC, Jensen ON: Proteomic analysis of glycosylphosphatidylinositol-anchored membrane proteins. Mol Cell Proteomics 2003, 2: 1261–1270. 10.1074/mcp.M300079-MCP200PubMedView ArticleGoogle Scholar
  11. Foster LJ, De Hoog CL, Mann M: Unbiased quantitative proteomics of lipid rafts reveals high specificity for signaling factors. Proc Natl Acad Sci U S A 2003, 100: 5813–5818. 10.1073/pnas.0631608100PubMed CentralPubMedView ArticleGoogle Scholar
  12. Gupta N, Wollscheid B, Watts JD, Scheer B, Aebersold R, DeFranco AL: Quantitative proteomic analysis of B cell lipid rafts reveals that ezrin regulates antigen receptor-mediated lipid raft dynamics. Nat Immunol 2006, 7: 625–633. 10.1038/ni1337PubMedView ArticleGoogle Scholar
  13. Jia JY, Lamer S, Schumann M, Schmidt MR, Krause E, Haucke V: Quantitative proteomics analysis of detergent-resistant membranes from chemical synapses: evidence for cholesterol as spatial organizer of synaptic vesicle cycling. Mol Cell Proteomics 2006, 5: 2060–2071. 10.1074/mcp.M600161-MCP200PubMedView ArticleGoogle Scholar
  14. Sprenger RR, Horrevoets AJ: Proteomic study of caveolae and rafts isolated from human endothelial cells. Methods Mol Biol 2007, 357: 199–213.PubMedGoogle Scholar
  15. Lu X, Zhu H: Tube-gel digestion: a novel proteomic approach for high throughput analysis of membrane proteins. Mol Cell Proteomics 2005, 4: 1948–1958. 10.1074/mcp.M500138-MCP200PubMed CentralPubMedView ArticleGoogle Scholar
  16. Ishmael JE, Safic M, Amparan D, Vogel WK, Pham T, Marley K, Filtz TM, Maier CS: Nonmuscle myosins II-B and Va are components of detergent-resistant membrane skeletons derived from mouse forebrain. Brain Res 2007, 1143: 46–59. 10.1016/j.brainres.2007.01.061PubMedView ArticleGoogle Scholar
  17. Kim KB, Lee JW, Lee CS, Kim BW, Choo HJ, Jung SY, Chi SG, Yoon YS, Yoon G, Ko YG: Oxidation-reduction respiratory chains and ATP synthase complex are localized in detergent-resistant lipid rafts. Proteomics 2006, 6: 2444–2453. 10.1002/pmic.200500574PubMedView ArticleGoogle Scholar
  18. Kisby GE, Standley M, Park T, Olivas A, Fei S, Jacob T, Reddy A, Lu X, Pattee P, Nagalla SR: Proteomic analysis of the genotoxicant methylazoxymethanol (MAM)-induced changes in the developing cerebellum. J Proteome Res 2006, 5: 2656–2665. 10.1021/pr060126gPubMedView ArticleGoogle Scholar
  19. Sheikh AM, Barrett C, Villamizar N, Alzate O, Miller S, Shelburne J, Lodge A, Lawson J, Jaggers J: Proteomics of cerebral injury in a neonatal model of cardiopulmonary bypass with deep hypothermic circulatory arrest. J Thorac Cardiovasc Surg 2006, 132: 820–828. 10.1016/j.jtcvs.2006.07.002PubMedView ArticleGoogle Scholar
  20. Spitzer AR, Chace D: Mass spectrometry in neonatal medicine and clinical diagnosis--the [corrected] potential use of mass spectrometry in neonatal brain [corrected] monitoring. Clin Perinatol 2006, 33: 729–44, viii. 10.1016/j.clp.2006.06.005PubMedView ArticleGoogle Scholar
  21. Yang ZJ, Appleby VJ, Coyle B, Chan WI, Tahmaseb M, Wigmore PM, Scotting PJ: Novel strategy to study gene expression and function in developing cerebellar granule cells. J Neurosci Methods 2004, 132: 149–160. 10.1016/j.jneumeth.2003.09.031PubMedView ArticleGoogle Scholar
  22. Kovacs WJ, Shackelford JE, Tape KN, Richards MJ, Faust PL, Fliesler SJ, Krisans SK: Disturbed cholesterol homeostasis in a peroxisome-deficient PEX2 knockout mouse model. Mol Cell Biol 2004, 24: 1–13. 10.1128/MCB.24.1.1-13.2004PubMed CentralPubMedView ArticleGoogle Scholar
  23. Edison R, Muenke M: The interplay of genetic and environmental factors in craniofacial morphogenesis: holoprosencephaly and the role of cholesterol. Congenit Anom (Kyoto) 2003, 43: 1–21.View ArticleGoogle Scholar
  24. Nissenkorn A, Michelson M, Ben-Zeev B, Lerman-Sagie T: Inborn errors of metabolism: a cause of abnormal brain development. Neurology 2001, 56: 1265–1272.PubMedView ArticleGoogle Scholar
  25. FitzPatrick DR, Keeling JW, Evans MJ, Kan AE, Bell JE, Porteous ME, Mills K, Winter RM, Clayton PT: Clinical phenotype of desmosterolosis. Am J Med Genet 1998, 75: 145–152. 10.1002/(SICI)1096-8628(19980113)75:2<145::AID-AJMG5>3.0.CO;2-SPubMedView ArticleGoogle Scholar
  26. Opitz JM, de la Cruz F: Cholesterol metabolism in the RSH/Smith-Lemli-Opitz syndrome: summary of an NICHD conference. Am J Med Genet 1994, 50: 326–338. 10.1002/ajmg.1320500406PubMedView ArticleGoogle Scholar
  27. Powers JM, Tummons RC, Moser AB, Moser HW, Huff DS, Kelley RI: Neuronal lipidosis and neuroaxonal dystrophy in cerebro-hepato-renal (Zellweger) syndrome. Acta Neuropathol (Berl) 1987, 73: 333–343. 10.1007/BF00688256View ArticleGoogle Scholar
  28. Li N, Shaw AR, Zhang N, Mak A, Li L: Lipid raft proteomics: analysis of in-solution digest of sodium dodecyl sulfate-solubilized lipid raft proteins by liquid chromatography-matrix-assisted laser desorption/ionization tandem mass spectrometry. Proteomics 2004, 4: 3156–3166. 10.1002/pmic.200400832PubMedView ArticleGoogle Scholar
  29. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, Resing KA, Ahn NG: Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 2005, 4: 1487–1502. 10.1074/mcp.M500084-MCP200PubMedView ArticleGoogle Scholar
  30. Mukherjee A, Arnaud L, Cooper JA: Lipid-dependent recruitment of neuronal Src to lipid rafts in the brain. J Biol Chem 2003, 278: 40806–40814. 10.1074/jbc.M306440200PubMedView ArticleGoogle Scholar
  31. Melkonian KA, Ostermeyer AG, Chen JZ, Roth MG, Brown DA: Role of lipid modifications in targeting proteins to detergent-resistant membrane rafts. Many raft proteins are acylated, while few are prenylated. J Biol Chem 1999, 274: 3910–3917. 10.1074/jbc.274.6.3910PubMedView ArticleGoogle Scholar
  32. Santoni V, Molloy M, Rabilloud T: Membrane proteins and proteomics: un amour impossible? Electrophoresis 2000, 21: 1054–1070. 10.1002/(SICI)1522-2683(20000401)21:6<1054::AID-ELPS1054>3.0.CO;2-8PubMedView ArticleGoogle Scholar
  33. Blonder J, Chan KC, Issaq HJ, Veenstra TD: Identification of membrane proteins from mammalian cell/tissue using methanol-facilitated solubilization and tryptic digestion coupled with 2D-LC-MS/MS. Nat Protoc 2006, 1: 2784–2790. 10.1038/nprot.2006.359PubMedView ArticleGoogle Scholar
  34. States DJ, Omenn GS, Blackwell TW, Fermin D, Eng J, Speicher DW, Hanash SM: Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 2006, 24: 333–338. 10.1038/nbt1183PubMedView ArticleGoogle Scholar
  35. Elmariah SB, Hughes EG, Oh EJ, Balice-Gordon RJ: Neurotrophin signaling among neurons and glia during formation of tripartite synapses. Neuron Glia Biol 2005, 1: 1–11. 10.1017/S1740925X05000189PubMed CentralView ArticleGoogle Scholar
  36. Foty RA, Steinberg MS: Cadherin-mediated cell-cell adhesion and tissue segregation in relation to malignancy. Int J Dev Biol 2004, 48: 397–409. 10.1387/ijdb.041810rfPubMedView ArticleGoogle Scholar
  37. Scheiffele P: Cell-cell signaling during synapse formation in the CNS. Annu Rev Neurosci 2003, 26: 485–508. 10.1146/annurev.neuro.26.043002.094940PubMedView ArticleGoogle Scholar
  38. Fetissov SO, Bergstrom U, Johansen JE, Hokfelt T, Schalling M, Ranscht B: Alterations of arcuate nucleus neuropeptidergic development in contactin-deficient mice: comparison with anorexia and food-deprived mice. Eur J Neurosci 2005, 22: 3217–3228. 10.1111/j.1460-9568.2005.04513.xPubMedView ArticleGoogle Scholar
  39. Yang JW, Rodrigo R, Felipo V, Lubec G: Proteome analysis of primary neurons and astrocytes from rat cerebellum. J Proteome Res 2005, 4: 768–788. 10.1021/pr049774vPubMedView ArticleGoogle Scholar
  40. Dremina ES, Sharov VS, Schoneich C: Protein tyrosine nitration in rat brain is associated with raft proteins, flotillin-1 and alpha-tubulin: effect of biological aging. J Neurochem 2005, 93: 1262–1271. 10.1111/j.1471-4159.2005.03115.xPubMedView ArticleGoogle Scholar
  41. Head BP, Patel HH, Roth DM, Murray F, Swaney JS, Niesman IR, Farquhar MG, Insel PA: Microtubules and actin microfilaments regulate lipid raft/caveolae localization of adenylyl cyclase signaling components. J Biol Chem 2006, 281: 26391–26399. 10.1074/jbc.M602577200PubMedView ArticleGoogle Scholar
  42. Chen S, Bawa D, Besshoh S, Gurd JW, Brown IR: Association of heat shock proteins and neuronal membrane components with lipid rafts from the rat brain. J Neurosci Res 2005, 81: 522–529. 10.1002/jnr.20575PubMedView ArticleGoogle Scholar
  43. Say YH, Hooper NM: Contamination of nuclear fractions with plasma membrane lipid rafts. Proteomics 2007, 7: 1059–1064. 10.1002/pmic.200600849PubMedView ArticleGoogle Scholar
  44. Bae TJ, Kim MS, Kim JW, Kim BW, Choo HJ, Lee JW, Kim KB, Lee CS, Kim JH, Chang SY, Kang CY, Lee SW, Ko YG: Lipid raft proteome reveals ATP synthase complex in the cell surface. Proteomics 2004, 4: 3536–3548. 10.1002/pmic.200400952PubMedView ArticleGoogle Scholar
  45. Igbavboa U, Eckert GP, Malo TM, Studniski AE, Johnson LN, Yamamoto N, Kobayashi M, Fujita SC, Appel TR, Muller WE, Wood WG, Yanagisawa K: Murine synaptosomal lipid raft protein and lipid composition are altered by expression of human apoE 3 and 4 and by increasing age. J Neurol Sci 2005, 229–230: 225–232. 10.1016/j.jns.2004.11.037PubMedView ArticleGoogle Scholar
  46. Thouvenot E, Lafon-Cazal M, Demettre E, Jouin P, Bockaert J, Marin P: The proteomic analysis of mouse choroid plexus secretome reveals a high protein secretion capacity of choroidal epithelial cells. Proteomics 2006, 6: 5941–5952. 10.1002/pmic.200600096PubMedView ArticleGoogle Scholar
  47. Pelkmans L, Puntener D, Helenius A: Local actin polymerization and dynamin recruitment in SV40-induced internalization of caveolae. Science 2002, 296: 535–539. 10.1126/science.1069784PubMedView ArticleGoogle Scholar
  48. Chang L, Goldman RD: Intermediate filaments mediate cytoskeletal crosstalk. Nat Rev Mol Cell Biol 2004, 5: 601–613. 10.1038/nrm1438PubMedView ArticleGoogle Scholar
  49. Franzen R, Tanner SL, Dashiell SM, Rottkamp CA, Hammer JA, Quarles RH: Microtubule-associated protein 1B: a neuronal binding partner for myelin-associated glycoprotein. J Cell Biol 2001, 155: 893–898. 10.1083/jcb.200108137PubMed CentralPubMedView ArticleGoogle Scholar
  50. Fu H, Subramanian RR, Masters SC: 14–3-3 proteins: structure, function, and regulation. Annu Rev Pharmacol Toxicol 2000, 40: 617–647. 10.1146/annurev.pharmtox.40.1.617PubMedView ArticleGoogle Scholar
  51. Watson K, Edwards RJ, Shaunak S, Parmelee DC, Sarraf C, Gooderham NJ, Davies DS: Extra-nuclear location of histones in activated human peripheral blood lymphocytes and cultured T-cells. Biochem Pharmacol 1995, 50: 299–309. 10.1016/0006-2952(95)00142-MPubMedView ArticleGoogle Scholar
  52. Lawen A, Ly JD, Lane DJ, Zarschler K, Messina A, De Pinto V: Voltage-dependent anion-selective channel 1 (VDAC1)--a mitochondrial protein, rediscovered as a novel enzyme in the plasma membrane. Int J Biochem Cell Biol 2005, 37: 277–282. 10.1016/j.biocel.2004.05.013PubMedView ArticleGoogle Scholar
  53. Andreev VP, Li L, Cao L, Gu Y, Rejtar T, Wu SL, Karger BL: A New Algorithm Using Cross-Assignment for Label-Free Quantitation with LC-LTQ-FT MS. J Proteome Res 2007.Google Scholar
  54. Wienkoop S, Larrainzar E, Niemann M, Gonzalez EM, Lehmann U, Weckwerth W: Stable isotope-free quantitative shotgun proteomics combined with sample pattern recognition for rapid diagnostics. J Sep Sci 2006, 29: 2793–2801. 10.1002/jssc.200600290PubMedView ArticleGoogle Scholar
  55. Le Bihan T, Goh T, Stewart, Salter AM, Bukhman YV, Dharsee M, Ewing R, Wisniewski JR: Differential analysis of membrane proteins in mouse fore- and hindbrain using a label-free approach. J Proteome Res 2006, 5: 2701–2710. 10.1021/pr060190yPubMedView ArticleGoogle Scholar
  56. Wang G, Wu WW, Zeng W, Chou CL, Shen RF: Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes. J Proteome Res 2006, 5: 1214–1223. 10.1021/pr050406gPubMedView ArticleGoogle Scholar
  57. Ru QC, Zhu LA, Silberman J, Shriver CD: Label-free semiquantitative peptide feature profiling of human breast cancer and breast disease sera via two-dimensional liquid chromatography-mass spectrometry. Mol Cell Proteomics 2006, 5: 1095–1104. 10.1074/mcp.M500387-MCP200PubMedView ArticleGoogle Scholar
  58. Cui XY, Hu QD, Tekaya M, Shimoda Y, Ang BT, Nie DY, Sun L, Hu WP, Karsak M, Duka T, Takeda Y, Ou LY, Dawe GS, Yu FG, Ahmed S, Jin LH, Schachner M, Watanabe K, Arsenijevic Y, Xiao ZC: NB-3/Notch1 pathway via Deltex1 promotes neural progenitor cell differentiation into oligodendrocytes. J Biol Chem 2004, 279: 25858–25865. 10.1074/jbc.M313505200PubMedView ArticleGoogle Scholar
  59. Brady ST, Lasek RJ: Nerve-specific enolase and creatine phosphokinase in axonal transport: soluble proteins and the axoplasmic matrix. Cell 1981, 23: 515–523. 10.1016/0092-8674(81)90147-1PubMedView ArticleGoogle Scholar
  60. Deininger SO, Rajendran L, Lottspeich F, Przybylski M, Illges H, Stuermer CA, Reuter A: Identification of teleost Thy-1 and association with the microdomain/lipid raft reggie proteins in regenerating CNS axons. Mol Cell Neurosci 2003, 22: 544–554. 10.1016/S1044-7431(03)00028-9PubMedView ArticleGoogle Scholar
  61. Hu QD, Ang BT, Karsak M, Hu WP, Cui XY, Duka T, Takeda Y, Chia W, Sankar N, Ng YK, Ling EA, Maciag T, Small D, Trifonova R, Kopan R, Okano H, Nakafuku M, Chiba S, Hirai H, Aster JC, Schachner M, Pallen CJ, Watanabe K, Xiao ZC: F3/contactin acts as a functional ligand for Notch during oligodendrocyte maturation. Cell 2003, 115: 163–175. 10.1016/S0092-8674(03)00810-9PubMedView ArticleGoogle Scholar
  62. Guirland C, Suzuki S, Kojima M, Lu B, Zheng JQ: Lipid rafts mediate chemotropic guidance of nerve growth cones. Neuron 2004, 42: 51–62. 10.1016/S0896-6273(04)00157-6PubMedView ArticleGoogle Scholar
  63. Kashihara M, Miyata S, Kumanogoh H, Funatsu N, Matsunaga W, Kiyohara T, Sokawa Y, Maekawa S: Changes in the localization of NAP-22, a calmodulin binding membrane protein, during the development of neuronal polarity. Neurosci Res 2000, 37: 315–325. 10.1016/S0168-0102(00)00132-2PubMedView ArticleGoogle Scholar
  64. Leshchyns'ka I, Sytnyk V, Morrow JS, Schachner M: Neural cell adhesion molecule (NCAM) association with PKCbeta2 via betaI spectrin is implicated in NCAM-mediated neurite outgrowth. J Cell Biol 2003, 161: 625–639. 10.1083/jcb.200303020PubMed CentralPubMedView ArticleGoogle Scholar
  65. Maekawa S, Morii H, Kumanogoh H, Sano M, Naruse Y, Sokawa Y, Mori N: Localization of neuronal growth-associated, microtubule-destabilizing factor SCG10 in brain-derived raft membrane microdomains. J Biochem (Tokyo) 2001, 129: 691–697.View ArticleGoogle Scholar
  66. Miyata S, Funatsu N, Matsunaga W, Kiyohara T, Sokawa Y, Maekawa S: Expression of the IgLON cell adhesion molecules Kilon and OBCAM in hypothalamic magnocellular neurons. J Comp Neurol 2000, 424: 74–85. 10.1002/1096-9861(20000814)424:1<74::AID-CNE6>3.0.CO;2-5PubMedView ArticleGoogle Scholar
  67. Roussel G, Nussbaum F, Schoentgen F, Jolles P, Nussbaum JL: Immunological investigation of a 21-kilodalton cytosolic basic protein in rat brain. Dev Neurosci 1988, 10: 65–74.PubMedView ArticleGoogle Scholar
  68. Schaeren-Wiemers N, Bonnet A, Erb M, Erne B, Bartsch U, Kern F, Mantei N, Sherman D, Suter U: The raft-associated protein MAL is required for maintenance of proper axon--glia interactions in the central nervous system. J Cell Biol 2004, 166: 731–742. 10.1083/jcb.200406092PubMed CentralPubMedView ArticleGoogle Scholar
  69. Schlicht K, Buttner A, Siedler F, Scheffer B, Zill P, Eisenmenger W, Ackenheil M, Bondy B: Comparative proteomic analysis with postmortem prefrontal cortex tissues of suicide victims versus controls. J Psychiatr Res 2007, 41: 493–501. 10.1016/j.jpsychires.2006.04.006PubMedView ArticleGoogle Scholar
  70. Yanagisawa M, Nakamura K, Taga T: Roles of lipid rafts in integrin-dependent adhesion and gp130 signalling pathway in mouse embryonic neural precursor cells. Genes Cells 2004, 9: 801–809. 10.1111/j.1365-2443.2004.00764.xPubMedView ArticleGoogle Scholar
  71. Yang JW, Suder P, Silberring J, Lubec G: Proteome analysis of mouse primary astrocytes. Neurochem Int 2005, 47: 159–172. 10.1016/j.neuint.2005.04.017PubMedView ArticleGoogle Scholar
  72. Schirle M, Heurtier MA, Kuster B: Profiling core proteomes of human cell lines by one-dimensional PAGE and liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 2003, 2: 1297–1305. 10.1074/mcp.M300087-MCP200PubMedView ArticleGoogle Scholar
  73. Yu H, Li M, Tint GS, Chen J, Xu G, Patel SB: Selective reconstitution of liver cholesterol biosynthesis promotes lung maturation but does not prevent neonatal lethality in Dhcr7 null mice. BMC Dev Biol 2007, 7: 27. 10.1186/1471-213X-7-27PubMed CentralPubMedView ArticleGoogle Scholar
  74. Hicks WA, Halligan BD, Slyper RY, Twigger SN, Greene AS, Olivier M: Simultaneous quantification and identification using 18O labeling with an ion trap mass spectrometer and the analysis software application "ZoomQuant". J Am Soc Mass Spectrom 2005, 16: 916–925. 10.1016/j.jasms.2005.02.024PubMed CentralPubMedView ArticleGoogle Scholar
  75. Mirza SP, Halligan BD, Greene AS, Olivier M: An improved method for the analysis of membrane proteins by mass spectrometry. Physiol Genomics 2007.Google Scholar
  76. Halligan BD, Slyper RY, Twigger SN, Hicks W, Olivier M, Greene AS: ZoomQuant: an application for the quantitation of stable isotope labeled peptides. J Am Soc Mass Spectrom 2005, 16: 302–306. 10.1016/j.jasms.2004.11.014PubMed CentralPubMedView ArticleGoogle Scholar
  77. Bielawski J, Szulc ZM, Hannun YA, Bielawska A: Simultaneous quantitative analysis of bioactive sphingolipids by high-performance liquid chromatography-tandem mass spectrometry. Methods 2006, 39: 82–91. 10.1016/j.ymeth.2006.05.004PubMedView ArticleGoogle Scholar

Copyright

© Yu et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement