Full Scan Quantitation based on Raw Data using IPeaks

The Ipeaks module of MsXelerator® contains 5 very fast algorithms for searching Light/Heavy isotope patterns based on predefined patterns. This module also lets you create your own patterns based on Light/Heavy mass differences and ratios.

Most of the Quantitative software programs on the market perform quantitation after the identification of peptides and proteins has been done using Search Engines like Mascot® or Sequest®. IPeaks algorithms operate on raw Full Scan LC/MS data and will detect all peaks in the data set showing the specified isotope pattern. Using this mode of operation, in general much more peaks will be detected. The many unidentified peptides are possible candidates for further analysis using e.g. de novo sequencing algorithms.

The following Predefined Isotope Patterns and Search routines are available from IPeaks:

SILAC – SITE - 14N/15N - 16O/18O – MIDAR- ECAT – ICAT
Natural Isotopes - Br, Cl, Cl2 etc.
GSH Reactive Metabolite Detection, see Metabolite Profiling section

SILAC labeling:

Cells growing in culture are labeled with heavy amino acids and incorporate these amino acids into their proteins. The same peptide from different samples will be labeled with amino acids with different masses and will therefore produce different reporter ions in the MS scan. IPeaks quantitation can be performed in dual mode or based on triple mode (comparing three cell populations). After fast peak picking and charge state determination, the algorithm automatically calculates all ratio’s.

SITE: Stable Isotope Tagging of Epitopes:

Site is based on metabolic labeling of endogenously synthesized proteins during infection1, 2. The technique involves asymmetric labeling and special pooling of cell batches. Infection induced peptides show up having a very specific binomial isotopic pattern. The small number of infection-induced peptides is detected from scanning the complete 2D LC/MS surface (Figure 2). The difference Isotope Patterns from a Self-Peptide and one that is associated with the infection in shown in Figure 3.

1 Meiring, H.D., Targeted identification of disease related MHC-presented epitopes, Thesis 2005,
   Utrecht, The Netherlands
2 H.D. Meiring, E.C. Soethout, A.P.J.M. de Jong, and C.A.C.M. van Els, Targeted Identification
   of Infection-Related HLA Class I–Presented Epitopes by Stable Isotope Tagging of Epitopes (SITE),
   Current Protocols in Immunology (2007) 16.3.1-16.3.20

Figure 3: Detection of infection related peptides and self-peptides based on Isotope Distribution Patterns

14N/15N Labeling:

IPeaks contains a highly dedicated algorithm  to detect 14N/15N ion doublets from metabolic labeling experiments. Because of the fact that no fixed mass difference can be set, IPeaks uses a very fast iterative algorithm to detect all doublets using sophisticated rules. A typical analysis can be performed in less then 30 seconds.

Figure 4: 14N/15N Full Scan MS spectrum[M+3H]3+

MIDAR: Mass Isotope Distribution of Amino Acid Residues:

Quantitation is often performed using ratio’s between the mass peak heights in centroided mode or integrating over the mass peak in profile mode. MIDAR was developed to perform accurate ratio determinations by integrating the mass data over the full elution profile of a chromatographic peak. It is based on detection of all chromatographic peaks. Peakwidths and integation range are set  automatically. MIDAR can be applied to any mass difference.

MIDAR Workflow to detect Isotope Patterns:

Example:

As an example, MIDAR3 was applied to a new isotope coded acetylation reagent that comprises a mixture of two chemicals that are differentially labelled, separated by 1 Da. The reagents are mixed asymmetrically (11% of the lighter, and 89% of the heavier variant). When peptides are labelled with this reagent, the isotope pattern of the products is complex, but the ratio of two specific ions (M and M-1) gives a precise measure of the number of amino groups in each peptide.

The approach, termed MIDAR (mass isotopomer distribution analysis of amino acid residues), is particularly amenable to strategies based on positional or terminal proteomics, one variant of which is the analysis of N-terminal peptides. Knowledge of the number of amino groups can provide an integral statistic that can be used as an added parameter in accurate mass and retention time strategies.

3. L. McDonald, R. Beynon, to be published