Free download. Book file PDF easily for everyone and every device. You can download and read online Quantitative Proteomics file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Quantitative Proteomics book. Happy reading Quantitative Proteomics Bookeveryone. Download file Free Book PDF Quantitative Proteomics at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Quantitative Proteomics Pocket Guide.

Where labelling cells with SILAC may take up to several days [ 8 ], chemical labelling protocols are usually performed in less than an hour [ 25 ]. Chemical labelling can be applied to any protein sample, not just metabolically active samples, and some of the techniques allow for a high number of samples to be analysed simultaneously [ 26 ]. However, since chemical labelling is done either at the protein level or at the peptide level and at a relatively late stage in the sample preparation protocol, systematic errors are introduced more readily.

Also, labelling at the protein level requires specific proteins such as cysteine or lysine, which makes peptides without these amino acids not quantifiable [ 10 , 24 ]. The first chemical labelling technique that was described for quantitative mass spectrometry was the isotope-coded affinity tag ICAT.

In ICAT, a thiol reactive group is used to conjugate the tag to cysteine residues in the protein. Apart from the reactive group, the tag has a linker and a biotin moiety. The linker has either eight hydrogen atoms for the light version or eight deuterium atoms for the heavy version, which are used to distinguish two differentially labelled conditions by the 8 Da shift in the mass spectrum [ 26 ]. The biotin moiety of the tag can be used to affinity purify the tagged peptides after trypsinisation. The weakness of ICAT lies in the requirement of cysteine residues to be present in the peptide, which leads to a limitation in the amount of peptides tagged.

Furthermore, the presence of deuterium causes a shift in elution times when peptides are fractionated using HPLC, which hampers subsequent data analysis [ 27 ]. This elution time shift problem was later solved by introducing 13 C instead of D into the linker moiety. ICAT labelling has, for instance, been used to investigate the redox state of proteins in a study to the formation of reactive oxygen species and the way this is dealt with by the cell [ 28 ].

The ability to use ICAT in human samples has been exploited in screening cerebrospinal fluid samples of Alzheimer patients to find novel prognostic biomarkers [ 29 ]. In ICPL, lysine residues in intact proteins are labelled, which are more common than cysteine residues. The mass difference between isotope pairs of the labelled and unlabelled peptides depends on the amount of labelled lysine residues in the peptide and can be determined fairly simply, which provides strong constraints for database searches [ 30 ]. A disadvantage of labelling lysine residues is that modifying the residue side chain makes it impossible for trypsin to cleave at this particular lysine residue.

As such, this results in much longer peptides after trypsin digestion, as cleavage will only occur after arginine residues, which may lead to proteolytic peptides that cannot be detected. It is therefore recommended to either use another or an additional protease for protein digestion, or to perform the labelling at the peptide level after proteolytic cleavage. A study on tumour cell senescence in which ICPL was successfully used is a good indicator for the power of quantitative proteomics in general.

Here, an effect of tumour cell senescence on several important tumourigenesis proteins such as cMYC and key metabolic enzymes such as ATP synthetases were found [ 31 ]. Here, the label is conjugated to the N-termini and lysine residues of peptides, so that in principle every peptide is labelled Figure 4.

The various isobaric tags themselves have different masses, but are balanced by a linker moiety that ensures identical intact masses for all possible combinations of tag plus linker. As a consequence, differentially labelled peptides end up in the same precursor peak in the mass spectrum. The relative peak intensities of the tags are then used for quantitation [ 26 ].

Since identical peptides end up in the same peak, the complexity of the MS spectrum is not altered as a result of the labelling procedure. Furthermore, there are commercial kits available with up to 10 different tags, providing the possibility to run and compare 10 samples simultaneously. The most prominent disadvantage of this method is that the tag, just like most other chemical tags, is incorporated at the peptide level.

Principle of isobaric tagging. Peptides are tagged with chemical labels that have identical masses due to a delicate balance between individual tag and linker masses. Labelled peptides are then mixed and measured by mass spectrometry. This allows for the relative abundance determination. This study showed that the changes in protein expression of different prion diseases are markedly similar, while most changes at the protein level were found in the cerebellum.

This study provides an excellent example of biomarker research using mass spectrometry and could be a step towards defining biomarkers for different prion diseases, which are otherwise difficult to classify. A simple method of labelling compounds at the peptide level for relative quantitation is dimethylation. Either light-labelled with H or heavy-labelled with D dimethyl groups are conjugated to the N-terminus of the peptides and to free lysine residue side chains. The advantages of dimethyl labelling include low cost, high speed and possibilities for automated sample preparation.

However, since labelling occurs at the peptide level, variation between runs is still inherent to the process [ 10 , 34 ]. The first incarnation of dimethyl labelling was limited to only two different flavours. Although this may still be lower than the amount of different labels that can be achieved using isobaric tagging, it is significantly cheaper.

Dimethyl labelling can be used for a variety of quantitative measurements, for instance, after a pulldown or immunoprecipitation enrichment protocol. Using an antibody to probe for phosphopeptides in combination with labelling allows one to quantitatively monitor phosphorylation events [ 36 ]. Another possibility that was recently introduced is using dimethylation to study DNA—protein interactions, e. These widely different applications show the power of dimethylation as a quantitative proteomics tool. Labelling schemes of triplex stable isotope dimethyl labelling.

Quantitative proteomics using mass spectrometry.

Figure adapted from [35]. Another way to differentially label samples for quantitative purposes is the use of heavy oxygen. This labelling method is different from other labelling protocols in that the label incorporation is achieved during the digestion of proteins into peptides.

By performing the digestion in water that contains 18 O instead of 16 O, the carboxyl terminus of every peptide will incorporate two 18 O atoms. This method can be incredibly fast, with reports of labelling being achieved in 15 min [ 25 ]. A potential pitfall is that the labelling may be incomplete when not performed in a correct manner, leading to multiple peaks in the MS spectrum and therefore resulting into difficulties in quantitation [ 25 , 38 ].

Our lab has described a protocol to avoid incomplete labelling and to assure full incorporation of the heavy oxygen label [ 39 ]. By using immobilized trypsin under acidic conditions, all proteolytic peptides could be fully labelled with heavy oxygen with no traces of back-exchange. The labelling protocol was implemented into a protein—protein interaction analysis pipeline to differentiate between bona fide interaction partners of the low-level expressing cell cycle regulator cyclin-dependent kinase 9 Cdk9 and non-specifically binding or background proteins Figure 6.

Previously known, as well as novel, interaction partners of Cdk9 were characterized, among which most notable are the Mediator complex and several other proteins involved in transcriptional regulation. It was shown that a differential proteomics approach based on 18 O labelling provides a valuable method for high-confidence determination of protein interaction partners and is easily implemented in protein network analysis workflows.

Another method to achieve consistent labelling is to use alternative proteases besides trypsin, e. All label-based approaches described above are geared towards generating relative quantitative measurements. In many cases though, it would be interesting to measure absolute quantities of proteins instead. In order to gain absolute quantitation results, synthesized peptides or proteins containing heavy isotope labels that correspond to the target peptide or protein of interest can be spiked into the sample at a known concentration, after which the intensities of target and standard can be compared to one another.

Obviously, the standard peptide can be modified with one or multiple post-translational modifications if needed [ 41 ]. Due to the fact that this spiked standard provides absolute rather than relative quantitation, this technique has been dubbed absolute quantitation AQUA.

References

Spike-in components that can be used for AQUA include peptides with stable isotopes incorporated into one or several amino acids [ 41 ], a construct in which several peptides are strung together which has the added advantage of being able to quantify multiple peptides in one run [ 42 ] , or an entirely labelled protein to quantify the amount of protein [ 43 ]. As with other quantitation techniques, the stage at which the label is incorporated largely determines the extent of the systematic quantitation error that is introduced into the sample.

In studying hormonal influence on blood pressure, and more specifically angiotensin II, spiking in the synthesized heavy labelled angiotensin has been used to absolutely quantify protein levels in plasma. As such, it was shown that chronic kidney disease patients had strongly increased levels of angiotensin II [ 44 ]. These results show that AQUA can be useful in the field of biomarker research, although it has many more applications, such as in assessing the levels of enzymes in prokaryotes [ 45 ]. With the development of better and faster mass spectrometers with higher sensitivity and heavier duty cycles, the number of studies that use label-free quantitation LFQ methods has increased over the past few years.

Furthermore, there is no need for often expensive labelling kits. There are two major approaches employed in label-free quantitation: spectral counting and intensity-based quantitation. However, there are several issues that should be taken into account here.

In general, larger proteins generate more proteolytic peptides, which increases the chance that multiple peptides for one such protein are detected. Furthermore, in principle every peptide has different physicochemical properties, which influence the ionizability and, therefore, the detectability in the mass spectrometer. To address this, several modifications of spectral counting have been developed, which incorporate mathematical corrections, such as introducing a normalised spectral abundance factor into the equation to account for protein length variability e.

We have developed the normalized spectral abundance factor NSAF approach for using spectral counting in quantitative proteomics Zybailov et al. This approach takes into account the sample-to-sample variation that is obtained when carrying out replicate analyses of a sample and the fact that longer proteins tend to have more peptide identifications than shorter proteins. Examples of the application of the NSAF approach to quantitative proteomic analysis include work on the expression changes of membrane proteins in S. We have recently improved the NSAF approach to better deal with peptides shared between multiple proteins.

It can be implemented using the open source software package "plgem" written in R and maintained by the BioConductor project. We have demonstrated that NSAF datasets share substantial statistical similarities with GeneChip data, suggesting that most GeneChip-specific statistical tools should be applicable to the analysis of NSAF datasets as well Pavelka et al.

Zybailov, B. Abstract Zybailov, B. If the cells are auxotroph for the selected amino acids, all proteins in a cells are generally completely labelled after several doublings [ 8 ]. Conversely, this means that the cells must be dividing, which precludes the use of this technique on primary tissue samples. A complication that has been described in the literature that could potentially interfere with quantitation of SILAC labelled proteins is the natural occurrence of arginine-to-proline conversion. While lysine and arginine are relatively stable in the cell, it is possible for the cell to produce proline from spare arginine, which can then lead to heavy labelled proline.

Obviously, this is undesirable and should be accounted for either experimentally or during data analysis see e. Typical SILAC workflow: cells representing two different biological conditions are grown in either light or heavy medium containing amino acid with stable heavy isotopes. Cells are then harvested and mixed in equal amounts and all sample preparation is performed on the mixed cell populations.

In the final mass spectrum, a tryptic peptide will be observed as a peak pair, which represents the two sample conditions. By calculating the peak intensity ratio, the conditions can be compared in a quantitative fashion. Labelling using SILAC can also be used to examine post-translational protein modifications such as phosphorylation and ubiquitination in a quantitative manner.

An example of this is a phosphoproteomic study in yeast after the knockout of a kinase that plays a role in growth and division [ 19 ]. SILAC can in principle be used for any cultured cell type. A recent study from our lab into hormonal signalling in Drosophila combined SILAC mass spectrometry with transcriptome analysis [ 20 ]. Drosophila Kc cells were stimulated with the key insect hormone ecdysone and both mRNA expression and protein expression were studied during a time course.

The results showed a correlation in the changing levels of mRNA and protein over time, although it became evident that in general there is a time delay between mRNA and protein expression. Not all mRNA—protein pairs showed this delay though, which could be attributed to post-transcriptional regulation events of mRNAs and to variable stability of proteins. Several interesting proteins linked to signalling pathways such as target of rapamycin TOR and Notch were identified as being regulated by ecdysone signalling, giving an indication of the scope of the ecdysone system.

This study shows the applicability of SILAC in studies where a significant number of proteins are changed, and the correlation between mRNA and protein levels show the quantitative power of SILAC technology, as well as the power of this method to identify signalling networks in cellular systems. In general, there is also a correlation, albeit weak, between steady-state levels of mRNA and protein Figure 3. This is mainly true for products that show relatively high expression, which has also been reported in other studies. From this plot, it becomes also clear that for many mRNA products no corresponding protein was identified, illustrating the technical limitations in proteomics that still prevent very low abundant proteins to be detected.

In addition, there were several protein products that could not be matched to mRNAs, indicating that, since the intensities of these proteins are generally similar to those with a matched mRNA, this could be attributed to the incomplete annotation of the Drosophila database.

The intensity distribution of proteins for which no corresponding hit in the transcriptome analysis was found is represented by the green box plot. This distribution is very similar to the distribution of overlapping hits blue data points.

Quantitative Proteomics (RSC Publishing)

An interesting technological progression in the recent years has been the emergence of fully labelled SILAC organisms, such as fruit flies, mice and rats, which allows for in vivo quantitative protein analysis [ 12 , 21 , 22 ]. This allows scientists to study alterations of protein levels in lab mice with as little variation possible, which in turn makes it possible to study the dynamic proteome in tissue. Currently, the generation of SILAC labelled mice is limited by cost considerations due to the expenses required to raise the mice on a diet of labelled food and this has prevented large-scale usage thus far.

By spiking all the samples with this standard, quantitation becomes possible without the necessity to label the samples themselves using SILAC. It should be noted that it is recommended to have a representative sample for the tissue to be studied in the SILAC standard, which limits the usage of this technique to tissues with a representative cell line.

For a more in-depth review on this topic, see [ 23 ]. The use of chemical labelling strategies for relative quantitation in proteomics dates back to the late s [ 24 ]. The major advantages of using chemical techniques rather than metabolic labelling are the reduced cost and the higher speed of sample processing and analysis. Where labelling cells with SILAC may take up to several days [ 8 ], chemical labelling protocols are usually performed in less than an hour [ 25 ]. Chemical labelling can be applied to any protein sample, not just metabolically active samples, and some of the techniques allow for a high number of samples to be analysed simultaneously [ 26 ].

However, since chemical labelling is done either at the protein level or at the peptide level and at a relatively late stage in the sample preparation protocol, systematic errors are introduced more readily. Also, labelling at the protein level requires specific proteins such as cysteine or lysine, which makes peptides without these amino acids not quantifiable [ 10 , 24 ].

The first chemical labelling technique that was described for quantitative mass spectrometry was the isotope-coded affinity tag ICAT. In ICAT, a thiol reactive group is used to conjugate the tag to cysteine residues in the protein. Apart from the reactive group, the tag has a linker and a biotin moiety.


  • Search form.
  • Quantitative proteomics: challenges and opportunities in basic and applied research?
  • Operations Research: An Introduction.

The linker has either eight hydrogen atoms for the light version or eight deuterium atoms for the heavy version, which are used to distinguish two differentially labelled conditions by the 8 Da shift in the mass spectrum [ 26 ]. The biotin moiety of the tag can be used to affinity purify the tagged peptides after trypsinisation. The weakness of ICAT lies in the requirement of cysteine residues to be present in the peptide, which leads to a limitation in the amount of peptides tagged.

Furthermore, the presence of deuterium causes a shift in elution times when peptides are fractionated using HPLC, which hampers subsequent data analysis [ 27 ]. This elution time shift problem was later solved by introducing 13 C instead of D into the linker moiety. ICAT labelling has, for instance, been used to investigate the redox state of proteins in a study to the formation of reactive oxygen species and the way this is dealt with by the cell [ 28 ].

The ability to use ICAT in human samples has been exploited in screening cerebrospinal fluid samples of Alzheimer patients to find novel prognostic biomarkers [ 29 ]. In ICPL, lysine residues in intact proteins are labelled, which are more common than cysteine residues. The mass difference between isotope pairs of the labelled and unlabelled peptides depends on the amount of labelled lysine residues in the peptide and can be determined fairly simply, which provides strong constraints for database searches [ 30 ].

A disadvantage of labelling lysine residues is that modifying the residue side chain makes it impossible for trypsin to cleave at this particular lysine residue. As such, this results in much longer peptides after trypsin digestion, as cleavage will only occur after arginine residues, which may lead to proteolytic peptides that cannot be detected. It is therefore recommended to either use another or an additional protease for protein digestion, or to perform the labelling at the peptide level after proteolytic cleavage.

A study on tumour cell senescence in which ICPL was successfully used is a good indicator for the power of quantitative proteomics in general. Here, an effect of tumour cell senescence on several important tumourigenesis proteins such as cMYC and key metabolic enzymes such as ATP synthetases were found [ 31 ]. Here, the label is conjugated to the N-termini and lysine residues of peptides, so that in principle every peptide is labelled Figure 4.

Quantitative Proteomics: Label-free

The various isobaric tags themselves have different masses, but are balanced by a linker moiety that ensures identical intact masses for all possible combinations of tag plus linker. As a consequence, differentially labelled peptides end up in the same precursor peak in the mass spectrum.

The relative peak intensities of the tags are then used for quantitation [ 26 ]. Since identical peptides end up in the same peak, the complexity of the MS spectrum is not altered as a result of the labelling procedure. Furthermore, there are commercial kits available with up to 10 different tags, providing the possibility to run and compare 10 samples simultaneously.

The most prominent disadvantage of this method is that the tag, just like most other chemical tags, is incorporated at the peptide level. Principle of isobaric tagging. Peptides are tagged with chemical labels that have identical masses due to a delicate balance between individual tag and linker masses.

Labelled peptides are then mixed and measured by mass spectrometry. This allows for the relative abundance determination. This study showed that the changes in protein expression of different prion diseases are markedly similar, while most changes at the protein level were found in the cerebellum. This study provides an excellent example of biomarker research using mass spectrometry and could be a step towards defining biomarkers for different prion diseases, which are otherwise difficult to classify.

A simple method of labelling compounds at the peptide level for relative quantitation is dimethylation. Either light-labelled with H or heavy-labelled with D dimethyl groups are conjugated to the N-terminus of the peptides and to free lysine residue side chains. The advantages of dimethyl labelling include low cost, high speed and possibilities for automated sample preparation. However, since labelling occurs at the peptide level, variation between runs is still inherent to the process [ 10 , 34 ]. The first incarnation of dimethyl labelling was limited to only two different flavours.

Although this may still be lower than the amount of different labels that can be achieved using isobaric tagging, it is significantly cheaper. Dimethyl labelling can be used for a variety of quantitative measurements, for instance, after a pulldown or immunoprecipitation enrichment protocol. Using an antibody to probe for phosphopeptides in combination with labelling allows one to quantitatively monitor phosphorylation events [ 36 ].