Abstract
In this technical note, the ZT Scan DIA 3.0 acquisition mode is demonstrated to deliver improved metabolomics data quality relative to earlier generations of ZT Scan DIA. By implementing a narrower scanning window, ZT Scan DIA 3.0 enables Q1 deconvolution to approximately 0.9 Da, substantially reducing chimeric MS/MS spectral interference and improving both metabolome coverage and quantitative performance using the ZenoTOF 8600 system.
ZT Scan DIA is a sliding-window data-independent acquisition (DIA) approach that enables post-acquisition data deconvolution to extract a narrow effective precursor isolation window, thereby improving compound identification compared to fixed- and variable-window DIA workflows. In earlier implementations of ZT Scan DIA, the effective deconvolved Q1 window was approximately 1.75 Da, which is narrower than conventional DIA (>3 Da) but still broader than that achieved with targeted MS/MS approaches such as MRMHR (~1.2 Da).
ZT Scan DIA 3.0 represents the latest evolution of this workflow, in which further narrowing of the sliding window enables a 0.9 Da Q1 resolution. This enhanced Q1 deconvolution improves precursor–fragment correlation by reducing chimeric interference, resulting in higher confidence metabolite identifications and more selective quantitative measurements at the MS/MS level. In addition, ZT Scan DIA 3.0 delivers increased sensitivity, as evidenced by improved signal-to-noise ratios (S/N) compared to DDA or ZT Scan DIA 2.0.
To evaluate these performance improvements, NIST SRM 1950 plasma was analyzed using multiple untargeted metabolomics workflows on the ZenoTOF 8600 system, including DDA and ZT Scan DIA versions 2.0 and 3.0. Data were processed using MS-DIAL 5.6.082520 to identify polar metabolites within the NIST 1950 plasma metabolome. The results demonstrate that ZT Scan DIA 3.0 provides broad metabolomic coverage while generating high-quality quantitative MS/MS data for both known and unknown metabolites. Importantly, ZT Scan DIA acquisition produces a comprehensive digital record of the sample, enabling retrospective qualitative and quantitative analysis without the need for additional data acquisition.
Key features for metabolomics analysis using the ZT Scan DIA 3.0 workflow on the ZenoTOF 8600 system
- ZT Scan DIA 3.0 employs a narrower scanning window, enabling effective Q1 deconvolution to ~0.9 Da and reducing chimeric MS/MS interference
- Using MS-DIAL 5.6 software, ZT Scan DIA 3.0 identified ~2-fold more metabolites than DDA and 39% more than ZT Scan DIA 2.0
- ZT Scan DIA 3.0 delivers higher sensitivity, with increased peak areas and improved S/N compared to DDA
- ZT Scan DIA generates a comprehensive digital record of the sample, enabling retrospective qualitative and quantitative data analyses
Introduction
Untargeted metabolomics is a widely used approach for characterizing the biochemical composition of biological systems by comprehensively analyzing endogenous small molecules [1-3]. Advances in high-resolution mass spectrometry have substantially expanded metabolomics capabilities by improving mass accuracy, scan speed, and dynamic range. Nevertheless, consistent acquisition of high-quality tandem mass spectrometry (MS/MS) data across complex metabolomes remains a central challenge for confident metabolite identification and accurate quantitation. Accordingly, continued refinement of data acquisition strategies that balance coverage, selectivity, and sensitivity is critical for advancing discovery-scale metabolomics.
Data-independent acquisition (DIA) strategies have gained broad adoption in small-molecule mass spectrometry due to their ability to generate comprehensive and reproducible MS/MS datasets [4]. By fragmenting all ions within defined precursor m/z ranges, DIA enables systematic sampling of the metabolome and facilitates retrospective data analysis. However, conventional DIA approaches are often limited by precursor co-isolation and chimeric fragment-ion complexity, effects that can diminish spectral specificity and complicate downstream spectral deconvolution, particularly in highly complex matrices such as plasma.
The implementation of the Zeno trap, an ion accumulation and pulsing device that enhances duty cycle and ion transmission efficiency, has markedly improved MS/MS sensitivity on quadrupole time-of-flight (QTOF) platforms [5,6]. When coupled with scanning DIA acquisition, Zeno Trap–enabled Scanning DIA (ZT Scan DIA) integrates continuous precursor isolation with synchronized ion pulsing, yielding higher fragment ion signal intensity while maintaining comprehensive DIA coverage [7]. Unlike fixed- or variable-window-based DIA methods, ZT Scan DIA uses a continuously sliding isolation window, enabling post-acquisition assignment of fragment ions to near-unit-resolution precursor m/z intervals. This strategy improves precursor specificity, reduces spectral congestion, and supports more confident metabolite annotation and quantitation.
Previous studies applying ZT Scan DIA to small-molecule analysis have demonstrated increased metabolome coverage, improved MS/MS spectral quality, and the ability to perform quantitation at the MS/MS level [8]. In addition, ZT Scan DIA generates a permanent digital record of the sample, enabling retrospective targeted or quantitative analyses without the need for additional data acquisition, thereby increasing analytical flexibility.
In the present work, we extend the ZT Scan DIA workflow by incorporating narrow-window scanning isolation, referred to here as ZT Scan DIA 3.0. By reducing the effective precursor isolation width during scanning, this approach is intended to further enhance precursor ion selectivity and reduce fragment ion interference, particularly for coeluting or isobaric species. Using NIST SRM 1950 human plasma as a well-characterized reference material [9], we evaluated the effects of ZT Scan DIA 3.0 on metabolite coverage, MS/MS spectral clarity, and quantitative performance.
Together, these results demonstrate that narrowing the scanning isolation window with ZT Scan DIA 3.0 further refines DIA-based metabolomics acquisition, enabling improved identification confidence and quantitative reliability in complex biological samples.
Materials and methods
Sample preparation: NIST SRM 1950 plasma was extracted with 4 volumes of ice-cold methanol and vortexed for 10 s. A 1:10 volume equivalent of QReSS internal standards to plasma was added, and the sample was vortexed for 10s. The mixture was allowed to rest for 1 hr at 4 ºC, vortexed again for 10 s, centrifuged at 15,000 × g for 10 min, and the supernatant was decanted. The supernatant was dried under a stream of nitrogen, and the metabolites were resuspended in water containing 0.1% formic acid to a final concentration of 1 µL of extract per 0.2 µL of plasma equivalents. The solution was directly analyzed by high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) using a 0.5 µL plasma-equivalent on-column injection (2.5 µL injection). The QReSS internal standards (Vial 1) were purchased from Cambridge Isotope Laboratories, and the stock solutions were prepared according to the manufacturer’s instructions [10].
Chromatography: Samples were analyzed using an Exion LC system with an Acquity Premier CSH Phenyl-Hexyl column (1.7 µm, 2.1 x 100 mm; Waters). Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in methanol. A simple linear gradient from 0 to 99% B was used at a flow rate of 0.4 mL/min. The HPLC rinse solvent was water/methanol/iso-propanol/acetonitrile (1:1:1:1, v/v). The gradient conditions are shown in Table 1. A 2.5 µL injection was used (0.5 µL plasma equivalents), and the column temperature was maintained at 50°C. The total runtime was 19 min.
Mass spectrometry: Mass spectrometry: Metabolomics analysis of NIST SRM 1950 plasma extracts was performed on the ZenoTOF 8600 system equipped with Metabolomics analysis of NIST SRM 1950 plasma extracts was performed on the ZenoTOF 8600 system equipped with an Optiflow Pro ion source and an electrospray ionization (ESI) probe. Instrument calibration was maintained using the automated calibrant delivery system (CDS), which calibrated every five samples with the new universal calibration solution used for both ionization modes. DDA and ZT Scan DIA experiments—with varying Q1 acquisition window widths—were performed using collision-induced dissociation (CID) in the positive- and negative-ion modes. Instrument parameter settings are listed in Table 2.
ZT SCAN DIA experiments are modeled after SWATH experiments by selecting a window size. In SCIEX OS software, a sliding scale illustrates the balance between coverage and selectivity. Opting for a narrow window size (i.e., high selectivity) enhances the deconvoluted Q1 resolution but may reduce coverage. With ZT Scan DIA 3.0, the Q1 acquisition window can be narrower than in previous versions, improving chimeric-spectrum deconvolution and reducing background interference. In these tests, the window was set to 8.4 Da, the smallest possible for ZT Scan DIA 2.0, and 4.3 Da, the limit for ZT Scan DIA 3.0. To find the deconvoluted Q1 resolution, divide these windows by 5. Therefore, for ZT Scan 2.0, the Q1 resolution is approximately 1.7 Da, and for ZT Scan DIA 3.0, ~ 0.9 Da. Keep in mind that the duty cycle and accumulation times depend on the window size, and the method automatically accounts for this effect. Note: ZT Scan DIA 3.0 is an incremental improvement over previous versions; all functionality from previous versions is retained in this upgrade.
To simplify data analysis in MS-DIAL software, ensure that data for each injection are saved to a separate data file. MS-DIAL software does not parse each experiment within an aggregate data file, so the data are processed as a composite, with a single results file generated. To separate individual experiments from a data file when multiple experiments have been saved under the same data file name, use the SCIEX MS Data Converter 2.0.1, available at SCIEX.com, or ProteoWizard (https://proteowizard.sourceforge.io/) to split them. When converting, the data format output should be centroid.
Data processing: Data acquired from DDA and ZT SCAN DIA experiments were processed using MS-DIAL 5.6.082520-alpha software, which will be referred to generically as MS-DIAL 5.6 in this report. This version of MS-DIAL, available on the MS-DIAL website (https://systemsomicslab.github.io/compms/msdial/main.html), was adapted from MS-DIAL 5.5 to process ZT SCAN DIA data. The software processes DDA and SWATH data as in the 5.5 versions [11,12] but only MS-DIAL 5.6 can deconvolute ZT Scan DIA data to resolve the Q1 dimension, thereby improving the correlation between the precursor ion and its corresponding TOF MS/MS spectrum. This is particularly important for small-molecule DIA data analysis, where convolved MS/MS spectra make it difficult to unambiguously identify metabolites. Optimal data processing settings for MS-DIAL 5.6 were determined through an iterative process using DDA data, as detailed in [8] and presented in Table 3. The same settings were applied to ZT Scan DIA data, except for 2 smoothing parameters, as indicated. The overall optimization process was governed by varying settings to achieve optimal results and maximize the number of high-quality reference-matched metabolites.
In MS-DIAL, quality scoring is guided by an algorithm that combines various dot-product types (i.e., dot product, reverse dot product, and weighted dot product), mass accuracy, and retention time to generate a combined quality score [12]. For this study, we focused on metabolites with quality scores of 1.6 or higher. This threshold was determined to be suitable for reliable compound identification based on manual data inspection. Referencing the raw data to verify findings should be standard practice for all metabolomics data, regardless of the processing software used. In this case, the different dot-product scores were adjusted to ensure that the number of reference-matched metabolites, which heavily depends on these scores, closely matched the number of metabolites with quality scores ≥ 1.6, which is independent of the dot product scores.
Quantitative data processing was performed using the SCIEX OS software Analytics module. DDA data can be used for quantitation at the TOFMS level, and ZT Scan DIA data can provide quantitative measurements based on either the MS or MS/MS data.
Processed metabolomics results were visualized using MS-DIAL 5.6 software and the Explore and Analytics modules of SCIEX OS.
Results
ZT Scan DIA analysis of NIST SRM 1950 plasma
As described previously [7,8], ZT Scan DIA data acquisition and processing differ fundamentally from both DDA and traditional DIA workflows. In conventional DDA and DIA experiments, the collision cell must be cleared between consecutive MS/MS events—either for each selected precursor in DDA or for each discrete isolation window in DIA—adding about 1–2 ms of overhead per MS/MS acquisition (7). At high acquisition speeds, this cumulative overhead reduces the effective duty cycle. In contrast, ZT Scan DIA continuously captures data, eliminating the need for collision cell clearing between MS/MS events and enabling significantly higher duty cycles at rapid acquisition rates. This results in more MS/MS spectra being collected and provides a more comprehensive digital representation of the sample.
Both DDA and DIA workflows inherently generate chimeric MS/MS spectra, as fragment ions from multiple co-isolated precursors are acquired simultaneously. ZT Scan DIA addresses this limitation by combining retention time (RT) alignment and Q1 deconvolution, thereby more accurately associating fragment ions with their corresponding precursor ions and effectively removing many chimeric spectral contributions.
Unlike DDA, ZT Scan DIA supports direct quantitation at the MS/MS level, offering improved molecular specificity and better signal-to-noise (S/N) [8]. The use of narrow effective precursor isolation windows in ZT Scan DIA, which can be set as low as 0.9 Da in ZT Scan DIA 3.0, further enhances S/N compared to traditional DIA methods, leading to increased sensitivity and more reliable quantitative results.
To evaluate gains in polar metabolite identification, NIST SRM 1950 plasma extracts were analyzed using ZT Scan DIA 2.0 and 3.0 acquisition modes on the ZenoTOF 8600 system. DDA data were also acquired for reference only and are not the focus of this technical note. A detailed comparison of DDA and ZT Scan DIA 2.0 performance is provided elsewhere [8]. Metabolite identification from ZT Scan DIA 3.0 data acquired in both positive and negative ion modes was performed using MS-DIAL 5.6 set with the processing parameters summarized in Table 3. The initial analysis yielded 293 reference-matched metabolites across both ionization modes (Table 4). However, this value overestimates the true number of unique polar metabolites, as the dataset includes plasticizers, lipids, and, in some cases, multiple database entries corresponding to the same molecule under different names. Accordingly, all identifications were manually curated and confirmed by inspection of the MS/MS spectra using MS-DIAL and SCIEX OS visualization tools.
After curation, a final list of confidently identified molecules was generated (full list not shown) and compared with results from DDA and ZT Scan DIA 2.0 (Figure 2). Relative to DDA, ZT Scan DIA 3.0 identified 85% more compounds (~ 2-fold increase). This improvement stems from the stochastic precursor selection inherent to DDA analysis and the improved Q1 deconvolution enabled by ZT Scan DIA, which reduces spectral chimerism and enhances precursor–fragment correlation. Additionally, the narrower isolation window in ZT Scan DIA 3.0, compared with version 2.0, further minimized interfering fragment ions, leading to a 39% increase in the number of uniquely identified molecules. These results show that ZT Scan DIA 3.0, with its better-than-unit Q1 resolution, is particularly effective for untargeted small-molecule omics analysis.
Deconvolution of chimeric spectra using MS-DIAL 5.6 software
Metabolomics research relies on high data quality, which directly impacts results from untargeted metabolomics methods. Traditional DIA approaches face the challenge of co-isolated precursor ions within each SWATH window, leading to convolved product ion spectra, or chimeric spectra. The narrowest traditional DIA window available on SCIEX instruments is 3 Da, which has proven too broad for compound specificity in metabolomics and lipidomics studies. Using the sliding Q1 window in ZT Scan DIA analysis, chimeric spectra arising from isobars and other interferences can be partially or fully eliminated by Q1 deconvolution and retention time alignment in MS-DIAL 5.6 software. The narrow Q1 window enabled by ZT Scan DIA 3.0 offers an effective Q1 resolution of approximately 0.9 Da, potentially exceeding the precursor ion specificity achievable with targeted MS/MS scan modes such as MRMHR.
To illustrate the impact of data deconvolution on metabolite identification, NIST SRM 1950 plasma extracts were analyzed using DDA, ZT Scan DIA 2.0, and ZT Scan DIA 3.0 acquisition modes on the ZenoTOF 8600 system, and the results were compared for the representative metabolite hydroxybupropion (Figure 3). MS-DIAL 5.6 software deconvolutes DDA data based solely on retention time (RT), whereas ZT Scan DIA data are deconvoluted using both RT and the Q1 dimension.
In the DDA analysis (Figure 3, top row), the TOFMS extracted ion chromatogram (XIC) for the precursor ion at m/z 256.1101 appears relatively clean (top row, left panel); however, a substantial interference at m/z 89.0601 persists in the deconvoluted MS/MS spectrum and is not removed by RT-based deconvolution alone (top row, right panel). The XIC for this fragment (Figure 4, left panel) indicates a poorly defined contaminant with a multi-apex profile, one of which co-elutes with hydroxybupropion. In contrast, the fragment at m/z 166.0417 represents a characteristic and identifying product ion for hydroxybupropion (Figure 4, center panel).
Analysis of the ZT Scan DIA 2.0 data (Figure 3, middle row) shows that Q1 deconvolution effectively removes the major chimeric fragment at m/z 89.0610; however, a lower-intensity fragment at m/z 85.0288— likely originating from an acylcarnitine—remains after both RT and Q1 deconvolution (Figure 4, left panel). In contrast, the ZT Scan DIA 3.0 results (Figure 3, bottom row) demonstrate the benefit of narrowwindow scanning. After combined RT and Q1 deconvolution, both chimeric fragments are eliminated, resulting in a cleaner MS/MS spectrum (Figure 3, bottom row, left panel; highlighted in red) with a higher overall quality score than those obtained using other acquisition modes.
Quantitative analysis of metabolites derived from ZT Scan DIA data
The traditional discovery experimental workflow for metabolomics, DDA, is effective for compound identification when suitable spectral libraries are available; however, it is inherently limited in its ability to provide highly specific and accurate quantitative information. In an ideal DDA experiment, 1-2 product ion spectra are acquired per compound. While this is sufficient for broad compound coverage, it does not support robust quantitation at the MS/MS level. Quantitation based on product ions is inherently more compound-specific than TOFMS-level measurements alone, regardless of instrument resolution, due to the extensive isobaric overlap characteristic of small-molecule omics experiments. ZT Scan DIA data provide both qualitative information for confident compound identification and quantitative measurements at the MS/MS level, enabling more specific and accurate metabolite quantitation.
A representative comparison of quantitative performance obtained using DDA (TOFMS) and ZT Scan DIA (TOF MS/MS) acquisition is shown in Figure 1. The left panel displays an extracted ion chromatogram (XIC) generated from the DDA TOFMS survey scan, which is conventionally used for quantitation in DDA workflows. As shown, the elevated baseline and multiple co-eluting interferences within the 10 mDa mass window of γ-glutamylleucine contribute to a relatively low calculated signal-to-noise ratio [S/N].
In contrast, the center panel shows the XIC derived from ZT Scan DIA 2.0 data acquired at the MS/MS level. Under these conditions, a single dominant chromatographic peak is observed, with substantially reduced background and a markedly improved S/N relative to DDA. The right panel illustrates the effect of further narrowing the effective Q1 isolation window in ZT Scan DIA 3.0. The resulting chromatographic peak exhibits the highest S/N among all acquisition modes evaluated, demonstrating potentially improved sensitivity enabled by enhanced precursor selectivity.
Although the percent coefficient of variation (%CV) for this compound was below 20% across all acquisition modes, no additional improvement was observed in the ZT Scan DIA workflows in this example. This result mainly stems from differences in accumulation time between the experiments. The TOFMS survey scan in DDA was performed with a 100 ms accumulation time, while accumulation times for ZT Scan DIA 2.0 and 3.0 were 8.6 ms and 4.3 ms, respectively. The longer accumulation time likely explains why the %CV was lowest for the DDA experiments. For ZT Scan DIA experiments, these values are automatically set in SCIEX OS based on cycle time and Q1 window width. As the Q1 window narrows to enhance compound specificity, the number of data bins per scan increases, requiring a proportional reduction in accumulation time to stay within the fixed cycle time. Increasing the cycle time will also increase the accumulation time, but that may reduce sampling density and overall coverage. As with all untargeted workflows, optimal performance therefore requires balancing interdependent acquisition parameters to maximize coverage while maintaining sufficient sensitivity and quantitative robustness.
To evaluate the quantitative performance of ZT Scan DIA 3.0, TOFMS, and TOF MS/MS target lists were generated for the QReSS standards added during plasma extraction. ZT Scan DIA 3.0 data acquired in positive-ion mode were used for all analyses. Because each ZT Scan DIA experiment includes a TOFMS survey scan that is equivalent to the TOFMS survey scan acquired in a DDA workflow, quantitative results derived from these TOFMS data provide a direct and meaningful comparison to DDA-based TOFMS quantitation, despite originating from different acquisition modes.
Quantitative data processing was performed using the Analytics module in SCIEX OS software. Target lists were generated using either accurate precursor masses for TOFMS-level quantitation or accurate precursor and fragment masses for MS/MS-level quantitation. The resulting quantitative metrics are summarized in Table 5. Quantitation based on TOFMS data yielded a mean peak area of 3.05 × 10⁶ with an average %CV of 8.2. In contrast, quantitation performed at the MS/MS level within the same ZT Scan DIA experiment produced a higher mean peak area of 7.55 × 10⁶ and a lower average %CV of 4.7. These improvements are attributed to the increased signal-to-noise ratios typically achieved when analytes are quantified using fragment-ion data at the MS/MS level.
The quantitative evaluation of ZT Scan DIA 3.0 was extended to a broader set of metabolites identified using MS-DIAL 5.6 software and is summarized in Table 6. Compounds selected for quantitation were grouped into three MS-DIAL quality score ranges (>1.85, 1.7–1.85, and 1.6–1.7) to assess the relationship between spectral match quality and quantitative performance. Data were acquired using either DDA or ZT Scan DIA 3.0 and processed using the same workflow as for the QReSS standards.
Across all quality score ranges, ZT Scan DIA generally produced larger peak areas and lower percent coefficients of variation (%CV) compared with DDA. On average, ZT Scan DIA data showed a 4.5-fold increase in peak area and a 22-fold increase in %CV relative to DDA, indicating improved sensitivity and quantitative reproducibility. Compound-level comparisons between ZT Scan DIA and DDA are provided in the right-most columns of Table 6, with aggregated performance metrics summarized in the lower-right corner of the table.
The greatest gains in signal intensity and precision were observed for compounds with MS-DIAL quality scores above 1.7, reflecting the benefit of improved precursor–fragment correlation achieved using ZT Scan DIA. For compounds within the lowest quality score range (1.6– 1.7), improvements in signal-to-noise and peak area were less pronounced. This trend is expected, as visual inspection of the corresponding MS/MS spectra in MS-DIAL revealed increased chimeric interference within this score range. In contrast, spectra associated with higher quality scores were relatively free of interfering fragment ions, resulting in more robust quantitative performance.
Overall, these results show that ZT Scan DIA 3.0 offers better quantitative performance than DDA and ZT Scan DIA 2.0, especially for confidently identified metabolites, while maintaining improved precision across a wide range of compound qualities.
Although this study used an untargeted metabolomics workflow, an important characteristic of ZT Scan DIA is its ability to interrogate data post-acquisition in a targeted manner. Each ZT Scan DIA experiment generates a comprehensive digital record of the sample, including MS/MS information for essentially all precursor ions within the defined mass range. As shown in Table 4, the number of detected features closely tracks the number of acquired MS/MS spectra, reflecting the high degree of fragmentation coverage achieved with this approach.
The use of a sliding Q1 window enables effective Q1 deconvolution to approximately 0.9 Da, which is narrower than the isolation widths employed in conventional fixed- or variable-window DIA workflows and is approximately 2-fold more narrow than that achieved with ZT Scan DIA 2.0. This increased precursor selectivity improves precursor–fragment correlation and reduces spectral complexity, facilitating more confident interpretation of MS/MS data across a broad range of compounds.
Together, these attributes position ZT Scan DIA 3.0 as a flexible acquisition strategy that bridges untargeted discovery and targeted data interrogation. While dedicated targeted workflows such as MRMHR remain appropriate for certain quantitative applications, such as for optimizing the LOD and LOQ for a particular molecule, the combination of high sensitivity, improved selectivity, and comprehensive MS/MS coverage achieved with ZT Scan DIA 3.0 suggests that, in many cases, follow-up targeted analyses can be performed directly from the original data set, without the need for additional experiments aimed at sample reanalysis.
Conclusions
In this study, ZT Scan DIA 3.0 was evaluated as an untargeted metabolomics acquisition strategy using NIST SRM 1950 plasma and benchmarked against DDA and earlier ZT Scan DIA implementations. The results demonstrate that narrowing the scanning isolation window enables effective Q1 deconvolution to approximately 0.9 Da, improving precursor–fragment correlation and reducing chimeric MS/MS interference in complex biological matrices. When coupled with appropriate data processing in MS-DIAL 5.6, these improvements translated into increased metabolite coverage, higher-quality MS/MS spectra, and enhanced quantitative performance at the MS/MS level relative to DDA and ZT Scan DIA 2.0.
Importantly, the ZT Scan DIA 3.0 acquisition generated a comprehensive digital record of the sample, providing MS/MS information for nearly all detected features and enabling flexible post-acquisition data interrogation. While targeted workflows remain necessary for specific quantitative applications, the combination of improved selectivity, sensitivity, and fragmentation coverage achieved with ZT Scan DIA 3.0 supports its use as a robust platform for discovery-scale metabolomics and, in many cases, for targeted follow-up analyses directly from the same dataset. Together, these findings highlight the value of narrow-window scanning DIA approaches for improving data quality and expanding the analytical capabilities of untargeted small-molecule mass spectrometry. In summary:
- ZT Scan DIA 3.0 enables effective Q1 deconvolution to ~0.9 Da, providing substantially higher precursor selectivity than conventional fixed- or variable-window DIA and approximately two-fold improvement over ZT Scan DIA 2.0.
- Improved Q1 deconvolution reduces chimeric MS/MS spectra, leading to better precursor–fragment correlation and more confident metabolite identification in complex samples such as plasma.
- Using MS-DIAL 5.6.082520-alpha software, ZT Scan DIA 3.0 identified ~ 2-fold more metabolites (+85%) than DDA and 39% more than ZT Scan DIA 2.0, which is the result of the improved Q1 resolution and better correlation between the precursor ion and its MS/MS spectrum.
- Compared with DDA, ZT Scan DIA 3.0 delivered higher sensitivity, as evidenced by increased peak areas and improved signal-to-noise ratios for MS/MS-level quantitation.
- Quantitative reproducibility was improved at the MS/MS level, particularly for metabolites with high spectral match quality, demonstrating the benefit of fragment-ion-based quantitation in small-molecule omics.
- ZT Scan DIA generates a comprehensive digital record of the sample, with the number of detected features closely matching the number of acquired MS/MS spectra, supporting retrospective qualitative and quantitative analyses.
- ZT Scan DIA 3.0 effectively bridges untargeted discovery and targeted interrogation, reducing the need for additional experiments in many follow-up scenarios, while maintaining the flexibility required for hypothesis-driven metabolomics studies.
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