Narrow Precursor Mass Range for DIA–MS Enhances Protein Identification and Quantification in Arabidopsis

Abstract

Data independent acquisition–mass spectrometry (DIA–MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA–MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400–1200 m/z. When combining several DIA–MS analyses with narrow precursor ranges (i.e., 400–650, 650–900 and 900–1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme

“…In essence, scientists must balance three key factors: (I) cycle time, (II) covered mass range, and (III) the size of the isolation window. Historically, gas phase fractionations were employed to alleviate slow acquisition speed, meaning that the same sample was injected repeatedly, each time focusing on a different narrow mass range. ,, Recently, it was shown that focusing on narrow mass ranges can benefit protein coverage. , The rationale can be compared to using a magnifying glass on a painting: while the overall picture is not represented, it is possible to observe intricate details that might be overlooked at a broader glance. As we do not need to detect and quantify every peptide of a protein for its quantitation, it is sufficient to capture a smaller mass range to quantify less precursors overall, which in turn represent more proteins.…”

“…6,29,30 Recently, it was shown that focusing on narrow mass ranges can benefit protein coverage. 31,32 The rationale can be compared to using a magnifying glass on a painting: while the overall picture is not represented, it is possible to observe intricate details that might be overlooked at a broader glance.…”

Deep Proteome of the Developing Chick Midbrain

“…Last, we refined the method of HRMS data acquisition, which fundamentally controls the success of peptide sequencing and hence the fidelity of protein identifications. During DDA, historically the most commonly employed approach in bottom-up proteomics, the most abundant precursor ions are preferred for fragmentation, albeit at the expense of quantitative bias toward reporting on molecules at high concentration. , As an alternative, DIA schedules fragmentation for precursor ions over a wide m / z window, thus enhancing the likelihood of sequencing low-abundance peptides for trace-level protein identification. − The DIA data acquisition parameters were kept consistent with the DDA method when possible to facilitate a direct comparison of the results between the two methods. We selected 12 m / z windows for the DIA method based on recent publications using Orbitrap hybrid and tribrid instruments. , Surprisingly, DIA of the same midbrain proteome returned 4470 proteins (Table S4), slightly lower than the 4684 proteins (Table S2) that were detected by the DDA method on the same sample (Figure ).…”

15N Metabolic Labeling Quantification Workflow in Arabidopsis Using Protein Prospector

“…A targeted quantification strategy is recommended for further analysis of proteins of interest because this provides more accurate quantification and is less likely to have missing values, particularly in the 15 N labeled samples ( Bi et al, 2021 ; Reyes et al, 2021 ). In addition to targeted analysis, data-independent acquisition (DIA) ( Zhang and Bensaddek, 2021 ) can also be utilized, which can be done in label free samples or combined with 15 N metabolic labeling in the future. DIA benefits from having few missing values, but more efforts will be needed to deconvolve the mixed MS 2 spectra in DIA datasets.…”