Quantitative proteomics: a tool to assess cell differentiation

Available online at www.sciencedirect.com

Quantitative proteomics: a tool to assess cell differentiationMichiel Vermeulen1 and Matthias Selbach2

During cell differentiation, gene expression is regulated at

multiple levels which is only partially captured by transcription

profiling. In recent years it became increasingly clear that post-

translational modifications of core histones and post-

transcriptional regulation by RNA-binding proteins and

microRNAs play an important role during differentiation. Recent

advances in mass spectrometry-based quantitative

proteomics now allow for genome-wide analyses at the protein

level. This technology provides a powerful toolbox that can be

used to study different levels of gene regulation and reveal their

importance during development of multi-cellular organisms.

We highlight recent studies and indicate how quantitative

proteomics can be employed to investigate cell differentiation

in the future.

Addresses1 Department of Physiological Chemistry and Cancer Genomics Centre,

University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands2 Max Delbruck Center for Molecular Medicine, Robert-Rossle-Str. 10,

D-13092 Berlin, Germany

Corresponding author: Vermeulen, Michiel

([email protected]) and Selbach, Matthias

([email protected])

Current Opinion in Cell Biology 2009, 21:761–766

This review comes from a themed issue on

Cell differentiation

Edited by Carmen Birchmeier

Available online 1st October 2009

0955-0674/$ – see front matter

# 2009 Elsevier Ltd. All rights reserved.

DOI 10.1016/j.ceb.2009.09.003

IntroductionCell differentiation can be interpreted as a series of events

that lead to a shift in the cellular gene expression profile in

response to external stimuli. Therefore, measuring

changes in mRNA levels using microarrays or deep sequen-

cing is a powerful tool to study differentiation. However,

since proteins rather than mRNAs are the principle players

in most cellular processes, mRNA profiling provides an

incomplete picture of differentiation events. Mass spec-

trometry-based proteomics can potentially fill this gap.

Recent advances in instrumentation, software and quanti-

fication now allow comprehensive analyses of cell differ-

entiation processes by mass spectrometry.

The most straightforward way of studying cell differen-

tiation using mass spectrometry is to investigate how the

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proteome changes when cells differentiate. This strategy

can be applied to all models of cell differentiation in-

cluding stem cells and recently spurred the Proteome

Biology of Stem Cells Initiative [1]. Mass spectrometry

can now define stem cell proteomes to a depth of more

than 5000 proteins and can capture low abundant tran-

scription factors like OCT4, SOX2 and NANOG [2].

Combined with quantification this approach has recently

been used to compare the proteome of self-renewing and

differentiating embryonic stem cells and to investigate

differentiation of myocytes, adipocytes and T-helper

cells [3–6].

Measuring changes in protein abundance is certainly

informative. However, since protein levels are regulated

at many steps, such proteome profiling approaches cannot

directly reveal the mechanisms involved. We believe that

the main strength of mass spectrometry as a quantitative

tool to assess differentiation is to directly investigate

specific levels of regulation. The number of questions

that can be addressed in this manner is huge and dis-

cussing all of them is beyond the scope of this review.

Instead, we will focus on chromatin dynamics and post-

transcriptional regulation — two central aspects of cell

differentiation where we believe mass spectrometry is

particularly powerful. To set the scene, we will begin with

a brief introduction to the technology.

Mass spectrometry-based proteomicsEarly attempts to define proteomes used classical

methods like two-dimensional gel electrophoresis and

Edman degradation. Nowadays, mass spectrometers are

the instruments of choice for proteomics because of their

high sensitivity and sequencing speed [7,8]. In a typical

workflow, highly complex protein mixtures are digested

with a protease (Figure 1). The resulting peptide mixture

is separated by reversed-phase high performance liquid

chromatography (LC). At the end of the chromatographic

column, the eluting peptides are transferred into the

orifice of a mass spectrometer by a process called electro-

spray ionisation (ESI). The mass spectrometer as the key

instrument in the analytical pipeline performs two

important tasks. First, it determines the masses and

intensities of the peptides that elute from the column

during the HPLC run (mass spectrum, MS). In complex

samples typically dozens of peptides are co-eluting at any

given time. Second, the instrument isolates selected

peptides, fragments them and records the masses of

the fragments (fragment mass spectrum, MS/MS). Mod-

ern instruments are capable of fragmenting several pep-

tides per second. Both the MS and MS/MS spectra

contain information about the peptides that can be used


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http://dx.doi.org/10.1016/j.ceb.2009.09.003

762 Cell differentiation

Figure 1

Quantitative shotgun proteomics. In this example, stable isotope labelling by amino acids in cell culture (SILAC) is used to label cultured cells. Protein

samples are combined, digested and peptides are separated by high performance liquid chromatography (HPLC). Eluting peptides are transferred into the

orifice of a mass spectrometer by electrospray ionisation (ESI). The mass spectrum (MS) reveals the masses and intensities of peptides eluting from the

column at any given time. Stable isotope labelled peptides occur as pairs with a mass shift and can be quantified based on their intensity ratios.

Fragmentation of individual peptides reveals the fragment mass spectrum (MS/MS), which contains information about the peptide sequence including

potential post-translational modifications. The data from MS and MS/MS spectra are used to identify and quantify peptides and the corresponding proteins.

to identify the corresponding proteins in a database. The

higher the mass accuracy of the instrument, the greater is

the confidence in the identification [9]. Currently, this

technology can be used to identify more than 1000

proteins in a single LC–MS/MS run. Pre-fractionation

of proteins or peptides boosts the number of identified

proteins considerably and allows the identification of

essentially all proteins expressed in simple eukaryotes

[10��]. Importantly, this technology can not only identify

the proteins but also systematically map post-translational

modifications (PTMs) in a site-specific manner [11–13].

Although identification of proteins is certainly important,

analysis of differentiation processes also requires quanti-

tative information at the proteomic scale. Mass spectrom-

etry is not inherently quantitative but in recent years,

several technologies have been developed that add a

quantitative dimension to mass spectrometry data

[14,15]. Most of these technologies involve the use of

stable (i.e. non-radioactive) isotope labelling. For example,

in stable isotope labelling by amino acids in cell culture (SILAC),

proteins are metabolically labelled by cultivating them in

growth medium containing heavy isotope-encoded essen-

tial amino acids [15]. The general concept is that introdu-

cing heavy stable isotopes results in a shift in peptide mass.

Therefore, differentially labelled samples can be mixed

and analysed together. The ratio of peptide peak inten-

sities reflects relative differences in abundance of the

corresponding proteins between both samples. Combined

with the workflow described above, mass spectrometry-

based quantitative proteomics can assess the dynamics of


protein abundance and PTMs occurring during differen-

tiation events, as illustrated below.

Chromatin dynamics during differentiationIn a eukaryotic nucleus, DNA is packed in a structural

polymer called chromatin. Chromatin serves to store

genetic material, but also plays an active role in regulating

processes such asDNArepair, replication andtranscription.

The nucleosome, an octamer of four different histone

proteins around which the DNA is wrapped, represents

the basic repeating unit within chromatin. Nucleosomes

pose a barrier for reading the stored DNA-sequence infor-

mation. Recently, a large number of transcription factors

have been identified and characterised that are able to alter

the structure of chromatin and by doing so are able to

regulate the accessibility and transcriptional activity of

genes. Of particular interest are proteins and protein com-

plexes that post-translationally modify histones (so-called

chromatin ‘writers’). These PTMs include acetylation,

phosphorylation, methylation and ubiquitination, which

are thought to provide an epigenetic ‘barcode’ that partly

determines the expression status of individual genes or

chromosomal loci [16]. During cell differentiation, PTM

patterns of core histones on developmentally regulated

target genes show a high degree of dynamics, as revealed

by genome-wide chromatin immunoprecipitation (ChIP)-

sequencing [17,18]. These studies have provided a wealth

of knowledge but the success of this approach relies on the

specificity and availability of antibodies that can be used to

immunoprecipitate a histone modification of interest.

Moreover, antigen recognitioncould be negatively affected


Mass spectrometry and quantitative proteomics Vermeulen and Selbach 763

by additional PTMs that can occur elsewhere on the

epitope. Finally, antibody-based assays have only a limited

capacity to study PTM interplay to reveal combinatorial

modification codes. MS-based proteomics can partially

overcome these drawbacks and therefore provides a power-

ful toolbox that can be used to identify PTMs on proteins,

including core histones [12,19,20]. Recent computational

and instrumental advances in mass spectrometry technol-

ogy now allow researchers to measure the mass of a peptide

with sub-ppm mass accuracy which means that PTMs that

have similar masses such as acetylation (mass 42.010565)

and trimethylation (mass 42.046950) can be unequivocally

assigned to peptides with very high confidence [21]. Phan-

stiel et al. used an ETD enabled LTQ-Orbitrap and a label

free quantitation approach that was first used by the Kel-

leher lab [22�] to study histone H4 PTM dynamics upon

TPA induced stem cell differentiation [23��]. They ident-

ified and quantified 74 unique histone H4 molecules carry-

ing different combinations of PTMs. Interestingly, H4R3

methylation was only observed in the presence of H4K20

dimethylation, suggesting that H4K20 dimethylation is

necessary for subsequent H4R3 methylation. This combi-

natorial methylation of an active mark (H4R3me) [24] and a

repressive methyl mark (H4K20me) is reminiscent of the

combinatorial histone H3K4 and H3K27 methylation that

is observed in stem cells [17]. Furthermore, upon TPA

induced differentiation, the histone H4 molecules gradu-

ally lost its acetyl groups, which are linked to activation of

transcription, whereas the repressive H4K20me sites

gradually became more abundant. This could reflect the

establishment of large regions of silent heterochromatin, of

which H4K20me is a hallmark modification [25], as the

stem cells differentiate towards a committed cell type.

Although the biological role of many histone PTMs is still

unclear, one important aspect appears to be the recruitment

or stabilisation of proteins that can subsequently exert their

function at the site of recruitment [26�]. Quantitative mass

spectrometry canbeused to identify such PTM-dependent

interactions. Unmodified and modified histone tail pep-

tides are immobilised on beads and used for pulldown

experiments from nuclear extracts derived from light or

heavy SILAC labelled cells, respectively. Interacting

proteins are eluted from both peptide forms, combined

and analysed. Protein ratios identify PTM-dependent

interaction partners. This method revealed that TFIID

is recruited to nucleosomes by trimethylated histone H3

lysine four [27�].

The above-described examples illustrate the power of

quantitative MS-based proteomics and how it can be

applied to study chromatin PTM dynamics during cell

proliferationanddifferentiation.However,onemajordraw-

back of this approach is the fact that bulk histones are

studied. Therefore, one can only identify global genome-

wide changes in histone modification patterns. It would

however be desirable to use MS-based quantitative pro-

teomics to study such dynamics for isolated genomic loci.


Thus far this has not been technically feasible yet mainly

due to sensitivity issues. However, in a pioneering study,

Dejardin and Kingston recently described technology

called PICh (Proteomics of Isolated Chromatin segments)

that can be used to isolate specific sequences of genomic

DNA and its associated proteins in sufficient quantities to

allow subsequent protein identification by mass spectrom-

etry [28��]. Owing to their relatively high abundance

(approximately 100 copies per cell) the authors focus on

telomeres and they illustrate the applicability of PICh by

identifying a large number of known and novel telomere

interacting proteins. Combined with a quantitative filter

and given the rapid speed with which mass spectrometry

equipment is continuously being developed this approach

could become a valuable tool to study the protein dynamics

of particular chromatin loci during cell stimulation or per-

turbation or during differentiation, including the histone

PTMs that are associated with the locus.

Post-transcriptional regulationOnce an mRNA is transcribed it interacts with a multitude

of proteins involved in splicing, transport, stability and

translation of the message. In contrast to transcriptional

regulation, the role of these post-transcriptional regulatory

events has long been neglected. This has changed drasti-

cally and RNA biology is one of the most active research

areas today (e.g. see special issue of Cell on RNA, February

2009). It is now clear RNA metabolism is extensively

regulated during differentiation. The key players regulat-

ing the fate of individual messages are RNA-binding

proteins (RBPs) and non-coding RNAs such as microRNAs

(miRNAs). Together, mRNAs, small RNAs and RBPs

constitute ribonucleoprotein complexes (RNP) that

regulate all aspects of RNA metabolism from processing

to transport, translation and degradation. For example,

more than 90% of all human genes undergo alternative

splicing, and splicing patterns differ greatly between tis-

sues [29]. Both miRNAs and RBPs can also bind to specific

sequence motifs the 30-untranslated region (30-UTR) of

target mRNAs and regulate their stability and translation

[30,31]. Post-transcriptional regulation appears to be

particularly important during developmental switches such

as cell fate decisions. For example, several players involved

were first identified because they change the cell lineage of

Caenorhabditis elegans [32,33]. Intriguingly, 30-UTRs rather

than promoters are the primary regulators of gene expres-

sion in the worm germline [34].

A popular method to study RNA–protein interactions is to

purify proteins and identify associated RNAs by micro-

arrays or deep sequencing using methods such as cross-

linking immunoprecipitation (CLIP) or RNP immuno-

precipitation (RIP) [35,36]. In a complementary

approach, mass spectrometry can be employed to system-

atically identify proteins associated with a purified RNA.

Unbiased screening for proteins has the advantage that

RBPs can be identified that would never have been



selected for a targeted CLIP or RIP experiments. This

method was recently employed to reveal the composition

of active spliceosomes and the pre-mRNA 30-processing

complex [37�,38�]. When combined with UV cross-link-

ing, this method enables identification of distinct regions

of proteins that directly interact with RNA and thus

allows the definition of novel putative RNA-binding

domains [39�]. A pervasive problem is to distinguish

between real interactions and non-specific contaminants.

RNA is particularly cumbersome because the negative

charge facilitates non-specific binding of positively

charged proteins. Quantitative proteomics can solve this

problem: similar to the peptide pulldown principle out-

lined above, bait and control RNAs are immobilised and

incubated with heavy or light lysate from differentially

SILAC labelled cells, respectively. After combining both

samples, mass spectrometry can be used to identify and

quantify the proteins. True interaction partners and con-

taminants can be differentiated by their abundance ratios

[40�]. The same strategy can also be used to identify

DNA-binding proteins [41�]. An alternative to RNA-pull-

down experiments is to precipitate RNPs via known

protein components. For example, purification of Argo-

naute-associated proteins was used to identify new com-

ponents of the RNA-induced silencing complex (RISC)

and cytoplasmic processing bodies (P-bodies) [42,43]. In

the future, quantitative methods will facilitate differen-

tiation between bona fide RNP components and contami-

nants [44]. In addition, quantitative proteomics can assess

Figure 2

Global approaches to assess cell differentiation. Proteomics and transcriptom

references of individual approaches. For more details see text.


dynamic changes in RNP composition in response to

differentiation stimuli.

miRNAs represent an evolutionarily conserved class of

small RNAs that regulate gene expression [32,33,45�].After being transcribed and processed, mature miRNAs

are incorporated into the RISC and target mRNAs by

partial Watson–Crick base pairing to complementary

sequences in 30-UTRs. This repression occurs via degra-

dation of the message and/or translational repression.

Thus, transcriptome profiling alone cannot reveal the

impact of miRNAs on gene expression. Arguably, the

most relevant read-out to assess miRNA-mediated regu-

lation is to measure changes in de novo protein synthesis.

The recently developed pulsed SILAC (pSILAC)

method revealed that a single miRNA directly represses

production of hundreds of proteins [46��]. Similar results

were obtained using conventional SILAC, although in

this case result interpretation is complicated by different

protein turnover times [47�,48�].

The human genome encodes at least �500 and perhaps

up to a 1000 proteins with RNA-binding domains. The

number of human miRNAs remains controversial, but at

least �600 have been identified so far — most of them

without any functional characterisation. Importantly,

gene expression can be regulated by both RNA-binding

proteins and miRNAs. Moreover, this regulation can

occur in a combinatorial fashion where one player promotes

ics can provide valuable information at different levels. Numbers indicate


Mass spectrometry and quantitative proteomics Vermeulen and Selbach 765

or inhibits binding of another. The post-transcriptional

circuitry emerging seems to be at least as complex as the

much better studied transcription factor networks. Until

recently, regulation at the level of translation was limited to

artificial reporter assays. Now, mass spectrometry-based

proteomics can assess cellular translation at the global scale.

The signal transduction pathways that link differentiation

cues to changes in post-transcriptional regulation are still

poorly characterised. Systematic identification of the

involved RBPs including their post-translational modifi-

cations will greatly facilitate this endeavour.

ConclusionsIn this review, we have highlighted several recent studies

that illustrate the applicability of quantitative proteomics

to study complex biological phenomena such as cell differ-

entiation. Given the increasing sensitivity of modern mass

spectrometers, quantitative information is essential to dis-

criminate between regulated proteins and non-regulated

ones following cell stimulation in any given experiment.

To obtain a comprehensive systems wide view of cell

differentiation, multiple levels of regulation should ideally

be studied in parallel. This can be achieved by combining

quantitative proteomics with deep sequencing approaches,

including recently developed ribosome profiling technol-

ogy that allows for genome-wide analysis of RNA trans-

lation at nucleotide resolution [49��]. Furthermore, it

would be desirable to study cell differentiation processes

in whole organisms rather than making use of established

cell lines which have only a limited potential to reveal the

relevant regulatory mechanisms that play a role during the

development of multi-cellular eukaryotes. Stable isotope

labelling of entire model organisms like mouse, rat, worms

and flies will facilitate in vivo quantitative proteomics

[50��,51,52]. Some 10 years after completion of the human

genome sequence the post-genomic revolution is in full

flow and we foresee a continuing central role for mass

spectrometry-based proteomics herein as a ‘Swiss army

knife’ (see Figure 2), capable of tackling many different

aspects of complex biological systems.

AcknowledgementsThe Vermeulen lab is supported by a grant from the Netherlands GenomicsInitiative/Netherlands Organization for Scientific Research. The Selbachlab receives funding from the Helmholtz Association and the NationalGenome Research Network of the German Federal Ministry of Educationand Research.

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This paper describes novel technology based on deep sequencing ofribosome protected mRNA fragments that can be used to study the rateof RNA translation in a global manner.

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Quantitative proteomics: a tool to assess cell differentiation

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