- Email: [email protected]
Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
Chapter 32
Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte Martin J. Pfeiffer1, Marcin Siatkowski2,3, Yogesh Paudel3, Sebastian T. Balbach1, Nicole Baeumer4, Nicola Crosetto5, Hannes C.A. Drexler6, Georg Fuellen2,3 and Michele Boiani1 1
Max-Planck-Institute for Molecular Biomedicine, Münster, Germany, 2DZNE, German Center for Neurodegenerative Disorders, Rostock, Germany,
3
Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany, 4Arrows Biomedical GmbH, Münster, Germany, 5Institute of Biochemistry II, Goethe University Medical School, Frankfurt am Main, Germany, 6Max-Planck-Institute for Molecular Biomedicine, Bioanalytical Mass Spectrometry Facility. Münster, Germany
Chapter Outline Introduction 407 Results 409 Discussion 412 Materials and Methods 414 Mice 414 Oocyte and Embryonic Stem Cell Sample Preparation and Processing for Mass Spectrometry 414 Sample Preparation 414 LC-MS/MS and Data Analysis 414
INTRODUCTION Deciphering the mechanisms underlying reprogramming is one of the major driving forces of the reprogramming field. A variety of methods do exist that enable the reversion of a somatic cell to a pluripotent state. The pioneering technology of somatic cell nuclear transfer (SCNT), i.e. the transplantation of a somatic cell nucleus into an “enucleated” oocyte, was extended by reprogramming after fusion of somatic cells with pluripotent cells. However, even several years after the development of direct reprogramming through the delivery of selected transcription factors to a cell [induced pluripotent stem (iPS) cell technology], the mechanistic bases of the process itself are still largely unclear. In terms of their applicability to studying reprogramming mechanisms, the different approaches have different advantages and disadvantages. While direct
Principles of Cloning. DOI: http://dx.doi.org/10.1016/B978-0-12-386541-0.00037-0 © 2012 2014 Elsevier Inc. All rights reserved.
Transcriptome 415 Database Search and Bioinformatics 415 Fixation of Germinal Vesicle Stage Oocytes and Fertilized Embryos and Processing for Immunofluorescence 415 Confocal Imaging and Image Analysis 416 Acknowledgements 416 References 416
reprogramming enables the easy generation of iPS cells from adult tissues, the heterogeneity of a cell population while being reprogrammed has hindered an advance in mechanistic insights. Cell fusion, while also being technically more demanding, suffers from tetraploidy in the resulting cells, which does not allow a clear picture of the reprogrammed genome. The possible solution of fusing somatic cells with pluripotent cytoplasts only leads to partial reprogramming of the somatic genome. Accordingly, the cytoplasm of an embryonic stem cell (ESC) promotes reprogramming, but does not lead to its completion. In this respect, the ooplasm is the only cytoplasm known to date that supports full reprogramming after transplantation of a somatic nucleus, and does so in a comparatively efficient and fast manner. In a routine experiment, 40% of SCNT mouse embryos develop to the blastocyst stage and
407
408
reprogramming marker reexpression can be observed after only a few cell cycles. A plethora of strategies have been applied to investigate the mechanisms of reprogramming. Many studies rely on the analysis of gene expression profiles of pools of cells sampled during the reprogramming process, thereby advancing our understanding of reprogramming on the basis of mRNA expression signatures. While these strategies are informative, they leave out a very important layer of information: Most processes within a cell depend on its proteomic makeup on protein modifications, and on their interaction partners. The addition or depletion of putative reprogramming factors or the activation/repression of potentially involved signaling pathways is a strategy that can be applied in this context and large-scale screenings have led to the discovery of new factors involved in the process. However, only few studies have followed a proteomic top-down approach in the context of finding novel proteins that facilitate reprogramming. One remarkable report published by Singhal and colleagues applied protein extracts of pluripotent ESCs to permeabilized somatic cells during the reprogramming process. The ESC protein extract was then fractionated and the fractions capable of promoting reprogramming were characterized by subsequent proteomic analyses. This comparative approach led to the identification of a fraction containing transcription activator BRG1/ATP-dependent helicase SMARCA4 and SWI/SNF complex subunit SMARCC1 as novel proteins that play a role in reprogramming (Singhal et al., 2010). Using oocyte extracts to discover reprogramming factors of the oocyte appears to be a straightforward approach; however, it is difficult to collect the amount of material that present-day proteomic techniques demand. In fact, even the proteome of the oocyte itself has not been defined properly, largely due to limited available material and technological constraints. However, better and better proteomic approaches are constantly on the rise. New technology is constantly being developed that allows more and more information to be extracted from ever tinier sample sizes, and several methods now allow comparative quantitative information to be obtained on protein levels in bulk. As the aim of this book chapter is not to review proteomic technologies in general, but to exemplify a proteomic approach to the reprogramming machinery of the mouse oocyte, the reader is referred to the recent review of Wright and colleagues, who give an overview of the current technological advances within the field of proteomics combined with a survey of their use in reproductive biology (Wright et al., 2012). The speed with which the field is actually growing is reflected by a search for the keyword “proteomics,” which reveals that the annual number of items published increased more than 10-fold over the last decade (957 papers in 2001 compared to 11,127 papers in 2011).
PART | VI SCNT as a Tool to Answer Biological Questions
The proteome of the oocyte itself has not been defined properly, largely due to limited available material and technological constraints.
Still only a handful of proteomic studies are designed to specifically analyze the mouse oocyte and even fewer to tackle oocyte-mediated reprogramming. In 2007, Vitale and colleagues were the first to search for proteins differentially expressed between mouse germinal vesicle (GV) and mouse metaphase II (MII) oocytes; they were able to identify 12 proteins that change during the GV-MII transition using two-dimensional (2D) gel-electrophoresis followed by mass spectrometry (MS) (Vitale et al., 2007). In the following year, the first study aiming to give a general picture of the proteomic composition of the oocyte was published by Ma and colleagues, who were able to identify 380 unique proteins in 80 µg total protein from MII oocytes (Ma et al., 2008). The oocyte protein catalogue was then extended to contain 625 unique proteins by Zhang and colleagues, who analyzed 2700 MII oocytes using 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by reverse-phase liquid chromatography tandem MS (RP-LC-MS/MS). In 2010, a study designed to identify highly abundant maternal factors that drive the egg-embryo transition, identified 185 proteins using a 500-oocyte sample (Yurttas et al., 2010). While all these studies furthered our knowledge of the protein identities present in an oocyte, their depth (i.e. the number of proteins) was still disappointingly small compared to the thousands of mRNAs that can be reliably detected by microarray analysis. However, later that year Wang and colleagues were able to deepen the proteomic catalogue even further by identifying 2781 proteins in GV oocytes, 2973 proteins in MII oocytes, and 2082 in zygotes using a total of 7000 mouse oocytes at different developmental stages (Wang et al., 2010). To date, only two proteomics-based studies have been published that specifically aimed to use the technology to learn about oocyte-mediated reprogramming in the mouse. The Latham group used proteomics to study mammalian oocyte spindles in the context of SCNT and a more general approach applied by our laboratory led to the identification of 3699 oocytic proteins and, in combination with a bioinformatics screen, to the proposal of 28 putative reprogramming factors in the oocyte (Han et al., 2010; Pfeiffer et al., 2011). The work of the Latham group was based on the observation that SCNT embryos suffer from deficiencies in spindle composition during the first cleavages, which are accompanied by chromosome congression defects. Latham and colleagues isolated 5000 spindle-chromosome complexes from MII oocytes and 5000 spindle-chromosome complexes from cloned constructs 2 h after SCNT.
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
Protein expression profiles of both samples were compared using gel electrophoresis liquid chromatography MS (GeLC-MS/MS). Over 200 proteins could be identified in the cloned constructs sample, whereas over 400 proteins were detected in the ooplasm sample. This effort, combined with immunofluorescence analyses, resulted in the identification of four proteins that are underrepresented in the spindle-chromosome complexes of early SCNT embryos, namely clathrin heavy chain 1 (encoded by Cltc), aurora kinase B (encoded by Aurkb), dynactin subunit 4 (encoded by Dctn4) and casein kinase I alpha (encoded by Csnk1a1). This study was a remarkable example of how proteomics can be applied to investigate the mechanisms underlying developmental arrest of cloned embryos but also demonstrates how cumbersome the approach is, given the fact that 6225 cloned embryos had to be generated to yield a single sample for MS (Han et al., 2010). In our laboratory, we applied proteomics in a more general way to the study of oocyte-mediated reprogramming. We were interested in defining which factors may be responsible for the remarkable reprogramming capacity of the oocyte. Nuclear transfer remains the most efficient reprogramming method to date, in terms of speed and its ability to enable the derivation of fully reprogrammed ESCs. In this context, Hanna and colleagues proposed that the mechanism of pluripotency induction is active and directed in oocytes (and also in ESCs, when performing reprogramming via cell fusion), as opposed to being passive in iPS cells (Hanna et al., 2009). We reckoned that the factors responsible for this remarkable trait should have catalytic activity, be localized in the nucleus, and act on chromatin. We defined the proteomic makeup of the MII oocyte to an unprecedented depth of 3699 protein identities and used this dataset as a springboard for our screen for putative active reprogramming factors. This dataset was superimposed on a proteomics dataset of 4723 proteins from undifferentiated mouse ESCs. A bioinformatics screen on the overlap of 2556 proteins for the gene ontology (GO) terms nuclear localization, chromatin modification, and catalytic activity yielded a short list of 28 putative active reprogramming factors, including the already discovered SMARCA4 (Pfeiffer et al., 2011). These experiments and some further preliminary data are described in detail in the following section.
RESULTS In order to define the mouse oocyte proteome to a sufficient depth for meaningful bioinformatics analysis, we collected 1884 MII oocytes from B6C3F1 female mice (free of cumulus cells and zona pellucida). In parallel, undifferentiated ESCs were collected and lysed to yield 100 µg total protein. Both samples were subjected to 1D SDSPAGE and the gel was cut into 29 slices (for MII oocytes)
409
and 27 slices (for ESCs). Trypsin digestion was performed on each slice and samples were then analyzed by liquid chromatography-MS (LC-MS) using an LTQ Orbitrap XL Velos mass spectrometer. The spectra were analyzed by MaxQuant and Mascot Server software for protein identification. A total of 3699 protein groups for MII oocytes and 4723 protein groups for ESCs were identified at a false discovery rate of 1%. Reliable detection was assumed if a protein was identified by at least two peptides of which one unique. Our oocyte dataset of 3699 identities exceeded the previous dataset from Wang and colleagues (2973 identities) by nearly one-quarter. However, even this high number may lie far from the actual number of proteins present in the oocyte, particularly considering post-translational modifications, which we did not include in our data analysis. Furthermore, we measured gene expression at the mRNA level via microarray using a B6C3F1 MII oocyte sample. Interestingly, matching mRNA data could only be obtained for 2842 of the 3699 proteins detected in oocytes. However, part of this discrepancy has to be ascribed to missing probes for certain genes on the microarray itself (236 instances) and to not matching database entries (125 instances). The remaining 496 gene identities, 13.4% of the overall detected, had therefore actually been present on the protein level in MII oocytes, while their corresponding mRNAs were undetectable by microarray analysis. A GO analysis for biological processes revealed that those proteins were mainly associated with metabolism and translation, as well as protein transport and localization. Furthermore, three (Atrx, Cdk1, Hdac2) of the 496 genes are regarded as oocyte-specific markers (De La Fuente et al., 2004; de Vant’ery et al., 1996; Segev et al., 2001). Although we did not detect mRNAs for these three factors in our oocyte transcriptome, they have been found in the deep transcriptome of mouse oocytes (Tang et al., 2009). Still, these findings highlight the possible mismatch of transcriptome and proteome data in certain specialized cell types such as oocytes und underlines the necessity of reaching beyond the transcriptional level when analyzing biological samples. If the quality of a proteome dataset from denuded MII oocytes is high, one would expect to find oocyte-specific proteins, while cumulus cell-specific proteins should be absent. Indeed, of 68 oocyte-specific proteins, 46 were present in our MII proteome while cumulus cell-specific proteins were largely absent, as would be expected for a clean oocyte preparation. These observations argue in favor of a high-quality dataset. When we analyzed the distribution of the protein identities discovered in MII oocytes and ESCs grouped across the main GO biological process categories, no differences above 1.5% were observed. Therefore, both datasets seem to be of similar quality. It is not known whether one or multiple reprogramming pathways exist, or which factors these involve.
410
However, as reprogramming by the oocyte and by ESCs is relatively fast, it is likely that they share common reprogramming agents. We therefore inspected the proteins of the intersection between oocytes and ESCs, consisting of 2556 shared protein identities. To pursue the reprogramming factors, we filtered the shared subproteome for the GO biological process terms nuclear localization, chromatin modification, and catalytic activity. The rationale for choosing these terms is based on the fact that putative active reprogramming factors have chromatin as substrate (hence, nuclear localization and chromatin modification) and are neither significantly consumed nor titrated, since the ooplasm of an oocyte has been proposed to hold enough reprogramming factors for up to 100 nuclei (hence, catalytic activity) (Miyamoto et al., 2009). In fact, the proposal of Miyamoto and colleagues was, at least partly, recently confirmed in our laboratory by the successful transplantation of two independent somatic nuclei into a single ooplasm and the subsequent derivation of tetraploid ESCs from tetraploid cloned
PART | VI SCNT as a Tool to Answer Biological Questions
blastocysts (Pfeiffer et al., 2012). Our multilevel bioinformatics screening led to the identification of 28 genes encoding putative active reprogramming factors: Baz1b, Brcc3, Carm1, Ccnb1, Chd4, Dnmt1, Dnmt3a, Eed, Ep400, Hat1, Hdac1, Hdac2, Hdac6, Hells, Kdm1a/Lsd1, Kdm6a/Utx, Mll3, Prmt1, Prmt5, Prmt7, Rnf2, Rnf20, Ruvbl1, Ruvbl2, Smarca4/Brg1, Smarca5, Smarcal1, and Usp16. An interaction network of these 28 proteins based on the Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) database is shown in Figure 32.1 (Szklarczyk et al., 2011). Interestingly, the mRNA levels of 17 of the 28 putative reprogramming factors are upregulated during the conversion of mouse fibroblasts to iPS cells (Figure 32.2) (Samavarchi-Tehrani et al., 2010). To date, confirmation of our proposed reprogramming factors in the literature is available for 3 of the 28 encoded proteins, that is SMARCA4 (Singhal et al., 2010), protein arginine N-methyltransferase 5 (PRMT5) (Nagamatsu et al., 2011), and lysine-specific demethylase 6A (encoded by Kdm6a) (Mansour et al., 2012). PRMT5 even is able to
FIGURE 32.1 Functional protein association network of the putative 28 reprogramming factors based on the STRING database. Stronger associations are represented by thicker lines.
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
promote reprogramming together with POU domain, class 5, transcription factor 1 (Oct4) and Krueppel-like factor 4 (encoded by Klf4) alone, thereby substituting for Sox2 during the process. A more detailed description and accompanying records for the data described above can be found in our recently published paper entitled “Proteomic analysis of mouse oocytes reveals 28 candidate factors of the “reprogrammome” (Pfeiffer et al., 2011). Currently we are investigating the role of our proposed 28 factors in reprogramming using iPS technology as a screening tool (Figure 32.2). By overexpressing the putative reprogramming factors in mouse embryonic fibroblasts simultaneously with different combinations of the classical reprogramming factors Oct4, Sox2, Klf4, and c-myc we have confirmed our proposed hits based on the timing and efficiency of reprogramming. Initial experiments have also led to promising results for some of the others factors; however, the data are too preliminary to be reported in this chapter.
In a second approach, we are currently analyzing the subcellular localization of the 28 factors during mouse pre-implantation development. Until now, we have focused on the PRMT family and have been able to characterize the expression patterns of protein arginine N-methyltransferase 1 (PRMT1; encoded by Prmt1), histone-arginine methyltransferase CARM1/PRMT4 (encoded by Carm1/Prmt4) and PRMT5. In this context, it is interesting to note that both, Prmt1 and Prmt5 showed increased levels of mRNA during embryo cleavage, as detected by microarray or RNA sequence analyses at the four- or eight-cell stage, respectively (Tang et al., 2009; Zeng et al., 2004); however, protein distribution during early embryo development has never been assessed. Therefore GV oocytes, two-cell, four-cell, eight-cell, morula, and blastocyst stage embryos were processed by immunocytochemistry and subjected to confocal imaging. The three PRMT proteins were expected to be found in the nucleus of the cells. The results indeed showed
7.00
Hdac6
6.00
Rnf2 Smarcal1
Fold change in mRNA levels
5.00
Hat1 Dnmt3a RuvbI2
4.00
Brcc3 Hells Ccnb1
3.00
Prmt7 Kdm1 RuvbI1
2.00
Eed Dnmt1 Hdac2
1.00
Prmt1 Prmt5 0.00 0
2 5 8 11 16 Days after doxycyclin induction of reprogramming
411
21
FIGURE 32.2 Relative mRNA expression levels of the 28 putative reprogramming factors during iPS cell formation. Reprogramming was triggered by adding doxycycline to secondary mouse embryonic fibroblasts carrying inducible transgenes for Oct4, Sox2, Klf4, and c-myc. Based on data published by Samavarchi-Tehrani and colleagues (Samavarchi-Tehrani et al., 2010).
412
PART | VI SCNT as a Tool to Answer Biological Questions
(A)
(B) DNA
PRMT1 Overlay
DNA
(C) PRMT4 Overlay
DNA
PRMT5 Overlay
GV
2-cell
4-cell
8-cell
Morula
Blastocyst
FIGURE 32.3 Immunofluorescence staining of mouse oocytes and early embryos for different members of the PRMT family. Images of the subcellular distribution of PRMT1 (A), CARM1/PRMT4 (B), and PRMT5 (C) were obtained at different developmental stages. The nuclear localization of the different PRMTs is highly dependent on the developmental stage of the embryo.
that the proteins were localized in the nucleus, although not exclusively, as a cytoplasmic signal was also obtained dependent on the developmental stage (Figure 32.3). Cases of exclusively cytoplasmic localization were also observed. GV-stage oocytes showed no presence of PRMT1 in the nucleus; it was expressed only in the cytoplasm. The protein appeared as dots in the cytoplasm. At the two-cell stage, the protein can be detected in the cytoplasm and within the nucleus but not in nucleoli. The same pattern was observed at the four-cell stage; however, about onethird of embryos at that stage showed PRMT1 expression solely in the cytoplasm. At the eight-cell stage, the protein was again present in both the nucleus and cytoplasm, but it appeared to be concentrated in fewer spots than at earlier stages. In the morula, PRMT1 was not present in the nucleus, but was specifically located in the cytoplasm, in fewer spots than at the eight-cell stage. At the blastocyst stage, only small amounts of the protein were detectable, located within the nucleus. An interesting pattern was also observed for CARM1. Even though it was detected by MS, this arginine methyl transferase could barely be detected in GV oocytes and one-cell stage embryos by immunofluorescence. A clear nuclear signal for CARM1 became apparent at the twocell stage and was observed throughout pre-implantation development to the blastocyst with increasing fluorescence intensity. Similar to CARM1/PRMT4, PRMT5 could also be weakly detected by immunofluorescence in GV oocytes.
However, starting from the one-cell stage, a weak nuclear signal became apparent in some embryos and a strong nuclear signal was observed at the two-cell stage. Interestingly, nuclear localization was lost later in development and PRMT5 localization was mainly restricted to the cytoplasm in morula and blastocyst stage embryos. The subcellular analysis of three of the 28 putative reprogramming factors during normal pre-implantation development indicates that they are localized in the nucleus during the time when the majority of reprogramming takes place, which is a necessary characteristic for a genuine reprogramming factor.
DISCUSSION To date, proteomic approaches such as MS have rarely been used in studies of oocyte-mediated reprogramming in mammals. The reason is mainly technical: mammalian oocytes provide small samples sizes that, until a few years ago, were simply not suitable for a meaningful large-scale proteomic analysis. In this chapter, we show that this technical limitation has been overcome, at least in part, and that meaningful data can be generated from minute specimens such as mouse oocytes. These data can be used, in conjunction with bioinformatics analysis, to discover novel players in oocyte-mediate reprogramming and to conceive new hypotheses regarding the underlying mechanisms. Our proteomic study was driven by the hypothesis that oocyte-mediated reprogramming cannot simply rely on transcription factors, given the unparalleled speed of
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
the process after nuclear transfer. Transcription factors can interact with DNA at sites of accessible chromatin and therefore their action may largely be dependent on cell divisions, which gives them a window of opportunity to exert their function (passive mechanisms). In contrast, the mode of action of chromatin-modifying enzymes is independent of the cell cycle and activity. Furthermore, changes within chromatin structure and the epigenome are a necessary requirement for changing cellular programs, in the case of SCNT from a somatic program back to pluri-/ totipotency. For these reasons, we filtered the shared proteome of oocytes and ESCs for the criteria nuclear localization, enzymatic activity, and chromatin remodeling. The result is a shortlist of 28 putative active reprogramming factors, of which three are already confirmed and others are soon to follow. Our approach reveals that the era of proteomics has successfully stepped into the field of oocyte-mediated reprogramming. However, the data generated with our approach is purely qualitative. The lack of quantitative information greatly hinders the application of systems biology. This is especially the case as proteomics suffers from large numbers of false negatives. A certain protein may well be present even if it cannot be detected in a crude sample extract in shotgun proteomics. This is particularly true for generally under-represented proteins like transcription factors. For example, even in our in deep proteomic catalogue of 3699 identities for mouse MII oocytes, certain well-known oocytic proteins, like Oct4, were absent. Not only for this reason will the application of quantitative proteomics approaches be of major importance in the future. Until now, only one study has reported quantitative data on the mouse oocyte proteome; however, a label-free quantitation was applied, which led the authors themselves to use the expression semiquantitative MS analysis (Wang et al., 2010). In a different approach, Powell and colleagues were able to identify 16 proteins that were differentially expressed in high- versus low-quality pig oocytes using ExacTag proteomics in an attempt to identify novel oocyte quality markers. ExacTag proteomics enables the acquisition of quantitative data by introducing stable isotope tags to proteins samples after their collection but has not been used in the context of mouse oocytes to date. In a classical quantitative proteomics study, these labels are introduced by growing cells in medium containing isotope-labeled amino acids. This “stable isotope labeling by amino acids in cell culture” (SILAC) methodology can even be applied to whole organisms by means of a special diet containing heavy amino acids that is offered as a food supply over several generations. The so-called “SILAC mouse” enables high fidelity quantitative proteomics to be done in vivo and may well revolutionize the
413
field (Krüger et al., 2008; Zanivan et al., 2012). However, given the high price of a mouse SILAC diet and the large numbers of animals necessary to generate usable sample sizes in the field of SCNT, this advance is not yet applicable as a routine tool to study oocyte-mediated reprogramming. Possible ways out of this dilemma are spike-in or super-SILAC approaches. In a spike-in SILAC experiment, an easy to obtain heavily labeled sample, i.e. from cell culture, is used as reference outgroup for quantitation of the samples of interest. The results represent relative protein expression levels from the sample of interest in comparison to the outgroup. By calculating ratios of ratios, these relative expression levels can be transformed to gain reliable quantitative proteomic data on unlabeled samples. The downside, however, is that data can only be obtained on proteins that are present in both the sample of interest and the outgroup, a challenging prerequisite when it comes to the analysis of specialized cell types such as oocytes or developing embryos. Recently, to overcome this limitation, the application of so-called super-SILAC spike-in mixtures has been proposed, whereby the reference outgroup consist of an assembly of heavily labeled cell lines or tissues to enable a high coverage of all possible proteins in the sample of interest during the experiment (Geiger et al., 2010; 2011). An important application of these methods in the future will be the assessment of reprogramming determinants in oocytes. A smart way to grasp them would be to compare the proteome makeups of oocytes already known to be good or not-so-good reprogrammers. We recently observed that oocytes of aged mice not only support mouse embryo development after SCNT, but actually do so with higher blastocyst rates. A comparison of the transcriptomes of normal and aged oocytes revealed 724 genes (4.33% of all transcripts detected) that were differently expressed between the two age groups (Esteves et al., 2011). However, whether the corresponding proteins would match these differences is still unclear. In an attempt to fill this gap, we are currently generating the quantitative proteomes of oocytes from prepubertal, middle-aged, and old mice using the spike-in SILAC methodology; we expect to find putative players at the heart of the differing reprogramming abilities of oocytes from differently aged mice. Another interesting approach is the comparison of oocytes from different mouse strains. When oocytes from mice with distinct genetic background are used for SCNT experiments, blastocyst rates vary substantially after activation (Kishigami et al., 2007). It is clear that this observation has to be linked to the oocyte, as similar donor nuclei were used in the different experiments. Therefore, defining quantitative proteomes of oocytes from different mouse strains will provide a great resource to learn
414
PART | VI SCNT as a Tool to Answer Biological Questions
about the mechanisms of oocyte-mediated reprogramming and to identify novel putative proteins that are crucial for the process.
Proteomic experiments aiming to understand the prerequisites of successful cloning and the unfolding processes during cloned embryo development are not only of interest to the understanding of reprogramming but will also be of great value for germ cell and developmental biology, as well as reproductive medicine.
It is important to mention that proteomic experiments aiming to understand the prerequisites of successful cloning and the processes unfolding during cloned embryo development are not only of interest to the understanding of reprogramming but will also be of great value for germ cell and developmental biology, as well as reproductive medicine. As the technical hurdles for proteomics can be expected to be eased in the near future, an increasing number of proteomics-based studies is expected to advance the field for the benefit of all reprogramming platforms, and cell biology in general.
MATERIALS AND METHODS Mice Six- to eight-week-old B6C3F1 (C57Bl/6J × C3H/HeN) mice were used as oocyte and cumulus cell donors. All mice were primed with 10 IU each pregnant mare serum gonadotropin (PMSG) and human chorionic gonadotropin (hCG) injected intraperitoneally 48 h apart, and sacrificed by cervical dislocation 15 h after hCG injection to collect the cumulus-oocyte complexes from the oviducts. Mice were maintained and used for experiments according to institutional guidelines.
Oocyte and Embryonic Stem Cell Sample Preparation and Processing for Mass Spectrometry Sample Preparation A total of 1884 MII oocytes were denuded using warm Tyrode acid solution and collected in SDS lysis buffer (4% SDS, 100 mM Tris-HCl pH 7.5, 0.1 M dithiothreitol (DTT); total volume 70 µl). The sample was sonicated using a Bioruptor sonication system (5 cycles of 30 s on and 30 s off at a high setting) to ensure total lysis and to shear DNA. ESCs from one 10 cm plate were subjected to double sedimentation and collected in SDS lysis buffer as described. Following the addition of 20 µl NuPAGE LDS
sample buffer and heating, oocyte lysate proteins were size fractionated by 1D gel electrophoresis in two neighboring lanes of a 4-20% NuPAGE gel (Invitrogen) and stained with a Colloidal Blue staining Kit (Invitrogen). Lanes containing protein were sliced into 29 (oocytes) and 27 (ESCs) pieces and processed for GeLC-MS/MS. Briefly, proteins within each gel piece were subjected to reduction (10 mM DTT; 45 min at 56°C) and alkylation (2-iodoacetamide; 30 min at room temperature, in the dark) followed by tryptic cleavage for 16 h at 37°C. Peptides were then extracted from the gel pieces as previously described (Shevchenko et al., 2006), desalted, and concentrated using Stage Tips (Rappsilber et al., 2003).
LC-MS/MS and Data Analysis As described in Pfeiffer et al. (2011), each fraction, which represented the peptide content of two neighboring gel pieces, was analyzed by reversed phase chromatography using an Easy-LC nanoflow system (Proxeon) coupled online via in-house packed fused silica capillary column emitters (length, 15 cm; internal diameter, 75 µm; resin ReproSil-Pur C18-AQ, 3 µm) and a nanoelectrospray source (Proxeon) to an LTQ Orbitrap Velos mass spectrometer (Thermo Scientific). Peptides were eluted from the C18 column by applying a linear gradient of 5-35% buffer B (80% acetonitril, 0.5% acetic acid) over 120 min followed by a gradient of 35-98% over 15 min. The mass spectrometer was operated in the positive ion mode (source voltage 2.2 kV), automatically switching in a datadependent fashion between survey scans in the mass range of m/z 300-1650 and MS/MS acquisition. Collision-induced MS/MS spectra from the 15 most intense ion peaks in the MS were collected (Target Value of the Orbitrap survey scan: 1,000,000; resolution R = 60,000; Lockmass set to 445.120025). Raw data files were then processed by MaxQuant software (version 1.0.12.36) in conjunction with Mascot database searches (Cox and Mann, 2008). Data were searched against the mouse International Protein Index (IPI) sequence database (version 3.60) concatenated with reversed sequence versions of all entries. The parameter settings were: trypsin as digesting enzyme, a minimum length of six amino acids, a maximum of two missed cleavages, carbamidomethylation at cysteine residues set as fixed, and oxidation at methionine residues as well as acetylation at the protein N-termini as variable modifications. The maximum allowed mass deviation was 7 ppm for MS and 0.5 Da for MS/MS scans. Protein groups were regarded as being unequivocally identified with a false discovery rate set to 1% for all protein and peptide identifications when there were at least two matching peptides, with one being unique to the protein group. MS data was also analyzed using the SEQUEST search algorithm and Proteome Discoverer software (Thermo Scientific).
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
Transcriptome We obtained microarray data so that we could compare the mouse oocyte proteome and transcriptome. As described in Pfeiffer et al. (2011), pools of 20 zona-enclosed B6C3F1 oocytes were each subjected to total RNA extraction followed by preamplification, reverse transcription, labeling, and hybridization. RNA (samples in 300 µl RLT buffer containing 1% β-mercaptoethanol) was isolated using RNeasy Micro Kit as described by the manufacturer (Qiagen, no DNase treatment). An examination of total RNA on an Agilent 2100 Bioanalyzer and RNA Pico 6000 Lab-Chip Kit confirmed the extraction of high-quality RNA, which was then prepared for gene expression profiling. A RiboAmp HS Plus Amplification Kit (MDS Analytical Technologies GmbH, Germany) was used to amplify total RNA (two rounds of amplification) according to manufacturer’s instructions. After the second round of amplification, amplified RNA was eluted with 15 µl RE buffer. The concentration of amplified RNA was measured using an Agilent Bioanalyzer 2100 and a RNA 6000 Lab-Chip Kit. Three micrograms of amplified RNA were labeled with Cy3 using a Turbo Labeling CY3 Kit (MDS Analytical Technologies GmbH, Germany). The concentration and frequency of incorporation were measured using a NanoPhotometer (Implen, Munich, Germany). Fragmentation (1650 ng Cy3-labeled amplified RNA) and hybridization were performed following the hybridization procedure recommended by the array manufacturer (Agilent Technologies), with a modification given by the manufacturer of the Turbo Labeling CY3 Kit. Microarray wash and detection of the labeled RNA on GeneChips were performed according to the instructions of Agilent Technologies. Gene expression profiling was performed using Agilent’s Whole Mouse Genome Oligo Microarrays (4 × 44 k, each array with 41,174 features). Array image acquisition and feature extraction was performed using the Agilent G2505B Microarray Scanner and Feature Extraction software version 9.5 (Agilent Technologies).
Database Search and Bioinformatics Analysis and preprocessing of data were performed with Bioconductor software (Gentleman et al., 2004) using the R statistical computing and graphics environment (R Development Core Team, 2009), as described in Pfeiffer et al. (2011). First, transcriptome data were analyzed by Agilent Feature Extraction software version 9.5. All Agilent microarray probe sets were mapped to PubMed ENTREZ, which was used as the common point of reference. Mapping was accomplished using the mgug4122a. db R package version 2.4.5. Positive hybridization for both samples was taken as evidence that the mRNA
415
corresponding to the probe was present. By this criterion, the dataset was reduced from 27,032 (flag filtering) to 15,476 probe sets (double positive), and the gene hits in the transcriptome were screened to eliminate duplicates and multiple probes. Thus, of the initial 16,157 genes (corresponding to 27,032 probe sets), 10,833 genes (corresponding to 15,476 probe sets) were retained for further analysis. Next, we processed the proteome dataset provided by the MaxQuant software. Frequent new releases of the IPI (Kersey et al., 2004) implied that an update of the MaxQuant annotation was required, so we utilized supporting history files obtained from the IPI website (ftp://ftp. ebi.ac.uk/pub/databases/IPI/current) to upgrade to version 3.76. Based on the ipi.MOUSE.xrefs.gz file downloaded from IPI data repository, two lists of proteins represented by PubMed ENTREZ identifiers were established from MaxQuant proteome datasets, which yielded 3574 proteins from the initial 3699 proteins detected in oocyte and 4588 proteins from the initial 4723 proteins detected in ESCs. MaxQuant creates protein groups if the identified peptide set of one protein is equal to or is present in another protein’s peptide set. Protein groups were processed by taking the IPI identifier for which the highest peptide count was measured. When the same highest peptide count was obtained for two proteins in a protein group, we considered this a tie and arbitrarily took the first protein so as to avoid having to discard such hits. Such a tie may happen in case of isoforms (splice variants), but may also happen in the case of close paralogues, where no unique peptides can be detected that reflect the difference between the paralogues. A tie may also happen if a protein (referred to by a specific IPI identifier) is mapped to more than one gene locus. The disadvantage of arbitrarily selecting one of the hits is that for the subsequent GO analysis, we may have considered the wrong protein in case of paralogues. However, since close paralogues usually have similar function and feature a similar GO annotation, this disadvantage is outweighed by the advantage of a more exhaustive GO analysis.
Fixation of Germinal Vesicle Stage Oocytes and Fertilized Embryos and Processing for Immunofluorescence Oocytes and embryos were fixed and permeabilized in fixation/permeabilization solution [1% paraformaldehyde in phosphate-buffered saline (PBS) containing 0.1% Triton X-100 (PBT)] in 96-well plates for 15 min. Zonae pellucidae were removed using warm Tyrode/polyvinylpyrrolidone solution. For blocking non-specific-binding sites and quenching residual paraformaldehyde, the embryos were treated with blocking solution [0.1% Tween20, 2% bovine serum albumin (BSA), 2% glycine and 5% donkey serum in PBS] for at least 1 h (or until further use) at 4°C.
416
The cells were then incubated in a dilution of the primary antibody (PRMT1, Abcam Ab73246; Carm1, Cell Signaling C31G9; PRMT5, Abcam Ab31751) in AB buffer [0.1% Tween 20, 0.5% BSA, 0.5% Glycine and 1.25% Donkey serum in PBS] overnight at 4°C. Prior to use, primary antibody dilutions were centrifuged for 20 min at 13.000 rpm to sediment particles. Afterwards, cells were washed twice with PBT for 5 min and incubated in a dilution of the secondary antibody [donkey anti-rabbit immunoglobulin G (IgG) Alexa Fluor 647 (Invitrogen A31573) in AB buffer] for 2 h at room temperature in the dark. Subsequently, cells were washed again with PBT. Nuclei were counterstained with 0.1% YO-PRO-1 in AB buffer for 10 min.
Confocal Imaging and Image Analysis Oocytes and embryos were transferred into microdrops of PBS (50 μl) for imaging and covered with mineral oil on an imaging-quality thin-bottom plastic dish (Lumox, Greiner Bio-One, Germany). An inverted microscope (TE-2000U, Nikon, Düsseldorf, Germany) coupled to an UltraVIEW RS3 spinning disk confocal imaging system (PerkinElmer LAS, Jürgensheim, Germany) with 40× magnification was used for imaging. Analysis of the captured images was performed ImageJ software (Abramoff et al., 2004).
ACKNOWLEDGEMENTS This study was supported by the special priority program (Schwerpunktprogramm) No. 1356 of the Deutsche Forschungsgemeinschaft (grants BO2540/3-1 and FU583/2-1) and by the Max Planck Society. We are grateful to Andrea Krüger and Fataneh Fathi Far for immunofluorescence confocal images presented in Figure 32.3.
REFERENCES Abramoff, M.D., Magalhaes, P.J., Ram, S.J., 2004. Image Processing with ImageJ.. Biophotonics Int. 11 (7), 36–42. Cox, J., Mann, M., 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26 (12), 1367–1372. (Epub 2008/11/26). De La Fuente, R., Viveiros, M.M., Wigglesworth, K., ATRX, Eppig J.J., 2004. a member of the SNF2 family of helicase/ATPases, is required for chromosome alignment and meiotic spindle organization in metaphase II stage mouse oocytes. Dev. Biol. 272 (1), 1–14. Esteves, T.C., Balbach, S.T., Pfeiffer, M.J., Arauzo-Bravo, M.J., Klein, D.C., Sinn, M., et al., 2011. Somatic cell nuclear reprogramming of mouse oocytes endures beyond reproductive decline. Aging Cell 10 (1), 80–95. (Epub 2010/10/26). Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R., Mann, M., 2010. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods. 7 (5), 383–385. (Epub 2010/04/07).
PART | VI SCNT as a Tool to Answer Biological Questions
Geiger, T., Wisniewski, J.R., Cox, J., Zanivan, S., Krüger, M., Ishihama, Y., et al., 2011. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 6 (2), 147–157. (Epub 2011/02/05). Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., et al., 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome. Biol. 5 (10), R80. (Epub 2004/10/06). Han, Z., Liang, C.G., Cheng, Y., Duan, X., Zhong, Z., Potireddy, S., et al., 2010. Oocyte spindle proteomics analysis leading to rescue of chromosome congression defects in cloned embryos. J. Proteome. Res. 9 (11), 6025–6032. (Epub 2010/10/05). Hanna, J., Saha, K., Pando, B., van Zon, J., Lengner, C.J., Creyghton, M.P., et al., 2009. Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462 (7273), 595–601. (Epub 2009/11/10). Kersey, P.J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E., Apweiler, R., 2004. The International Protein Index: an integrated database for proteomics experiments. Proteomics 4 (7), 1985–1988. Kishigami, S., Bui, H.T., Wakayama, S., Tokunaga, K., Van Thuan, N., Hikichi, T., et al., 2007. Successful mouse cloning of an outbred strain by trichostatin A treatment after somatic nuclear transfer. J. Reprod. Dev. 53 (1), 165–170. (Epub 2006/11/02). Krüger, M., Moser, M., Ussar, S., Thievessen, I., Luber, C.A., Forner, F., et al., 2008. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134 (2), 353–364. (Epub 2008/07/30). Ma, M., Guo, X., Wang, F., Zhao, C., Liu, Z., Shi, Z., et al., 2008. Protein expression profile of the mouse metaphase-II oocyte. J. Proteome. Res. 7 (11), 4821–4830. (Epub 2008/09/23). Mansour, A.A., Gafni, O., Weinberger, L., Zviran, A., Ayyash, M., Rais, Y., et al., 2012. The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming. Nature (Epub 2012/07/18). Miyamoto, K., Tsukiyama, T., Yang, Y., Li, N., Minami, N., Yamada, M., et al., 2009. Cell-free extracts from mammalian oocytes partially induce nuclear reprogramming in somatic cells. Biol. Reprod. 80 (5), 935–943. Nagamatsu, G., Kosaka, T., Kawasumi, M., Kinoshita, T., Takubo, K., Akiyama, H., et al., 2011. A germ cell-specific gene, Prmt5, works in somatic cell reprogramming. J. Biol. Chem. 286 (12), 10641–10648. (Epub 2011/01/29). Pfeiffer, M., Stehling, M., Jauch, A., Boiani, M., 2012. ES Cell Lines from Tetraploid Mouse Blastocysts. In: Baharvand, H., Aghdami, N. (Eds.), Advances in Stem Cell Research Humana Press, New York, pp. 1–16. Pfeiffer, M.J., Siatkowski, M., Paudel, Y., Balbach, S.T., Baeumer, N., Crosetto, N., et al., 2011. Proteomic analysis of mouse oocytes reveals 28 candidate factors of the “reprogrammome.”. J. Proteome. Res. 10 (5), 2140–2153. (Epub 2011/02/25). R Development Core Team, 2009. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Rappsilber, J., Ishihama, Y., Mann, M., 2003. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75 (3), 663–670. Samavarchi-Tehrani, P., Golipour, A., David, L., Sung, H.K., Beyer, T.A., Datti, A., et al., 2010. Functional genomics reveals a BMP-driven
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
mesenchymal-to-epithelial transition in the initiation of somatic cell reprogramming. Cell Stem Cell 7 (1), 64–77. (Epub 2010/07/14). Segev, H., Memili, E., First, N.L., 2001. Expression patterns of histone deacetylases in bovine oocytes and early embryos, and the effect of their inhibition on embryo development. Zygote 9 (2), 123–133. Shevchenko, A., Tomas, H., Havlis, J., Olsen, J.V., Mann, M., 2006. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1 (6), 2856–2860. Singhal, N., Graumann, J., Wu, G., Arauzo-Bravo, M.J., Han, D.W., Greber, B., et al., 2010. Chromatin-Remodeling Components of the BAF Complex Facilitate Reprogramming. Cell 141 (6), 943–955. Szklarczyk D., Franceschini A., Kuhn M., Simonovic M., Roth A., Minguez P., et al., The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic acids research. 2011;39(Database issue):D561–8. Epub 2010/11/04. Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., et al., 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods. 6 (5), 377–382. (Epub 2009/04/08). Vitale, A.M., Calvert, M.E., Mallavarapu, M., Yurttas, P., Perlin, J., Herr, J., et al., 2007. Proteomic profiling of murine oocyte maturation. Mol. Reprod. Dev. 74 (5), 608–616. (Epub 2006/10/18).
417
Wang, S., Kou, Z., Jing, Z., Zhang, Y., Guo, X., Dong, M., et al., 2010. Proteome of mouse oocytes at different developmental stages. Proc. Natl. Acad. Sci. USA 107 (41), 17639–17644. (Epub 2010/09/30). Wright, P.C., Noirel, J., Ow, S.Y., Fazeli, A., 2012. A review of current proteomics technologies with a survey on their widespread use in reproductive biology investigations. Theriogenology 77 (4), 738–765 e52. Yurttas, P., Morency, E., Coonrod, S.A., 2010. Use of proteomics to identify highly abundant maternal factors that drive the egg-to-embryo transition. Reproduction 139 (5), 809–823. (Epub 2010/01/29). Zanivan, S., Krueger, M., Mann, M., 2012. In vivo quantitative proteomics: the SILAC mouse. Methods Mol. Biol. 757, 435–450. (Epub 2011/09/13). Zeng, F., Baldwin, D.A., Schultz, R.M., 2004. Transcript profiling during preimplantation mouse development. Dev. Biol. 272 (2), 483–496. (Epub 2004/07/30). de Vant’ery, C., Gavin, A.C., Vassalli, J.D., Schorderet-Slatkine, S., 1996. An accumulation of p34cdc2 at the end of mouse oocyte growth correlates with the acquisition of meiotic competence. Dev. Biol. 174 (2), 335–344.
Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte Martin J. Pfeiffer1, Marcin Siatkowski2,3, Yogesh Paudel3, Sebastian T. Balbach1, Nicole Baeumer4, Nicola Crosetto5, Hannes C.A. Drexler6, Georg Fuellen2,3 and Michele Boiani1 1
Max-Planck-Institute for Molecular Biomedicine, Münster, Germany, 2DZNE, German Center for Neurodegenerative Disorders, Rostock, Germany,
3
Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany, 4Arrows Biomedical GmbH, Münster, Germany, 5Institute of Biochemistry II, Goethe University Medical School, Frankfurt am Main, Germany, 6Max-Planck-Institute for Molecular Biomedicine, Bioanalytical Mass Spectrometry Facility. Münster, Germany
Chapter Outline Introduction 407 Results 409 Discussion 412 Materials and Methods 414 Mice 414 Oocyte and Embryonic Stem Cell Sample Preparation and Processing for Mass Spectrometry 414 Sample Preparation 414 LC-MS/MS and Data Analysis 414
INTRODUCTION Deciphering the mechanisms underlying reprogramming is one of the major driving forces of the reprogramming field. A variety of methods do exist that enable the reversion of a somatic cell to a pluripotent state. The pioneering technology of somatic cell nuclear transfer (SCNT), i.e. the transplantation of a somatic cell nucleus into an “enucleated” oocyte, was extended by reprogramming after fusion of somatic cells with pluripotent cells. However, even several years after the development of direct reprogramming through the delivery of selected transcription factors to a cell [induced pluripotent stem (iPS) cell technology], the mechanistic bases of the process itself are still largely unclear. In terms of their applicability to studying reprogramming mechanisms, the different approaches have different advantages and disadvantages. While direct
Principles of Cloning. DOI: http://dx.doi.org/10.1016/B978-0-12-386541-0.00037-0 © 2012 2014 Elsevier Inc. All rights reserved.
Transcriptome 415 Database Search and Bioinformatics 415 Fixation of Germinal Vesicle Stage Oocytes and Fertilized Embryos and Processing for Immunofluorescence 415 Confocal Imaging and Image Analysis 416 Acknowledgements 416 References 416
reprogramming enables the easy generation of iPS cells from adult tissues, the heterogeneity of a cell population while being reprogrammed has hindered an advance in mechanistic insights. Cell fusion, while also being technically more demanding, suffers from tetraploidy in the resulting cells, which does not allow a clear picture of the reprogrammed genome. The possible solution of fusing somatic cells with pluripotent cytoplasts only leads to partial reprogramming of the somatic genome. Accordingly, the cytoplasm of an embryonic stem cell (ESC) promotes reprogramming, but does not lead to its completion. In this respect, the ooplasm is the only cytoplasm known to date that supports full reprogramming after transplantation of a somatic nucleus, and does so in a comparatively efficient and fast manner. In a routine experiment, 40% of SCNT mouse embryos develop to the blastocyst stage and
407
408
reprogramming marker reexpression can be observed after only a few cell cycles. A plethora of strategies have been applied to investigate the mechanisms of reprogramming. Many studies rely on the analysis of gene expression profiles of pools of cells sampled during the reprogramming process, thereby advancing our understanding of reprogramming on the basis of mRNA expression signatures. While these strategies are informative, they leave out a very important layer of information: Most processes within a cell depend on its proteomic makeup on protein modifications, and on their interaction partners. The addition or depletion of putative reprogramming factors or the activation/repression of potentially involved signaling pathways is a strategy that can be applied in this context and large-scale screenings have led to the discovery of new factors involved in the process. However, only few studies have followed a proteomic top-down approach in the context of finding novel proteins that facilitate reprogramming. One remarkable report published by Singhal and colleagues applied protein extracts of pluripotent ESCs to permeabilized somatic cells during the reprogramming process. The ESC protein extract was then fractionated and the fractions capable of promoting reprogramming were characterized by subsequent proteomic analyses. This comparative approach led to the identification of a fraction containing transcription activator BRG1/ATP-dependent helicase SMARCA4 and SWI/SNF complex subunit SMARCC1 as novel proteins that play a role in reprogramming (Singhal et al., 2010). Using oocyte extracts to discover reprogramming factors of the oocyte appears to be a straightforward approach; however, it is difficult to collect the amount of material that present-day proteomic techniques demand. In fact, even the proteome of the oocyte itself has not been defined properly, largely due to limited available material and technological constraints. However, better and better proteomic approaches are constantly on the rise. New technology is constantly being developed that allows more and more information to be extracted from ever tinier sample sizes, and several methods now allow comparative quantitative information to be obtained on protein levels in bulk. As the aim of this book chapter is not to review proteomic technologies in general, but to exemplify a proteomic approach to the reprogramming machinery of the mouse oocyte, the reader is referred to the recent review of Wright and colleagues, who give an overview of the current technological advances within the field of proteomics combined with a survey of their use in reproductive biology (Wright et al., 2012). The speed with which the field is actually growing is reflected by a search for the keyword “proteomics,” which reveals that the annual number of items published increased more than 10-fold over the last decade (957 papers in 2001 compared to 11,127 papers in 2011).
PART | VI SCNT as a Tool to Answer Biological Questions
The proteome of the oocyte itself has not been defined properly, largely due to limited available material and technological constraints.
Still only a handful of proteomic studies are designed to specifically analyze the mouse oocyte and even fewer to tackle oocyte-mediated reprogramming. In 2007, Vitale and colleagues were the first to search for proteins differentially expressed between mouse germinal vesicle (GV) and mouse metaphase II (MII) oocytes; they were able to identify 12 proteins that change during the GV-MII transition using two-dimensional (2D) gel-electrophoresis followed by mass spectrometry (MS) (Vitale et al., 2007). In the following year, the first study aiming to give a general picture of the proteomic composition of the oocyte was published by Ma and colleagues, who were able to identify 380 unique proteins in 80 µg total protein from MII oocytes (Ma et al., 2008). The oocyte protein catalogue was then extended to contain 625 unique proteins by Zhang and colleagues, who analyzed 2700 MII oocytes using 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by reverse-phase liquid chromatography tandem MS (RP-LC-MS/MS). In 2010, a study designed to identify highly abundant maternal factors that drive the egg-embryo transition, identified 185 proteins using a 500-oocyte sample (Yurttas et al., 2010). While all these studies furthered our knowledge of the protein identities present in an oocyte, their depth (i.e. the number of proteins) was still disappointingly small compared to the thousands of mRNAs that can be reliably detected by microarray analysis. However, later that year Wang and colleagues were able to deepen the proteomic catalogue even further by identifying 2781 proteins in GV oocytes, 2973 proteins in MII oocytes, and 2082 in zygotes using a total of 7000 mouse oocytes at different developmental stages (Wang et al., 2010). To date, only two proteomics-based studies have been published that specifically aimed to use the technology to learn about oocyte-mediated reprogramming in the mouse. The Latham group used proteomics to study mammalian oocyte spindles in the context of SCNT and a more general approach applied by our laboratory led to the identification of 3699 oocytic proteins and, in combination with a bioinformatics screen, to the proposal of 28 putative reprogramming factors in the oocyte (Han et al., 2010; Pfeiffer et al., 2011). The work of the Latham group was based on the observation that SCNT embryos suffer from deficiencies in spindle composition during the first cleavages, which are accompanied by chromosome congression defects. Latham and colleagues isolated 5000 spindle-chromosome complexes from MII oocytes and 5000 spindle-chromosome complexes from cloned constructs 2 h after SCNT.
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
Protein expression profiles of both samples were compared using gel electrophoresis liquid chromatography MS (GeLC-MS/MS). Over 200 proteins could be identified in the cloned constructs sample, whereas over 400 proteins were detected in the ooplasm sample. This effort, combined with immunofluorescence analyses, resulted in the identification of four proteins that are underrepresented in the spindle-chromosome complexes of early SCNT embryos, namely clathrin heavy chain 1 (encoded by Cltc), aurora kinase B (encoded by Aurkb), dynactin subunit 4 (encoded by Dctn4) and casein kinase I alpha (encoded by Csnk1a1). This study was a remarkable example of how proteomics can be applied to investigate the mechanisms underlying developmental arrest of cloned embryos but also demonstrates how cumbersome the approach is, given the fact that 6225 cloned embryos had to be generated to yield a single sample for MS (Han et al., 2010). In our laboratory, we applied proteomics in a more general way to the study of oocyte-mediated reprogramming. We were interested in defining which factors may be responsible for the remarkable reprogramming capacity of the oocyte. Nuclear transfer remains the most efficient reprogramming method to date, in terms of speed and its ability to enable the derivation of fully reprogrammed ESCs. In this context, Hanna and colleagues proposed that the mechanism of pluripotency induction is active and directed in oocytes (and also in ESCs, when performing reprogramming via cell fusion), as opposed to being passive in iPS cells (Hanna et al., 2009). We reckoned that the factors responsible for this remarkable trait should have catalytic activity, be localized in the nucleus, and act on chromatin. We defined the proteomic makeup of the MII oocyte to an unprecedented depth of 3699 protein identities and used this dataset as a springboard for our screen for putative active reprogramming factors. This dataset was superimposed on a proteomics dataset of 4723 proteins from undifferentiated mouse ESCs. A bioinformatics screen on the overlap of 2556 proteins for the gene ontology (GO) terms nuclear localization, chromatin modification, and catalytic activity yielded a short list of 28 putative active reprogramming factors, including the already discovered SMARCA4 (Pfeiffer et al., 2011). These experiments and some further preliminary data are described in detail in the following section.
RESULTS In order to define the mouse oocyte proteome to a sufficient depth for meaningful bioinformatics analysis, we collected 1884 MII oocytes from B6C3F1 female mice (free of cumulus cells and zona pellucida). In parallel, undifferentiated ESCs were collected and lysed to yield 100 µg total protein. Both samples were subjected to 1D SDSPAGE and the gel was cut into 29 slices (for MII oocytes)
409
and 27 slices (for ESCs). Trypsin digestion was performed on each slice and samples were then analyzed by liquid chromatography-MS (LC-MS) using an LTQ Orbitrap XL Velos mass spectrometer. The spectra were analyzed by MaxQuant and Mascot Server software for protein identification. A total of 3699 protein groups for MII oocytes and 4723 protein groups for ESCs were identified at a false discovery rate of 1%. Reliable detection was assumed if a protein was identified by at least two peptides of which one unique. Our oocyte dataset of 3699 identities exceeded the previous dataset from Wang and colleagues (2973 identities) by nearly one-quarter. However, even this high number may lie far from the actual number of proteins present in the oocyte, particularly considering post-translational modifications, which we did not include in our data analysis. Furthermore, we measured gene expression at the mRNA level via microarray using a B6C3F1 MII oocyte sample. Interestingly, matching mRNA data could only be obtained for 2842 of the 3699 proteins detected in oocytes. However, part of this discrepancy has to be ascribed to missing probes for certain genes on the microarray itself (236 instances) and to not matching database entries (125 instances). The remaining 496 gene identities, 13.4% of the overall detected, had therefore actually been present on the protein level in MII oocytes, while their corresponding mRNAs were undetectable by microarray analysis. A GO analysis for biological processes revealed that those proteins were mainly associated with metabolism and translation, as well as protein transport and localization. Furthermore, three (Atrx, Cdk1, Hdac2) of the 496 genes are regarded as oocyte-specific markers (De La Fuente et al., 2004; de Vant’ery et al., 1996; Segev et al., 2001). Although we did not detect mRNAs for these three factors in our oocyte transcriptome, they have been found in the deep transcriptome of mouse oocytes (Tang et al., 2009). Still, these findings highlight the possible mismatch of transcriptome and proteome data in certain specialized cell types such as oocytes und underlines the necessity of reaching beyond the transcriptional level when analyzing biological samples. If the quality of a proteome dataset from denuded MII oocytes is high, one would expect to find oocyte-specific proteins, while cumulus cell-specific proteins should be absent. Indeed, of 68 oocyte-specific proteins, 46 were present in our MII proteome while cumulus cell-specific proteins were largely absent, as would be expected for a clean oocyte preparation. These observations argue in favor of a high-quality dataset. When we analyzed the distribution of the protein identities discovered in MII oocytes and ESCs grouped across the main GO biological process categories, no differences above 1.5% were observed. Therefore, both datasets seem to be of similar quality. It is not known whether one or multiple reprogramming pathways exist, or which factors these involve.
410
However, as reprogramming by the oocyte and by ESCs is relatively fast, it is likely that they share common reprogramming agents. We therefore inspected the proteins of the intersection between oocytes and ESCs, consisting of 2556 shared protein identities. To pursue the reprogramming factors, we filtered the shared subproteome for the GO biological process terms nuclear localization, chromatin modification, and catalytic activity. The rationale for choosing these terms is based on the fact that putative active reprogramming factors have chromatin as substrate (hence, nuclear localization and chromatin modification) and are neither significantly consumed nor titrated, since the ooplasm of an oocyte has been proposed to hold enough reprogramming factors for up to 100 nuclei (hence, catalytic activity) (Miyamoto et al., 2009). In fact, the proposal of Miyamoto and colleagues was, at least partly, recently confirmed in our laboratory by the successful transplantation of two independent somatic nuclei into a single ooplasm and the subsequent derivation of tetraploid ESCs from tetraploid cloned
PART | VI SCNT as a Tool to Answer Biological Questions
blastocysts (Pfeiffer et al., 2012). Our multilevel bioinformatics screening led to the identification of 28 genes encoding putative active reprogramming factors: Baz1b, Brcc3, Carm1, Ccnb1, Chd4, Dnmt1, Dnmt3a, Eed, Ep400, Hat1, Hdac1, Hdac2, Hdac6, Hells, Kdm1a/Lsd1, Kdm6a/Utx, Mll3, Prmt1, Prmt5, Prmt7, Rnf2, Rnf20, Ruvbl1, Ruvbl2, Smarca4/Brg1, Smarca5, Smarcal1, and Usp16. An interaction network of these 28 proteins based on the Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) database is shown in Figure 32.1 (Szklarczyk et al., 2011). Interestingly, the mRNA levels of 17 of the 28 putative reprogramming factors are upregulated during the conversion of mouse fibroblasts to iPS cells (Figure 32.2) (Samavarchi-Tehrani et al., 2010). To date, confirmation of our proposed reprogramming factors in the literature is available for 3 of the 28 encoded proteins, that is SMARCA4 (Singhal et al., 2010), protein arginine N-methyltransferase 5 (PRMT5) (Nagamatsu et al., 2011), and lysine-specific demethylase 6A (encoded by Kdm6a) (Mansour et al., 2012). PRMT5 even is able to
FIGURE 32.1 Functional protein association network of the putative 28 reprogramming factors based on the STRING database. Stronger associations are represented by thicker lines.
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
promote reprogramming together with POU domain, class 5, transcription factor 1 (Oct4) and Krueppel-like factor 4 (encoded by Klf4) alone, thereby substituting for Sox2 during the process. A more detailed description and accompanying records for the data described above can be found in our recently published paper entitled “Proteomic analysis of mouse oocytes reveals 28 candidate factors of the “reprogrammome” (Pfeiffer et al., 2011). Currently we are investigating the role of our proposed 28 factors in reprogramming using iPS technology as a screening tool (Figure 32.2). By overexpressing the putative reprogramming factors in mouse embryonic fibroblasts simultaneously with different combinations of the classical reprogramming factors Oct4, Sox2, Klf4, and c-myc we have confirmed our proposed hits based on the timing and efficiency of reprogramming. Initial experiments have also led to promising results for some of the others factors; however, the data are too preliminary to be reported in this chapter.
In a second approach, we are currently analyzing the subcellular localization of the 28 factors during mouse pre-implantation development. Until now, we have focused on the PRMT family and have been able to characterize the expression patterns of protein arginine N-methyltransferase 1 (PRMT1; encoded by Prmt1), histone-arginine methyltransferase CARM1/PRMT4 (encoded by Carm1/Prmt4) and PRMT5. In this context, it is interesting to note that both, Prmt1 and Prmt5 showed increased levels of mRNA during embryo cleavage, as detected by microarray or RNA sequence analyses at the four- or eight-cell stage, respectively (Tang et al., 2009; Zeng et al., 2004); however, protein distribution during early embryo development has never been assessed. Therefore GV oocytes, two-cell, four-cell, eight-cell, morula, and blastocyst stage embryos were processed by immunocytochemistry and subjected to confocal imaging. The three PRMT proteins were expected to be found in the nucleus of the cells. The results indeed showed
7.00
Hdac6
6.00
Rnf2 Smarcal1
Fold change in mRNA levels
5.00
Hat1 Dnmt3a RuvbI2
4.00
Brcc3 Hells Ccnb1
3.00
Prmt7 Kdm1 RuvbI1
2.00
Eed Dnmt1 Hdac2
1.00
Prmt1 Prmt5 0.00 0
2 5 8 11 16 Days after doxycyclin induction of reprogramming
411
21
FIGURE 32.2 Relative mRNA expression levels of the 28 putative reprogramming factors during iPS cell formation. Reprogramming was triggered by adding doxycycline to secondary mouse embryonic fibroblasts carrying inducible transgenes for Oct4, Sox2, Klf4, and c-myc. Based on data published by Samavarchi-Tehrani and colleagues (Samavarchi-Tehrani et al., 2010).
412
PART | VI SCNT as a Tool to Answer Biological Questions
(A)
(B) DNA
PRMT1 Overlay
DNA
(C) PRMT4 Overlay
DNA
PRMT5 Overlay
GV
2-cell
4-cell
8-cell
Morula
Blastocyst
FIGURE 32.3 Immunofluorescence staining of mouse oocytes and early embryos for different members of the PRMT family. Images of the subcellular distribution of PRMT1 (A), CARM1/PRMT4 (B), and PRMT5 (C) were obtained at different developmental stages. The nuclear localization of the different PRMTs is highly dependent on the developmental stage of the embryo.
that the proteins were localized in the nucleus, although not exclusively, as a cytoplasmic signal was also obtained dependent on the developmental stage (Figure 32.3). Cases of exclusively cytoplasmic localization were also observed. GV-stage oocytes showed no presence of PRMT1 in the nucleus; it was expressed only in the cytoplasm. The protein appeared as dots in the cytoplasm. At the two-cell stage, the protein can be detected in the cytoplasm and within the nucleus but not in nucleoli. The same pattern was observed at the four-cell stage; however, about onethird of embryos at that stage showed PRMT1 expression solely in the cytoplasm. At the eight-cell stage, the protein was again present in both the nucleus and cytoplasm, but it appeared to be concentrated in fewer spots than at earlier stages. In the morula, PRMT1 was not present in the nucleus, but was specifically located in the cytoplasm, in fewer spots than at the eight-cell stage. At the blastocyst stage, only small amounts of the protein were detectable, located within the nucleus. An interesting pattern was also observed for CARM1. Even though it was detected by MS, this arginine methyl transferase could barely be detected in GV oocytes and one-cell stage embryos by immunofluorescence. A clear nuclear signal for CARM1 became apparent at the twocell stage and was observed throughout pre-implantation development to the blastocyst with increasing fluorescence intensity. Similar to CARM1/PRMT4, PRMT5 could also be weakly detected by immunofluorescence in GV oocytes.
However, starting from the one-cell stage, a weak nuclear signal became apparent in some embryos and a strong nuclear signal was observed at the two-cell stage. Interestingly, nuclear localization was lost later in development and PRMT5 localization was mainly restricted to the cytoplasm in morula and blastocyst stage embryos. The subcellular analysis of three of the 28 putative reprogramming factors during normal pre-implantation development indicates that they are localized in the nucleus during the time when the majority of reprogramming takes place, which is a necessary characteristic for a genuine reprogramming factor.
DISCUSSION To date, proteomic approaches such as MS have rarely been used in studies of oocyte-mediated reprogramming in mammals. The reason is mainly technical: mammalian oocytes provide small samples sizes that, until a few years ago, were simply not suitable for a meaningful large-scale proteomic analysis. In this chapter, we show that this technical limitation has been overcome, at least in part, and that meaningful data can be generated from minute specimens such as mouse oocytes. These data can be used, in conjunction with bioinformatics analysis, to discover novel players in oocyte-mediate reprogramming and to conceive new hypotheses regarding the underlying mechanisms. Our proteomic study was driven by the hypothesis that oocyte-mediated reprogramming cannot simply rely on transcription factors, given the unparalleled speed of
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
the process after nuclear transfer. Transcription factors can interact with DNA at sites of accessible chromatin and therefore their action may largely be dependent on cell divisions, which gives them a window of opportunity to exert their function (passive mechanisms). In contrast, the mode of action of chromatin-modifying enzymes is independent of the cell cycle and activity. Furthermore, changes within chromatin structure and the epigenome are a necessary requirement for changing cellular programs, in the case of SCNT from a somatic program back to pluri-/ totipotency. For these reasons, we filtered the shared proteome of oocytes and ESCs for the criteria nuclear localization, enzymatic activity, and chromatin remodeling. The result is a shortlist of 28 putative active reprogramming factors, of which three are already confirmed and others are soon to follow. Our approach reveals that the era of proteomics has successfully stepped into the field of oocyte-mediated reprogramming. However, the data generated with our approach is purely qualitative. The lack of quantitative information greatly hinders the application of systems biology. This is especially the case as proteomics suffers from large numbers of false negatives. A certain protein may well be present even if it cannot be detected in a crude sample extract in shotgun proteomics. This is particularly true for generally under-represented proteins like transcription factors. For example, even in our in deep proteomic catalogue of 3699 identities for mouse MII oocytes, certain well-known oocytic proteins, like Oct4, were absent. Not only for this reason will the application of quantitative proteomics approaches be of major importance in the future. Until now, only one study has reported quantitative data on the mouse oocyte proteome; however, a label-free quantitation was applied, which led the authors themselves to use the expression semiquantitative MS analysis (Wang et al., 2010). In a different approach, Powell and colleagues were able to identify 16 proteins that were differentially expressed in high- versus low-quality pig oocytes using ExacTag proteomics in an attempt to identify novel oocyte quality markers. ExacTag proteomics enables the acquisition of quantitative data by introducing stable isotope tags to proteins samples after their collection but has not been used in the context of mouse oocytes to date. In a classical quantitative proteomics study, these labels are introduced by growing cells in medium containing isotope-labeled amino acids. This “stable isotope labeling by amino acids in cell culture” (SILAC) methodology can even be applied to whole organisms by means of a special diet containing heavy amino acids that is offered as a food supply over several generations. The so-called “SILAC mouse” enables high fidelity quantitative proteomics to be done in vivo and may well revolutionize the
413
field (Krüger et al., 2008; Zanivan et al., 2012). However, given the high price of a mouse SILAC diet and the large numbers of animals necessary to generate usable sample sizes in the field of SCNT, this advance is not yet applicable as a routine tool to study oocyte-mediated reprogramming. Possible ways out of this dilemma are spike-in or super-SILAC approaches. In a spike-in SILAC experiment, an easy to obtain heavily labeled sample, i.e. from cell culture, is used as reference outgroup for quantitation of the samples of interest. The results represent relative protein expression levels from the sample of interest in comparison to the outgroup. By calculating ratios of ratios, these relative expression levels can be transformed to gain reliable quantitative proteomic data on unlabeled samples. The downside, however, is that data can only be obtained on proteins that are present in both the sample of interest and the outgroup, a challenging prerequisite when it comes to the analysis of specialized cell types such as oocytes or developing embryos. Recently, to overcome this limitation, the application of so-called super-SILAC spike-in mixtures has been proposed, whereby the reference outgroup consist of an assembly of heavily labeled cell lines or tissues to enable a high coverage of all possible proteins in the sample of interest during the experiment (Geiger et al., 2010; 2011). An important application of these methods in the future will be the assessment of reprogramming determinants in oocytes. A smart way to grasp them would be to compare the proteome makeups of oocytes already known to be good or not-so-good reprogrammers. We recently observed that oocytes of aged mice not only support mouse embryo development after SCNT, but actually do so with higher blastocyst rates. A comparison of the transcriptomes of normal and aged oocytes revealed 724 genes (4.33% of all transcripts detected) that were differently expressed between the two age groups (Esteves et al., 2011). However, whether the corresponding proteins would match these differences is still unclear. In an attempt to fill this gap, we are currently generating the quantitative proteomes of oocytes from prepubertal, middle-aged, and old mice using the spike-in SILAC methodology; we expect to find putative players at the heart of the differing reprogramming abilities of oocytes from differently aged mice. Another interesting approach is the comparison of oocytes from different mouse strains. When oocytes from mice with distinct genetic background are used for SCNT experiments, blastocyst rates vary substantially after activation (Kishigami et al., 2007). It is clear that this observation has to be linked to the oocyte, as similar donor nuclei were used in the different experiments. Therefore, defining quantitative proteomes of oocytes from different mouse strains will provide a great resource to learn
414
PART | VI SCNT as a Tool to Answer Biological Questions
about the mechanisms of oocyte-mediated reprogramming and to identify novel putative proteins that are crucial for the process.
Proteomic experiments aiming to understand the prerequisites of successful cloning and the unfolding processes during cloned embryo development are not only of interest to the understanding of reprogramming but will also be of great value for germ cell and developmental biology, as well as reproductive medicine.
It is important to mention that proteomic experiments aiming to understand the prerequisites of successful cloning and the processes unfolding during cloned embryo development are not only of interest to the understanding of reprogramming but will also be of great value for germ cell and developmental biology, as well as reproductive medicine. As the technical hurdles for proteomics can be expected to be eased in the near future, an increasing number of proteomics-based studies is expected to advance the field for the benefit of all reprogramming platforms, and cell biology in general.
MATERIALS AND METHODS Mice Six- to eight-week-old B6C3F1 (C57Bl/6J × C3H/HeN) mice were used as oocyte and cumulus cell donors. All mice were primed with 10 IU each pregnant mare serum gonadotropin (PMSG) and human chorionic gonadotropin (hCG) injected intraperitoneally 48 h apart, and sacrificed by cervical dislocation 15 h after hCG injection to collect the cumulus-oocyte complexes from the oviducts. Mice were maintained and used for experiments according to institutional guidelines.
Oocyte and Embryonic Stem Cell Sample Preparation and Processing for Mass Spectrometry Sample Preparation A total of 1884 MII oocytes were denuded using warm Tyrode acid solution and collected in SDS lysis buffer (4% SDS, 100 mM Tris-HCl pH 7.5, 0.1 M dithiothreitol (DTT); total volume 70 µl). The sample was sonicated using a Bioruptor sonication system (5 cycles of 30 s on and 30 s off at a high setting) to ensure total lysis and to shear DNA. ESCs from one 10 cm plate were subjected to double sedimentation and collected in SDS lysis buffer as described. Following the addition of 20 µl NuPAGE LDS
sample buffer and heating, oocyte lysate proteins were size fractionated by 1D gel electrophoresis in two neighboring lanes of a 4-20% NuPAGE gel (Invitrogen) and stained with a Colloidal Blue staining Kit (Invitrogen). Lanes containing protein were sliced into 29 (oocytes) and 27 (ESCs) pieces and processed for GeLC-MS/MS. Briefly, proteins within each gel piece were subjected to reduction (10 mM DTT; 45 min at 56°C) and alkylation (2-iodoacetamide; 30 min at room temperature, in the dark) followed by tryptic cleavage for 16 h at 37°C. Peptides were then extracted from the gel pieces as previously described (Shevchenko et al., 2006), desalted, and concentrated using Stage Tips (Rappsilber et al., 2003).
LC-MS/MS and Data Analysis As described in Pfeiffer et al. (2011), each fraction, which represented the peptide content of two neighboring gel pieces, was analyzed by reversed phase chromatography using an Easy-LC nanoflow system (Proxeon) coupled online via in-house packed fused silica capillary column emitters (length, 15 cm; internal diameter, 75 µm; resin ReproSil-Pur C18-AQ, 3 µm) and a nanoelectrospray source (Proxeon) to an LTQ Orbitrap Velos mass spectrometer (Thermo Scientific). Peptides were eluted from the C18 column by applying a linear gradient of 5-35% buffer B (80% acetonitril, 0.5% acetic acid) over 120 min followed by a gradient of 35-98% over 15 min. The mass spectrometer was operated in the positive ion mode (source voltage 2.2 kV), automatically switching in a datadependent fashion between survey scans in the mass range of m/z 300-1650 and MS/MS acquisition. Collision-induced MS/MS spectra from the 15 most intense ion peaks in the MS were collected (Target Value of the Orbitrap survey scan: 1,000,000; resolution R = 60,000; Lockmass set to 445.120025). Raw data files were then processed by MaxQuant software (version 1.0.12.36) in conjunction with Mascot database searches (Cox and Mann, 2008). Data were searched against the mouse International Protein Index (IPI) sequence database (version 3.60) concatenated with reversed sequence versions of all entries. The parameter settings were: trypsin as digesting enzyme, a minimum length of six amino acids, a maximum of two missed cleavages, carbamidomethylation at cysteine residues set as fixed, and oxidation at methionine residues as well as acetylation at the protein N-termini as variable modifications. The maximum allowed mass deviation was 7 ppm for MS and 0.5 Da for MS/MS scans. Protein groups were regarded as being unequivocally identified with a false discovery rate set to 1% for all protein and peptide identifications when there were at least two matching peptides, with one being unique to the protein group. MS data was also analyzed using the SEQUEST search algorithm and Proteome Discoverer software (Thermo Scientific).
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
Transcriptome We obtained microarray data so that we could compare the mouse oocyte proteome and transcriptome. As described in Pfeiffer et al. (2011), pools of 20 zona-enclosed B6C3F1 oocytes were each subjected to total RNA extraction followed by preamplification, reverse transcription, labeling, and hybridization. RNA (samples in 300 µl RLT buffer containing 1% β-mercaptoethanol) was isolated using RNeasy Micro Kit as described by the manufacturer (Qiagen, no DNase treatment). An examination of total RNA on an Agilent 2100 Bioanalyzer and RNA Pico 6000 Lab-Chip Kit confirmed the extraction of high-quality RNA, which was then prepared for gene expression profiling. A RiboAmp HS Plus Amplification Kit (MDS Analytical Technologies GmbH, Germany) was used to amplify total RNA (two rounds of amplification) according to manufacturer’s instructions. After the second round of amplification, amplified RNA was eluted with 15 µl RE buffer. The concentration of amplified RNA was measured using an Agilent Bioanalyzer 2100 and a RNA 6000 Lab-Chip Kit. Three micrograms of amplified RNA were labeled with Cy3 using a Turbo Labeling CY3 Kit (MDS Analytical Technologies GmbH, Germany). The concentration and frequency of incorporation were measured using a NanoPhotometer (Implen, Munich, Germany). Fragmentation (1650 ng Cy3-labeled amplified RNA) and hybridization were performed following the hybridization procedure recommended by the array manufacturer (Agilent Technologies), with a modification given by the manufacturer of the Turbo Labeling CY3 Kit. Microarray wash and detection of the labeled RNA on GeneChips were performed according to the instructions of Agilent Technologies. Gene expression profiling was performed using Agilent’s Whole Mouse Genome Oligo Microarrays (4 × 44 k, each array with 41,174 features). Array image acquisition and feature extraction was performed using the Agilent G2505B Microarray Scanner and Feature Extraction software version 9.5 (Agilent Technologies).
Database Search and Bioinformatics Analysis and preprocessing of data were performed with Bioconductor software (Gentleman et al., 2004) using the R statistical computing and graphics environment (R Development Core Team, 2009), as described in Pfeiffer et al. (2011). First, transcriptome data were analyzed by Agilent Feature Extraction software version 9.5. All Agilent microarray probe sets were mapped to PubMed ENTREZ, which was used as the common point of reference. Mapping was accomplished using the mgug4122a. db R package version 2.4.5. Positive hybridization for both samples was taken as evidence that the mRNA
415
corresponding to the probe was present. By this criterion, the dataset was reduced from 27,032 (flag filtering) to 15,476 probe sets (double positive), and the gene hits in the transcriptome were screened to eliminate duplicates and multiple probes. Thus, of the initial 16,157 genes (corresponding to 27,032 probe sets), 10,833 genes (corresponding to 15,476 probe sets) were retained for further analysis. Next, we processed the proteome dataset provided by the MaxQuant software. Frequent new releases of the IPI (Kersey et al., 2004) implied that an update of the MaxQuant annotation was required, so we utilized supporting history files obtained from the IPI website (ftp://ftp. ebi.ac.uk/pub/databases/IPI/current) to upgrade to version 3.76. Based on the ipi.MOUSE.xrefs.gz file downloaded from IPI data repository, two lists of proteins represented by PubMed ENTREZ identifiers were established from MaxQuant proteome datasets, which yielded 3574 proteins from the initial 3699 proteins detected in oocyte and 4588 proteins from the initial 4723 proteins detected in ESCs. MaxQuant creates protein groups if the identified peptide set of one protein is equal to or is present in another protein’s peptide set. Protein groups were processed by taking the IPI identifier for which the highest peptide count was measured. When the same highest peptide count was obtained for two proteins in a protein group, we considered this a tie and arbitrarily took the first protein so as to avoid having to discard such hits. Such a tie may happen in case of isoforms (splice variants), but may also happen in the case of close paralogues, where no unique peptides can be detected that reflect the difference between the paralogues. A tie may also happen if a protein (referred to by a specific IPI identifier) is mapped to more than one gene locus. The disadvantage of arbitrarily selecting one of the hits is that for the subsequent GO analysis, we may have considered the wrong protein in case of paralogues. However, since close paralogues usually have similar function and feature a similar GO annotation, this disadvantage is outweighed by the advantage of a more exhaustive GO analysis.
Fixation of Germinal Vesicle Stage Oocytes and Fertilized Embryos and Processing for Immunofluorescence Oocytes and embryos were fixed and permeabilized in fixation/permeabilization solution [1% paraformaldehyde in phosphate-buffered saline (PBS) containing 0.1% Triton X-100 (PBT)] in 96-well plates for 15 min. Zonae pellucidae were removed using warm Tyrode/polyvinylpyrrolidone solution. For blocking non-specific-binding sites and quenching residual paraformaldehyde, the embryos were treated with blocking solution [0.1% Tween20, 2% bovine serum albumin (BSA), 2% glycine and 5% donkey serum in PBS] for at least 1 h (or until further use) at 4°C.
416
The cells were then incubated in a dilution of the primary antibody (PRMT1, Abcam Ab73246; Carm1, Cell Signaling C31G9; PRMT5, Abcam Ab31751) in AB buffer [0.1% Tween 20, 0.5% BSA, 0.5% Glycine and 1.25% Donkey serum in PBS] overnight at 4°C. Prior to use, primary antibody dilutions were centrifuged for 20 min at 13.000 rpm to sediment particles. Afterwards, cells were washed twice with PBT for 5 min and incubated in a dilution of the secondary antibody [donkey anti-rabbit immunoglobulin G (IgG) Alexa Fluor 647 (Invitrogen A31573) in AB buffer] for 2 h at room temperature in the dark. Subsequently, cells were washed again with PBT. Nuclei were counterstained with 0.1% YO-PRO-1 in AB buffer for 10 min.
Confocal Imaging and Image Analysis Oocytes and embryos were transferred into microdrops of PBS (50 μl) for imaging and covered with mineral oil on an imaging-quality thin-bottom plastic dish (Lumox, Greiner Bio-One, Germany). An inverted microscope (TE-2000U, Nikon, Düsseldorf, Germany) coupled to an UltraVIEW RS3 spinning disk confocal imaging system (PerkinElmer LAS, Jürgensheim, Germany) with 40× magnification was used for imaging. Analysis of the captured images was performed ImageJ software (Abramoff et al., 2004).
ACKNOWLEDGEMENTS This study was supported by the special priority program (Schwerpunktprogramm) No. 1356 of the Deutsche Forschungsgemeinschaft (grants BO2540/3-1 and FU583/2-1) and by the Max Planck Society. We are grateful to Andrea Krüger and Fataneh Fathi Far for immunofluorescence confocal images presented in Figure 32.3.
REFERENCES Abramoff, M.D., Magalhaes, P.J., Ram, S.J., 2004. Image Processing with ImageJ.. Biophotonics Int. 11 (7), 36–42. Cox, J., Mann, M., 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26 (12), 1367–1372. (Epub 2008/11/26). De La Fuente, R., Viveiros, M.M., Wigglesworth, K., ATRX, Eppig J.J., 2004. a member of the SNF2 family of helicase/ATPases, is required for chromosome alignment and meiotic spindle organization in metaphase II stage mouse oocytes. Dev. Biol. 272 (1), 1–14. Esteves, T.C., Balbach, S.T., Pfeiffer, M.J., Arauzo-Bravo, M.J., Klein, D.C., Sinn, M., et al., 2011. Somatic cell nuclear reprogramming of mouse oocytes endures beyond reproductive decline. Aging Cell 10 (1), 80–95. (Epub 2010/10/26). Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R., Mann, M., 2010. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods. 7 (5), 383–385. (Epub 2010/04/07).
PART | VI SCNT as a Tool to Answer Biological Questions
Geiger, T., Wisniewski, J.R., Cox, J., Zanivan, S., Krüger, M., Ishihama, Y., et al., 2011. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 6 (2), 147–157. (Epub 2011/02/05). Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., et al., 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome. Biol. 5 (10), R80. (Epub 2004/10/06). Han, Z., Liang, C.G., Cheng, Y., Duan, X., Zhong, Z., Potireddy, S., et al., 2010. Oocyte spindle proteomics analysis leading to rescue of chromosome congression defects in cloned embryos. J. Proteome. Res. 9 (11), 6025–6032. (Epub 2010/10/05). Hanna, J., Saha, K., Pando, B., van Zon, J., Lengner, C.J., Creyghton, M.P., et al., 2009. Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462 (7273), 595–601. (Epub 2009/11/10). Kersey, P.J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E., Apweiler, R., 2004. The International Protein Index: an integrated database for proteomics experiments. Proteomics 4 (7), 1985–1988. Kishigami, S., Bui, H.T., Wakayama, S., Tokunaga, K., Van Thuan, N., Hikichi, T., et al., 2007. Successful mouse cloning of an outbred strain by trichostatin A treatment after somatic nuclear transfer. J. Reprod. Dev. 53 (1), 165–170. (Epub 2006/11/02). Krüger, M., Moser, M., Ussar, S., Thievessen, I., Luber, C.A., Forner, F., et al., 2008. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134 (2), 353–364. (Epub 2008/07/30). Ma, M., Guo, X., Wang, F., Zhao, C., Liu, Z., Shi, Z., et al., 2008. Protein expression profile of the mouse metaphase-II oocyte. J. Proteome. Res. 7 (11), 4821–4830. (Epub 2008/09/23). Mansour, A.A., Gafni, O., Weinberger, L., Zviran, A., Ayyash, M., Rais, Y., et al., 2012. The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming. Nature (Epub 2012/07/18). Miyamoto, K., Tsukiyama, T., Yang, Y., Li, N., Minami, N., Yamada, M., et al., 2009. Cell-free extracts from mammalian oocytes partially induce nuclear reprogramming in somatic cells. Biol. Reprod. 80 (5), 935–943. Nagamatsu, G., Kosaka, T., Kawasumi, M., Kinoshita, T., Takubo, K., Akiyama, H., et al., 2011. A germ cell-specific gene, Prmt5, works in somatic cell reprogramming. J. Biol. Chem. 286 (12), 10641–10648. (Epub 2011/01/29). Pfeiffer, M., Stehling, M., Jauch, A., Boiani, M., 2012. ES Cell Lines from Tetraploid Mouse Blastocysts. In: Baharvand, H., Aghdami, N. (Eds.), Advances in Stem Cell Research Humana Press, New York, pp. 1–16. Pfeiffer, M.J., Siatkowski, M., Paudel, Y., Balbach, S.T., Baeumer, N., Crosetto, N., et al., 2011. Proteomic analysis of mouse oocytes reveals 28 candidate factors of the “reprogrammome.”. J. Proteome. Res. 10 (5), 2140–2153. (Epub 2011/02/25). R Development Core Team, 2009. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Rappsilber, J., Ishihama, Y., Mann, M., 2003. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75 (3), 663–670. Samavarchi-Tehrani, P., Golipour, A., David, L., Sung, H.K., Beyer, T.A., Datti, A., et al., 2010. Functional genomics reveals a BMP-driven
Chapter | 32 Proteomic Approach to the Reprogramming Machinery of the Mouse Oocyte
mesenchymal-to-epithelial transition in the initiation of somatic cell reprogramming. Cell Stem Cell 7 (1), 64–77. (Epub 2010/07/14). Segev, H., Memili, E., First, N.L., 2001. Expression patterns of histone deacetylases in bovine oocytes and early embryos, and the effect of their inhibition on embryo development. Zygote 9 (2), 123–133. Shevchenko, A., Tomas, H., Havlis, J., Olsen, J.V., Mann, M., 2006. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1 (6), 2856–2860. Singhal, N., Graumann, J., Wu, G., Arauzo-Bravo, M.J., Han, D.W., Greber, B., et al., 2010. Chromatin-Remodeling Components of the BAF Complex Facilitate Reprogramming. Cell 141 (6), 943–955. Szklarczyk D., Franceschini A., Kuhn M., Simonovic M., Roth A., Minguez P., et al., The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic acids research. 2011;39(Database issue):D561–8. Epub 2010/11/04. Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., et al., 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods. 6 (5), 377–382. (Epub 2009/04/08). Vitale, A.M., Calvert, M.E., Mallavarapu, M., Yurttas, P., Perlin, J., Herr, J., et al., 2007. Proteomic profiling of murine oocyte maturation. Mol. Reprod. Dev. 74 (5), 608–616. (Epub 2006/10/18).
417
Wang, S., Kou, Z., Jing, Z., Zhang, Y., Guo, X., Dong, M., et al., 2010. Proteome of mouse oocytes at different developmental stages. Proc. Natl. Acad. Sci. USA 107 (41), 17639–17644. (Epub 2010/09/30). Wright, P.C., Noirel, J., Ow, S.Y., Fazeli, A., 2012. A review of current proteomics technologies with a survey on their widespread use in reproductive biology investigations. Theriogenology 77 (4), 738–765 e52. Yurttas, P., Morency, E., Coonrod, S.A., 2010. Use of proteomics to identify highly abundant maternal factors that drive the egg-to-embryo transition. Reproduction 139 (5), 809–823. (Epub 2010/01/29). Zanivan, S., Krueger, M., Mann, M., 2012. In vivo quantitative proteomics: the SILAC mouse. Methods Mol. Biol. 757, 435–450. (Epub 2011/09/13). Zeng, F., Baldwin, D.A., Schultz, R.M., 2004. Transcript profiling during preimplantation mouse development. Dev. Biol. 272 (2), 483–496. (Epub 2004/07/30). de Vant’ery, C., Gavin, A.C., Vassalli, J.D., Schorderet-Slatkine, S., 1996. An accumulation of p34cdc2 at the end of mouse oocyte growth correlates with the acquisition of meiotic competence. Dev. Biol. 174 (2), 335–344.