Swammerdam Institute for Life Sciences

Stable-isotope labeling by amino acids in

cell culture (SILAC)

Literature thesis MSc Chemistry, track Analytical Sciences

15 June 2009

Robin van der Ploeg (0312592)

Supervisor: Prof. dr. C.G. de Koster

Second reviewer: dr. W. Th. Kok

2

Abstract

Stable isotope labeling by amino acids in cell culture (SILAC) is a relatively new mass

spectrometry-based method in proteomics. In a typical SILAC experiment, a cell culture

is grown on a medium with isotope labeled amino acids. During the time the cell culture

spends on the medium, proteins will be synthesized from the labeled amino acids. The

ratios of peak abundances of labeled and unlabeled peptides in the mass spectra can be

used to quantify protein turnover. In this literature thesis the reliability of published

SILAC measurements will be investigated. Also, dynamic SILAC experiments and

applications in biology and medicine will be discussed.

Samenvatting

Stable isotope labeling by amino acids in cell culture (SILAC) is een relatief nieuwe, op

massaspectrometrie gebaseerde methode in proteomics. In een typisch SILAC experiment

wordt een cultuur cellen gekweekt op een medium met isotoopgelabelde aminozuren.

Gedurende de tijd dat de cellen op het medium groeien worden de nieuwe eiwitten

gesynthetiseerd uit gelabelde aminozuren. De piekverhoudingen tussen de gelabelde en

de ongelabelde peptiden in de massaspectra kunnen gebruikt worden om de omzetting

van eiwitten te kwantificeren. In deze literatuurscriptie zal de betrouwbaarheid van

gepubliceerde SILAC metingen onderzocht worden. Ook zullen dynamische (tijd

opgeloste) SILAC experimenten en toepassingen in biologie en geneeskunde worden

besproken.

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Table of contents

Abstract ............................................................................................................................... 2

Samenvatting....................................................................................................................... 2 Table of contents ................................................................................................................. 3 1. Introduction ................................................................................................................. 4

1.1 Classical protein analysis .......................................................................................... 4 1.2 Chemical labeling techniques ................................................................................... 5

2. Description of SILAC .................................................................................................. 7

3. Research question ...................................................................................................... 10 4. Analytical Parameters ................................................................................................ 11

4.1 Experimental errors introduced by sample preparation .......................................... 12 4.2 Role of the mass analyzer ....................................................................................... 13 4.3 Accuracy study with

13C6-arginine ......................................................................... 14

4.4 Absolute quantitation and the dynamic range ......................................................... 16

5. The heat shock response as a potential application field for SILAC ............................ 17 5.1 Quantitation of dynamic SILAC measurements ..................................................... 18

5.2 Some examples of dynamic SILAC ........................................................................ 19 6. Static SILAC.............................................................................................................. 24 7. Applications in biology and medicine ....................................................................... 26

8. Conclusions ............................................................................................................... 27

9. References ................................................................................................................. 28

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1. Introduction

The collection of genes in a cell is called the genome. Humans have 24,000 genes made

up of 3,200,000,000 base pairs [1]. The genome itself is only able to store and replicate

genetic information. Cells have the ability to transcribe the genetic information encoded

in the DNA (deoxyribonucleic acid) to messenger RNA (ribonucleic acid). The entire

collection of the mRNA molecules in a cell is called the transcriptome. The mRNA is

then translated into proteins, which can fulfill tasks inside the cell such as catalyzing

metabolic reactions, being a part of signaling networks, serve as chemical sensors or form

structural elements.

The proteome is the entire collection of proteins that are expressed in a cell. As a result of

alternative splicing and post-translational modifications, the human proteome is at least

ten times larger than the genome. The expressed proteins differ in copy number, place in

the cell and in time. In human serum for example, it has a dynamic range in

concentrations of 1012

and changes every moment as a result of external and internal

stimuli [2].

The genomes of many organisms have been sequenced and the mRNA of the

transcriptome can be studied with micro array technology. However, there are several

reasons why the use of mRNA measurements has its limitations [3]. Firstly, there is no

1:1 correlation between the presence of mRNA and the protein concentration. Secondly,

certain disease processes only involve changes in protein activity and none in mRNA

activity. Drugs acting on disease mechanisms are normally designed to target proteins,

not mRNA. Thirdly, protein concentrations in clinical samples are more stable than

mRNA concentrations. Fourthly, proteins fulfill a variety of functions in a cell, whereas

mRNA is, as the name implies, just a messenger.

1.1 Classical protein analysis

All proteins have residues that can be neutral, positively or negatively charged. For every

protein there exists a pH at which the average charge is zero. This is called the isoelectric

point (pI). With isoelectric focusing (IEF) proteins can be separated according to their pI.

It is performed by casting a gel made of either agarose or polyacrylamide with a pH

gradient. This can be done by either suspending amphoteric molecules in the gel or

charging the side groups of the polyacrylamide with a gradient of strong acid or base. The

protein mixture is injected into the gel and a voltage is applied. As a result of their net

charge the proteins will move to the region in the gel where the pH equals their pI.

Proteins can also be separated with a technique called SDS polyacrylamide-gel

electrophoresis (SDS-PAGE). It involves injecting the protein sample into a gel

consisting of a cross-linked polyacrylamide. The proteins are solubilized with SDS, a

negatively charged surfactant. The solubilized proteins are injected into the gel and when

a voltage is applied, the proteins start migrating towards the cathode (positive pole). The

friction that each protein endures is dependent on the pore size of the gel and the size of

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the proteins. Small proteins endure less friction and migrate further through the gel than

large proteins. The method can be calibrated with standard mixtures of proteins of known

molecular weights (MWs).

More proteins can be analyzed at once when these two techniques are combined. IEF can

be used as a first-dimension separation and SDS-PAGE as second dimension separation

to form Two-dimensional Gel Electrophoresis (2D-E) [4]. 2D-E was developed by

O‟Farrel in 1975 [5] and can separate up to 2500 proteins. The proteins can be made

visible selectively with Western blotting, or non-selectively with for example Coomassie

Blue.

2D-E however, has its limitations. First, membrane proteins are hydrophobic and

aggregate in the first dimension gels, leaving them out of the analysis. Second, only the

most abundant proteins can be seen with the staining technique. The large dynamic range

of the proteome causes the least abundant proteins to be invisible with this technique.

Quantification based on blot intensities has a limited precision. Third, very basic and very

acidic proteins fall outside the pI range that can be separated.

At present, mass spectrometry (MS) is the technique of choice for qualitative and

quantitative measurements in proteomes. The ionization techniques most often used for

biomacromolecules are matrix-assisted laser desorption-ionisation (MALDI) and

(nano)electrospray ionization ((n)ESI). 2D-E is often combined with MS by cutting out a

spot of interest in the gel, subjecting it to trypsine digestion and either directly placing the

sample on a MALDI plate or first subjecting the peptide mixture to a separation in the

liquid phases before mass spectrometric detection (LC-MS). In the latter mode it is also

possible to use mass analyzers that allow several mass analysis steps to form LC-MS/MS

or LC-MSn.

1.2 Chemical labeling techniques

Mass spectrometry is not in itself inherently quantitative. Different molecules in a sample

have different ionization efficiencies, resulting in different sensitivities. Stable isotope

labeled analogs of substances have been used as standards for quantitative measurements

with mass spectrometry for a long time. Because isotope labeled molecules have similar

physical, chemical and biological properties as the natural form, they are ideal as internal

standards.

Chemical techniques that enable quantitative protein analysis in mass spectrometry have

recently been developed and the following will be discussed below: Isotope-encoded

affinity tags (ICAT), 18

O labeling and Stable Isotope Labeling by Amino Acids in Cell

cultures (SILAC). Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) is also a

chemical labeling technique but will not be discussed in this work.

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ICAT was developed by Aebersold et al. in 1999 [6]. It exists of three elements (Figure

1): the biotina end group, a linker that can be labeled with heavy or light isotopes and a

group that is reactive towards the thiol group in cysteine.

Figure 1: ICAT reagent [6]

In an ICAT experiment, two cell cultures are grown in parallel (Figure 3). After

extraction, the protein contents are reduced so that cysteine residues exist as free thiol

groups. The heavy and light isotope labeled ICAT reagents are added to the two cell

cultures respectively. After the labeling step, the two samples are combined and the

proteins are digested with a proteolytic enzyme. The resulting lysate is purified by

affinity chromatography using an avidinb column. The purified peptides are then analyzed

with LC-MS. Using the extracted ion chromatograms (XIC), the ratio between peak

intensities of the light reagent in the one cell culture and the heavy reagent in the other

culture can be used to determine the fold ratio of protein concentrations in the two cell

cultures (Figure 2).

Figure 2: determination of fold ratios from the XIC [7]

Another technique for isotope labeling is by enzymatic degradation in 18

O labeled water

[8]. Again, two cell cultures are grown in two different states. The proteins of one cell

culture are digested with trypsin in water with a natural H218

O / H216

O ratio and the other

a Biotin is also known as vitamin H or B7.

b Avidin is a biotin binding protein that naturally occurs in the oviducts of birds, reptiles and amphibians.

7

is digested with trypsin in water with an artificially high H218

O / H216

O ratio. Upon

digestion, 16

O atoms are built into the peptides in the one sample and 18

O atoms into the

other. The cell lysate can be analyzed with LC-MS and from the XICs of the peptides the

fold ratios of the 18

O / 16

O peptides, the fold ratio of the proteins can be determined.

2. Description of SILAC

In the techniques described above the molecules of interest are subjected to chemical

reactions. This introduces a source of errors, id est the efficiency of the derivatization

reaction can be lower than 100%. Other ways to incorporate a label into living organisms

is by metabolic labeling. This has historically been done with unstable isotopes of amino

acids of which the radioactive decay could be measured in time, so-called pulse-chase

labeling. 35

S labeled methionine can be used for this purpose. In such an experiment [9], a

cell culture is first starved on a methonine-free medium for some time and then briefly

placed on a 35

S methionine containing medium (the pulse). Samples are taken in time and

from the increase in radioactivity, synthesis constants can be determined.

In the chase mode, the cell culture is grown on 35

S labeled methionine until all

methionines are labeled. The cells are then resuspended into a medium with unlabeled

amino acids. From the decrease in radioactivity of a time series of samples, the

degradation constants can be determined. The measurements in both cases can be done by

precipitating the whole protein contents of the samples or by first separating the proteins

by 2-DE and selecting a certain proteins of interest.

When grown on 15

N containing salts, simple organisms like bacteria and yeast will

incorporate 15

N into their proteins which can be measured with MS [7]. This method has

the limitation that the mass difference in peptides depends on the amino acid sequence of

the peptide.

In the technique stable isotope labeling by amino acids in cell culture, SILAC, stable

isotopes are incorporated in the cells via the metabolism. SILAC was first described by

Ong et al. [10] in 2002.

A typical SILAC experiment starts with growing two cell cultures. One cell culture is

grown in a medium in which one or two amino acids are replaced by a stable isotope

labeled amino acid. A wide variety has been used in the literature: 13

C6-arginine, D3-

leucine, D10-leucine, D4-lysine 13

C6-lysine or D8-valine are examples where one element

is labeled with a heavy isotope. 15

N212

C6-lysine, 15

N213

C6- lysine and 15

N413

C6-arginine

are examples where two different elements are labeled. The choice of isotope labeled

amino acid is not arbitrary. Four factors are taken into account. First, arginine and lysine

are often used because trypsin cleaves proteins and peptides after the carboxyl ends of

these amino acids except when it is adjacent to a proline. Second, the abundance in

peptides matters. Arginine for example occurs in 52% of all peptides of five amino acids

or longer. Third, when a reversed phase separation step is used in the analysis, the labeled

and the unlabeled peptides of arginine coelute, thus facilitating the analysis. With leucine

this is not the case: the labeled and the unlabeled peptides can differ in retention time,

8

caused by the replacement of hydrogen atoms by deuterium atoms. Fourth, the mass

accuracy of the mass analyzer is taken into account. The mass difference between leucine

and D3 leucine is 3 Da whereas between arginine and 13

C6-arginine it is 6 Da. A larger

mass difference minimizes the chance of overlapping signals in the mass spectrum.

The other cell culture is grown with native, unlabeled amino acids. During the time the

cells spend in the medium, the cell‟s metabolism incorporates the labeled amino acids

into the proteins it synthesizes. The newly formed proteins have a predictable mass

difference, for example plus 3 Da for every D3-leucine.

Essential amino acids (those that cannot be synthesized by the cell itself) such as arginine

will be incorporated. Non-essential amino acids will also be incorporated if available in

high concentrations.

One exception of an amino acid that is often used in SILAC that can be metabolized into

proline is arginine [11]. This is however not a problem in quantitation: the labeled

prolines that are found in MS are distinguishable from natural proline.

After several cell doublings, the complete proteome of the cells will contain the kind of

labeled or unlabeled amino acids that are available in the medium. After optional

purification by for example ultrafiltration, the samples are combined and digested with

trypsin. The resulting tryptic digest is analyzed by LC-MS. The labeled and the unlabeled

peptides are separated in the mass analyzer. A labeled peptide with one leucine for

example will have a peak 3 Da higher than its unlabeled counterpart, see Figure 4.

In contrast to ICAT, no chemical labeling step or affinity purification step is required.

The workflows of SILAC and ICAT are shown next to each other in Figure 3 to show the

difference in required steps. Note that in SILAC the sample handling of the cell cultures

is done simultaneously whereas in ICAT the handling has to be done parallel and exactly

in the same way to allow relative quantitation. This results in a better chance of

reproducing experiments accurately. Another difference is that SILAC uses the

metabolism of a cell whereas ICAT can also be used on a nonliving biological sample.

Isotope-labeled amino acids are commercially available and quantitation is

straightforward, see Figure 4. The peak intensities can be compared in time, thus giving a

measure for how fast a protein is synthesized or degraded. The time resolved variant of

SILAC, or dynamic SILAC will be discussed in paragraph 5.

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Figure 3: Comparison of SILAC and ICAT labeling [10]

10

Figure 4: Incorporation of D3-leucine in a protein in a SILAC experiment [10]. The peptide in

question is the triply charged APEEHPVLLTEAPLNPK, containing 3 leucines. The signal marked

with the * is an unrelated peptide.

3. Research question

An important issue that has been identified is the reliability of the experiments. In this

work it will be assessed whether there is an agreed way to report the reliability of data.

More questions rose and will also be discussed. What is the effect of sample preparation

on the experimental error in the intensity ratio measurements? How can experimental

errors be reduced to a minimum? Which mass analyzer is to be preferred? Several studies

will be evaluated regarding how they report analytical parameters such as precision and

dynamic range.

There is a need for techniques that obtain time resolved quantitative information about

the concentration of proteins in a cell. The heat shock response (HSR) will be introduced

as a potential application field for SILAC. The events in this pathway take place in on a

timescale of several minutes. However, in many SILAC experiments the timescales are in

the order of days rather than minutes. The suitability of SILAC as an analytical technique

for this phenomenon will be evaluated by looking at the timescales of several studies

Because there has also been a substantial amount of SILAC research wherein two cell

cultures are compared in a steady-state fashion, experiments of this type will also be

discussed.

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4. Analytical Parameters

The International Union of Pure and Applied Chemistry (IUPAC) has devised standards

to report numerical outcomes and the statistical certainty of experimental work

unambiguously. Definitions used in this work are stated in Table 1.

Table 1: IUPAC definitions for the presentations of results

Term Definition Source

Accuracy The closeness of agreement between a test result and the true

value.

[12]

Precision The closeness of agreement between independent test results

obtained by applying the experimental procedure under

stipulated conditions. A measure of precision (or

imprecision) is the standard deviation.

[12]

Repeatability The closeness of agreement between independent results

obtained with the same method on identical test material,

under the same conditions (same operator, same apparatus,

same laboratory and after short intervals of time).

[12]

Reproducibil

ity

The closeness of agreement between independent results

obtained with the same method on identical test material but

under different conditions (different operators, different

apparatus, different laboratories and/or after different

intervals of time).

[12]

Sensitivity in

analytical

chemistry

The slope of the calibration curve. If the curve is in fact a

„curve‟, rather than a straight line, then of course sensitivity

will be a function of analyte concentration or amount.

http://www.iu

pac.org/goldbo

ok/S05606.pdf

Sensitivity in

MS

IUPAC has the following addition to the definition of

sensitivity in MS: “[Sensitivity] depends upon the observed

change in ion current for a particular amount or change of

flow rate of sample though the ion source.”

http://goldboo

k.iupac.org/S0

5605.html

Dynamic

range

The ratio between the maximum usable indication and the

minimum usable indication (detection limit). A distinction

may be made between the linear dynamic range, where the

response is directly proportional to concentration, and the

dynamic range where the response may be non-linear,

especially at higher concentrations.

http://goldboo

k.iupac.org/D0

1874.html

Mass

accuracy

The mass accuracy indicates the accuracy of the m/z

provided by the mass analyzer and is often expressed in

parts per million (ppm). It is largely dependent on the

resolution.

[13]

Resolution R = m / Δm where m is the mass and Δm is the width of the

peak at 50% of its maximum.

[13]

In the context of SILAC, actual repeatability is ideally measured by repeating both the

instrumental part (i.e. the gel electrophoresis separation and LC-MS(-MS)) and the

biological part (the growing of cell cultures under specific conditions) of the experiment.

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In reality, this is not always the case. Blagoev et al. [14] for example, report SILAC

ratios with relative standard deviations of proteins based on the SILAC ratios of the

peptides belonging to the proteins. No mention is made of repeating the biological part of

the experiment.

Reproducibility is measured under different conditions as can be read in Table 1. There

are many examples in the literature where the authors deviate from this definition and

write about reproducibility where repeatability is actually the better term according to the

IUPAC definition. Kim et al. [15] for example compare quantitative results between

different labeling strategies SILAC, ICAT and decoupled labeling. No other labs are

mentioned in the article, so it is assumed that they actually intended to investigate

repeatability.

4.1 Experimental errors introduced by sample preparation

Zhang et al. [16] have investigated the errors in measured SILAC ratios introduced with

sample preparation steps by experimental design. Since the sample preparation of the

labeled and unlabeled cell culture are mixed and done in one go, the experimental error

can be smaller than with chemical labeling techniques where the sample preparation is

done in parallel. The more steps are performed the larger the error. Although

experimental errors can never be completely eliminated, it is desirable to estimate how

large they are. In their work Zhang et al. investigate the experimental error of three

consecutive sample preparation steps: immunoprecipitation (IP), SDS-PAGE

fractionation and in-gel digestion.

In the experiment, two cultures of NG108 cells (mouse neuroblastoma and rat glioma

hybrid) were grown. In one medium, 13

C6-arginine and 13

C6-lysine were used as heavy

labeled amino acids and in the other normal isotopic arginine and lysine were used as

light amino acids. Apart from the isotope labels, the conditions were identical. After 1:1

combination, the cell lysates were analyzed with LC-MS/MS using a linear ion trap-

Orbitrap (LTQ-Orbitrap). As the conditions were identical, the expected SILAC ratio for

each and every protein was 1. Data analysis was done using MaxQuant software. Proteins

were identified and quantified based on two or more peptides. The number of proteins

identified and quantified varied per experiment, ranging from 240 to over 700.

The authors determined the variability as a relative standard deviation (rsd) of several

replicates of IP, SDS-PAGE fractionation and in-gel digestion and of the whole

procedure. According to the IUPAC definition (Table 1) this would be called the

repeatability of the individual steps and of the whole method. This study is thorough in

determining the precision of sample preparation steps, but no mention is made of growing

the cell culture multiple times as should be the case when biological variation is taken

into account.

First, IP with anti-phosphotyrosine of cell lysate was repeated N times (N = 1, 3 and 6).

The rsd of the SILAC ratios was the smallest when the results of N = 6 were pooled. The

difference between the N = 3 and N = 6 experiment was very small, so the authors

conclude that N = 3 is sufficient for IP. Second, they investigate the error of in-gel

digestion by N repetitions. The samples were separated by one-dimensional SDS-PAGE,

selected gel bands were subjected to digestion with trypsin. The rsds in SILAC ratios

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were very similar for all N. The authors conclude that N = 1 is sufficient for in-gel

digestion. Third, the optimal number of repetitions for SDS-PAGE fractionation is

determined using N = 3 for IP and N = 3 for in-gel precipitation. The rsd was the smallest

for N = 6, but virtually indistinguishable from N = 4. The authors conclude that the

optimal number of sample preparation steps can be determined by employing

experimental design.

4.2 Role of the mass analyzer

In their research to Erythropoietin-producing carcinoma B (EphB) pathway, Zhang et al.

have screened for interacting proteins using SILAC with a quadrupole time-of-flight

(QTOF) mass analyzer [17] and an LTQ-Orbitrap [18]. Again, NG108 cells were labeled

with 13

C6-arginine and 13

C6-lysine as heavy labeled amino acids.

They report two biological replicates. In one replicate they identified 683 proteins and in

the other 532. 411 proteins were identified in both replicates. The LTQ-Orbitrap based

method could quantify 777 proteins, whereas the QTOF based method could only

quantify 127. The LTQ-Orbitrap based method performed strikingly better than the

QTOF based method. It had a larger dynamic range and a lower noise level. From the 95

proteins that could be quantified with both methods, the SILAC ratios from the LTQ-

Orbitrap based method were higher than determined with the QTOF based method. This

is a result of the lower noise levels in the Orbitrap mass analyzer [18]. An example of the

difference in SILAC ratio L/H found with the two different mass analyzers is given in

Figure 5.

Figure 5: Illustration of the lower noise level in the Orbitrap as compared to the QTOF [18].

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De Godoy et al. [19] report the use of a hybrid linear ion trap-Fourier transform mass

spectrometer (LTQ-FT), combining the trapping capability of the LTQ with an ion

cyclotron resonance trap (ICR) for sensitive detection.

Two cultures of yeast cells (Saccharomyces cerevisiae) were grown in media, containing

either 15

N213

C6- lysine as heavy labeled amino acid or normal lysine.

In their analysis of the whole yeast cell proteome they identified about 2000 proteins and

the dynamic range was 1000. These are the highest reported numbers for a SILAC

experiment. The method had effective sensitivity to 500 femtomoles (10-15

moles).

4.3 Accuracy study with 13C6-arginine

Ong et al. [11] investigate the variability in quantification of SILAC in NIH 3T3 mouse

fibroblast cells using 13

C6-arginine and an LC-MS/MS method. The mass analyzer was a

QTOF. To assess the analytical properties of quantification of their method the authors

mixed labeled and unlabeled cell lysates in ratios 1:1, 1:5, 1:10 and 5:1 and separated

them by one-dimensional SDS-PAGE. Gel bands around 200 kDa were cut out and

digested with trypsin. An 80 minute LC-MS/MS analysis was done on the tryptic digest.

Quantification was done by integrating the peaks of a specific peptide in the XIC as

shown in Figure 6.

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Figure 6: Extracted Ion Chromatograms (XIC) of the peptide VVFQEFR at different mixing ratios

[11].

Relative standard deviations of the measurements were calculated at two levels. First, the

rsd‟s of peptides were calculated from the different measuring points across the XIC

peaks. Second, the rsd‟s of proteins were calculated from the rsd‟s of the peptides that are

part of the proteins in question. This was done for over 60 proteins.

Note that this rsd was calculated based on one LC-MS/MS run. The authors comment

that this rsd is much smaller than possible biological or sample preparation errors, but

16

don‟t give a suggestion of approximately how much smaller that is. They also identify

possible pitfalls in quantification. Peptides that are present in the tryptic digest in low

abundances may give rise to MS signals in very few scans, thus increasing the chance of

inaccurate quantification and a larger rsd than with an abundant peptide that is present in

twenty scans. The dynamic range of the mass spectrometer plays a role in the accuracy of

quantification. When a peptide signal is present, but the intensity is comparable to the

background noise, the abundance is less accurate. Similarly, if a peptide is so abundant

that it saturates the detector, the concentration of the abundant peptide can be

underestimated. In the analysis, also some fully 13

C-labeled proline showed up. It is

known that some cell lines are able to metabolize argine to proline. This is especially so

in the presence of high arginine concentrations. However, when this is known, the results

can be corrected by adding the peak intensity of the heavy proline peptide to the light

one.

4.4 Absolute quantitation and the dynamic range

Hanke et al. [20] have showed that SILAC can accurately quantify proteins down to 150

attomoles (10-18

moles) in cell lysate using an Orbitrap mass spectrometer without

fractionation prior to LC-MS analysis. 13

C615

N4-arginine and 13

C615

N2-lysine were used

as heavy labeled amino acids.

The linear range of SILAC ratios between 1:300 and 300:1 was investigated for maltose

binding protein (MBP) in E. coli AT713, a strain that is auxotrophic for arginine and

lysine, by keeping the amount of MBP constant at 150 fmol. The linearity was excellent

between 1:100 and 100:1, showing that SILAC can have a dynamic range of four orders

of magnitude (104). The rsd of ratio measurements was typically < 5 %. The precision of

the measurements is mostly dependent on the S/N, so the most abundant peaks could be

quantified more accurately.

Having shown that absolute and quantitative SILAC is possible in a model system, an

LTQ-Orbitrap method was developed to cope with the complexity of real biological

samples. When a protein of interest is recognized as a peptide in the full-scan mode, the

mass analyzer is automatically switched to the Selected Ion Monitoring (SIM) mode and

uses the ability of the LTQ-Orbitrap to enrich the ions of light isotopic peptide thus

increasing the S/N. This acquisition strategy is schematically shown in Figure 7.

Figure 7: Acquisition strategy of the LTQ-Orbitrap method [20].

Using this method the authors were able to determine the number of Grb2 protein

molecules in several cell lines. Grb2 is a 25 kDa protein that plays a key role in the

17

transduction of growth signals from receptors to intracellular effectors. The experiment

for each cell line was repeated five times and 7 to 14 peptides were quantified. HeLa cells

had 5.55 ± 0.26 x 105 copies of Grb2, HepG2 liver cells 8.80 ± 0.12 x 10

5 copies and

C2C12 murine muscle cells 5.67 ± 0.20 x 105 copies. The number of copies of a protein

in a cell can be used as input for computational systems biology models.

5. The heat shock response as a potential application field for SILAC

A well studied phenomenon in molecular cell biology is the heat shock response (HSR).

As a reaction to a sudden increase in temperature, a collection of proteins called heat

shock proteins (hsp‟s) are upregulated. In E. coli, the transcription of hsp‟s is initiated by

one protein, the sigma factor σ32

[21]. The level and activity of σ32

is regulated at three

levels of feedback loops. At temperatures ≥ 37 ºC, σ32

is upregulated. Then, degradation,

which keeps the amount of σ32

low, is inhibited. Finally, the activity of σ32

is negatively

regulated (Figure 8). Each of these feedback loops has been modeled. The process of heat

shock response takes place at a timescale of several minutes. It would be desirable to

have a method that is able to measure the concentrations of σ32

and the other proteins in

the HSR pathway in a time-resolved way. SILAC has been used to do time resolved

experiments. A research question that will be investigated in this thesis will be: is SILAC

suitable for quantitative proteomics experiments on a timescale as short as the HSR?

Figure 8: activity and amount of sigma factor σ

32 upon temperature increase [21]

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5.1 Quantitation of dynamic SILAC measurements

The dynamics of protein concentration in a cell can be approximated to the following

equation. The concentration of a protein B is dependent on the rate of synthesis, k1 and

the rate of degradation k2.

][][][ 21 CBA kk Equation 1

Where [A] is the concentration of precursor to protein B and [C] is the concentration of

degradation products. Pratt et al. [22] describe an approach to determine the degradation

constants k2 of proteins in vivo. In their experiment they grew haploid yeast cells on a

D10-leucine medium in a chemostat for at least seven cell doublings to ensure full

labeling. At t = 0 they placed the cells in a medium with light leucine and from that

moment onwards the cell started to break down the labeled proteins and synthesize

unlabeled proteins. Samples were taken at t = 0, 0.167, 0.667, 1, 2, 4, 6, 8, 10, 12, 24.5

and 51 hours. The lysates of the samples were subjected to 2-DE and spots of interest

were sliced out of the gel and analyzed by MALDI-TOF MS. At each time point the RIAt

was measured as defined in Equation 2.

)( HL

Ht

AA

ARIA

Equation 2

Where AH is the monoisotopic intensity of the heavy peptide and AL is the intensity of the

monoisotopic light peptide.

The RIA changes over time as a result of the degradation of labeled proteins and the

dilution of the chemostat (cells are lost because they flow out of the chemostat). The

formula for RIAt can be written generically as:

)exp()( 0 tkRIARIARIARIA losst Equation 1

Pratt et al. simplified the formula by fixing values of RIA0 and RIA∞ at their average

measured values, leaving kloss as the only variable in the formula.

)exp(985.0 tkRIA losst Equation 2

The RIAt values were measured at different time points and fitted into a nonlinear curve.

Values for kloss were determined.

tRIAk tloss /)985.0/ln( Equation 3

Finally the correction for dilution in the chemostat was made to obtain k2.

Dkk loss 2 Equation 4

19

A schematic overview of the quantification strategy is shown in Figure 9.

Figure 9: chase experiment with deuterated leucine. Note that kdeg is k2 in this work.

With the described method the authors were able to determine k2 values for 52 abundant

proteins in h-1

with rsd‟s of around 5%.

5.2 Some examples of dynamic SILAC

As mentioned in the introduction, there are several ways in which the cellular proteome is

complex. Differences in protein concentrations by location in the cell (i.e. by organelle)

can be tackled by prefractionating the cell lysate with ultrafiltration. Differences in time

can be tackled by doing time-resolved experiments and these will be described in this

paragraph.

Doherty et al. [23] describe an experiment in which they measure protein turnover in

intact organisms. Layer chickens (Gallus gallus) were fed a synthetic diet containing a

sufficient amount of essential amino acids for optimal growth. At t = 0, the valine in the

diet was replaced with a 50:50 mixture light valine : D8-valine. Pectoral muscle was

sampled at t = 0, 1, 2, 4, 8, 14, 24, 30, 32, 48, 55, 72, 96 and 120 hours. The samples

were homogenized and the protein contents were separated by SDS-PAGE. The 20 most

abundant proteins were analyzed by MALDI-TOF. Since the mass range used was 950-

3500 Th, the ratio calculations were corrected for longer peptides in which more than one

heavy valine was incorporated. Error margins of RIAs in proteins were based on the

consistency of the intensity ratios of different peptides of a protein. The authors conclude

20

that they have proven that SILAC can not only be used for cell cultures, but also for

intact complex organisms.

More recently, Doherty et al. [24] describe a SILAC experiment on relatively short

timescales with human adenocarcinoma A549 cells. The cell culture is labeled with 13

C6-

arginine and at t = 0 switched to light arginine. Sampling was done at 0, 0.25, 0.50, 1, 2,

4 and 8 hours. The lysates were separated by SDS-PAGE and the lanes were divided into

40 slices that were each analyzed with LC-MS/MS with an LTQ as the mass analyzer.

Degradation constants k2 of 576 proteins could be determined, ranging from 2 x 10-5

± 9 x

10-7

to 5.4 ± 0.4 h-1

. Using the degradation constants and identifications of proteins

several hypotheses about what triggers protein degradation could be tested. They did not

find a correlation between degradation speed and molecular weight, pI, gene ontology

classification, the presence of stabilizing residues, the presence of PEST sequencesc. The

degree of structural order did seem to make a difference. Proteins that consisted

completely of disordered residues were degraded more rapidly than proteins that

contained no disordered residues. By structural disorder, the lack of a well defined 3D

structure is meant [25]. The turnover of different subunits of large protein complexes was

also examined. The turnover rates of proteins belonging to the 40S and 60S ribosomal

subunits were not all equal. A drawback of the method used by the authors is that it

provides no information about whether or not a subunit is assembled in a complex or

present in solution as a „spare part‟.

The authors stress that the results do not apply for other cell lines or conditions.

Mintz et al. [26] investigated protein turnover in the endoplasmic reticulum (ER) under

the influence of stress inducers tunicamycin (Tun) and thapsigargin (Thp) of human

primary fibroblast cells. One cell culture was not subjected to the stress factors and was

grown on heavy 13

C615

N2-lysine and 13

C6-arginine. The other cell culture was subjected to

the stress factors and was grown on light, 12

C614

N2-lysine and 12

C6-arginine. Two heavy

labeled amino acids are used to be able to quantify more peptides than with only one

labeled amino acid. Samples were taken at 0, 6, 12 and 24 hours. The samples are mixed

1:1 and the ER is separated from the other organelles by centrifugation. The protein

contents are analyzed by SDS PAGE followed by LC-MS/MS using an LTQ mass

analyzer. Peak intensity ratios of ER proteins from the treated and untreated cells were

plotted. Significant fold change as a reaction to the stress inducers could be measured for

22 abundant proteins (Figure 10). Quantification of lower abundance proteins however

was not “reproducible”. The authors probably mean repeatable by the IUPAC definition

(Table 1).

c A PEST sequence is a peptide motif rich in proline, glutamate, serine and threonine that is recognized by

the cell‟s protease machinery and triggers degradation.

21

Figure 10: Change in abundance ratios of 22 abundant ER proteins as a result of Thp and Tun

induced stress after 24 hours [26].

To investigate protein degradation of the human Major Histocompatibility Complex

(MHC), Milner et al. [27] grew human cancer cell line UCI-107 labeled with D3-leucine

for 7 days. On t = 0 the medium was changed to light leucine. After cell lysis and affinitiy

purification, the proteins were digested with trypsin and analyzed with LC-MS/MS using

an ion trap as a mass analyzer. Degradation profiles of several proteins could be drawn

based on the relative abundance of heavy leucine (Figure 11). Note that the sampling

interval (t = 0, 3, 6, 9, 12, 18, 24, 48, 96) is not optimal for peptides with fast turnover

rates.

Figure 11: Degradation profile of three MHC peptides with different turnover times [27].

22

Blagoev et al. [28] report a dynamic SILAC experiment with the shortest reported

timescale. They focus on time resolved phosphorylation of tyrosine residues in HeLa cell

cultures that were treated with epidermal growth factor (EGF) for a certain amount of

time. Isotopic labeling occurred in a slightly modified way. Rather than having two

isotopic species, the authors use three: light 12

C614

N4-arginine, medium 13

C614

N4-arginine

and heavy 13

C615

N4-arginine. Two three-state experiments were carried out. In the first

experiment three cell cultures were grown on light, medium and heavy arginine

respectively and treated with EGF for 0, 1 and 10 minutes respectively. In the second

experiment three other cell cultures were treated with EGF for 0, 5 and 20 minutes. The

cells are lysed and the lysates are mixed 1:1:1. After affinity purification, the proteins are

digested with trypsin. The tryptic digest is analyzed with LC-MS using a QTOF mass

analyzer (Figure 12a). The mass spectrum of each peptide in the experiment shows three

isotope peaks (Figure 12b).

Figure 12:a) schematic overview a three-state experiment, b) mass spectra of three peptides of

different proteins [28].

Activation profiles of some important proteins in the EGF receptor signaling network are

shown in Figure 13.

23

Figure 13: activation profiles of some important proteins in the EGF receptor signaling network. The

profiles were generated by interpolating protein ratio’s with KaleidaGraph software using the

Stineman function [28].

Each measurement was done relative to the common zero point (i.e. the amount in

unstimulated cells). Absolute copy numbers were not measured. The profiles that could

be seen were obtained by intrapolating the measured values into systems of rate equations

such as the one in Equation 2. It can be seen that the activity of EGF receptor EGFR

increases rapidly within the first minute of stimulation, stays constant for 5 minutes and

finally steadily declines. Eps15 and c-Cbl are related to the early stage of the pathway

and follow the same trend, but their activation peaks at a lower level. Ubiquitin is

involved in the next step, and consecutively ubiquitinilation is recognized by Hrs and

STAM2. The order in which protein activities peak is in line with what is known about

this pathway in the literature. The results were verified by Western blotting.

Activation profiles of 202 proteins were obtained. 81 of these were significantly

upregulated upon EGF stimulation. The reliability of ratio measurements was

investigated for some proteins. Variability of the ratio measurements of different peptides

within a protein had rsd‟s of <20%. Ratios >25 had higher rsd‟s of up to 30%.

The studies described in this paragraph show examples of time-resolved SILAC

experiments that can be done. We will now evaluate whether they can be applied on the

heat shock reaction.

The three phases of the heat shock response of σ32

take 15 minutes (Figure 8). When the

method as described by Blagoev et al. is used it will give only four measurements: 0, 1, 5

and 10 minutes. Preferably the concentration of σ32

is measured at around 9 time points,

e.g. 0, 1, 3, 5, 7, 9, 11, 13 and 15 minutes. Although it is the method with the shortest

timescale yet reported, it is still not sufficient for σ32

. Another potential limiting factor for

the method is the time it takes before a measured ratio is significantly different from the

initial value. If the S/N is low, it can take long before an emerging signal is noted.

24

6. Static SILAC

Most of the SILAC studies reported in the literature are static SILAC experiments. Two

states are compared, for example, disease influenced cells and healthy cells. This will be

discussed briefly in the next paragraph. In this paragraph, a special application of static

SILAC will be discussed.

Schwanhäusser et al. describe a technique called pulsed SILAC. In their approach, HeLa

cells are grown in light (L) amino acids lysine and arginine and at t = 0 the medium is

changed to either medium (M) amino acids D4-lysine and 13

C6-arginine or heavy (H)

amino acids 13

C615

N2-lysine and 13

C615

N4-arginine. Samples were taken at t = 2 and 10

hours.

The protein contents of the cells were separated by SDS-PAGE and digested with trypsin.

The tryptic digests were analyzed with LC-MS/MS using and LTQ-Orbitrap instrument.

In the mass spectra different peaks occur for the H, M and L peptides. The H/L ratio

provides information about the turnover rate, but no information about the translation

rate. The H/M ratio on the other hand is a measure for translation rate, because from t = 0

all new proteins in each state will consist of H or M labeled amino acids (Figure 14).

Degradation also takes place, but this is assumed equal for each state.

Figure 14: pulsed SILAC approach [29]

The method was compared to the luciferase system, in which the expression of luciferase

is measured as the intensity of luminescence. They find that pulsed SILAC can determine

differences in translation rates in complex mixtures over at least two orders of magnitude.

25

The method was validated with more proteins, now using yeast cells. Yeast cells were

mixed in four different L/M/H ratios (0.5:1:1, 0.5:1:2, 0.5:1:4, 0.5:1:8). Of each protein

the log2 fold H/M ratiod and total peptide intensities in the mass spectrum were as a point

in the graph in Figure 15.

Figure 15: validation of the pulsed SILAC method with different H/M ratios [29]

First, it can be seen that proteins with higher abundances can be quantified more

accurately. Second, proteins with lower abundances don‟t often deviate more than 0.3log2

times from their true value.

The method was applied in a system where the iron homeostasis of HeLa cells was

investigated. One cell culture was grown on H amino acids and was treated with iron in

the form of ferric ammonium citrate. The other cell culture was grown on M amino acids

and was not treated with iron. The H/M ratios could be used to determine which proteins

d An H/M ratio of 4:1 for example corresponds with log2 fold change of log2 4 = log 4 / log 2 = 2

26

in the iron homeostasis pathway were upregulated and which proteins were

downregulated as a result of the iron treatment.

7. Applications in biology and medicine

The analytical development of SILAC is strongly influenced by the demand for

techniques from the molecular biology community. It is beyond the scope of this work to

discuss all the available literature. In this paragraph a number of applications will be

presented in Table 2. The articles are listed by three themes: first, articles in which

specific proteins or pathways are analyzed, second, articles in which the whole proteome

is screened, and third, articles that are applied to cancer researche.

Table 2: SILAC applications

Short description Reference

Targeted pathways and proteins

New hydroxylation sites were found on Factor Inhibiting

Hypoxia inducible factor (HIF) substrates.

[30]

Phosphorylation in the insulin pathway. [31]

The interaction between proteins and DNA. [32]

Protein methylation in the proteome of HeLa cells. [33]

Phosphorylation in the yeast pheromone signaling pathway. [34]

The functioning of red blood cells of isotope labeled knockout

mice.

[35]

The interaction of O-GlcNAcylation and O-phosphorylation. [36]

Tyrosine phosphorylation of the ErbB receptor family. [37]

The phosphorylation of a potassium ion channel. [38]

Protein-protein interactions of the ubiquitin proteasome. [39]

Phosphorylation in human embryonic kidney cells. [40]

Methylation, phosphorylation and acetylation of human

histones.

[41]

Global analysis

The proteome of differentiating adypocytes. [42]

Comparison of protein expression in cell cultures and tissues. [43]

Comparison of protein expression in differentiating stem cells.

and self-renewing stem cells.

[44]

Comparison of membrane proteins in whole cell membrane and

detergent resistant membrane.

[45]

Global analysis of protein-protein analysis. [46]

Cell differentiation in B lymphocytes. [47]

Global analysis of rat liver cells. [48]

e Cancer was the number one cause of death in the Netherlands in 2008. http://www.cbs.nl/nl-

NL/menu/themas/gezondheid-welzijn/publicaties/artikelen/archief/2009/2009-2687-wm.htm

27

Cancer research

Substrates for metalloproteases involved in tumor metastasis. [49]

The whole proteome of hepatocellular carcinoma cells with

high and low metastasis potential.

[50]

The treatment of human retinal pigment epithelial cells with

Tumor Necrosis Factor alpha.

[51]

The nuclear proteome of T leukemia cells were screened for

proteins involved in apoptosis.

[52]

Secreted proteins from pancreatic cancer derived cells and

healthy cancer cells were screened for biomarkers.

[53]

The whole proteome of prostate cancer cells were compared to

those of healthy cells.

[54]

8. Conclusions

SILAC offers benefits compared to chemical labeling techniques in that the sample

handling requires fewer steps. It can be applied to living cells as well as whole living

organisms. The labeling is done through the metabolism as opposed to a chemical

reaction that might be incomplete.

SILAC is already widely used by molecular biologists to study whole proteomes, specific

pathways, post-translational modifications and is used in cancer research.

The precision of SILAC ratios is the highest (rsd‟s of <5%) when the true value is close

to 1:1 and several peptides are used. A high abundance in the mass spectrum also benefits

the precision of the ratio measurements.

Very low amounts down to 150 amol of proteins can be analyzed and a dynamic range of

104 could be attained using the hybrid LTQ-Orbitrap mass analyzer. The highest number

of identified and quantified proteins reported was achieved using a hybrid LTQ-FT mass

analyzer.

When an approach based on the multiple state approach of Blagoev et al. [28] is used,

SILAC can possibly be used to do measurements on the heat shock response protein σ32

if the S/N is high enough to be able to measure an upcoming signal shortly after the start

of the experiment.

True studies of the reproducibility of SILAC i.e. with different instruments, different

analysts in different labs have not been reported.

28

9. References

1. Alberts B, J.A., Lewis J, Raff M, Roberts K, Walter P, Molecular Biology of the

Cell. Fifth ed. 2008, New York: Garland Science, Taylor & Francis Group LLC.

2. Krijgsveld, J., et al., Quantitative proteomics of fruit fly development by metabolic

labeling. Molecular & Cellular Proteomics, 2005. 4(8): p. S324-S324.

3. Anderson, N.L. and N.G. Anderson, Proteome and proteomics: New technologies,

new concepts and new words. Electrophoresis, 1998. 19(11): p. 1853-1861.

4. de Koster, C., University of Amsterdam M.Sc. Course Mass Spectrometry Lecture

Notes. 2007.

5. O'Farrell, P., High resolution two-dimensional electrophoresis of proteins.

Journal of Biological Chemistry, 1975. 250(10): p. 4007-4021.

6. Gygi, S.P., et al., Quantitative analysis of complex protein mixtures using isotope-

coded affinity tags. Nat Biotechnol, 1999. 17(10): p. 994-9.

7. Ong, S.E. and M. Mann, Mass spectrometry-based proteomics turns quantitative.

Nat Chem Biol, 2005. 1(5): p. 252-62.

8. Stewart, I.I., T. Thomson, and D. Figeys, O-18 Labeling: a tool for proteomics.

Rapid Communications in Mass Spectrometry, 2001. 15(24): p. 2456-2465.

9. Fisher, D.E., Pulse labeling of small nuclear ribonucleoproteins in vivo reveals

distinct patterns of antigen recognition by human autoimmune antibodies. Proc.

Natl. Acad. Sci. USA, 1984. 81(10): p. 3185-3189.

10. Ong, S.E., et al., Stable isotope labeling by amino acids in cell culture, SILAC, as

a simple and accurate approach to expression proteomics. Mol Cell Proteomics,

2002. 1(5): p. 376-86.

11. Ong, S.E., I. Kratchmarova, and M. Mann, Properties of 13C-substituted arginine

in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res,

2003. 2(2): p. 173-81.

12. Currie, L.A. and G. Svehla, Nomenclature for the presentation of results of

chemical analysis (IUPAC Recommendations 1994). Pure & Appl. Chem., 1994.

66(3): p. 595-608.

13. De Hoffmann, E. and V. Stroobant, Mass Spectrometry: Principles and

Applications. Third ed. 2007, Chichester, UK: John Wiley & Sons Ltd.

14. Blagoev, B., et al., A proteomics strategy to elucidate functional protein-protein

interactions applied to EGF signaling. Nat Biotechnol, 2003. 21(3): p. 315-8.

15. Kim, Y.J., et al., Reproducibility assessment of relative quantitation strategies for

LC-MS based proteomics. Analytical Chemistry, 2007. 79(15): p. 5651-5658.

16. Zhang, G., Evaluation of the Variation in Sample Preparation for Comparative

Proteomics Using Stable Isotope Labeling by Amino Acids in Cell Culture. J

Proteome Res, 2009. 8(3): p. 1285-1292.

17. Zhang, G., et al., Quantitative phosphotyrosine proteomics of EphB2 signaling by

stable isotope labeling with amino acids in cell culture (SILAC). J Proteome Res,

2006. 5(3): p. 581-8.

18. Zhang, G., D. Fenyo, and T.A. Neubert, Screening for EphB signaling effectors

using SILAC with a linear ion trap-orbitrap mass spectrometer. J Proteome Res,

2008. 7(11): p. 4715-26.

29

19. de Godoy, L.M., et al., Status of complete proteome analysis by mass

spectrometry: SILAC labeled yeast as a model system. Genome Biol, 2006. 7(6):

p. R50.

20. Hanke, S., et al., Absolute SILAC for accurate quantitation of proteins in complex

mixtures down to the attomole level. J Proteome Res, 2008. 7(3): p. 1118-30.

21. Guisbert, E., et al., Convergence of molecular, modeling, and systems approaches

for an understanding of the Escherichia coli heat shock response. Microbiol Mol

Biol Rev, 2008. 72(3): p. 545-54.

22. Pratt, J.M., et al., Stable isotope labelling in vivo as an aid to protein

identification in peptide mass fingerprinting. Proteomics, 2002. 2(2): p. 157-63.

23. Doherty, M.K., et al., Proteome dynamics in complex organisms: using stable

isotopes to monitor individual protein turnover rates. Proteomics, 2005. 5(2): p.

522-33.

24. Doherty, M.K., et al., Turnover of the Human Proteome: Determination of

Protein Intracellular Stability by Dynamic SILAC. J Proteome Res, 2008.

25. Tompa, P., Structural disorder serves as a weak signal for intracellular protein

degradation. Proteins, 2008. 71(2): p. 903-909.

26. Mintz, M., et al., Time series proteome profiling to study endoplasmic reticulum

stress response. J Proteome Res, 2008. 7(6): p. 2435-44.

27. Milner, E., et al., The turnover kinetics of major histocompatibility complex

peptides of human cancer cells. Mol Cell Proteomics, 2006. 5(2): p. 357-65.

28. Blagoev, B., et al., Temporal analysis of phosphotyrosine-dependent signaling

networks by quantitative proteomics. Nat Biotechnol, 2004. 22(9): p. 1139-45.

29. Schwanhausser, B., et al., Global analysis of cellular protein translation by

pulsed SILAC. Proteomics, 2008. 9(1): p. 205-209.

30. Cockman, M.E., et al., Proteomic-based identification of novel factor inhibiting

HIF (FIH) substrates indicates widespread asparaginyl hydroxylation of ankyrin

repeat domain-containing proteins. Mol Cell Proteomics, 2008.

31. Hanke, S. and M. Mann, The phosphotyrosine interactome of the insulin receptor

family and its substrates IRS-1 and IRS-2. Mol Cell Proteomics, 2008.

32. Mittler, G., F. Butter, and M. Mann, A SILAC-based DNA protein interaction

screen that identifies candidate binding proteins to functional DNA elements.

Genome Res, 2009.

33. Ong, S.E., G. Mittler, and M. Mann, Identifying and quantifying in vivo

methylation sites by heavy methyl SILAC. Nat Methods, 2004. 1(2): p. 119-26.

34. Gruhler, A., et al., Quantitative phosphoproteomics applied to the yeast

pheromone signaling pathway. Mol Cell Proteomics, 2005. 4(3): p. 310-27.

35. Kruger, M., et al., SILAC mouse for quantitative proteomics uncovers kindlin-3 as

an essential factor for red blood cell function. Cell, 2008. 134(2): p. 353-64.

36. Wang, Z., A. Pandey, and G.W. Hart, Dynamic interplay between O-linked N-

acetylglucosaminylation and glycogen synthase kinase-3-dependent

phosphorylation. Mol Cell Proteomics, 2007. 6(8): p. 1365-79.

37. Schulze, W.X., L. Deng, and M. Mann, Phosphotyrosine interactome of the ErbB-

receptor kinase family. Mol Syst Biol, 2005. 1: p. 2005 0008.

38. Park, K.S., et al., Graded regulation of the Kv2.1 potassium channel by variable

phosphorylation. Science, 2006. 313(5789): p. 976-9.

30

39. Guerrero, C., et al., An integrated mass spectrometry-based proteomic approach:

quantitative analysis of tandem affinity-purified in vivo cross-linked protein

complexes (QTAX) to decipher the 26 S proteasome-interacting network. Mol

Cell Proteomics, 2006. 5(2): p. 366-78.

40. Ibarrola, N., et al., A proteomic approach for quantitation of phosphorylation

using stable isotope labeling in cell culture. Analytical Chemistry, 2003. 75(22):

p. 6043-6049.

41. Bonenfant, D., et al., Analysis of dynamic changes in post-translational

modifications of human histones during cell cycle by mass spectrometry. Mol Cell

Proteomics, 2007. 6(11): p. 1917-32.

42. Molina, H., et al., Temporal Profiling of the Adipocyte Proteome during

Differentiation Using a Five-Plex SILAC Based Strategy. J Proteome Res, 2008.

43. Pan, C., et al., Comparative proteomic phenotyping of cell lines and primary cells

to assess preservation of cell type specific functions. Mol Cell Proteomics, 2008.

44. Prokhorova, T.A., et al., SILAC-labeling and quantitative comparison of the

membrane proteomes of self-renewing and differentiating human embryonic stem

cells. Mol Cell Proteomics, 2009.

45. Zheng, Y.Z., K.B. Berg, and L.J. Foster, Mitochondria do not contain lipid rafts

and lipid rafts do not contain mitochondrial proteins. J Lipid Res, 2009.

46. Trinkle-Mulcahy, L., et al., Identifying specific protein interaction partners using

quantitative mass spectrometry and bead proteomes. J Cell Biol, 2008. 183(2): p.

223-39.

47. Romijn, E.P., et al., Expression clustering reveals detailed co-expression patterns

of functionally related proteins during B cell differentiation: a proteomic study

using a combination of one-dimensional gel electrophoresis, LC-MS/MS, and

stable isotope labeling by amino acids in cell culture (SILAC). Mol Cell

Proteomics, 2005. 4(9): p. 1297-310.

48. Wu, C.C., et al., Metabolic labeling of mammalian organisms with stable isotopes

for quantitative proteomic analysis. Anal Chem, 2004. 76(17): p. 4951-9.

49. Esselens, C.W., et al., Metastasis-associated C4.4A, a GPI-anchored protein

cleaved by ADAM10 and ADAM17. Biol Chem, 2008. 389(8): p. 1075-84.

50. Chen, N., et al., Quantitative proteome analysis of HCC cell lines with different

metastatic potentials by SILAC. Proteomics, 2008. 8(23-24): p. 5108-18.

51. An, E., H. Gordish-Dressman, and Y. Hathout, Effect of TNF-alpha on human

ARPE-19-secreted proteins. Mol Vis, 2008. 14: p. 2292-303.

52. Hwang, S.I., et al., Systematic characterization of nuclear proteome during

apoptosis: a quantitative proteomic study by differential extraction and stable

isotope labeling. Mol Cell Proteomics, 2006. 5(6): p. 1131-45.

53. Gronborg, M., et al., Biomarker discovery from pancreatic cancer secretome

using a differential proteomic approach. Mol Cell Proteomics, 2006. 5(1): p. 157-

71.

54. Everley, P.A., et al., Quantitative cancer proteomics: stable isotope labeling with

amino acids in cell culture (SILAC) as a tool for prostate cancer research. Mol

Cell Proteomics, 2004. 3(7): p. 729-35.