Abstract
Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
Highlights
Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments
Unlike with single-injection dependent acquisition (DDA), we find that there is little technical variation between single-injection DIA runs, and that these 2ϫ gas-phase fractionation (GPF)-DIA approaches can be preferable to performing technical replicates with DDA
In an extreme case for performing exhaustive detection work on a single biological sample, we find that PECAN analysis of 6 GPF-DIA injections can perform comparably to 6ϫ strong cation exchange or high-pH reverse-phase fractionated DDA injections with dramatically less sample material and preparation effort [41]
Summary
Data independent acquisition (DIA) is an attractive method for quantitative proteomics. Most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. Despite the wide appeal of DIA for quantitative proteomics, one drawback is that many approaches commonly require generating comprehensive DDA-based spectrum libraries [12] before interpreting any DIA data [9, 13, 14] This approach produces high-performance libraries with instrument-specific fragmentation and retention times [15], it does so at the expense of instrument time, sample, and significant. We focus on collecting DIA data, constructing DIA-only libraries, and analyzing DIA data with open-source software using Proteowizard [20], Skyline [21] and EncyclopeDIA [19] Much of this intuition is transferable to ToF-based experiments, here we use DIA methods for Orbitraps as examples. It should be noted that other open-source (e.g. DIA-Umpire [17]) and commercial (e.g. ProteinLynx Global Server [16], Scaffold DIA or Spectronaut Pulsar) software for detecting peptides without spectrum libraries can be used
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