In classic shotgun proteomics, the data is recorded using data dependent acquisition (DDA). Individual precursors are selected for fragmentation in a semi stochastic manner, favoring the most intense peaks. As a result, a very low percentage of the detectable peptides get fragmented, and an even lower number are reliably identified (Michalski et al. 2011). This usually results in poor reproducibility even among technical replicates. Furthermore, since only the selected precursors are fragmented, a large proportion of the information ‑which depends in part on the complexity of the sample‑ is lost. In DDA workflows, peptide identification is done by subjecting the fragmented data to database search to query the experimental spectra against theoretical fragmentation spectra.
In DIA (data independent acquisition), in contrast, all precursors are fragmented and MS2 data is acquired for all fragment ions. This allows to record MS1 and MS2 data from virtually all peptides in a sample without the loss of information. Because of the complexity of this kind of data, dedicated strategies for protein identification have been developed. These strategies are either peptide‑centric, using spectral libraries, or spectrum‑centric, like the directDIA™ workflow developed by Biognosys. Furthermore, Biognosys has stablished its own optimized library‑based DIA workflow called HRM™. Learn more about HRM here.
Michalski A, Cox J, and Mann M (2011) More than 100,000 Detectable Peptide Species Elute in Single Shotgun Proteomics Runs but the Majority is Inaccessible to Data-Dependent LC−MS/MS. J Proteome Res 10:1785–1793, American Chemical Society.
Created by SEZ. Last update 2018-03-15 by SEZ