Data-independent acquisition (DIA) mass spectrometry may change how proteomic data are generated.
In traditional data-dependent acquisition (DDA), a proteomic sample is digested into peptides, ionized and analyzed by mass spectrometry. Peptide signals that rise above the noise in a full-scan mass spectrum are selected for fragmentation, producing tandem (MS/MS) mass spectra that can be matched to spectra in a database. Although extremely powerful, the mass spectrometer randomly samples peptides for fragmentation and is biased to pick those with the strongest signal. Thus, it remains a challenge to reproducibly quantify especially low-abundance peptides.
In targeted proteomics, most notably selected reaction monitoring (SRM), mass spectrometry assays are deployed to very sensitively detect peptides representing proteins of interest with high quantitative accuracy. Despite also being quite powerful (it was our 2012 Method of the Year), this approach is not suitable for discovery-based applications.
Many eyes in the proteomics community are now trained on data-independent acquisition (DIA), which in theory combines the advantages of DDA and SRM. In a DIA analysis, all peptides within a defined mass-to-charge (m/z) window are subjected to fragmentation; the analysis is repeated as the mass spectrometer marches up the full m/z range. This results in accurate peptide quantification without being limited to profiling predefined peptides of interest.
Although the DIA concept was introduced a decade ago, interest has been rekindled as several practical DIA implementations have recently been developed. Whereas many in the proteomics field are excited about the potential of DIA to overcome the sampling problems seen with DDA, others have yet to be impressed. Further applications to challenging biological questions are needed to showcase the advantages of DIA.
Another requirement for broader adoption of DIA will be the development of robust data analysis tools. Because multiple peptides in an m/z window are fragmented together in DIA, the resulting MS/MS spectra are very complex and require deconvolution. A few software tools were published this year (Nat. Biotechnol. 32, 219–223, 2014; Nat. Methods 11, 167–170, 2014), and others are likely on the horizon. We will be watching to see whether DIA can live up to its potential.
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