A data independent acquisition technique employed on the TripleTOF® 6600 System
Zuzana Demianova1; Cyrus Papan1; Joerg Dojahn1; Baljit K. Ubhi2
1SCIEX, Germany; 2SCIEX, USA
SWATH® Acquisition, a data independent acquisition (DIA) workflow is well adopted in quantitative discovery proteomics, but still not commonly used in discovery metabolomics. Here, it is described how SWATH Acquisition enables the identification of a higher number of metabolites for untargeted metabolomics workflows compared to traditional data dependent acquisition (DDA) approaches, thus enabling a broader profile of the metabolome. The results show that SWATH Acquisition using variable windows improves metabolite coverage using the Accurate Mass Metabolite Spectral Library (AMMSL) compared with the traditional DDA approach.
SWATH® Acquisition, a data independent acquisition (DIA) workflow is well adopted in quantitative discovery proteomics1, but still not commonly used in discovery metabolomics. Traditional data dependent acquisition (DDA) techniques are heavily employed in the field of metabolomics and workflows on the mass spectrometers have been adapted so that as much data as possible can be captured. This has led to a two-injection workflow in the community; one injection to collect the MS and mine the precursor data and a second for the MS/MS to confirm the metabolite identification. Researchers were limited by the speed of their mass spectrometers because they could not scan fast enough to capture both the MS and MS/MS data in a single injection. Also, the stochastic nature of data dependent workflows often means MS/MS of low abundant metabolites are often missed.
The TripleTOF 6600 System allows both the MS and MS/MS data to be collected in a single injection allowing the collection of a digitized map of every detectable metabolite in the sample - meaning no need to go back and re-run a sample but just re-mine the data as the hypothesis changes. Here, it is demonstrated how SWATH Acquisition enables the identification of a higher number of metabolites for untargeted metabolomics workflows2 compared to traditional data dependent acquisition (DDA) approaches thus enabling a broader profile of the metabolome.2 Results show that SWATH Acquisition using variable Q1 window acquisition3 improves metabolite coverage using the Accurate Mass Metabolite Spectral Library (AMMSL) compared with the traditional DDA approach.
Sample preparation: Human urine and commercially available human plasma were processed according to standard extraction protocols. Urine was diluted with water at a ratio of 1:4 (v/v) and centrifuged for prior analysis, whilst plasma was extracted 1:4 (v/v) with ice-cold methanol allowing for protein precipitation.
Chromatography: Separation was performed on an Agilent Technologies 1290 Infinity II using an Acquity BEH C18 column with dimensions; 100mm x 2.1 mm ID, 1.7 µm (Waters, Milford, USA) using a flow rate 200 µL/min. A gradient was employed from 1-10 minutes from 2-98% of 0.1% formic acid in acetonitrile, total length of LC separation was 14 minutes and column oven temperature was set to 40oC. Injection volume was 5 µL for both type of samples.
Mass spectrometry: The SWATH Acquisition and DDA experiments were acquired on a TripleTOF 6600 System in positive ion mode. The source settings were Curtain Gas 35 psi, GS1 40 psi, GS2 40 psi, ISVF 5500 V, Source temp. 600°C, Declustering Potential 80 V. MS/MS was acquired with a collision energy was 30 V with 15 V spread.
For the DDA acquisition, the top 5, 10, 15, 20 and 25 precursor ions per cycle were selected for MS/MS, described hereon in as top5, top10, top15, top 20 and top25 (Table 1). For SWATH Acquisition, the number of Q1 windows was varied using 15, 20 and 30 windows, both fixed window (fw) and variable window (vw) widths. All these settings were used to test which parameters resulted in the highest number of identifications and coverage of metabolites in plasma and urine extracts.
The Top20 DDA acquisition method was used to calculate variable windows using a SWATH Variable Window Assay Calculator version 1.1 with minimum window size 3 Da.4 Example graphs illustrating the correlation of the SWATH Acquisition window sizes versus the precursor ion density is presented in Figure 2.
Data processing: Data was processed using SCIEX OS Software or MasterView™ Software and the Accurate Mass Metabolite Spectral Library (AMMSL) using search settings accordingly: candidate search algorithm, results sorted by Purity (for DDA data) or Fit (for SWATH Acquisition data). A combined score (isotope distribution pattern, fragmentation pattern, mass error) of >70% was used to evaluate the confidence in the metabolite identification.
In the first part of the study, the traditional DDA acquisition strategy was evaluated by comparing the number of precursors selected for MS/MS analysis. An identical accumulation time of 25ms was used across these DDA experiments to be able to compare the data. The coverage of these different methods was evaluated by matching the metabolites to the spectral library (AMMSL) which contains over 550 exogenous and endogenous metabolites to a human plasma extract (Figure 3). A library score of 70% and above was used as the cutoff criteria for a high quality confirmed metabolite match.
The data demonstrate a significant improvement of metabolite coverage at the MS/MS level when comparing the top5 to the top25 DDA method (Figure 3). In Figure 3, an increase in metabolite coverage over 100% was observed in plasma extracts by increasing the number of selected precursor ions for DDA acquisition from top5 to top25. This result highlights the capability of the TripleTOF 6600 System for fast MS/MS acquisition, which allows for the fragmentation of a large number of precursors in a single DDA cycle, leading to a larger number of metabolites identified.
In the second part of this study, the SWATH Acquisition strategy was tested with various fixed (fw) and variable window (vw) sizes with similar cycle time in a plasma extract. As shown in Figure 4, increasing the number of fixed windows resulted in ~30% gain in metabolite coverage. Using the variable window method resulted in a ~70% gain in metabolite coverage.
These results show that decreasing the window size and varying the window size, depending on the precursor ion mass density, improves the overall ion selectivity as shown in Figure 5. This highlights an example of the MS/MS spectrum of D-Lysine with a precursor mass 147.1125 m/z. Here one can visualize a Q1 window from 127.1-149.4 m/z (top) and Q1 window from 137.7-149.4 m/z (bottom) acquired using a SWATH Acquisition method with 15 variable windows and 30 variable windows, respectively. Figure 5 (top panel) clearly show many fragments from other co-eluting metabolites infiltrating the MS/MS spectrum within the mass range from 127.1 m/z to 149.4 m/z. This highlights the need for a higher number of SWATH Acquisition windows with narrower widths, allowing fewer precursor ions selected for MS/MS fragmentation, resulting in higher specificity and selectivity, necessary for confident metabolite identification.
In the third step, identification rate was compared between SWATH Acquisition and traditional DDA acquisition in a plasma extract. At first glance DDA acquisition presents a higher number of metabolites identified solely based at the MS1 level (Figure 4, MS acquired); however when the MS/MS is used to confirm the metabolites, the numbers of identified metabolites drops significantly; most likely due to the sheer number of false positives using just the MS1 data (and mass accuracy alone). These numbers drop further when the library score is set to 70% and above meaning that the MS1, MS/MS, isotope distribution and retention time must have a combined scoring of 70% and above to be considered a high level identification (Figure 6).
The SWATH Acquisition method is using information obtained from both the MS and MS/MS spectrum. Due to this, metabolites are identified not only on their exact mass, but also based on their molecular structure. Figure 5 also shows an increased number of metabolites identified at the MS/MS level for samples measured using SWATH Acquisition.
Finally, these experimental approaches were applied to common matrices used in metabolomics studies, namely urine and extracted plasma. Figure 6 illustrates that SWATH Acquisition applying 20 variable windows can identify up to 55% more metabolites than a traditional top20 DDA acquisition (in a urine extract). More confident MS/MS based identifications lead to higher quantifiable metabolites in a metabolite expression experiment, which at the end allows better understanding of the biology. When comparing the performance in extracted plasma it can be observed that applying SWATH Acquisition with 20 variable windows allows significant gains in metabolite coverage (around 55%) versus the top20 DDA acquisition, similar gains as seen in the urine extract (Figure 7),
Lastly note that the larger the library, the more coverage one can gain from a sample. The library used for these experiments even though small in size, contained high quality spectra from biochemically relevant metabolites generated on TripleTOF Systems.