Global relative and absolute quantitation in microbial proteomics

Global relative and absolute quantitation in microbial proteomicsAndreas Otto, Jorg Bernhardt, Michael Hecker and Dorte Becher

Available online at www.sciencedirect.com

Proteomic studies are designed to yield either qualitative

information on proteins (identification, distribution,

posttranslational modifications, interactions, structure and

function) or quantitative information (abundance, distribution

within different localizations, temporal changes in abundance

due to synthesis and degradation or both). To this end these

studies can draw upon a wide range of qualitative and

quantitative gel-based and gel-free techniques. This review

summarizes current proteomic workflows for global relative or

absolute protein quantitation and their application in microbial

physiology.

Address

Institute for Microbiology, Ernst Moritz Arndt University Greifswald,

F.-L.-Jahn-Strasse 15, 17487 Greifswald, Germany

Corresponding author: Becher, Dorte ([email protected])

Current Opinion in Microbiology 2012, 15:364–372

This review comes from a themed issue on Microbial proteomics

Edited by Bertrand Seraphin and Robert Hettich

For a complete overview see the Issue and the Editorial

Available online 21st March 2012

1369-5274/$ – see front matter, # 2012 Elsevier Ltd. All rights

reserved.

http://dx.doi.org/10.1016/j.mib.2012.02.005

IntroductionTo meet the demand of life sciences to monitor and to

understand cellular processes, -omics techniques have

evolved into elaborate and powerful tools for elucidating

regulatory processes on different cellular levels. In this

respect proteomics is particularly important, since it

focuses on proteins as the ‘workhorses of life’.

Modern proteomic techniques aim at obtaining a com-

prehensive description of the protein inventory of a cell

and at following changes in protein levels either on a

relative or on an absolute basis, enabling the scientist to

resolve processes of life in depth as well as at a global scale

at the same time. Whilst relative proteomic studies com-

pare, for example, different cell states, strains or (sub)

cellular localizations to elucidate relative changes in the

samples under investigation, absolute proteome studies

are more focused on the determination of absolute protein

levels of, for example, single metabolic pathways, ideally

comprising all detectable proteins in a complete sample.

Consequently, the analysis of protein composition and

its changes greatly facilitates the characterization of


regulatory pathways and metabolic responses to external

and internal stimuli.

In fulfilling its goals, large-scale proteomics is principally

subject to a range of general requirements or limiting

factors. Most experiments are today designed to simul-

taneously identify and quantify large numbers of proteins

to delineate subgroups of significantly changing proteins.

Therefore, pitfalls are found predominantly with respect

to the number and comprehensiveness of identified/

quantified proteins at a given sample/condition, sample

complexity that has to be resolved with the pursued

proteomics approach, the type of quantification approach

(and therefore sample preparation) and quality assess-

ment of data/statistical evaluation. Furthermore, the bio-

logical questions have to match the technical aspects of

the selected proteomics approach [1].

The aim of this review is to highlight concepts and

workflows applied in current large-scale gel-based and

gel-free quantitative studies in the field of microbial

proteomics to elucidate global regulatory events.

Two-dimensional gel electrophoresisThe success of proteomics as a distinct and important

area in life sciences was closely related to the invention

of 2DE [2]. 2DE is an orthogonal separation technique

that resolves protein species from a complex mixture

according to their pI and molecular weight. Proteins

separated on 2D gels are visualized, for example, by

staining, identified on the basis of MALDI-MS and

compared in silico by differential gel image analysis.

The collected relative quantitative information reflects

differences related to the specific experimental setup,

for example, comparisons between stressed and non-

stressed cells, different time points or different mutants

of the same organism [3,4]. Gel-based proteomics is to

date unparalleled in terms of resolution of complex

protein mixtures. Moreover, differential protein expres-

sion and changes due to, for example, PTM of proteins

are directly made visible either by comparison of differ-

ent 2D gel images by appropriate software or by soph-

isticated methods like DIGE allowing multiplexed

analyses on a single 2D gel (Table 1). The combination

of resolving power on protein level with analytical

specificity of highly sensitive and accurate MALDI

TOF-TOF mass spectrometry even uncovers cases of

multiple proteins found in the same spot on a 2D gel,

thus avoiding false quantification values, and is able to

detect PTMs of specific protein species not otherwise

distinguishable on peptide level (e.g. methylation or

acetylation).

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Table 1

Quantitation strategies used in microbial large-scale proteomics studies.

Dynamic

range

Basis of

quantitation

Type of

quantitation

approach

Multiplexing Type of

information

used for

quantification

Level of

quantitative

information

Advantages Disadvantages

Gel-based

2D gels

(classical)

Up to 4

orders of

magnitude

Differential image

analysis

Relative/

absoluteaNot limited/

inter-gel

comparison

Signal intensity/

staining

Protein Quantitation on

protein level

Limited in pI

range/hydrophobicity

2D gels (DIGE) Up to 4

orders of

magnitude

Differential image

analysis of different

imaging channels

of the same gel

Relative 3 Signal intensity/

staining

Protein Quantitation on

protein level

samples to be

compared run

on the same gel

Limited in pI

range/hydrophobicity

Gel-free/mass spectrometry-based chemical labeling

iTRAQ, TMT 2 Isobaric mass tags Relative 2–8 MS/MS Protein/

peptide

Multiplexing up

to 8 samples

Late introduction of

label; highly

reproducible sample

processing required

Metabolic labeling

SILAC 1–2 Heavy labeling of

alkaline amino acids

in organisms/cell

cultures

Relative 2–3 MS Peptide Excellent method

for determination

of small changes

in protein amount;

suited for subcellular

fractionation due to

early introduction

of label

Auxotrophy required

15N labeling 1–2 Labeling of complete

organisms with heavy

nitrogen/cell cultures

Relative 2 MS Peptide Excellent method

for determination

of small changes

in protein amount;

suited for subcellular

fractionation due to

early introduction

of label

Variable mass

changes depending

on primary sequence

of the peptide

Spiked-in peptides

AQUA 2 Comparison of artificial,

heavy peptides to

endogenous proteotypic

peptides

Relative/

absolute

Not limited MS Peptide Highly accurate;

very sensitive if used

with QQQ mass

spectrometers

Expensive; targeted

analysis of only a

subset of proteins/

peptides

Label free quantitation

Hi3 absolute

quantitation

3–4

(with MSE

acqusitionb)

Signal intensity of the

three most abundant

peptides of each

protein

Relative/

absolute

Not limited;

inter-run

comparison

MS Peptide Determination of

absolute quantitative

protein data for large

scale proteomics

experiments

Sample processing

steps and digestion/

recovery efficiency

must be determined

and kept constant

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366 Microbial proteomics

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For instance, concurrent mapping of the main vegetative

metabolic pathways at a glance was achieved for the

quantitative profiling of PTM in a study on S-Bacillithio-

lation in Bacillus subtilis elucidating intracellular regulat-

ory processes caused by hypochloric acid [5].

Additionally, protein synthesis and stability can be

examined by using 2DE in combination with radioactive

pulse chase labeling as performed in a study on long-term

glucose starvation in Staphylococcus aureus COL [6]. This

study illustrates the high versatility of 2DE as substan-

tiated by the powerful concurrent visualization and

characterization of changes in the main metabolic routes.

2DE proteomics data were successfully collated into a

database (Protecs) supporting the analysis of complex

biological questions. The database allowed comprehen-

sive analyses of preexisting and newly generated data on

anaerobiosis of S. aureus with statistical tools resulting in

the classification of exogenous stimuli previously sup-

posed to be unrelated into discrete groups with similar

physiological response patterns [7]. A related approach

was used in expression pattern analyses to determine the

targets of antimicrobial agents [8�].

Limitations of the classical 2D gel electrophoresis relat-

ing to issues on the reproducibility between gels are

overcome by the use of the DIGE technology. In a study

on ClpP-mediated proteolysis in S. aureus, DIGE was

used to discern the differences between three mutants on

a single gel revealing the importance of ClpP for phage

regulatory switches in the system under investigation [9].

In general, the application of gel-based DIGE workflows

in studies comparing closely related organisms on protein

level is extremely useful: in contrast to peptide-centered

shot-gun proteomics, small differences in protein primary

sequences of homologous proteins do not have a major

effect on the protein patterns on a 2D gel.

Despite other mass spectrometry-based technologies that

emerged with the technological progress of electrospray

instrumentation, 2DE in combination with MALDI-MS

still remains a pivotal methodology in proteomics that can

be routinely applied for parallel quantitative expression

profiling even by laboratories not associated to large

proteomics core facilities [3,10].

Gel-free proteomicsDuring the last decade, the preponderance of gel-cen-

tered analyses in proteomics has rapidly changed: gel-

free mass spectrometry-based proteomic methods have

revolutionized the whole field in terms of comprehen-

siveness, sensitivity and versatility. They enabled and

significantly improved the analysis of proteins previously

excluded from detection owing to their physico-chemical

properties.

A huge variety of methods exists, depending on the type

of mass analyzer and quantitation approach. In general, a


Quantitative analysis and regulation Otto et al. 367

typical workflow in gel-free proteomics involves the

enzymatic digest of a complex protein mixture into pep-

tides that are further analyzed by ESI-LC–MS/MS. After

the determination of exact peptide masses, primary struc-

ture information is computed by analyzing the fragment

spectra obtained in the LC–MS/MS run. Subsequently,

the peptide identification data are processed into protein

identification data, and possible post-translational modi-

fications are annotated [11].

Proteomic analyses have in common that they suffer from

a limited resolution in the dynamic range as compared to

the range of abundance and number of protein species in

the samples. To overcome this limitation, global proteo-

mic studies almost exclusively rely on fractionation,

either conducted at subcellular, protein or peptide level,

although this constraint may be abolished in the future

[12��,13�].

Gel-free quantitation methods in global proteomic stu-

dies are subdivided into targeted methods that are used

for absolute quantitation and methods that include a

differential mass tag that is introduced either chemically,

enzymatically or metabolically [14]. Furthermore, a

rapidly growing group of relative and absolute quanti-

tation methods rely on label-free techniques. These

workflows imply a direct comparison of LC–MS/MS

datasets incorporating the signal intensities of the LC

peaks rather than comparisons of artificially introduced

isotope labels.

Because of the wide range of different methods a detailed

description is out of the scope of this review. Here, we

focus on exemplary studies of global microbial quantitat-

ive proteomics and refer to excellent reviews given else-

where.

Global relative quantitative gel-free proteomicstudiesGlobal studies in microbial proteomics yield information

that has not been available before: having looked at only

small aspects of the physiology of an organism, single

complexes and pathways or single cell states, proteomics

researchers are now put in the position to survey the

overall metabolic state(s) of the organism under the given

conditions from a ‘bird’s view’ and then to zoom in on the

molecular level in the areas of interest.

Metabolic labeling (Table 1) for quantitative gel-free

proteomic analyses of the model organism B. subtiliswas implemented as early as 2004 [15] and applied to

proteome investigations for various topics [16]. Two

global approaches for this gram-positive bacterium were

published in 2010 covering 1928 and 2142 proteins,

respectively [17,18�]. The first by Soufi et al. employed

SILAC to relatively quantify changes of protein abun-

dance between two physiological conditions and to


simultaneously detect and quantify the dynamics in ser-

ine, threonine and tyrosine phosphorylation on a global

scale. The authors broadly confirmed previous gel-based

data and gained novel insights into the basic physiology

and changes in the kinome of phosphate-starved and

glucose-starved cells [17]. The second study comprises

conjoined transcriptome and proteome analyses to

describe global changes in glucose-starved B. subtilis cells

[18�]. Comprehensive proteomics data were obtained for

four different subcellular localizations including the ana-

lytically challenging membrane proteome. Altered levels

of gene expression and changes in protein amount were

traced over five time points. Time-dependent data were

visualized by Voronoi treemaps (Figure 1). It could be

inferred that degradation of ribosomes is a main source of

building blocks and energy in stationary phase cells.

Specific degradation of distinct components of metabolic

pathways as well as the unambiguous spatial localization

of a substantial fraction of the proteome could be deter-

mined. The proteome and transcriptome data from grow-

ing cells correlated, whereas in non-growing cells proteins

were still present but not synthesized any more [19�]. The

authors of the large-scale study on differences between

the aerobic and anaerobic lifestyle of Thermoplasma acid-ophilum explained discrepancies between proteome and

transcriptome data by extensive posttranscriptional regu-

latory mechanisms [20]. Conclusively, concurrent

sampling of proteome and transcriptome data in global

studies is highly eligible as this yields comprehensive and

complementary information on two different levels of

cellular regulation.

Considerable efforts in proteomic research are under-

taken for pathogenic bacteria [21–23]. As the emergence

and propagation of multiresistant strains, for example, of

S. aureus, are a major threat in our health systems today

[24], there is a strong need for novel antibiotic strategies.

Granting access to potentially novel targets, extensive

proteomic studies of S. aureus resolved more than 80% of

the proteome in growing cells [19�] and gained valuable

information on the composition and changes of the sur-

face proteome of staphylococci [25–28]. Quantitative

proteomics is also pushed forward to in situ conditions

by monitoring physiological changes of the pathogen after

internalization by human host cells [29�].

Besides stable isotopic labeling of complete organisms,

differential mass tag approaches like iTRAQ are inten-

sively made use of especially in highly multiplexed

studies. Addressing the host–pathogen interaction in

the Arabidopsis-Pseudomonas syringae system, Kaffarnick

et al. determined changes in the host’s extracellular

proteome pathosystem [30]. Taking advantage of even

two different quantitative proteomics techniques, Jayapal

et al. estimated protein turnover rates in Streptomycescoelicolor cultures undergoing transition from exponential

growth to stationary phase in a SILAC-iTRAQ



Figure 1

2

-2

CV

z-sc

ore

0.25 0.5 exponential growth 120 min stationary phase

Current Opinion in Microbiology

Voronoi treemaps as a powerful visualization tool. To support an in-depth physiological and functional data interpretation protein data should be

enriched with meta-information, which may be derived from functional ontologies such as GO (gene ontology), KEGG Brite, regulatory hierarchies,

COG or other functional categorization schemes (e.g. the SubtiWiki functional categorization as shown here). Voronoi treemaps are able to support a

simultaneous visualization of all protein expression data and complex gene functional categories with functionally most related protein clusters closest

to each other on limited space. Within the treemap the expression data of a proteome sample are mapped as color shades (blue — below, gray —

near, orange — above average expression level), uncertainty as color saturation level. The figure panels show samples from exponential growth phase

as well as from 1200 stationary phase of a Bacillus subtilis glucose-starved culture. Proteins within the same functional cluster with similar profiles

illustrate co-regulated cellular functions which are controlled in response to the acting stimulus.

multitagging strategy [31]. In this study the advantages of

the different approaches become apparent: while meta-

bolic labeling allows extensive fractionation schemes and

thus comprehensive studies on most complete pro-

teomes, the multiplex chemical labeling with isobaric

mass tags opens up clear-cut benefits for strain/mutant/

condition comparisons and at the same time avoids miss-

ing values in time course data sets.

Absolute determination of protein abundanceFor an integration of proteomic data into datasets for

systems biology it is crucial to obtain absolute quantitat-

ive proteomic data. Detecting the absolute protein con-

centration makes it possible to determine protein

stoichiometries from small functional units like metabolic

pathways up to complete proteomes and to compare

different samples in virtually limitless combinations

[32��]. To fulfill the daunting task of absolute quantifi-

cation of proteins and proteomes, currently two different

approaches are mainly applied: workflows based on the

targeted AQUA approach [33] and concepts based on

extracted signal intensities [34]. Other approaches relying


on tagging complete genomes impress by their indepen-

dent methodological and analytical approach of fluor-

escence-aided proteome quantification leading to the

same results as compared to proteomic methods, but their

applicability for the broader scientific community seems

to be questionable in terms of analysis costs and time

[35,36].

In the AQUA approach synthetic isotopically labeled

peptides of known concentrations are spiked into the

endogenous peptides of the sample. The peptide mixture

is then subjected to targeted mass spectrometry [33],

resulting in absolute quantitative information for the

identified proteins. Having a massive impact on the

scientific community already, particularly in proteomics

studies looking at smaller functional units of single

proteins, this approach has recently been further concep-

tually refined to meet the requirements for global quan-

titative analyses.

The groundbreaking work published in 2009 by Mal-

mstrom et al. for absolute quantitation on a global scale



Figure 2

protein amountsare calculated

from Hi3reference &Hi3 protein

peptides

(a) – AQUA peptide supported 2D gel based (b) – AQUA peptide supported LCMS based (c) – Hi3 label-free abs.quantitation

trypticdigest

imaging and 2Dpattern analysis

lowvariancestaining

measuredproteinsamples

2D gels spike in ofheavy (AQUA)peptides

trypticdigest

LCMS&MS/MS

determines lightpeptide/heavypeptide ratios

anchor proteinbased quantitation

of all detectableproteins


spike in ofheavy (AQUA)peptides

trypticdigest

LCMS &MS/MS

determines lightpeptide/heavypeptide ratios


spike in of diges-ted referenceproteins

tRm/z

LCMS & MS/MS

ref. protein proteins

anchor peptideconcentrations

serve as referen -ces for quan -titation of all

other peptides

m/z

tRm/z

LCMS runs

tR

label-freequanti-tation

tR

m/z

proteinsproteins

Current Opinion in Microbiology

Absolute quantitation approaches in global studies. Absolute quantitative proteomic data are needed for integration into datasets for systems biology.

(a) The AQUA peptide supported 2D gel-based method relies on protein separation and detection in 2D gels (represented by blue spots/bars) with

absolute quantitation of a distinct set of anchor proteins (orange spots/bars) by the AQUA method (red bars indicate labeled reference peptides). (b)

Similarly, this approach may be performed on aligned label-free LC–MS datasets. Relative peptide abundances are derived from LC–MS maps and

absolute peptide abundances are determined by AQUA. (c) Label-free LC–MS datasets with a spiked-in reference protein serve as basis for absolute

quantitation approaches via the Hi3 method. The three most abundant peptides of all proteins detected in an LC–MS dataset are compared to the

three most intense peptides of a spiked-in reference protein of known concentration (red bars indicate the three most intense peptides of the spiked-in

reference protein and blue bars indicate the three most intense peptide of other proteins detected and compared).

combined the AQUA methodology with label-free quan-

titation and spectral libraries [37]. In follow-up studies,

the workflow was applied to investigate changes in 25

different states of Leptospira interrogans coping with anti-

biotic stresses [38��] as well as in a comprehensive multi-

omics study on Mycoplasma pneumonia [39�]. A unique

feature of these studies is the generation of very extensive

and consistent absolute quantitative data allowing a sys-

tematic interrogation of the cell’s physiological state.

Because of the absence of missing values these datasets

may be regarded as proteomic arrays comparable to arrays

in transcriptome studies [40].

Another approach for absolute protein quantitation on a

global scale is based on targeted mass spectrometry in

combination with 2DE [41��]. Absolute protein concen-

trations for a specific preselected subset of proteins of a

complex sample are determined by AQUA and the

respective protein extracts are subjected to 2DE


(Figure 2). In analogy to Malmstrom et al. [37], the final

step includes the calibration of the 2D gels according to

the absolute concentration of the anchor proteins. This

fast and cost-effective method is applicable even in

laboratories where high-end mass spectrometry is not

available. Because of the possibility of intergel calibra-

tions the need for measurement capacity is small and once

set up, this approach may be used to complement existing

routine 2D workflows currently only covering relative

quantitation based on differential gel imaging. This

approach combines the advantages of gel-based and

gel-free analyses as well as of absolute and relative

quantitation and offers a unique platform for the precise

and extensive characterization of diverse stress and

environmental trigger signatures.

Label-free quantitation methods, although technically

challenging, are emerging in the field of absolute global

proteome quantitation studies since no cost-intensive



artificial heavy labeled peptides are needed. A majority of

the label-free absolute quantitation methods rely on the

concept of accurate mass retention time pairs combined

with the integration of the three most abundant peptides

of a protein compared to a spiked-in reference [34,42].

Implementations of this concept, either from academics

[43,44] or as part of a complete proteomics technology

[34,42,45] have greatly succeeded in label-free absolute

proteomics. In microbial proteomics this was used, for

example, for the analysis of different mutants of S. aureusSCV [46] and the probing of the exoproteome of Coryne-bacterium pseudotuberculosis [47].

Despite the success of absolute quantitation on a global

scale, this task remains to be of highest complexity and

exigency compared to relative proteomics studies or

studies aiming on absolute quantification of single

proteins.

Conclusion/outlookThe range of possibilities for global quantitation studies

in proteomics is manifold, offering microbiologists power-

ful experimental options for their lines of research. Whilst

spatio-temporal studies multiply costs and efforts, the

generation of absolute data requires precise compliance to

workflows. In all cases the design of a specific quantitative

study should be worked out with due diligence based on

the objective: the choice of the type of labeling and

proteomics technique determines the quantitation

accuracy, depth of proteome coverage and reproducibility

of the study [1,48].

In our opinion, the elucidation of PTM on a global scale

[11,49,50] and the determination of protein turnover

[51,52,53�] as well as quantitative studies on complex

microbial consortia [54,55�,56] are currently important

fields of progress for microbial proteomics.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest

�� of outstanding interest

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12.��

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13.�

Thakur SS, Geiger T, Chatterjee B, Bandilla P, Frohlich F, Cox J,Mann M: Deep and highly sensitive proteome coverage byLC–MS/MS without prefractionation. Mol Cell Proteomics 2011,10:003699.

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18.�

Otto A, Bernhardt J, Meyer H, Schaffer M, Herbst FA, Siebourg J,Mader U, Lalk M, Hecker M, Becher D: Systems-wide temporalproteomic profiling in glucose-starved Bacillus subtilis. NatCommun 2010, 1:137.

Global study following the changes in gene expression and changes in theprotein amount for five different time points in glucose starved cells ofBacillus subtilis. Spatio-temporal study with implemented visualization ofquantitative data with Voronoi treemaps.

19.�

Becher D, Hempel K, Sievers S, Zuhlke D, Pane-Farre J, Otto A,Fuchs S, Albrecht D, Bernhardt J, Engelmann S et al.: A proteomicview of an important human pathogen — towards thequantification of the entire Staphylococcus aureus proteome.PLoS One 2009, 4:e8176.

Study comprising more than 80% of the expressed cytosolic proteome ina growing staphylococcal cell covering four different cellular localizationsin a single proteomic study. High proteome coverage is yielded byextensive fractionation schemes combined with stable isotope labeling.



20. Sun N, Pan C, Nickell S, Mann M, Baumeister W, Nagy I:Quantitative proteome and transcriptome analysis of thearchaeon Thermoplasma acidophilum cultured under aerobicand anaerobic conditions. J Proteome Res 2011,9:4839-4850.

21. de Souza GA, Wiker HG: A proteomic view of mycobacteria.Proteomics 2011, 11:3118-3127.

22. Bumann D: Pathogen proteomes during infection: a basis forinfection research and novel control strategies. J Proteomics2010, 73:2267-2276.

23. Poetsch A, Haussmann U, Burkovski A: Proteomics ofcorynebacteria: from biotechnology workhorses topathogens. Proteomics 2011, 11:3244-3255.

24. Francois P, Scherl A, Hochstrasser D, Schrenzel J: Proteomicapproaches to study Staphylococcus aureus pathogenesis.J Proteomics 2010, 73:701-708.

25. Hempel K, Pane-Farre J, Otto A, Sievers S, Hecker M, Becher D:Quantitative cell surface proteome profiling for SigB-dependent protein expression in the human pathogenStaphylococcus aureus via biotinylation approach. J ProteomeRes 2010, 9:1579-1590.

26. Hempel K, Herbst FA, Moche M, Hecker M, Becher D:Quantitative proteomic view on secreted, cell surface-associated, and cytoplasmic proteins of the methicillin-resistant human pathogen Staphylococcus aureus under iron-limited conditions. J Proteome Res 2011, 10:1657-1666.

27. Dreisbach A, van Dijl JM, Buist G: The cell surface proteome ofStaphylococcus aureus. Proteomics 2011, 11:3154-3168.

28. Solis N, Cordwell SJ: Current methodologies for proteomics ofbacterial surface-exposed and cell envelope proteins.Proteomics 2011, 11:3169-3189.

29.�

Schmidt F, Scharf SS, Hildebrandt P, Burian M, Bernhardt J,Dhople V, Kalinka J, Gutjahr M, Hammer E, Volker U: Time-resolved quantitative proteome profiling of host–pathogeninteractions: the response of Staphylococcus aureus RN1HGto internalisation by human airway epithelial cells. Proteomics2010, 10:2801-2811.

Changes in the proteome of internalized Staphylococcus aureus RN1HGinternalized by human airway epithelial cells were kept track of by acombination of pulse-chase stable isotope labeling and high capacity cellsorting. Excellent approach of in vivo quantitative proteomics with lowsample consumption.

30. Kaffarnik FA, Jones AM, Rathjen JP, Peck SC: Effector proteinsof the bacterial pathogen Pseudomonas syringae alter theextracellular proteome of the host plant, Arabidopsis thaliana.Mol Cell Proteomics 2009, 8:145-156.

31. Jayapal KP, Sui S, Philp RJ, Kok YJ, Yap MG, Griffin TJ,Hu WS: Multitagging proteomic strategy to estimate proteinturnover rates in dynamic systems. J Proteome Res 2010,9:2087-2097.

32.��

Malmstrom L, Malmstrom J, Aebersold R: Quantitativeproteomics of microbes: Principles and applications tovirulence. Proteomics 2011, 11:2947-2956.

A milestone in absolute quantitation on a global scale: combination of theAQUA methodology with label free quantification and spectral libraries.

33. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP:Absolute quantification of proteins and phosphoproteins fromcell lysates by tandem MS. Proc Natl Acad Sci U S A 2003,100:6940-6945.

34. Silva JC, Denny R, Dorschel CA, Gorenstein M, Kass IJ, Li GZ,McKenna T, Nold MJ, Richardson K, Young P et al.: Quantitativeproteomic analysis by accurate mass retention time pairs.Anal Chem 2005, 77:2187-2200.

35. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A,Dephoure N, O’Shea EK, Weissman JS: Global analysis ofprotein expression in yeast. Nature 2003, 425:737-741.

36. Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M,DeRisi JL, Weissman JS: Single-cell proteomic analysis of S.cerevisiae reveals the architecture of biological noise. Nature2006, 441:840-846.


37. Malmstrom J, Beck M, Schmidt A, Lange V, Deutsch EW,Aebersold R: Proteome-wide cellular protein concentrations ofthe human pathogen Leptospira interrogans. Nature 2009,460:762-765.

38.��

Schmidt A, Beck M, Malmstrom J, Lam H, Claassen M,Campbell D, Aebersold R: Absolute quantification of microbialproteomes at different states by directed mass spectrometry.Mol Syst Biol 2011, 7:510.

Study on pathogenic progression and antibiotic defense of Leptospirainterrogans. Most comprehensive proteome pattern comparison basedon absolute proteome data to date.

39.�

Maier T, Schmidt A, Guell M, Kuhner S, Gavin AC, Aebersold R,Serrano L: Quantification of mRNA and protein and integrationwith protein turnover in a bacterium. Mol Syst Biol 2011, 7:511.

Survey on protein turnover in Mycoplasma pneumoniae. This studycombines mRNA and absolute protein data together with different metadata to describe physiological perturbations in a systems biology study.

40. Cox J, Mann M: Is proteomics the new genomics? Cell 2007,130:395-398.

41.��

Maass S, Sievers S, Zuhlke D, Kuzinski J, Sappa PK, Muntel J,Hessling B, Bernhardt J, Sietmann R, Volker U et al.: Efficient,global-scale quantification of absolute protein amounts byintegration of targeted mass spectrometry and two-dimensionalgel-based proteomics. Anal Chem 2011, 83:2677-2684.

Absolute protein quantification by targeted mass spectrometry in com-bination with 2DE. This workflow allows absolute quantitative studies witha relatively fast and cost effective methodology combined with theadvantages of 2DE.

42. Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ:Absolute quantification of proteins by LCMSE: a virtue ofparallel MS acquisition. Mol Cell Proteomics 2006, 5:144-156.

43. Grossmann J, Roschitzki B, Panse C, Fortes C, Barkow-Oesterreicher S, Rutishauser D, Schlapbach R: Implementationand evaluation of relative and absolute quantification inshotgun proteomics with label-free methods. J Proteomics2011, 73:1740-1746.

44. Andreev VP, Li L, Cao L, Gu Y, Rejtar T, Wu SL, Karger BL: A newalgorithm using cross-assignment for label-free quantitationwith LC-LTQ-FT MS. J Proteome Res 2007, 6:2186-2194.

45. Geromanos SJ, Vissers JP, Silva JC, Dorschel CA, Li GZ,Gorenstein MV, Bateman RH, Langridge JI: The detection,correlation, and comparison of peptide precursor and productions from data independent LC–MS with data dependant LC–MS/MS. Proteomics 2009, 9:1683-1695.

46. Kriegeskorte A, Konig S, Sander G, Pirkl A, Mahabir E, Proctor RA,von Eiff C, Peters G, Becker K: Small colony variants ofStaphylococcus aureus reveal distinct protein profiles.Proteomics 2011, 11:2476-2490.

47. Pacheco LG, Slade SE, Seyffert N, Santos AR, Castro TL,Silva WM, Santos AV, Santos SG, Farias LM, Carvalho MA et al.: Acombined approach for comparative exoproteome analysis ofCorynebacterium pseudotuberculosis. BMC Microbiol 2011,11:12.

48. Li Z, Adams RM, Chourey K, Hurst GB, Hettich RL, Pan C:Systematic comparison of label-free, metabolic labeling, andisobaric chemical labeling for quantitative proteomics on LTQorbitrap velos. J Proteome Res 2012, 11:1582-1590.

49. Choudhary C, Kumar C, Gnad F, Nielsen ML, Rehman M,Walther TC, Olsen JV, Mann M: Lysine acetylation targetsprotein complexes and co-regulates major cellular functions.Science 2009, 325:834-840.

50. Eberl HC, Mann M, Vermeulen M: Quantitative proteomics forepigenetics. Chembiochem 2010, 12:224-234.

51. Ahmad Y, Boisvert FM, Lundberg E, Uhlen M, Lamond AI:Systematic analysis of protein pools, isoforms andmodifications affecting turnover and subcellular localisation.Mol Cell Proteomics 2012, 11 M111.013680.

52. Boisvert FM, Ahmad Y, Gierlinski M, Charriere F, Lamond D,Scott M, Barton G, Lamond AI: A quantitative spatial proteomicsanalysis of proteome turnover in human cells. Mol CellProteomics 2012, 11 M111.011429.



53.�

Kramer G, Sprenger RR, Nessen MA, Roseboom W, Speijer D, deJong L, de Mattos MJ, Back J, de Koster CG: Proteome-widealterations in Escherichia coli translation rates uponanaerobiosis. Mol Cell Proteomics 2010, 9:2508-2516.

The authors present a methodology to identify stable and labile proteins ina global study to compare relative translation rates with relative mRNAlevels. Thereupon, possible discrepancies between mRNA expression/protein turnover can be used to uncover the basis for regulation bytranslation or by transcription.

54. Pan C, Fischer CR, Hyatt D, Bowen BP, Hettich RL, Banfield JF:Quantitative tracking of isotope flows in proteomes ofmicrobial communities. Mol Cell Proteomics 2011, 10M110.006049.


55.�

Belnap CP, Pan C, Denef VJ, Samatova NF, Hettich RL,Banfield JF: Quantitative proteomic analyses of the responseof acidophilic microbial communities to different pHconditions. ISME J 2011, 5:1152-1161.

Quantitative analyses of microbial communities. Global study enabling forin-depth characterization of changes in complex microbial consortiatriggered by environmental changes.

56. Jehmlich N, Fetzer I, Seifert J, Mattow J, Vogt C, Harms H,Thiede B, Richnow HH, von Bergen M, Schmidt F: Decimalplace slope, a fast and precise method for quantifying13C incorporation levels for detecting the metabolic activityof microbial species. Mol Cell Proteomics 2010,9:1221-1227.


Global relative and absolute quantitation in microbial proteomics

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