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Quantitative proteomics: a tool to assess cell differentiation
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Quantitative proteomics: a tool to assess cell differentiation Michiel 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 posttranslational modifications of core histones and posttranscriptional 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. Addresses 1 Department of Physiological Chemistry and Cancer Genomics Centre, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands 2 Max Delbru¨ck Center for Molecular Medicine, Robert-Ro¨ssle-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
Introduction Cell 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 sequencing 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 spectrometry-based proteomics can potentially fill this gap. Recent advances in instrumentation, software and quantification now allow comprehensive analyses of cell differentiation processes by mass spectrometry. The most straightforward way of studying cell differentiation using mass spectrometry is to investigate how the www.sciencedirect.com
proteome changes when cells differentiate. This strategy can be applied to all models of cell differentiation including 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 transcription 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 discussing all of them is beyond the scope of this review. Instead, we will focus on chromatin dynamics and posttranscriptional 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 proteomics Early 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 electrospray 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). Modern instruments are capable of fragmenting several peptides per second. Both the MS and MS/MS spectra contain information about the peptides that can be used Current Opinion in Cell Biology 2009, 21:761–766
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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 quantitative information at the proteomic scale. Mass spectrometry 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 essential amino acids [15]. The general concept is that introducing 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 intensities reflects relative differences in abundance of the corresponding proteins between both samples. Combined with the workflow described above, mass spectrometrybased quantitative proteomics can assess the dynamics of Current Opinion in Cell Biology 2009, 21:761–766
protein abundance and PTMs occurring during differentiation events, as illustrated below.
Chromatin dynamics during differentiation In 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 as DNA repair, replication and transcription. 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 information. 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 complexes 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 recognition could be negatively affected www.sciencedirect.com
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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 powerful toolbox that can be used to identify PTMs on proteins, including core histones [12,19,20]. Recent computational and instrumental advances in mass spectrometry technology 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]. Phanstiel et al. used an ETD enabled LTQ-Orbitrap and a label free quantitation approach that was first used by the Kelleher lab [22] to study histone H4 PTM dynamics upon TPA induced stem cell differentiation [23]. They identified and quantified 74 unique histone H4 molecules carrying 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 combinatorial 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 gradually 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 can be used to identify such PTM-dependent interactions. Unmodified and modified histone tail peptides 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].
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 spectrometry [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 perturbation or during differentiation, including the histone PTMs that are associated with the locus.
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 proliferation and differentiation. However, one major drawback of this approach is the fact that bulk histones are studied. Therefore, one can only identify global genomewide changes in histone modification patterns. It would however be desirable to use MS-based quantitative proteomics to study such dynamics for isolated genomic loci.
A popular method to study RNA–protein interactions is to purify proteins and identify associated RNAs by microarrays or deep sequencing using methods such as crosslinking immunoprecipitation (CLIP) or RNP immunoprecipitation (RIP) [35,36]. In a complementary approach, mass spectrometry can be employed to systematically identify proteins associated with a purified RNA. Unbiased screening for proteins has the advantage that RBPs can be identified that would never have been
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Post-transcriptional regulation Once 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 drastically 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 regulating 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 tissues [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 expression in the worm germline [34].
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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-linking, 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 outlined 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 contaminants 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-pulldown experiments is to precipitate RNPs via known protein components. For example, purification of Argonaute-associated proteins was used to identify new components of the RNA-induced silencing complex (RISC) and cytoplasmic processing bodies (P-bodies) [42,43]. In the future, quantitative methods will facilitate differentiation between bona fide RNP components and contaminants [44]. In addition, quantitative proteomics can assess
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 degradation 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 regulation 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
Figure 2
Global approaches to assess cell differentiation. Proteomics and transcriptomics can provide valuable information at different levels. Numbers indicate references of individual approaches. For more details see text. Current Opinion in Cell Biology 2009, 21:761–766
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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 modifications will greatly facilitate this endeavour.
embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics 2008, 7:672-683. 3.
Prokhorova TA, Rigbolt KT, Johansen PT, Henningsen J, Kratchmarova I, Kassem M, Blagoev B: Stable isotope labeling by amino acids in cell culture (SILAC) and quantitative comparison of the membrane proteomes of self-renewing and differentiating human embryonic stem cells. Mol Cell Proteomics 2009, 8:959-970.
4.
Cui Z, Chen X, Lu B, Park SK, Xu T, Xie Z, Xue P, Hou J, Hang H, Yates JR 3rd et al.: Preliminary quantitative profile of differential protein expression between rat L6 myoblasts and myotubes by stable isotope labeling with amino acids in cell culture. Proteomics 2009, 9:1274-1292.
5.
Molina H, Yang Y, Ruch T, Kim JW, Mortensen P, Otto T, Nalli A, Tang QQ, Lane MD, Chaerkady R et al.: Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J Proteome Res 2009, 8:48-58.
6.
Filen JJ, Filen S, Moulder R, Tuomela S, Ahlfors H, West A, Kouvonen P, Kantola S, Bjorkman M, Katajamaa M et al.: Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation. Mol Cell Proteomics 2009, 8:32-44.
7.
Cox J, Mann M: Is proteomics the new genomics? Cell 2007, 130:395-398.
8.
Cravatt BF, Simon GM, Yates JR 3rd: The biological impact of mass-spectrometry-based proteomics. Nature 2007, 450:991-1000.
9.
Zubarev R, Mann M: On the proper use of mass accuracy in proteomics. Mol Cell Proteomics 2007, 6:377-381.
Conclusions In this review, we have highlighted several recent studies that illustrate the applicability of quantitative proteomics to study complex biological phenomena such as cell differentiation. Given the increasing sensitivity of modern mass spectrometers, quantitative information is essential to discriminate 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 technology that allows for genome-wide analysis of RNA translation 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.
Acknowledgements The Vermeulen lab is supported by a grant from the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research. The Selbach lab receives funding from the Helmholtz Association and the National Genome Research Network of the German Federal Ministry of Education and Research.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest
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Quantitative proteomics: a tool to assess cell differentiation Michiel 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 posttranslational modifications of core histones and posttranscriptional 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. Addresses 1 Department of Physiological Chemistry and Cancer Genomics Centre, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands 2 Max Delbru¨ck Center for Molecular Medicine, Robert-Ro¨ssle-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
Introduction Cell 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 sequencing 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 spectrometry-based proteomics can potentially fill this gap. Recent advances in instrumentation, software and quantification now allow comprehensive analyses of cell differentiation processes by mass spectrometry. The most straightforward way of studying cell differentiation using mass spectrometry is to investigate how the www.sciencedirect.com
proteome changes when cells differentiate. This strategy can be applied to all models of cell differentiation including 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 transcription 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 discussing all of them is beyond the scope of this review. Instead, we will focus on chromatin dynamics and posttranscriptional 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 proteomics Early 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 electrospray 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). Modern instruments are capable of fragmenting several peptides per second. Both the MS and MS/MS spectra contain information about the peptides that can be used Current Opinion in Cell Biology 2009, 21:761–766
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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 quantitative information at the proteomic scale. Mass spectrometry 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 essential amino acids [15]. The general concept is that introducing 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 intensities reflects relative differences in abundance of the corresponding proteins between both samples. Combined with the workflow described above, mass spectrometrybased quantitative proteomics can assess the dynamics of Current Opinion in Cell Biology 2009, 21:761–766
protein abundance and PTMs occurring during differentiation events, as illustrated below.
Chromatin dynamics during differentiation In 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 as DNA repair, replication and transcription. 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 information. 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 complexes 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 recognition could be negatively affected www.sciencedirect.com
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 powerful toolbox that can be used to identify PTMs on proteins, including core histones [12,19,20]. Recent computational and instrumental advances in mass spectrometry technology 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]. Phanstiel et al. used an ETD enabled LTQ-Orbitrap and a label free quantitation approach that was first used by the Kelleher lab [22] to study histone H4 PTM dynamics upon TPA induced stem cell differentiation [23]. They identified and quantified 74 unique histone H4 molecules carrying 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 combinatorial 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 gradually 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 can be used to identify such PTM-dependent interactions. Unmodified and modified histone tail peptides 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].
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 spectrometry [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 perturbation or during differentiation, including the histone PTMs that are associated with the locus.
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 proliferation and differentiation. However, one major drawback of this approach is the fact that bulk histones are studied. Therefore, one can only identify global genomewide changes in histone modification patterns. It would however be desirable to use MS-based quantitative proteomics to study such dynamics for isolated genomic loci.
A popular method to study RNA–protein interactions is to purify proteins and identify associated RNAs by microarrays or deep sequencing using methods such as crosslinking immunoprecipitation (CLIP) or RNP immunoprecipitation (RIP) [35,36]. In a complementary approach, mass spectrometry can be employed to systematically identify proteins associated with a purified RNA. Unbiased screening for proteins has the advantage that RBPs can be identified that would never have been
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Post-transcriptional regulation Once 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 drastically 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 regulating 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 tissues [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 expression in the worm germline [34].
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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-linking, 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 outlined 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 contaminants 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-pulldown experiments is to precipitate RNPs via known protein components. For example, purification of Argonaute-associated proteins was used to identify new components of the RNA-induced silencing complex (RISC) and cytoplasmic processing bodies (P-bodies) [42,43]. In the future, quantitative methods will facilitate differentiation between bona fide RNP components and contaminants [44]. In addition, quantitative proteomics can assess
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 degradation 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 regulation 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
Figure 2
Global approaches to assess cell differentiation. Proteomics and transcriptomics can provide valuable information at different levels. Numbers indicate references of individual approaches. For more details see text. Current Opinion in Cell Biology 2009, 21:761–766
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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 modifications will greatly facilitate this endeavour.
embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics 2008, 7:672-683. 3.
Prokhorova TA, Rigbolt KT, Johansen PT, Henningsen J, Kratchmarova I, Kassem M, Blagoev B: Stable isotope labeling by amino acids in cell culture (SILAC) and quantitative comparison of the membrane proteomes of self-renewing and differentiating human embryonic stem cells. Mol Cell Proteomics 2009, 8:959-970.
4.
Cui Z, Chen X, Lu B, Park SK, Xu T, Xie Z, Xue P, Hou J, Hang H, Yates JR 3rd et al.: Preliminary quantitative profile of differential protein expression between rat L6 myoblasts and myotubes by stable isotope labeling with amino acids in cell culture. Proteomics 2009, 9:1274-1292.
5.
Molina H, Yang Y, Ruch T, Kim JW, Mortensen P, Otto T, Nalli A, Tang QQ, Lane MD, Chaerkady R et al.: Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J Proteome Res 2009, 8:48-58.
6.
Filen JJ, Filen S, Moulder R, Tuomela S, Ahlfors H, West A, Kouvonen P, Kantola S, Bjorkman M, Katajamaa M et al.: Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation. Mol Cell Proteomics 2009, 8:32-44.
7.
Cox J, Mann M: Is proteomics the new genomics? Cell 2007, 130:395-398.
8.
Cravatt BF, Simon GM, Yates JR 3rd: The biological impact of mass-spectrometry-based proteomics. Nature 2007, 450:991-1000.
9.
Zubarev R, Mann M: On the proper use of mass accuracy in proteomics. Mol Cell Proteomics 2007, 6:377-381.
Conclusions In this review, we have highlighted several recent studies that illustrate the applicability of quantitative proteomics to study complex biological phenomena such as cell differentiation. Given the increasing sensitivity of modern mass spectrometers, quantitative information is essential to discriminate 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 technology that allows for genome-wide analysis of RNA translation 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.
Acknowledgements The Vermeulen lab is supported by a grant from the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research. The Selbach lab receives funding from the Helmholtz Association and the National Genome Research Network of the German Federal Ministry of Education and Research.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest
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