Intelligent data acquisition blends targeted and discovery methods

Abstract

A mass spectrometry (MS) method is described here that can reproducibly identify hundreds of peptides across multiple experiments. The method uses intelligent data acquisition to precisely target peptides while simultaneously identifying thousands of other, nontargeted peptides in a single nano-LC-MS/MS experiment. We introduce an online peptide elution order alignment algorithm that targets peptides based on their relative elution order, eliminating the need for retention-time-based scheduling. We have applied this method to target 500 mouse peptides across six technical replicate nano-LC-MS/MS experiments and were able to identify 440 of these in all six, compared with only 256 peptides using data-dependent acquisition (DDA). A total of 3757 other peptides were also identified within the same experiment, illustrating that this hybrid method does not eliminate the novel discovery advantages of DDA. The method was also tested on a set of mice in biological quadruplicate and increased the number of identified target peptides in all four mice by over 80% (826 vs 459) compared with the standard DDA method. We envision real-time data analysis as a powerful tool to improve the quality and reproducibility of proteomic data sets.

Figure 1

Overlap of peptide identification among the analysis of six technical replicates. Six nano-LC–MS/MS experiments produced 23 919 unique peptide identifications in total, but only one-fifth of the identifications were observed in all six replicates. A large percentage (31.2%) of the peptides were only detected in one of the six experiments.

Figure 2

To assess the deviation in retention times for matched samples, we ran two identical nano-LC–MS/MS experiments 10 days apart on the same LC–MS system. (A) The relationship between apex retention times of the 7589 unique peptides common between experiments displays a high degree of linearity (R² = 0.9989) but a skewed slope and nonzero intercept (m = 1.033; b = −0.647). (B) Average deviation from unity was nearly a minute off (μ = −0.805 min), with a broad distribution over 2 min wide. (C) Peptides ranked by their relative elution order exhibit a normal distribution around zero (μ = −1.097).

Figure 3

Real-time elution order alignment algorithm. 46.3 min into a nano-LC–MS/MS experiment, an MS¹ scan is performed (A) and m/z features are matched to a 2D ion map stored on the instrument. (B) 21 of the peaks match 80 features in the ion map at a 10 ppm tolerance. Of these, over half (41 of 80) were mapped to one elution order bin (51 elution order). (C) A rolling elution order range is continually updated throughout the nano-LC–MS/MS experiment.

Figure 4

Following determination of the current elution order range (A), target peptides (B) sharing a similar elution order value are selected (C, rectangles represent individual peptides). Peptide targets within the elution order range are filtered based on when they were last sampled for MS/MS (D), leaving only targets that have been waiting the longest (e.g., > 5 s, highlighted rectangles). Those filtered peptides are then immediately sampled by MS/MS, regardless of MS¹ detection (D). Unfilled MS/MS events are automatically filled with m/z features picked by the intensity-based DDA algorithm using normal sampling parameters (e.g., dynamic exclusion, intensity threshold, charge state exclusion, etc.).

Figure 5

Subset of 500 mouse peptides were targeted with DDA, an accurate mass inclusion list (INC), and our intelligent data acquisition (IDA) method in hexplicate. (A) IDA identified the most target peptides of the three methods (error bars represent the 1 σ). (B) Discovery identifications by three methods show only a slight decline in the total number of peptides identified using IDA. (C) 74% of the targets were observed in all six technical replicates when IDA was used compared with <20% for the inclusion list or data-dependent acquisition.

Figure 6

(A) Four C57Bl/6 mice were sacrificed at 10 weeks of age, and eight organs were harvested from each mouse. (B) Peptides resulting from a tryptic digestion of lysates from each organism were labeled with TMT 8-plex tags in a randomized order. (C) 165 min nano-LC–MS/MS experiments using DDA top-15 method identified only 3969 peptides in all four mice. (D) A subset of 1500 peptide targets was selected from peptides detected in only two or three of all four mice.

Figure 7

(A) In four subsequent nano-LC–MS/MS experiments, only 810 of 1500 mouse peptide targets were identified with DDA. The identifications improve to 1072 when IDA is used (error bars represent 1 σ). (B) In total, 826 target peptides were identified in all four mice when IDA was used to target. This number falls to only 459 peptides when DDA is used. (C) The number of statistically significant differences (p value <0.05) quantified when each tissue is compared with liver is greater with IDA than DDA. (D) When comparing target peptides identified in the muscle versus the liver, IDA quantified 826 statistically significant peptides compared with only 531 when DDA was used, a 56% increase.