EPFL/ETHZ BXD Liver, Soluble Proteins CD (Jun14) SWATH

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SWATH MS is a novel technique that is based on data-independent acquisition (DIA) which aims to complement traditional mass spectrometry-based proteomics techniques such as shotgun and SRM methods. In principal, it allows a complete and permanent recording of all fragment ions of all peptide precursors in a biological sample and can thus potentially combine the advantages of shotgun (high throughput) with those of SRM (high reproducibility and sensitivity).

The method uniquely combines a DIA methods with a innovative data analysis approach based on targeted data extraction developed in the Aebersold lab. Like in other DIA methods, the mass spectrometer cycles through precursor acquisition windows designed to cover the whole range of 400-1200 m/z - in which most of the proteotypic peptide precursors of an organism fall - within 2-4 seconds. During each cycle, the mass spectrometer will fragment all precursors from a given precursors window (e.g. 475 - 500 m/z for 25 Da windows) and record a complete, high accuracy fragment ion spectrum. The same range will be fragmented again in the next cycle, thus providing a time-resolved recording of fragment ions that elute on the chromatography. Thus the SWATH method provides highly multiplexed fragment ion spectra that are deterministically recorded over the complete chromatographic time.

In the Malmstroem group, we are interested in the data-analysis challenge that is posed by DIA / SWATH data. Traditionally, DIA methods have been analyzed by trying to reconstruct the lineage of precursor and fragment ions based on their chromatographic elution profile, and then analysing the data in a workflow similar to those used in shotgun proteomics. However, these approaches suffered from low sensitivity and propagation of errors due to mis-assignment of fragment ions to precursor ions. We are thus working on automating targeted methods that are conceptually similar to SRM and allow querying the data multiple times with specific hypothesis, thus giving the researcher more control and specificity in the bioinformatic data analysis step. With these novel algorithms, it is potentially possible to explore a much larger part of the data that is present and obtain a nearly complete picture of a proteome.


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