sulfur-36s stable isotope labeling of amino acids for quantification (sulaq)

TECHNICAL BRIEF

Sulfur-36S stable isotope labeling of amino acids

for quantification (SULAQ)

Nico Jehmlich1,5, Frank-Dieter Kopinke2, Steffi Lenhard1,3, Carsten Vogt4, Florian-AlexanderHerbst1, Jana Seifert1, Ulrike Lissner 5, Uwe Volker 5, Frank Schmidt1,5 and Martin von Bergen1,6

1 Department of Proteomics, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany2 Department of Environmental Engineering, Helmholtz Centre for Environmental Research – UFZ, Leipzig,

Germany3 Biotechnology Center of the TU Dresden, Tatzberg, Dresden, Germany4 Department of Isotope Biogeochemistry, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany5 Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-

Arndt-University, Greifswald, Germany6 Department of Metabolomics, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

Received: February 1, 2011

Revised: October 12, 2011

Accepted: October 25, 2011

We introduce a universal metabolic labeling strategy using elemental heavy 36Sulfur (36S)

called 36Sulfur stable isotope labeling of amino acids for quantification (SULAQ). In the proof

of principle experiment, Pseudomonas putida KT2440 was grown in defined minimal medium

with sodium benzoate or sodium succinate as the sole carbon and 32S- or 36S-sodium sulfate

as the sole sulfur sources. Quantification using mass spectrometry resulted in 562 proteins

with 1991 unique peptides. SULAQ technology can be a valuable alternative strategy for the

quantitative comparisons in MS-based proteomics approaches characterizing bacterial and

other biological samples in different growth conditions.

Keywords:

Cysteine / Metabolic labeling / Methionine / Pseudomonas putida / Stable isotope

labeling / 36Sulfur incorporation / Technology

Mass spectrometry (MS) is the method of choice in

proteomics for protein identification due to remarkable

improvements in mass accuracy, sensitivity and speed.

Besides protein identification, relative quantification of

dynamic changes in the proteome plays a key role in nearly

all systems biology approaches.

In the past two decades, several quantitative proteomics

techniques coupling stable isotope labeling and MS-analysis

have been successfully developed. In these studies, two

or more biological samples are cultivated under

two different conditions: one sample is cultivated in

light media and one sample in medium with heavy

isotopes. By means of high mass accuracy MS, isotope

clusters of each stable isotope can be measured and the

intensities of light and heavy peptides can be used to

calculate the relative amount of proteins. Up to now,

stable hydrogen, carbon or nitrogen isotopes (2H, 13C, or15N) have been provided as inorganic salts or labeled

organic carbon sources as well as amino acids have been

used to label newly synthesized proteins (for review

see [1, 2]). Besides these dominating elements of the

protein backbone, the chemical element sulfur (S) is also

ubiquitous in all natural proteomes, because it is incorpo-

rated into the side chains of cysteine and methionine, and

plays a central role in cell metabolism, cell physiology and

health [3]. An overview of absolute quantitation of thiol

and sulfur-containing amino acid metabolites in yeast

extracts by MS-based methods is decribed (see review [4]).Abbreviation: SULAQ, 36S stable isotope labeling for MS-based

quantification

Correspondence: Dr. Frank Schmidt, Interfaculty Institute for

Genetics and Functional Genomics, Department of Functional

Genomics, Ernst-Moritz-Arndt-University Greifswald, Friedrich-

Ludwig-Jahn-Strasse 15a, 17487 Greifswald, Germany

E-mail: [email protected]

Fax: 149-3834-86-795871

& 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Proteomics 2012, 12, 37–42 37DOI 10.1002/pmic.201100057

For metabolic labeling of proteins, stable isotope labeling

by amino acids in cell culture (SILAC) using 15N- and 13C-

containing amino acids is the ‘gold standard’ [5]. SILAC has

been applied in numerous quantitative proteomics analyses,

demonstrating its potential as a powerful and versatile

approach for proteome-wide quantification, which can also

be extended to the monitoring of post-translational modifi-

cations, e.g. protein phosphorylation [6–9]. The strength of

the SILAC approach is based on the predictable incorpora-

tion of amino acids resulting into well-defined mass shifts.

A B FractionationCultivation

Pseudomonas putida KT2440

control

SDS-PAGE1200

kDa

stress condition

85100120150

Sulfurminimalmedium

506070

Na2SO4Na-benzoate

Na2SO4Na-succinate

40

Sample preparation andprotein extract mixed 1:1 N

umbe

r of

cut

ted

band

s

30

25

20

nanoLC-Orbitrap MS/MS24

15

Database search and data analysis Observation of retention time shiftC D

Δ RT min (heavy labeled non-oxidized peptidesvs. heavy labeled oxidized peptides)

Chromatogram View

Δ RT min (heavy labeled oxidized peptidesvs. light unlabeled oxidized peptides)

light - unlabeled

heavy - labeled

Δ 3.02 minSpectrum View retention time [min] peptide-

heavy - labeledlight -unlabeled- SFAFGSISSTSGSLM(heavy)PR -non-oxidized pair

peptide-SFAFGSISSTSGSLM (heavy/ox)PR -

m/zrela

tive

ab

un

dan

ce [

%]

32 36 40 44

oxidized pair

retention time [min]

E Quantitative interpretation

Control analysis Biological analysis

32S / 36S 1:1 mixture5

Benzoate / Succinategel-free, n=284 1D-PAGE, n=5625

100 200 300

0

log

2 ra

tio

200 400 600

0

log

2 ra

tio

-5number of proteins

-5number of proteins

Example: IGIMPGHIHLPGK, Charge =4, Δ =1.002 m/z

36S32S

25.2 25.8 26.4 27.0

38 N. Jehmlich et al. Proteomics 2012, 12, 37–42


In contrast, methods using stable isotope labeled substrates

instead of labeled amino acids require metabolization by

microbes potentially resulting in unpredictable and dynamic

incorporation into proteins [10].

Microorganisms are very versatile in terms of their

physiological capabilities and are thus also capable of

synthesizing amino acids de novo. Owing to this fact the

application of SILAC to microbes is limited to strains

that are auxotrophic for at least one abundant amino acid

[11, 12].

In this study, we introduce an alternative metabolic

labeling strategy using heavy sulfur 36S stable isotope

labeling for MS-based quantification (SULAQ). This strategy

meets the following requirements for valuable quantifica-

tion approaches: easy distinction of sample and control,

accurate quantification, comprehensive coverage of the

proteome and high-throughput capacity. The incorporation

of 36S into defined amino acids has several advantages for

quantitative protein analysis: (i) a constant mass shift

between the control versus treated sample of 4 Da for each

sulfur-containing amino acid per peptide, (ii) incorporation

into cysteine allows the implementation of cysteine-specific

tagging strategies for enrichment [13], or direct probing by

disulfide exchange chromatography [14, 15], and (iii) enables

chromatographic isolation of either cysteine- or methionine-

containing peptides using combined fractional diagonal

chromatography (COFRADIC) [16, 17].

In the past, metabolic incorporation of radioactively

labeled amino acids such as 35S-methionine and -cysteine

was used succesfully in several applications to study

e.g. the proteome response to oxidative stress in Sacchar-omyces cerevisiae [18] or Candida albicans [19], intracellular

proteolysis patterns of Bacillus subtilis during glucose star-

vation [20], or the thiolation of the Arabidopsis thalianaproteome [21]. However, radioactive material requires

appropiate safety precautions. For example, 35S-labeled

solutions have been found to release volatile radioactive

substances [22] and are therefore not applicable in standard

laboratories.

Therefore, alternative labeling strategies are still needed

to facilitate quantitative approaches, particularly for micro-

bial proteomics in order to gain new insights into such

important fields like infection biology, biotechnology and

environmental microbiology.

In order to verify the 36S incorporation rate, time

constant and proteome stability, the model bacterium

Pseudomonas putida KT2440 (DSM number 6125) was

cultivated containing 10 mM sodium succinate but two

different sulfur sources (32S or 36S sodium sulfate). Best-fit

calculation of the 32S and 36S P. putida growth curve was

performed using the exponential growth equation of

GraphPad Prism v5.02 that describes the growth with a

constant doubling time. The 32S culture has a doubling time

of approx. 2.08 h and the 36S culture a doubling time of

1.97 h. The rate constant (k) of both cultures were 0.33 and

0.35 and were in a comparable range suggesting minor

effects triggered by the different sulfur sources (Supporting

Information Fig. 1). In a proteome analysis of the latest

time-point (t6) we were able to quantify up to 344 proteins

(Z1 peptide used for quantification) out of 713 identified

proteins (protein prophet40.95) resulting in a media ratio

(light/heavy) of 0.27. This is proportional to a labeling effi-

ciency of 73%. To get a higher labeling efficiency, we claim

that the use of an already labeled pre-culture is necessary.

Figure 1. Schematic overview of proposed workflow. P. putida KT2440 was cultivated in batch culture in defined minimal medium with

two different carbon- and energy sources, succinate and benzoate, respectively. For quantification using SULAQ the succinate grown

culture received 36S-sodium sulfate as the sulfur source and the culture growing with succinate 32S-sodium sulfate (A). Cells of both

cultures were harvested after entry into the stationary growing phase. After sample preparation and protein extraction, the protein

extracts were mixed in equal amounts (1:1) and separated by a 15-cm-long 12.5% SDS-PAGE (B). The 1-D gel was cut into 24 bands as

indicated in the figure, followed by in-gel proteolytic digestion and nano-LC separation and Orbitrap MS/MS measurement. (C) Identi-

fication and quantification of sulfur-containing peptides were performed by the software package of Rosetta Elucidator (Ceiba solutions

v3). As an example, a chromatogram and spectrum view of a quantified 36S-labeled peptide is shown. The chromatographic view (upper

panel) of the peptide (IGIMPGHIHLPGK, charge 5 4, PP_4185, succinyl-CoA synthetase, a subunit (sucD), P. putida KT2440) indicates the

extracted ion current (XIC) of the 36S-labeled (red) peptide and the 32S-unlabeled (blue) peptide at the given retention times whereas a

detailed spectrum view of the relative abundance of the peptide peaks is indicated in the lower panel. (D) Negative retention time shifts of

oxidized peptides in comparison to their non-oxidized counterparts are shown. Upper panel, Whisker-Box-Plot displays the retention time

differences of pairs of heavy 36S-labeled non-oxidized peptides and heavy 36S-labeled oxidized peptides (n 5 231). The oxidized peptides

eluted with an average of 3.8373.48 min (n 5 231) earlier than the non-oxidized peptide (median 5 3.37 min, Whisker 5% percentile 5

1.37 min, Whisker 95% percentile 5 6.55 min). The lower Whisker-Box-Plot shows the retention time difference between heavy 36S-labeled

oxidized peptides and light 32S-unlabeled oxidized peptides. Both types of peptides (light and heavy) eluted almost at the same retention

time with an average difference of 0.00570.027 min (n 5 231) (median 5 0.002 min, Whisker 5% percentile 5�0.01 min, Whisker 95%

percentile 5 0.03 min). As an example, a detailed chromatogram view of one selected quantified peptide (SFAFGSISSTSGSLMPR,

charge 5 2, PP_0824, phosphate ABC transporter, periplasmic phosphate-binding protein, P. putida KT2440) is shown in the lower part for

which the retention time between the oxidized and non-oxidized peptide pairs (light and heavy) differed by 3.02 min. This illustrates that

the obtained ratios of oxidized and non-oxidized peptides are comparable (coefficient of determination 5 0.954). (E) Quantitative

proteome analysis can be performed with e.g. histogram calculation (log2 (ratio benzoate/succinate), n 5 562, mean 5�0.14, SD 5 1.54) or

XY-plot. To validate the quantitation values, two experiments were compared (control versus biological analysis). As a result, the control

analysis (32S versus 36S analysis; one substrate) showed only slightly changes in the proteome similar to other control experiments used

for SILAC or ICAT. On the other side, biological analysis showed a remarkable higher rate of significant regulated proteins.

3

Proteomics 2012, 12, 37–42 39


As expected, the median ratios found in six analyzed

time-points continuously decreased during cultivation from

4.5 (t1) to 0.27 (t6) (Supporting Information Fig. 2), due to

the replacement of 32S by 36S. At the last time point, we

finally mixed the 32S and 36S culture using a bacteria ratio of

approx. 1:1 estimated by OD600. Subsequent proteome

analysis resulted in 725 identified proteins of which 284

proteins (Z1 peptide) could be quantified using the SULAQ

approach. The median ratio (light/heavy) was 1.32

confirming the initial light/heavy labeling ratio of 0.27 in

the 36S culture only. Five up-regulated and six down-regu-

lated proteins out of 284 quantified proteins showed a fold

change greater than 2 and a p-value o0.01 that are criteria

usually applied to detect significant biological changes

within a proteome. Four of them clearly indicating reduced

labeling turnover at time point 6 in the single 36S approach.

Remaining seven proteins were quantified based on just one

peptide quantification which accounts for only 3% of the

total single peptide quantifications. Regarding these find-

ings, we assume that quantification quality by 36S is similar

to the most common quantitation approaches such as

SILAC or 2-DE and can be successfully applied to determine

relevant biological changes in the proteome when compar-

ing different physiological states.

In the second experiment, the proteome of P. putidaKT2440 during the growth with two different carbon sources

was compared. P. putida KT2440 was cultivated in batch

cultures overnight either in minimal medium supplemented

with 2 mM sodium benzoate and 2 mM 32S-sodium sulfate

or in the same medium containing 10 mM sodium succinate

as the sole carbon source and 2 mM 36S-Na2SO4 (Fig. 1A).

Cells were incubated at 301C with horizontal shaking at

125 rpm. The synthetic minimal medium without any sulfur

source contained 760 mg/L NH4Cl, 680 mg/L KH2PO4,

871 mg/L K2HPO4, 5.5 mg/L CaCl2 � 6H2O, 0.25 mg/L Na2

MoO4 � 4H2O, 20 g/L MgCl2, pH 7.0. P. putida was preculti-

vated in minimal medium in 10 mM sodium succinate as the

carbon source and 2 mM of 32S sodium sulfate. 36S-labeled

Na2SO4 was synthesized by a three-step oxidation cascade.

Briefly, a glass vial containing about 3 mg of elemental sulfur

(from Campro Scientific, 99.24% 36S-isotopic enrichment)

was transferred into a quartz tube and purged with a

continuous air flow (20 mL/min). The quartz tube was

heated with a burner such that the sulfur evaporated and

passed the hot zone where it was oxidized into SO2 in the air-

stream. The gas flow was washed with 7 mL of water

(Millipore quality) containing 0.2 mM of NaOH in order to

absorb the resulting SO2 completely. The outlet of the gas

flow was a capillary generating a flow of small gas bubbles in

the water phase. After complete purging of the quartz tube

(30 min) the pH value of the absorption solution was still

alkaline (pH 9.33). Later, 100mL of a 30% H2O2 were added

in order to oxidize sulfite to sulfate completely. The solution

was heated to about 801C until no residual H2O2 was

detectable (about 6 h, iodide-starch test). Ion chromato-

graphic analysis of the resulting solution (diluted 1:200)

revealed 1.15 g/L sulfate (11.5 mM) and no other contam-

inating anions. This concentration corresponds to a sulfate

yield of about 97%. The solution was adjusted to pH 7.4

using diluted HCl (24mmol, yielding a final chloride

concentration of about 3 mM) and for cell cultivation 2 mM

of 36S sodium sulfate was used.

For proteome analysis, crude protein extracts were

prepared as described in [23] and the extracts containing

heavy and light sulfur were pooled in a ratio of 1:1 (32S-extract

60mg136S-extract 60mg). About 120mg of protein was sepa-

rated by 1-D gel electrophoresis (Fig. 1B) and the gel lane was

cut into 24 gel bands. In-gel digestion with trypsin (Promega,

Mannheim, Germany) was performed as previously descri-

bed [23]. Tryptic peptides were measured with a nano-LTQ-

Orbitrap-MS/MS mass spectrometer [24]. LC separation was

performed using a constant flow rate of 300 nL/min, solvent

A (2% ACN, 0.1% formic acid) mixed in a 54-min gradient

with 5–100% solvent B (98% ACN, 0.1% formic acid).

Detailed chromatographic conditions were as follows:

0–5 min isocratic 5% solvent B, 5–43 min: 5–35% solvent B,

43–48 min: 35–60% solvent B, 48–50 min isocratic 60%

solvent B, 50–52 min: 60–100% solvent B, 52–54 min isocratic

100% solvent B, reconstitution of the column with solvent A

from 54 to 64 min.

Data analysis was done using the software Rosetta

Elucidator (Ceiba Solutions v3.3.0.SP3.19, MA, USA) with the

following settings: 1000 count intensity threshold, 10 ppm

mass accuracy, 10 min alignment search distance applied

for the binning process. For quantitative analysis, the data

pairs were built using a binning tolerance of 5 ppm, a

retention time location tolerance of 0.2 min and a mass label

shift of 3.995 Da. For peptide identification the following

data-dependent settings were applied: full tryptic digest,

10 ppm precursor tolerance, 1.00 Da fragment ion mass

tolerance, mass shift of methionine and cysteine by

3.995 Da, one missed proteolytic cleavage, oxidation on

methionine as ‘variable’ modification. All data searches were

performed with SEQUEST/Sorcerer (Sorcerer v3.5, Sage-N

Research, CA, USA) using a P. putida FASTA sequence

(downloaded from NCBI repository, 2009) and identified

peptides/proteins were accepted if they exceeded a Pepti-

deTeller score of 0.8 and a ProteinTeller score of 0.95 [25,

26]. For quantification, only labeled pairs were considered if

at least one of the isotope groups in the labeled pair was

annotated by peptide identification, reaching a labeled pair

status of ‘‘good’’ (Fig. 1C).

As a result, 1991 peptides accounting for 562 proteins

were quantified using the SULAQ approach based on

methionine-containing peptides (Supporting Information

Table 1). Many of the identified and quantified proteins

belonged to important metabolic processes such as the

tricarboxylic acid (TCA) cycle (Supporting Information

Fig. 3), the sulfur metabolism (Supporting Information

Fig. 4) or the cysteine and methionine metabolism

(Supporting Information Fig. 5). So far only methionine-

containing peptides were used for quantitation.



We also observed a subset of 231 peptide pairs with a

methionine oxidation that results in a mass shift difference

of 116 Da between the non-oxidized and oxidized peptides

(Fig. 1D). The retention times for oxidative peptides changed

during chromatography: oxidized eluted earlier (median 5

3.37 min, n 5 231) than non-oxidized peptides. This obser-

vation can be explained by the fact that oxidation of

methionine to sulfoxide or sulfone (and cysteine to sulfenic

acid, sulfinic acid and sulfonic acid) leads to a decreased

hydrophobicity, thus reducing the peptide retention time on

a reversed phase column [27]. As expected, the retention

times of the unlabeled and the heavy labeled peptides did not

differ (median 5 0.002 min, n 5 231). Furthermore, quanti-

fication of peptide intensities yielded similar data for

oxidized and the non-oxidized peptides (coefficient

R2 5 0.954). The ratios of oxidized and non-oxidized peptides

differed by only 4.8% from the average of the ratios (n 5 231,

SD 5 14.4%) and therefore both ratios can be used for rela-

tive quantification. Overall, the ability to quantify oxidized

and non-oxidized peptides is a clear benefit for targeted

methods studying e.g. oxidative damage of proteins.

After database searching and data analysis, results of

quantitative proteome analysis can be displayed in multiple

ways, e.g. histogram calculation followed by pathway and

genome database comparisons for functional analysis [28].

In order to validate our alternative labeling strategy

(SULAQ), we cultured P. putida KT2440 with succinate as

control substrate and the monocyclic aromatic compound

benzoate as the stressor substrate. However, addition of

succinate itself caused the up-regulations of several proteins

including amino acid transporters, stress proteins or anti-

oxidants [29, 30]. We were able to confirm the up-regulation

via SULAQ of the branched-chain amino acid ABC trans-

porter (PP_1141, braC) with an average fold change of �1.9

(32S-benzoate/36S-succinate) or the adenylate kinase (amino

acid metabolism, PP_1506, adk, average fold change of

�4.5) indicating higher levels of these proteins during

growth with succinate. In the presence of benzoate as

stressor, P. putida KT2440 is expected to produce higher

levels proteins involved in degradation, prevention of

uptake, and neutralization of the acid by phosphate [31]. The

degradation of benzoate is known to proceed via catechol

and the X-ketoadipate pathway [32]. Strongly increased levels

of X-ketodipyl CoA thiolase (PP_1377, pcaF, average fold

change4100) [29] were observed in our experiment in the

presence of benzoate. Increased cellular levels of succinyl-

CoA synthetase (PP_4185, sucD, average fold change 5.8)

[31] or translation elongation factor Tu (PP_0440, tuf-1,

average fold change 4.1; PP_0452, tuf-2, average fold change

3.7) are also compatible with the better growth of P. putidaKT2440 observed with benzoate.

Since the usage of sulfur-isotope-labeled substrates are

important for proteomics studies, 34S labeled substrates can

be a perfect alternative to 36S because of their favorable price

and availability. However, currently the major bottleneck for

using 34S-labeled substrates instead of 36S is that bioinfor-

matic tools for automatic cluster de-isotoping are missing

because 34S provides only a mass shift between light and

heavy isotope of 2 Da; nevertheless we believe such tool

will be available in the near future. To demonstrate a

general feasibility, we performed an additional experi-

ment using 34S instead of 36S. Initial results are promis-

ing and indicate high coverage of quantifiable peptides

and concomitantly no significantly proteome changes

using 34S (data not shown).

Our proof of principle study indicates that SULAQ is a

valuable tool for studying protein changes in microorgan-

isms. Furthermore, this labeling approach can also be

applied for the analysis of protein–protein interactions,

protein functions or signaling processes. In contrast to the

traditional proteomic set-ups, SULAQ can be also used for

detecting the sulfur flux in environmental microbiology

where sulfur-containing metabolites are metabolized.

This work was supported by funding from the BMBF withinthe framework of 01 ZIK-FunGene. Patent information: FrankSchmidt, Frank-Dieter Kopinke, and Martin von Bergen (2009).Verfahren zur relativen Quantifizierung von Peptiden nachEinbau von 36S nach metabolischer Markierung. German Patentno. DE 10 2008 043 241.

The authors have declared no conflict of interest.

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sulfur-36s stable isotope labeling of amino acids for quantification (sulaq)

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