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Proteomic analysis of cell fate decision
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Proteomic analysis of cell fate decision Jenny Hansson and Jeroen Krijgsveld The field of proteomics is progressing at a rapid pace, developing from primarily a specialist technology to a valuable tool in biological research. Importantly, the establishment of mass spectrometry as a quantitative method, miniaturisation of liquid chromatography techniques, and improved sensitivity of mass-spectrometric instrumentation now enable nearcomplete monitoring of cellular proteome dynamics. An increasing number of studies are therefore now applying quantitative proteomics to study proteins and posttranslational modifications in stem cells, to reveal molecular mechanisms and pathways underlying pluripotency, differentiation and reprogramming. Addresses European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany Corresponding author: Krijgsveld, Jeroen ([email protected])
Current Opinion in Genetics & Development 2013, 23:540–547
levels and posttranslational modifications in a comprehensive and quantitative manner, however the tools to achieve this goal have been lacking until recently. The field of mass spectrometry-based proteomics has progressed rapidly over the past few years, now enabling the routine characterization of 5000 proteins in single samples [9,10]. This even extends to >10,000 proteins in cases where availability of sample and mass spectrometry time are not restricted, thus creating data sets that are thought to represent complete cellular proteomes [11,12]. This evolution is due to several key advances in instrumentation and methodology (Box 1) and their combination into integrated workflows (Figure 1). Although there are multiple variations in this basic workflow [13], the key point is that they all aim to generate dense and unbiased data sets representing a large proportion of the proteome. This is an important prerequisite for subsequent bioinformatic analyses to derive biologically meaningful information.
This review comes from a themed issue on Cell reprogramming Edited by Huck Hui Ng and Patrick Tam For a complete overview see the Issue and the Editorial Available online 10th August 2013 0959-437X/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gde.2013.06.004
Introduction Stem cell biology is currently one of the most active and fast-progressing areas in biology. This is driven by the notion that principles of self-renewal, pluripotency and differentiation are fundamental to the earliest stages of human development as well as to diseases such as cancer. The prospect of using stem cells in regenerative medicine is another motivation explaining intense efforts to characterise molecular mechanisms underlying stem cell plasticity, including reprogramming. Numerous transcriptomic and epigenetic studies have revealed transcriptional profiles and chromatin states of stem cells and differentiating cells [1–5]. However, it is becoming increasingly clear that mRNA levels poorly correlate with protein abundance [6], and that during early differentiation expression of multiple proteins is regulated posttranscriptionally [7,8]. In addition, the functioning of a protein can be modulated by posttranslational modifications (PTMs), including phosphorylation, acetylation, methylation, ubiquitination and sumoylation. Clearly, to fully understand the mechanistic details of stem cell dynamics there is a strong need for monitoring protein Current Opinion in Genetics & Development 2013, 23:540–547
In this review, we discuss recent advances in the field of stem cell proteomics, with an emphasis on studies of the last two years that have taken a quantitative time course analysis approach to understand changes in cell fate.
Proteomic analysis of cell differentiation and reprogramming Quantitative proteomics has been extensively used to compare cellular states in a binary fashion [9,17–20]. For instance, a number of studies have compared iPSCs and ESCs to assess if the functional similarity of these cells is also reflected in their proteomes. Kim and colleagues compared human newborn foreskin fibroblasts (hFFs), hiPSCs and hESCs by 2D-gel electrophoresis [21]. Although this approach suffers from low sensitivity thus excluding many low-abundant proteins, the study showed that 15 proteins were differentially expressed between hESCs and hiPSCs. Two other groups have achieved much greater proteome coverage of hESCs and hiPSCS, using stable isotope labelling, SCX chromatography for peptide fractionation and high-massaccuracy MS [10,22]. Phanstiel and colleagues compared four hESC and four hiPSC lines, while Munoz et al. performed two experiments each comparing an hiPSCs cell line to its precursor cell line and to hESCs. Although both studies achieved great depth identifying 6761 [10] and 10,628 proteins [22] only 2234 and 2683 proteins were quantified in all replicates in the respective studies. This represents only 25–33% of the proteins identified, thus excluding the majority from the statistical analysis. Nevertheless, 293 and 58 proteins were found to be www.sciencedirect.com
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Box 1 Major advances in proteomic technologies Achieving full coverage of cellular proteomes is challenged by two major factors inherent to most biological samples: proteome complexity and dynamic range. Facing this problem, a number of developments in methodology and instrument design, ranging from sample preparation to chromatography and mass spectrometry have contributed to the improved analytical power of proteomic workflows. When used in combination, they now permit to dig deep into the proteome identifying and quantifying proteins even in the lowest abundance range and in small sample sizes (10,000–100,000 cells). The most salient advances are listed below, the following reviews and references therein provide an excellent entry point for a more detailed overview: [13–16]. Sample preparation Protocols for generic and unbiased extraction of proteins from cells. Integration of protein isolation and digestion in miniaturized devices (on-filter, on-column). Stable isotope labelling approaches for protein quantification (e.g. SILAC, TMT, iTRAQ). Peptide fractionation techniques (e.g. SCX, IEF, HILIC) to reduce sample complexity thereby increasing overall sampling depth. Enrichment of posttranslationally modified proteins (e.g. TiO2 and IMAC for phosphopeptides; modification-specific antibodies). Chromatography Miniaturization of liquid chromatography columns (20–75 mm internal diameter) to enhance sensitivity. Decreased particle size of chromatographic sorbents (1.5–5 mm) and extended column length (30–50 cm) to improve peak shape and peak capacity. Ultra performance liquid chromatography (UPLC) systems delivering consistent low flow rates (100–300 nl/min). Mass spectrometry Efficient ion transfer optics improving overall sensitivity. Fast peptide fragmentation (10 Hz) boosting the number of peptide identifications per time unit. Availability of various modes of peptide fragmentation (CID, HCD, ETD) with overlapping performance for peptide subsets (e.g. with/ without PTMs). Sensitive detection with high resolution and high mass accuracy for protein identification at low false discovery rate (e.g. Orbitrap, time-of-flight). Bioinformatics Integrated workflows for raw data processing, peak detection, protein identification and quantification, and data quality evaluation (e.g. MaxQuant, Proteome Discoverer).
differentially expressed between hESCs and hiPSCs with statistical significance, respectively, however with low overlap between the two studies [23]. Importantly, the study by Phanstiel and colleagues showed that expanding the number of replicate analyses from 1 to 3 increased the number of differentially expressed proteins detected with statistical significance from 5 to 293 proteins [22]. This is a clear demonstration of how experimental design enables more powerful statistical evaluation, thus determining the overall outcome of the study especially when proteomic www.sciencedirect.com
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differences are small. Another remaining question emerging from these studies is whether the small overlap between the data sets is due to the use of different cell lines, different technologies and instrumentation, or both. This touches on a general conundrum in the field where data produced in one lab cannot always be fully reproduced in another lab, because of differences in data collection or processing. Determining proteome changes in cells during their transition from one state to another is best done in a time-series to capture temporal dynamics of protein expression. Several such studies have been conducted recently, in different contexts of cellular differentiation and development [24–26,27,28–31,32]. To save in mass spectrometry time, multiplexed approaches using isobaric mass tags (e.g. TMT or iTRAQ) have been popularly used, offering the capability to compare several proteomic states (e.g. up to eight time points) in a single experiment. However, these approaches still suffer from decreased proteome sampling depth [33] and quantification accuracy [34], for which improvements have started to emerge [35] but are not yet fully resolved [36]. iTRAQ has been used to investigate proteome dynamics during ESC differentiation by embroid body formation [37] or by transfer of ESCs to medium that lacked the factors needed for stem cell maintenance [38]. While these studies led to the characterisation of 575 and 1032 protein expression profiles, respectively, a greater depth has been achieved in the analysis of differentiation of ESCs into oligodendrocyte progenitor cells [39]. Here, 4-plex iTRAQ combined with SCX fractionation and analysis by high-resolution nano LC–MS/MS resulted in the quantification of about 3000 proteins, including novel markers (e.g. AMBRA1, NID1 and CRYAB) of different steps during oligodendrocyte differentiation. The first proteomic study of cellular reprogramming was recently reported [40], monitoring the dynamics of protein expression over time, from fibroblasts to the induced pluripotent state (Figure 2). Specifically, FACSsorting on the basis of Thy1, SSEA1 and Oct4-GFP expression was used to collect cells destined to reach the pluripotent state [41], at 3-day intervals over the entire 15 days of reprogramming. Stable isotope labelling via reductive dimethylation was then used to quantify proteome changes between each of the consecutive time points. Peptide fractionation by isoelectric focusing and analysis by high-resolution nano LC–MS/MS led to the quantification of close to 8000 proteins. The data revealed a two-step resetting of the proteome during the first and last three days of reprogramming, indicating that the proteome composition of the intermediate states is remarkably different from either fibroblasts or iPSCs. In addition, the study showed that proteins within complexes and protein families change in abundance in a highly synchronous fashion, suggesting a shared regulatory mechanism. Current Opinion in Genetics & Development 2013, 23:540–547
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Quantitative proteomic workflow. A typical proteomic experiment for comparison of cellular proteomes consists of lysis of collected cells, protein extraction, enzymatic digestion of proteins into peptides, followed by a fractionation step to reduce the complexity of the peptide mixture. If a specific class of peptides is targeted, for example, phosphorylated peptides, enrichment strategies are employed. Each peptide fraction is then analysed by reversed-phase liquid chromatography coupled to mass spectrometry (LC–MS), in which peptides are fragmented by tandem MS (MS/MS). Search algorithms are used to match experimental to theoretical fragmentation patterns of peptide sequences predicted from the genome sequence, thus leading to peptide and protein identity. Protein quantification is typically achieved through the use of stable isotope labels, incorporated either metabolically at the protein level or chemically at the peptide level. A multitude of bioinformatic approaches can then be used to interpret the data and to infer biological insight by expression, cluster and network analysis. Current Opinion in Genetics & Development 2013, 23:540–547
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Quantitative proteomic analysis of cellular reprogramming. (a) Cells undergoing reprogramming to pluripotency were FACS-sorted at three-day intervals. Extracted proteins were digested, labelled by stable isotopes, fractionated via isoelectric focusing and analysed by LC–MS/MS. Close to 8000 proteins were identified, which were analysed bioinformatically to derive insights at various levels: (b) grouping all proteins in a heatmap revealed large protein abundance changes early and late during reprogramming, while small changes occurred in the intermediate phase. (c) Clustering of proteins with common expression profile along reprogramming revealed enrichment of biological processes within each cluster. (d) Proteins within protein complexes and families showed strongly coordinated expression changes during reprogramming. (e) In stark contrast to the rest of the 26 quantified nuclear pore proteins, Nup210 was strongly increased, a phenomenon that was demonstrated to be essential for reprogramming (adapted from Ref. [40]). www.sciencedirect.com
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Phosphoproteomic analysis of early ESC differentiation. (a) ESCs were cultured in SILAC medium and then induced to differentiate in a nondirected manner. After protein extraction and digestion, peptides were fractionated with SCX, followed by phosphopeptide enrichment using TiO2 and analysis by LC–MS/MS. (b) Among 6500 proteins and 15,000 phosphopeptides, changes in the phosphoproteome were more extensive than the changes in protein abundance. (c) Extensive changes in phoshorylation was found in the N-terminal regions of DNA-methyltransferases (DNMTs). (d) A specific interaction of DNMTs with the PAF1 transcriptional elongation complex (PAF1C) was found during early differentiation (adapted from Ref. [32]).
Exceptions to this pattern, where the expression profile of a single protein deviates from the rest of the proteins within a complex, was proposed to indicate a specific functionality during reprogramming. Such a deviating pattern was observed for the nuclear pore protein Nup210, which was indeed demonstrated to have a critical regulatory role in cell cycle progression and reprogramming, evidenced by the failure to generate iPSCs upon knock-down of Nup210 (Figure 2) [40].
Posttranslational modifications during stem cell differentiation A major benefit of mass spectrometry-based proteomics is its potential to identify posttranslational modifications. Phosphorylation is among the most prominent examples, fulfilling an important role in cell signalling. Since the stoichiometry of protein phosphorylation is typically very low, this has triggered the development of enrichment strategies to facilitate the detection of phosphorylated peptides, finding their way to phosphoproteome analysis in stem cells [22,42–45]. Current Opinion in Genetics & Development 2013, 23:540–547
First insight into global temporal phosphorylation dynamics in hESCs was provided in a study applying quantitative MS to analyse the phosphoproteome of hESCs during the first four hours of bone morphogenic protein (BMP)-induced differentiation [45]. Amongst others, this revealed that Sox2, a key pluripotency factor, is itself subject to phosphorylation. An expanded understanding was provided more recently by Rigbolt and colleagues, who performed quantitative proteomic and phosphoproteomic analyses of hESCs during the first 24 hours after induction of nondirected hESC differentiation [32]. Importantly, two distinct differentiation protocols allowed for discrimination between treatment-specific and common events associated with induction of differentiation. Using a SILAC-approach combined with SCX fractionation, TiO2 phosphopeptide enrichment and high-resolution LC–MS/MS, an in-depth phosphoproteomic dataset was achieved, identifying 6521 proteins and mapping 14,865 phosphopeptides (Figure 3). In addition to highlighting dynamic phosphorylation on DNA methyltransferases, the data revealed that alteration of the phosphoproteome was www.sciencedirect.com
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more extensive than the changes in protein abundance (30– 45% sites showed a change in phosphorylation status while 17–19% proteins changed in abundance) (Figure 3). Indeed, of 216 identified transcription factors, including many with an established role in stem cell pluripotency and differentiation, only about 10% changed in abundance within 24 hours of differentiation, while almost half of all 714 phosphorylation sites on transcription factors changed more than twofold over the same period. Interestingly, a study investigating FGF-2-assisted stem cell maintenance also identified transcription factors as one of the largest classes of regulated phosphoproteins [46], suggesting that dynamics of phosphorylation intersects with transcription factor activity. Beyond phosphorylation, other posttranslational modifications that may regulate stem cell properties should not be neglected [47]. Recently, quantitative mass spectrometry was successfully employed to map the global changes in ubiquitination in pluripotent and differentiated ESCs, identifying multiple members of the core pluripotency machinery to be ubiquitinylated [48]. The authors therefore suggest that the ubiquitin-proteasome system is an additional mode of regulation of stem cell pluripotency.
Conclusions and outlook Quantitative proteomics is still a young field where continuous technical and methodological advances indicate that full maturation has not been reached yet. Future developments will continue to focus on increasing sensitivity and throughput to maximize the information content obtained from small cell populations. Future trends facilitated by increased speed and sensitivity of mass spectrometers will likely include minimizing the number of sample handling steps [49] to avoid protein losses along the way. Importantly, this will permit a decrease in the number of cells required for in-depth proteome analysis, opening the way to analyse highly purified rare cell populations obtained by FACS-sorting or tissue micro-dissection [50]. In addition, further development of approaches for multiplexed data collection will facilitate dynamic proteome profiling across multiple time points or conditions [51,52]. All of this will help to bridge proteomic methodologies to in vitro and in vivo systems used in stem cell biology, thereby enabling the identification of markers and mechanisms pertinent to many facets of cell fate decision.
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Proteomic analysis of cell fate decision Jenny Hansson and Jeroen Krijgsveld The field of proteomics is progressing at a rapid pace, developing from primarily a specialist technology to a valuable tool in biological research. Importantly, the establishment of mass spectrometry as a quantitative method, miniaturisation of liquid chromatography techniques, and improved sensitivity of mass-spectrometric instrumentation now enable nearcomplete monitoring of cellular proteome dynamics. An increasing number of studies are therefore now applying quantitative proteomics to study proteins and posttranslational modifications in stem cells, to reveal molecular mechanisms and pathways underlying pluripotency, differentiation and reprogramming. Addresses European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany Corresponding author: Krijgsveld, Jeroen ([email protected])
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levels and posttranslational modifications in a comprehensive and quantitative manner, however the tools to achieve this goal have been lacking until recently. The field of mass spectrometry-based proteomics has progressed rapidly over the past few years, now enabling the routine characterization of 5000 proteins in single samples [9,10]. This even extends to >10,000 proteins in cases where availability of sample and mass spectrometry time are not restricted, thus creating data sets that are thought to represent complete cellular proteomes [11,12]. This evolution is due to several key advances in instrumentation and methodology (Box 1) and their combination into integrated workflows (Figure 1). Although there are multiple variations in this basic workflow [13], the key point is that they all aim to generate dense and unbiased data sets representing a large proportion of the proteome. This is an important prerequisite for subsequent bioinformatic analyses to derive biologically meaningful information.
This review comes from a themed issue on Cell reprogramming Edited by Huck Hui Ng and Patrick Tam For a complete overview see the Issue and the Editorial Available online 10th August 2013 0959-437X/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gde.2013.06.004
Introduction Stem cell biology is currently one of the most active and fast-progressing areas in biology. This is driven by the notion that principles of self-renewal, pluripotency and differentiation are fundamental to the earliest stages of human development as well as to diseases such as cancer. The prospect of using stem cells in regenerative medicine is another motivation explaining intense efforts to characterise molecular mechanisms underlying stem cell plasticity, including reprogramming. Numerous transcriptomic and epigenetic studies have revealed transcriptional profiles and chromatin states of stem cells and differentiating cells [1–5]. However, it is becoming increasingly clear that mRNA levels poorly correlate with protein abundance [6], and that during early differentiation expression of multiple proteins is regulated posttranscriptionally [7,8]. In addition, the functioning of a protein can be modulated by posttranslational modifications (PTMs), including phosphorylation, acetylation, methylation, ubiquitination and sumoylation. Clearly, to fully understand the mechanistic details of stem cell dynamics there is a strong need for monitoring protein Current Opinion in Genetics & Development 2013, 23:540–547
In this review, we discuss recent advances in the field of stem cell proteomics, with an emphasis on studies of the last two years that have taken a quantitative time course analysis approach to understand changes in cell fate.
Proteomic analysis of cell differentiation and reprogramming Quantitative proteomics has been extensively used to compare cellular states in a binary fashion [9,17–20]. For instance, a number of studies have compared iPSCs and ESCs to assess if the functional similarity of these cells is also reflected in their proteomes. Kim and colleagues compared human newborn foreskin fibroblasts (hFFs), hiPSCs and hESCs by 2D-gel electrophoresis [21]. Although this approach suffers from low sensitivity thus excluding many low-abundant proteins, the study showed that 15 proteins were differentially expressed between hESCs and hiPSCs. Two other groups have achieved much greater proteome coverage of hESCs and hiPSCS, using stable isotope labelling, SCX chromatography for peptide fractionation and high-massaccuracy MS [10,22]. Phanstiel and colleagues compared four hESC and four hiPSC lines, while Munoz et al. performed two experiments each comparing an hiPSCs cell line to its precursor cell line and to hESCs. Although both studies achieved great depth identifying 6761 [10] and 10,628 proteins [22] only 2234 and 2683 proteins were quantified in all replicates in the respective studies. This represents only 25–33% of the proteins identified, thus excluding the majority from the statistical analysis. Nevertheless, 293 and 58 proteins were found to be www.sciencedirect.com
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Box 1 Major advances in proteomic technologies Achieving full coverage of cellular proteomes is challenged by two major factors inherent to most biological samples: proteome complexity and dynamic range. Facing this problem, a number of developments in methodology and instrument design, ranging from sample preparation to chromatography and mass spectrometry have contributed to the improved analytical power of proteomic workflows. When used in combination, they now permit to dig deep into the proteome identifying and quantifying proteins even in the lowest abundance range and in small sample sizes (10,000–100,000 cells). The most salient advances are listed below, the following reviews and references therein provide an excellent entry point for a more detailed overview: [13–16]. Sample preparation Protocols for generic and unbiased extraction of proteins from cells. Integration of protein isolation and digestion in miniaturized devices (on-filter, on-column). Stable isotope labelling approaches for protein quantification (e.g. SILAC, TMT, iTRAQ). Peptide fractionation techniques (e.g. SCX, IEF, HILIC) to reduce sample complexity thereby increasing overall sampling depth. Enrichment of posttranslationally modified proteins (e.g. TiO2 and IMAC for phosphopeptides; modification-specific antibodies). Chromatography Miniaturization of liquid chromatography columns (20–75 mm internal diameter) to enhance sensitivity. Decreased particle size of chromatographic sorbents (1.5–5 mm) and extended column length (30–50 cm) to improve peak shape and peak capacity. Ultra performance liquid chromatography (UPLC) systems delivering consistent low flow rates (100–300 nl/min). Mass spectrometry Efficient ion transfer optics improving overall sensitivity. Fast peptide fragmentation (10 Hz) boosting the number of peptide identifications per time unit. Availability of various modes of peptide fragmentation (CID, HCD, ETD) with overlapping performance for peptide subsets (e.g. with/ without PTMs). Sensitive detection with high resolution and high mass accuracy for protein identification at low false discovery rate (e.g. Orbitrap, time-of-flight). Bioinformatics Integrated workflows for raw data processing, peak detection, protein identification and quantification, and data quality evaluation (e.g. MaxQuant, Proteome Discoverer).
differentially expressed between hESCs and hiPSCs with statistical significance, respectively, however with low overlap between the two studies [23]. Importantly, the study by Phanstiel and colleagues showed that expanding the number of replicate analyses from 1 to 3 increased the number of differentially expressed proteins detected with statistical significance from 5 to 293 proteins [22]. This is a clear demonstration of how experimental design enables more powerful statistical evaluation, thus determining the overall outcome of the study especially when proteomic www.sciencedirect.com
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differences are small. Another remaining question emerging from these studies is whether the small overlap between the data sets is due to the use of different cell lines, different technologies and instrumentation, or both. This touches on a general conundrum in the field where data produced in one lab cannot always be fully reproduced in another lab, because of differences in data collection or processing. Determining proteome changes in cells during their transition from one state to another is best done in a time-series to capture temporal dynamics of protein expression. Several such studies have been conducted recently, in different contexts of cellular differentiation and development [24–26,27,28–31,32]. To save in mass spectrometry time, multiplexed approaches using isobaric mass tags (e.g. TMT or iTRAQ) have been popularly used, offering the capability to compare several proteomic states (e.g. up to eight time points) in a single experiment. However, these approaches still suffer from decreased proteome sampling depth [33] and quantification accuracy [34], for which improvements have started to emerge [35] but are not yet fully resolved [36]. iTRAQ has been used to investigate proteome dynamics during ESC differentiation by embroid body formation [37] or by transfer of ESCs to medium that lacked the factors needed for stem cell maintenance [38]. While these studies led to the characterisation of 575 and 1032 protein expression profiles, respectively, a greater depth has been achieved in the analysis of differentiation of ESCs into oligodendrocyte progenitor cells [39]. Here, 4-plex iTRAQ combined with SCX fractionation and analysis by high-resolution nano LC–MS/MS resulted in the quantification of about 3000 proteins, including novel markers (e.g. AMBRA1, NID1 and CRYAB) of different steps during oligodendrocyte differentiation. The first proteomic study of cellular reprogramming was recently reported [40], monitoring the dynamics of protein expression over time, from fibroblasts to the induced pluripotent state (Figure 2). Specifically, FACSsorting on the basis of Thy1, SSEA1 and Oct4-GFP expression was used to collect cells destined to reach the pluripotent state [41], at 3-day intervals over the entire 15 days of reprogramming. Stable isotope labelling via reductive dimethylation was then used to quantify proteome changes between each of the consecutive time points. Peptide fractionation by isoelectric focusing and analysis by high-resolution nano LC–MS/MS led to the quantification of close to 8000 proteins. The data revealed a two-step resetting of the proteome during the first and last three days of reprogramming, indicating that the proteome composition of the intermediate states is remarkably different from either fibroblasts or iPSCs. In addition, the study showed that proteins within complexes and protein families change in abundance in a highly synchronous fashion, suggesting a shared regulatory mechanism. Current Opinion in Genetics & Development 2013, 23:540–547
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Quantitative proteomic workflow. A typical proteomic experiment for comparison of cellular proteomes consists of lysis of collected cells, protein extraction, enzymatic digestion of proteins into peptides, followed by a fractionation step to reduce the complexity of the peptide mixture. If a specific class of peptides is targeted, for example, phosphorylated peptides, enrichment strategies are employed. Each peptide fraction is then analysed by reversed-phase liquid chromatography coupled to mass spectrometry (LC–MS), in which peptides are fragmented by tandem MS (MS/MS). Search algorithms are used to match experimental to theoretical fragmentation patterns of peptide sequences predicted from the genome sequence, thus leading to peptide and protein identity. Protein quantification is typically achieved through the use of stable isotope labels, incorporated either metabolically at the protein level or chemically at the peptide level. A multitude of bioinformatic approaches can then be used to interpret the data and to infer biological insight by expression, cluster and network analysis. Current Opinion in Genetics & Development 2013, 23:540–547
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Quantitative proteomic analysis of cellular reprogramming. (a) Cells undergoing reprogramming to pluripotency were FACS-sorted at three-day intervals. Extracted proteins were digested, labelled by stable isotopes, fractionated via isoelectric focusing and analysed by LC–MS/MS. Close to 8000 proteins were identified, which were analysed bioinformatically to derive insights at various levels: (b) grouping all proteins in a heatmap revealed large protein abundance changes early and late during reprogramming, while small changes occurred in the intermediate phase. (c) Clustering of proteins with common expression profile along reprogramming revealed enrichment of biological processes within each cluster. (d) Proteins within protein complexes and families showed strongly coordinated expression changes during reprogramming. (e) In stark contrast to the rest of the 26 quantified nuclear pore proteins, Nup210 was strongly increased, a phenomenon that was demonstrated to be essential for reprogramming (adapted from Ref. [40]). www.sciencedirect.com
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Phosphoproteomic analysis of early ESC differentiation. (a) ESCs were cultured in SILAC medium and then induced to differentiate in a nondirected manner. After protein extraction and digestion, peptides were fractionated with SCX, followed by phosphopeptide enrichment using TiO2 and analysis by LC–MS/MS. (b) Among 6500 proteins and 15,000 phosphopeptides, changes in the phosphoproteome were more extensive than the changes in protein abundance. (c) Extensive changes in phoshorylation was found in the N-terminal regions of DNA-methyltransferases (DNMTs). (d) A specific interaction of DNMTs with the PAF1 transcriptional elongation complex (PAF1C) was found during early differentiation (adapted from Ref. [32]).
Exceptions to this pattern, where the expression profile of a single protein deviates from the rest of the proteins within a complex, was proposed to indicate a specific functionality during reprogramming. Such a deviating pattern was observed for the nuclear pore protein Nup210, which was indeed demonstrated to have a critical regulatory role in cell cycle progression and reprogramming, evidenced by the failure to generate iPSCs upon knock-down of Nup210 (Figure 2) [40].
Posttranslational modifications during stem cell differentiation A major benefit of mass spectrometry-based proteomics is its potential to identify posttranslational modifications. Phosphorylation is among the most prominent examples, fulfilling an important role in cell signalling. Since the stoichiometry of protein phosphorylation is typically very low, this has triggered the development of enrichment strategies to facilitate the detection of phosphorylated peptides, finding their way to phosphoproteome analysis in stem cells [22,42–45]. Current Opinion in Genetics & Development 2013, 23:540–547
First insight into global temporal phosphorylation dynamics in hESCs was provided in a study applying quantitative MS to analyse the phosphoproteome of hESCs during the first four hours of bone morphogenic protein (BMP)-induced differentiation [45]. Amongst others, this revealed that Sox2, a key pluripotency factor, is itself subject to phosphorylation. An expanded understanding was provided more recently by Rigbolt and colleagues, who performed quantitative proteomic and phosphoproteomic analyses of hESCs during the first 24 hours after induction of nondirected hESC differentiation [32]. Importantly, two distinct differentiation protocols allowed for discrimination between treatment-specific and common events associated with induction of differentiation. Using a SILAC-approach combined with SCX fractionation, TiO2 phosphopeptide enrichment and high-resolution LC–MS/MS, an in-depth phosphoproteomic dataset was achieved, identifying 6521 proteins and mapping 14,865 phosphopeptides (Figure 3). In addition to highlighting dynamic phosphorylation on DNA methyltransferases, the data revealed that alteration of the phosphoproteome was www.sciencedirect.com
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more extensive than the changes in protein abundance (30– 45% sites showed a change in phosphorylation status while 17–19% proteins changed in abundance) (Figure 3). Indeed, of 216 identified transcription factors, including many with an established role in stem cell pluripotency and differentiation, only about 10% changed in abundance within 24 hours of differentiation, while almost half of all 714 phosphorylation sites on transcription factors changed more than twofold over the same period. Interestingly, a study investigating FGF-2-assisted stem cell maintenance also identified transcription factors as one of the largest classes of regulated phosphoproteins [46], suggesting that dynamics of phosphorylation intersects with transcription factor activity. Beyond phosphorylation, other posttranslational modifications that may regulate stem cell properties should not be neglected [47]. Recently, quantitative mass spectrometry was successfully employed to map the global changes in ubiquitination in pluripotent and differentiated ESCs, identifying multiple members of the core pluripotency machinery to be ubiquitinylated [48]. The authors therefore suggest that the ubiquitin-proteasome system is an additional mode of regulation of stem cell pluripotency.
Conclusions and outlook Quantitative proteomics is still a young field where continuous technical and methodological advances indicate that full maturation has not been reached yet. Future developments will continue to focus on increasing sensitivity and throughput to maximize the information content obtained from small cell populations. Future trends facilitated by increased speed and sensitivity of mass spectrometers will likely include minimizing the number of sample handling steps [49] to avoid protein losses along the way. Importantly, this will permit a decrease in the number of cells required for in-depth proteome analysis, opening the way to analyse highly purified rare cell populations obtained by FACS-sorting or tissue micro-dissection [50]. In addition, further development of approaches for multiplexed data collection will facilitate dynamic proteome profiling across multiple time points or conditions [51,52]. All of this will help to bridge proteomic methodologies to in vitro and in vivo systems used in stem cell biology, thereby enabling the identification of markers and mechanisms pertinent to many facets of cell fate decision.
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49. Mann M, Kulak NA, Nagaraj N, Cox J: The coming age of complete, accurate, and ubiquitous proteomes. Mol Cell 2013, 49:583-590. 50. Altelaar AFM, Heck AJR: Trends in ultrasensitive proteomics. Curr Opin Chem Biol 2012, 16:206-213. 51. Dephoure N, Gygi SP: Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the
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dynamic response to rapamycin in yeast. Sci Signal 2012, 5:rs2. 52. McAlister GC, Huttlin EL, Haas W, Ting L, Jedrychowski MP, Rogers JC, Kuhn K, Pike I, Grothe RA, Blethrow JD et al.: Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal Chem 2012, 84:7469-7478.
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