Introduction to Proteomics: Principles and Applications: 52 (Methods of Biochemical Analysis)
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In survey MS spectra, the definition of very low and very strong signals can be problematic. At very low signal, peptide ions are often difficult to distinguish from background noise Fig. In practice, saturation is more often observed for quadrupole TOF instruments than ion traps because these latter devices can control the number of ions before detection [ 70 ].
For quantification in tandem MS spectra, saturation effects are rarely a problem.
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Instead, low-intensity spectra are frequently obtained and may result in less robust quantitation values due to poor ion statistics. All ions present in this window will contribute to the signal of the, e. As a result, it is not always clear to what extent quantification was contributed by the peptide of interest or by background. This can sometimes lead to a large underestimation of true changes, especially for very weak peptide signals. Taken together, the limits to quantification of complex proteomes by stable isotopes is first and foremost an issue of signal interference caused by co-eluting components of similar mass.
Therefore, the most straightforward way for optimizing quantitative analyses is to decrease sample complexity by increasing HPLC gradient times or by biochemical fractionation prior to LC-MS analysis. Currently, two widely used but fundamentally different label-free quantification strategies can be distinguished: a measuring and comparing the mass spectrometric signal intensity of peptide precursor ions belonging to a particular protein and b counting and comparing the number of fragment spectra identifying peptides of a given protein.
For low-resolution mass spectra this is typically done by creating extracted ion chromatograms XICs for the mass to charge ratios determined for each peptide [ 74 ]. More recently, this concept has been extended to high-resolution data to include contributions of 13 C isotopes to the overall signal intensities [ 75 ]. The intensity value for each peptide in one experiment can then be compared to the respective signals in one or more other experiments to yield relative quantitative information [ 74 , 76 , 77 , 78 , 79 , 80 ].
For proteomic analysis of very complex peptide mixtures, three important experimental parameters affect the analytical accuracy of quantification by ion intensities. This is not a trivial task and special software has been developed to align LC-runs prior to identifying corresponding peptides [ 81 , 82 , 83 , 84 ]. While extensive peptide sequencing by tandem MS is required to identify as many proteins as possible in complex mixtures, a robust quantitative reading by ion intensities requires multiple sampling of the chromatographic peak by survey mass spectra.
Typically, multiple fragment spectra are acquired for every survey spectrum at acquisition rates ranging from 0. Still, even for fast sampling instruments, better quantification accuracy will inevitably mean poorer proteome coverage and vice versa. In these approaches, matching of integrated peak intensities to identified peptides is performed by using a combination of accurate mass and retention time [ 84 , 85 , 86 ].
Obviously, there are challenges with analyzing such data from complex samples as many fragmentation spectra will be populated with sequence ions from multiple peptides each contributing differently to the overall spectral content. The peptide or more recently introduced spectral counting approach [ 91 , 92 , 93 ] is based on the empirical observation that the more of a particular protein is present in a sample, the more tandem MS spectra are collected for peptides of that protein.
Hence, relative quantification can be achieved by comparing the number of such spectra between a set of experiments. However, the commonly employed dynamic exclusion of ions that have already been selected for fragmentation is detrimental for accurate quantification [ 94 ].
introduction to proteomics principles and applications 52 methods of biochemical analysis Manual
Although very intuitive and attractive in practical terms, the spectrum counting approach is still controversial because it does not measure any direct physical property of a peptide. It further assumes that the linearity of response is the same for every protein. In fact, the spectrum count response is different for every peptide because, e. Therefore, even reasonable quantification requires the observation of many spectra for a given protein.
Old et al. At the same time, saturation effects will be observed at higher spectral counts and saturation levels will be different for all proteins which renders the assessment of the dynamic range of observed changes difficult. Nevertheless, the correlation between amount of protein and number of tandem mass spectra does hold and has led researchers to extend the concept to the estimation of absolute protein expression levels.
In the first of a series of papers, Rappsilber et al. In a subsequent refinement, the same group transformed the PAI into an exponentially modified form emPAI [ 96 ] which showed a better correlation to known protein amounts. Further advances have been made by using computational models that predict which peptides of a given protein are likely to be detected by the mass spectrometer in the first place and thus would form a better basis for quantification [ 97 , 98 , 99 , 66 ]. For example, results obtained by the absolute protein expression profiling APEX method [ 99 ] suggest that absolute protein expression can be determined to within the correct order of magnitude.
Label-free approaches are certainly the least accurate among the mass spectrometric quantification techniques when considering the overall experimental process because all the systematic and non-systematic variations between experiments are reflected in the obtained data Fig. Consequently, the number of experimental steps should be kept to a minimum and every effort should be made to control reproducibility at each step. Nonetheless, label-free quantification is worth considering for a number of reasons.
In simple practical terms, the time-consuming steps of introducing a label into proteins or peptides can be omitted and there are no costs for labeling reagents.
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In terms of analytical strategy, the following points may also be important: i there is no principle limit to the number of experiments that can be compared. This is certainly an advantage over stable isotope labeling techniques that are typically limited to 2—8 experiments that can be directly compared. However, particularly for spectral counting, this comes at the cost of unclear linearity and relatively poor accuracy [ 94 ].
When contemplating a data analysis strategy for proteomic data generated by quantitative mass spectrometry, it is worth reconsidering a couple of principle points. Quantitative proteomic data are typically very complex, and often of variable quality. This is in part because the data are incomplete: even the most advanced mass spectrometers, which can acquire several tandem MS spectra per second, are often overwhelmed by the number of peptides present in a sample. As a consequence, only a subset of all proteins present can be identified in any one analysis [ ].
For protein quantification, it is further mandatory to detect a protein in all experiments that should be compared. As a result, often only a subset of identified proteins can actually be quantified Fig. Identification and quantification rates are direct functions of sample complexity. While a large fraction of proteins present in, e. This clearly limits the confidence in quantification results.
Generic data processing and analysis workflow for quantitative mass spectrometry. Yellow icons indicate steps common to all quantification approaches with or without the use of stable isotopes. Blue icons in the boxed area refer to extra steps required when using mass spectrometric signal intensity values for quantification.
For protein quantification based on spectrum counting, the data processing steps are basically identical to the general protein identification workflow in proteomics which is one of the reasons why this approach has become so popular. Researchers can choose from a variety of methods available for automated protein identification and subsequent probabilistic validation of spectrum-to-peptide matches for a recent review see Ref.
It should be emphasized that for any quantification method it is mandatory to consider only those spectrum-to-peptide matches that are unique for a particular protein [ 11 ]. Quantification methods based on ion intensities, regardless of whether employing stable isotope labeling or not, require a number of additional steps prior to protein quantification boxed area in Fig. Two particular elements are important to mention here: intensity integration i within the mass spectrum centroiding and ii across the chromatographic peak.
For low-resolution MS data, both aspects are carried out in one operation by extracting the ion chromatograms from the LC-MS data. For high-resolution MS data, the procedure is more complex and typically performed in two steps. Each method has its merits and detractions: monoisotopic peak integration is relatively straightforward to implement but not very sensitive particularly for larger peptides for which the monoisotopic peaks only constitute a minority of the total signal intensity. In addition, the use of heavy isotopes distorts the relative isotope distribution of peptides which leads to inaccuracies.
In contrast, the summed area of the entire isotope cluster is the most sensitive and accurate method [ ] as it utilizes all of the data but is more difficult to implement computationally.
As discussed in a previous section, signal intensity integration over the chromatographic time scale is primarily required for label-free quantification as well as those stable isotope reagents that lead to significant differences in chromatographic behavior. For methods which do not suffer from this shortcoming, time integration can be performed but is not required.
Instead, collection of several spectra for each peptide is generally useful in order to obtain several quantitative readings. There are several sources of potential error in the mass spectrometric readout of an LC-MS experiment that can negatively affect the results of peptide quantification. Spectra for which these errors are detected should be filtered out prior to computing quantification values.
The first of these issues is the presence and variability of spectral background noise Fig. A second common issue is the presence of interfering signals other than background noise Fig. Third, strong signal intensities can lead to detector saturation for some mass spectrometers particularly quadrupole TOF instruments, Fig.
For stable isotope labeling, further quality criteria must be considered. One very simple and often incurred problem is systematic bias introduced by imperfections in mixing the two protein populations. Mixing errors can most of the time be determined experimentally and apply uniformly to all protein quantification values and are thus easily corrected for. Although this may not appear to be a significant source of uncertainty and, again, can be easily corrected for, isotope impurities lead to increased spectral interferences and, more importantly, limit the dynamic range of detectable differences between samples.
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A similar argument applies to incomplete incorporation of the isotope label into proteins and peptides. Again, while isotope incorporation can be measured and correction factors can be applied, the combination of the above items limits the dynamic range of detectable differences between samples to approximately 20— Consequently, determined changes are often smaller than their true values. It is important to keep in mind that this effect can be much more pronounced when spectral background contributes significantly to overall spectral intensity.
Diamonds represent intensity readings from individual spectra. The red line indicates the expected ratio of 2. It is evident that variations in change determination are much larger for low-intensity spectra than for medium- or high-intensity spectra. Diamonds represent intensity readings from individual spectra for samples 1 and 2 same data as in a. The slope of the two-sided regression line approximates the expected twofold difference in protein quantity between the two samples. Not surprisingly, precision increases with increasing number of spectra. Diamonds represent individual protein fold changes in ascending order.
However, these data points may contain many false negatives small but significant changes.
Quantitative mass spectrometry in proteomics: a critical review
Classification methods e. Raw data from quantitative MS experiments are generally not suitable for statistical analysis, thus a number of preparative steps are required. First, raw data are typically not normally distributed, an assumption made by many statistical tests. Therefore, data are frequently log-transformed assuming that the data are lognormal-distributed. This operation typically also harmonizes the variance of data otherwise high values would have large variances and vice versa. If replicates of the experiment have been generated, normalization of their data is mandatory because technical bias may overshadow the underlying biological effects for details on normalization techniques, see Refs.
As discussed above, technical effects include sample mixing errors, incomplete isotope incorporation, or isotope impurity. In many cases, systematic technical bias can be measured directly but in some cases requires dedicated experimentation e. The resulting information is used to build correction functions that are consecutively applied to the data.