Method and mass spectrometry system for acquiring mass spectral data

Abstract

To provide an improved method for acquiring mass spectral data.SOLUTION: There is provided a method for acquiring mass spectral data of a sample across at least a portion of an m / z range, wherein the m / z range includes a plurality of sets of m / z sub-ranges and each set includes one or more m / z sub-ranges. The method includes determining a first set of m / z sub-ranges of the plurality of sets of m / z sub-ranges and determining a second set of m / z sub-ranges of the plurality of sets of m / z sub-ranges, wherein the first set includes a first m / z sub-range and the second set includes a second m / z sub-range. The step of determining the first and second sets of m / z sub-ranges includes setting the first and second sets of m / z sub-ranges such that a relatively high transmission region of a first response profile at least partially overlaps a relatively high transmission region of a second response profile.

Description

[Technical Field]

[0001] The present disclosure relates to methods for acquiring mass spectral data of a sample over a range of mass-to-charge ratios (m / z), and to mass spectrometry systems for carrying out such methods. [Background technology]

[0002] In mass analyzers that use an ion trap mass analyzer, such as the Orbitrap™ mass analyzer manufactured by Thermo Fisher Scientific™, the number of ions entering the analyzer must be limited to a specific range to avoid undesirable effects due to trap overfilling, such as space charge effects. In Orbitrap™ instruments, a trap (C-trap) typically injects ions into the orbitrap mass analyzer. Such traps are often referred to as "extraction traps." A typical target value for the number of ions or total ion current (TIC) in a complete mass spectrometry (MS) scan in an orbitrap instrument is 1×10 6 ~3×10 6 However, such an upper limit naturally limits the dynamic range of the mass spectrometry scan as well as the signal-to-noise (S / N) ratio that can be achieved for lower abundance analytes in the sample. This drawback is particularly pronounced in the analysis of biological samples, which are often dominated by a few highly abundant species. For example, in human plasma, the protein serum albumin constitutes approximately 50% of the plasma proteins. Effective analysis of low abundance species in a sample requires either removal of the dominant species (which may not be possible in all circumstances) or increasing the dynamic range to resolve both high and low abundance species.

[0003] To record a typical full scan covering a wide m / z range, ions derived from a sample are accumulated in a trap (e.g., a C trap) for a certain amount of time and then injected into a mass analyzer (e.g., an orbital trap mass analyzer). The total time the ions are accumulated (referred to herein as the "injection time") is typically determined by an automatic gain control (AGC) mechanism that controls the accumulation time in the C trap. The AGC typically utilizes both the observed TIC of the m / z range of interest as well as the associated injection time of a previous scan (e.g., a full scan or a short, low-resolution "prescan"), but can also utilize an additional electrometer device to estimate the injection time required to reach a specified number of ions in the ion trap (also referred to as the AGC target value). Like the TIC itself, the resulting injection time is primarily determined by the most abundant species in the sample. Considering that the injection time is applied equally to all sample components and that typically all ions from the m / z range enter the ion trap simultaneously, lower abundance species are either not resolved at all or are detected with a low signal-to-noise ratio, limiting the dynamic range of the analysis.

[0004] Approaches to increasing the signal-to-noise ratio of m / z ranges as well as the overall dynamic range of MS scans have been previously described. WO 2006 / 129083 describes a method involving sequential injection of ions from multiple selected m / z ranges followed by mass analysis of a combined sample of ions, using an AGC mechanism to achieve a target number of ions.

[0005] AD Southam et al. (Anal. Chem. 2007, 79, 4595) describe a method for increasing the dynamic range of Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry, specifically for direct injection nanoelectrospray applications, which involves the sequential injection and mass analysis of multiple adjacent overlapping m / z windows, followed by stitching these windows to generate a continuous full-scan spectrum. The method includes a dedicated stitching algorithm primarily aimed at maintaining or even improving the high mass accuracy of the FT-ICR mass analyzer.

[0006] F. Meier et al. (Nat. Methods 2018, 15, 440) describe an acquisition method called "BoxCar," and International Publication No. WO 2018 / 134346 describes a similar approach. These publications aim to increase the signal-to-noise ratio (S / N) of low-abundance species and the overall dynamic range of a full scan by sequentially injecting ions from a segment of the full-scan m / z range and performing mass analysis on two or more combined samples of ions, which can then be stitched together by a post-processing algorithm to generate a full-scan spectrum. Specifically, a wide m / z range is segmented into multiple adjacent, overlapping m / z windows, which are alternately assigned to two or more "BoxCar scans" (partial spectra). For example, in two BoxCar scans, windows #1, #3, #5, ... are assigned to scan #1, and windows #2, #4, #6, ... are assigned to scan #2. For each scan, ions from the assigned windows are injected sequentially and then collectively measured in a mass analyzer. F. Meier et al. focus on proteomics applications, and the authors demonstrate improved performance in terms of sensitivity and number of proteins detected per unit time compared to a standard full scan on the Q Exactive™ HF.

[0007] The BoxCar method (WO 2018 / 134346) seeks to provide an approach to replacing standard full scans without interfering with established workflows (such as data-dependent acquisition in proteomics) and without compromising acquisition speed. Based on the basic method of multiplexing selected m / z ranges through their sequential injection and collective mass analysis, as described in WO 2006 / 129083, the BoxCar method demonstrates that the dynamic range of a full scan can be effectively increased by acquiring multiplexed partial scans. Because each m / z window in a partial scan has the same AGC target value, the individual injection times allocated by the AGC are highly dependent on the abundance of the species contained in the window. For example, low-abundance species are assigned longer injection times than high-abundance species, thereby reducing the proportion of high-abundance species in favor of low-abundance species and better utilizing the total available ion injection time (which is not limited by the highest abundance species as in the case of a standard full scan).

[0008] The concept of dividing the precursor m / z scan range into multiple m / z windows of fixed or variable width is described in U.S. Pat. Nos. 8,809,770, 9,269,553, and 9,543,134. U.S. Pat. No. 8,809,770 uses different m / z selection windows to generate fragmentation spectra and searches a library of fragmentation spectra for known compounds. U.S. Pat. Nos. 9,269,553 and 9,543,134 describe the use of multiple wide precursor m / z selection windows that cover the entire m / z range of interest to obtain multiple fragmentation spectra. However, none of these documents describe the use of multiple precursor (MS) selection windows through sequential injection and ensemble mass analysis of the precursors. 1 ) scans. In particular, each of these documents does not address aspects of increasing the dynamic range of MS scans. 1 Not the law, but MS 2 Mass spectrometry methods are described.

[0009] While the above-described techniques have provided improvements in the acquisition of mass spectral data, it is an object of the present disclosure to provide improved methods for acquiring mass spectral data. In particular, one object of the present disclosure is to obtain a high dynamic range when obtaining mass spectral data. Summary of the Invention

[0010] Some embodiments of the present disclosure relate to mass spectrometry (MS) using an automatic gain control (AGC) mechanism that determines the injection time of analyte sample ions to control the number of ions entering an ion trap mass analyzer (e.g., an orbital trap mass analyzer or a trapped time of flight (ToF) where ions enter the trap and are ejected from there to a ToF mass analyzer). The injection time is a parameter that may also be referred to as the "accumulation time" or "fill time." The number of ions entering the mass analyzer of an ion trap mass analyzer can be controlled by filling the extraction ion trap for a calculated period of time. Ions are ejected from the extraction trap into the mass analyzer so that ions can only enter the mass analyzer at a specified time. This calculated period is the injection time, accumulation time, or fill time, and some embodiments of the present disclosure relate to improvements in determining this parameter.

[0011] In some embodiments of the present disclosure, automatic partitioning of the scan range is used to separate m / z regions with high-abundance signals from those with low-abundance signals, thus enabling dynamic adjustment of the high dynamic range (HDR) window (also referred to herein as m / z subrange) according to the composition of the sample. Because the method allows for dynamic adjustment, the methods described herein may be particularly advantageous when the composition of the sample is highly time-dependent, as may be the case in chromatographic experiments. Compared to previous methods such as the BoxCar method, improved allocation of dynamic m / z windows can be achieved. In particular, the embodiments described herein enable dynamic focusing on low-abundance regions on a relatively short time scale, such that automatic repartitioning of the m / z range can, in principle, be performed at high frequency, e.g., on a scan-by-scan basis.

[0012] In general terms, the present disclosure provides a method for acquiring mass spectral data of a sample across at least a portion of an m / z range. The method includes partitioning the m / z range into one or more sets of m / z subranges, each set including one or more m / z subranges, by receiving mass spectral data of the sample across the m / z range, dividing the m / z range into a plurality of m / z bins, determining an ion abundance index for each m / z bin based on the mass spectral data, and forming one or more sets of m / z subranges by assigning m / z bins having ion abundances corresponding to at least a threshold degree to the formed m / z subranges. The method further includes performing mass analysis on the sample for each set of m / z subranges, thereby obtaining one or more partial mass spectral data sets. By partitioning the m / z range in this manner based on the mass spectral data, m / z subranges for mass analysis can be dynamically determined. This allows multiple sets of m / z subranges to be rapidly determined to prevent high-abundance species from dominating low-abundance species when performing mass analysis. This process of partitioning an m / z range by dividing it into bins and then grouping the bins together can be referred to as "clustering." The result of this "clustering" is one or more m / z bins grouped together, which can then be processed to determine m / z subranges for mass analysis.

[0013] In some embodiments, the process of forming m / z subranges for mass analysis may include (i) dividing the entire m / z range into bins (which may be equidistant) and determining a TIC value (or other measure of ion abundance, such as any measure of signal intensity) for each bin; (ii) grouping bins of similar TIC size into clusters of various sizes; and (iii) processing the list of clusters to partition the spectrum into m / z subranges (m / z windows). Preferably, the entire spectral range is partitioned in each iteration. The final processing step (iii) may include forming m / z subranges that are wider than the clusters formed in step (ii). In some scenarios, using a relatively wide overlap between m / z subranges (compared to the BoxCar method) can avoid the need to rescale the intensities of peaks obtained within the lateral regions of the mass filter (e.g., the lateral sides of the quadrupole). As a result, HDR experiments do not require recording a standard full scan simultaneously with the HDR full scan. This can save time and reduce the need for post-processing workflows.

[0014] Thus, in general terms, the present disclosure also provides a method for acquiring mass spectral data of a sample over at least a portion of an m / z range, the m / z range including a plurality of sets of m / z subranges, each set including one or more m / z subranges. The method includes determining a first set of m / z subranges of the plurality of sets of m / z subranges (e.g., for performing a first subscan) and determining a second set of m / z subranges of the plurality of sets of m / z subranges (e.g., for performing a second subscan), the first set including (at least) the first m / z subrange and the second set including (at least) the second m / z subrange. The method further includes mass filtering the sample using a first mass filter having a first response profile corresponding to a first m / z subrange to isolate ions in a first set of m / z subranges, the first response profile having a relatively high transmission region and one or more relatively low transmission regions, and performing mass analysis on the sample across the first set of m / z subranges to obtain a first partial mass spectral data set. The first mass analysis can be described as a first subscan. The method then includes mass filtering the sample using a second mass filter having a second response profile corresponding to a second m / z subrange to isolate ions in a second set of m / z subranges, the second response profile having a relatively high transmission region and one or more relatively low transmission regions, and performing mass analysis on the sample across the second set of m / z subranges to obtain a second partial mass spectral data set. The second mass analysis can be described as a second subscan.

[0015] The first mass filter and the second mass filter can be the same mass filter or two different mass filters. For example, the first and second mass filters can be two quadrupoles or a single quadrupole. However, other numbers and types of filters can be used. In a preferred embodiment, a single mass filter is used to perform each subscan. However, in some alternative implementations, one subscan can be performed using one instrument and another subscan can be performed on another instrument, and the resulting partial spectra can be stitched together.

[0016] The step of determining the first and second sets of m / z subranges includes setting the first and second sets of m / z subranges such that a relatively high transmission region of the first response profile at least partially overlaps a relatively high transmission region of the second response profile. The first and second response profiles may be adjacent to each other on the m / z axis with a degree of overlap high enough to ensure that the high transmission regions coincide. Overlapping the relatively high transmission regions of the response profiles may ensure that, for any given m / z value, the resulting mass spectral data was obtained from a relatively high transmission portion of the response profile. This means that the imperfect (e.g., trapezoidal) nature of the mass filter response profile does not need to be compensated for, since data is obtained from outside the low transmission side of the response profile. Therefore, less data post-processing may be required.

[0017] Each set of m / z subranges may include one or more m / z subranges. For each subscan across the set of m / z subranges, the m / z subranges may be injected sequentially. For each m / z subrange injected, a mass filter is preferably individually tuned, with the mass filter having an individual response profile associated with the particular m / z subrange. Furthermore, an HDR scan may include more than two subscans. For example, two, three, four, or more different sets of m / z subranges may be identified.

[0018] Some embodiments of the present disclosure provide methods for adjusting injection times for mass spectral analysis. For example, embodiments of the present disclosure enable the redistribution of accumulated unused injection time among m / z windows, thereby improving sensitivity in m / z regions containing relatively low-abundance ions. In particular, if the AGC algorithm determines a set of injection times that exceeds the actual time available, at least some of the injection times may be reduced. This may be achieved by preferentially shortening certain injection times over others to maintain dynamic range. For example, this may be achieved by redistributing "spare" injection time from m / z windows that are allocated less injection time than a fair (e.g., even) distribution of injection times. However, all injection times may be reduced by an equal factor. The total available injection time may be user-defined and / or based on the sample, experiment, etc., and may be constrained by the repetition rate of the instrument. For example, the repetition rate may depend on how frequently measurements are required across each chromatographic (e.g., liquid chromatography (LC)) peak. Thus, adjusting infusion times according to the methods of the present disclosure can improve utilization of available infusion time while still adhering to constraints on available time.

[0019] Thus, in generalized terms, the present disclosure also provides a method for acquiring mass spectral data of a sample over at least a portion of an m / z range, the m / z range including a set of one or more m / z subranges. The method includes determining an initial distribution of injection times, the initial distribution including an initial injection time for each m / z subrange of the set of one or more m / z subranges. Based on determining that the total time of the initial distribution of injection times exceeds the total available injection time for acquiring the mass spectral data, the method determines an adjusted distribution of injection times, the adjusted distribution including an adjusted injection time for each m / z subrange. One or more adjusted injection times in the adjusted distribution may be the same as (i.e., equal to) the corresponding initial injection time of the initial distribution. That is, not all m / z subranges have accumulation times adjusted from their initial values. Rather, in embodiments of the present disclosure, at least one injection time of the adjusted distribution is different from the corresponding injection time of the initial distribution.

[0020] The method then includes performing mass analysis for each m / z subrange according to the adjusted injection time distribution to obtain a partial mass spectral data set. Determining the adjusted distribution of injection times includes reducing at least one of the initial injection times for each m / z subrange such that the total time of the adjusted distribution of injection times for the set of one or more m / z subranges is less than or equal to the total available injection time for acquiring the mass spectral data.

[0021] Partial mass spectral datasets (data obtained from separate subscans) can be combined to provide MS data covering the entire scan range. Therefore, the present disclosure also provides a scan stitching procedure that can be used to convert two or more partial mass spectral datasets into an HDR full scan on the fly, which can be processed like a standard full scan by a post-acquisition software tool. Thus, additional, potentially time-consuming post-processing steps can be reduced or avoided. In the stitching procedure, two adjacent m / z subranges resulting from separate partial mass spectral datasets when obtained using the HDR techniques described herein are stitched together at their overlap region. The stitching boundary within this overlap region can be variable and optimized to preserve the isotope distribution occurring in the border regions of both windows.

[0022] The present disclosure also provides a hybrid approach, which involves acquiring a standard full scan of an m / z range and a single (or a few) HDR "zoom" scans that include selected, non-overlapping m / z subranges. The standard full scan can be used as a reference point for quantification. In some cases, the standard full scan can provide sufficient information about highly abundant peaks by itself. The HDR "zoom" scan can provide deeper insight into sparse regions of the full scan or specific regions of interest. These regions in the standard full scan can be replaced with corresponding m / z windows from an HDR subscan (or multiple subscans) to obtain a hybrid HDR full scan. When this hybrid HDR workflow includes two scan events, scan rate performance can be comparable to an approach using two HDR subscans.

[0023] Thus, in general terms, the present disclosure also provides a method for acquiring mass spectral data of a sample across an m / z range, the method including: performing a first mass analysis on the sample across the m / z range to thereby obtain a first mass spectral dataset; partitioning the m / z range into one or more sets of m / z subranges, each set including one or more m / z subranges; and performing a mass analysis on the sample for each set of m / z subranges to thereby obtain one or more partial mass spectral datasets. Thus, the first mass spectral dataset can provide information about the sample across the entire m / z range, while the one or more partial mass spectral datasets can serve as "zoom-in" scans of specific subranges of the full m / z range. The partitioning can be performed as described above based on the first mass spectral dataset. The partitioning can also be performed based on a previous mass spectral dataset (e.g., obtained from a previous scan performed on the same sample). The first mass spectral dataset and one or more partial mass spectral datasets may be stitched together to provide a mass spectral dataset that is effectively an enhanced version of a single full scan.

[0024] These and other advantages will become apparent from the following detailed description.

[0025] The present disclosure will now be described, by way of example only, with reference to the accompanying figures. [Brief explanation of the drawings]

[0026] [Figure 1A] 1 illustrates a method for acquiring mass spectral data. [Figure 1B] FIG. 1 illustrates a method for segmenting a scan range. [Figure 2] 1 shows the relationship between the width of the quadrupole transmission side and the separation width. [Figure 3A] An example of how to partition the m / z range is shown. [Figure 3B] An example of how to partition the m / z range is shown. [Figure 3C] An example of how to partition the m / z range is shown. [Figure 3D] An example of how to partition the m / z range is shown. [Figure 3E] An example of how to partition the m / z range is shown. [Figure 3F] An example of how to partition the m / z range is shown. [Figure 3G] An example of how to partition the m / z range is shown. [Figure 4A] 1 shows a schematic representation of a quadrupole transmission side. [Figure 4B] A set of response profiles for m / z subranges is shown. [Figure 5] 1 shows a schematic representation of a mass spectrometry system for implementing the methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0027] Existing approaches, such as the BoxCar method, represent a fundamental approach to effectively increasing the dynamic range and signal-to-noise ratio of full scans. However, known systems typically do not provide a fully online (i.e., limited to MS instruments) workflow for generating continuous full scan spectra, and post-processing software and / or calibration procedures may be required prior to actual data acquisition to obtain full scan spectra (as opposed to partial spectra). In contrast, embodiments of the present disclosure can be used to implement a comprehensive online workflow capable of generating high dynamic range full scans for a variety of applications without the need for individual calibration for each application. This not only saves users time but also enables further online analysis of full scans, taking into account algorithms designed to operate on full scans.

[0028] The high dynamic range (HDR) scan method described herein provides an improved approach for recording and processing full MS scans with improved dynamic range compared to standard full scans. The HDR scan processor (a) sets up an HDR scan by analyzing the full spectrum and separating m / z regions with high signal from m / z regions with low signal, (b) fully utilizes the available sample injection time by extending the AGC, and (c) implements algorithmic steps to stitch together HDR scan portions each containing a subset of the separated m / z regions. The resulting scan is an HDR full scan spectrum that can be used as a replacement for a standard full scan. The method described herein is compatible with established mass spectrometry systems and workflows and reduces the need for additional pre-processing calibration steps and post-processing rescaling or stitching steps.

[0029] A scenario in which the HDR method described here is particularly advantageous is in experiments with low sample loads, especially in single-cell proteomics. A known set of the 1000-3000 most abundant proteins can be identified by dedicated MS analysis of isolated precursors. 2 The full ion fragmentation scan after each full scan may be sufficient to obtain further information about the sample components.

[0030] Apart from the increased dynamic range, HDR full scan also provides a better resolution than standard full scan. 2 This allows for better prediction of the injection time of the scan, since in a standard full scan, the ion optics settings (such as the S-lens or RF amplitude of the ion funnel) are adjusted towards the lower end of the spectral m / z range, potentially reducing the transmission of ions in the higher m / z region. In contrast, MS analysis of isolated precursors from the higher m / z region 2In a scan, the transmission of precursors is likely to be higher due to a much narrower isolated m / z range. Therefore, when the AGC predicts the injection time of precursors in a narrow m / z window based on a standard full scan, the resulting injection time may be too high due to an underestimation of the TIC, resulting in a poor MS performance. 2 The actual TIC within a scan may overshoot the desired AGC target if appropriate. However, in HDR scans, ion optics settings (e.g., RF, etc.) can be set individually for each window (each m / z subrange), allowing for a full scan and MS 2 The transmission difference between scans is reduced, resulting in improved AGC performance.

[0031] Therefore, the present disclosure also provides MS 1 MS scan based 2 It provides a method for controlling the AGC injection time in a scan where the ion optics settings are varied across the m / z range. HDR methods can provide improved AGC prediction accuracy, but this is not possible in MS 1 Scans can be performed by combining a small number of injections with a smaller mass window, which is typically the case when a narrow mass window is selected, such as in SIM or MS. 2 For example, the method can be used to measure the MS of a sample over at least a portion of the m / z range. 2

[0010] The method can be described in general terms as a method for acquiring mass spectral data, comprising performing one or more first mass analyses on a sample for each m / z subrange of a set of m / z subranges, thereby obtaining one or more partial mass spectral data sets, each first mass analysis being performed using an ion optics set for each m / z subrange of a respective set of m / z subranges. 1 Mass spectrometry, acquiring and m / z subrange MS 2performing a second mass analysis on the sample for each m / z subrange of the set, the second mass analysis comprising: 2 MS performed with the ion optics set for each m / z subrange of the set 2 Mass spectrometry is performed using a subrange of m / z. 2 The set can be determined based on one or more partial mass spectral data sets. The first mass analysis can be an HDR scan as described herein.

[0032] With regard to the use of multiple precursor m / z windows, known methods (such as those described in U.S. Pat. Nos. 8,809,770, 9,269,553, and 9,543,134) involve fragmentation (MS 2 While some (but not all) embodiments of the present disclosure employ selective m / z windows to enhance the precursor (MS ) scan and associated data independent workflow, 1 ) scan. The HDR methods described herein generally do not require any assumptions about the fragmentation scan of the selected precursor. Rather, the HDR methods described herein can be used in conjunction with common "top N" data-dependent experiments, where precursors are selected from a precursor survey scan and fragmented to obtain product ion spectra.

[0033] More specifically, existing solutions have the following limitations: Scan range partitioning: The m / z windows or subranges multiplexed in the "BoxCar" method are often fixed and must be determined and optimized in advance for a particular sample or application (e.g., proteomics, as in F. Meier et al.) depending on the typical distribution of species across the m / z range of interest. Furthermore, even when optimized for a given application type, a fixed set of windows may not always be optimal during acquisitions involving chromatography, where the composition of the injected sample naturally changes over time. Furthermore, WO 2018 / 134346 refers to data-dependent partitioning, but explains that partitioning is performed such that high-abundance species are assigned to narrow regions on the m / z axis. However, this approach may not reflect the realistic distribution of peaks within many samples. Therefore, the automated partitioning algorithm described herein offers greater flexibility and is more generally applicable. Notably, WO 2018 / 134346 focuses solely on "high-abundance species" assigned to a "narrow region on the m / z axis," effectively limiting the method disclosed therein to samples with a small number of high-abundance species present in a well-defined, narrow m / z region that is clearly separated from the remaining low-abundance species. However, this is unlikely to reflect the practical reality of most samples, and injection times are determined based on the overall TIC (total intensity of included signals) of the window, not just the most prominent signal. Thus, larger windows with low-abundance signals may have longer injection times than smaller windows with high-abundance species. The present disclosure can take this into account in the partitioning procedure. Furthermore, WO 2018 / 134346 suggests that partitioning be based on a dichotomous decision between "low" and "high" abundance of a species, but this is an extreme case. Typically, a wide range of abundances is observed, and signals of various intensities frequently overlap across a given m / z window. Therefore, it is rarely (or only in certain applications) possible to adjust the window so that the high abundance species is "the only species actually present."In contrast, the present disclosure provides a broadly applicable technique that can automatically (without user intervention or prior knowledge) segment a given spectrum based on its ion abundance distribution.

[0034] Stitching m / z windows: Ions from m / z windows are typically separated by the quadrupole, and their transmission profile has a trapezoidal shape rather than an ideal boxcar shape. The width of the trapezoid sides typically increases with the separation width. As a result, the intensities of ions detected at the leading and trailing edges of the trapezoid must be corrected to avoid distortion of relative abundances. The correction function must either be determined in advance for a selected set of windows or derived from a comparison between peak intensities in multiple HDR scans and those in a standard full scan in post-processing analysis. The first option is applicable only to a fixed set of m / z windows and requires a dedicated calibration procedure, while the latter option requires the acquisition of standard full scans at regular intervals during analysis, which can further slow down the experiment. In contrast, the embodiments described herein can reduce the need for additional scans or calibrations.

[0035] Injection time determination by AGC: For example, in known implementations of AGC on Exactive™ and Exploris™ instruments, each multiplexed m / z window can be assigned an individual maximum injection time (referred to as max IT). To easily replace a standard full scan with an HDR full scan without exposing additional parameters in the user interface, the user-specified max IT for the scan can be evenly distributed among the m / z windows. However, because windows with higher TICs can be assigned injection times below the upper limit given by their max IT allocation, the overall max IT for the entire scan may not be utilized to its full extent. For example, with an overall max IT of 100 ms and 10 m / z windows, each window is assigned an individual max IT of 10 ms. If the AGC determines an injection time of only 1 ms for a high TIC window, the remaining 9 ms can be utilized by other low TIC windows, due to the greater need to fully utilize the available injection time and compensate for their low TIC. If half of the windows use only 5 ms, the unused time further accumulates to 25 ms. Thus, in some embodiments, if it is determined that the total IT required for multiple m / z subranges exceeds the total IT available, the IT of at least one m / z subrange can be reduced to bring the total IT within limits. In some embodiments, "spare" injection time can be distributed from subranges that have less than an equal allocation of injection time.

[0036] To address the limitations of existing solutions as outlined above and thus create a holistic HDR scanning workflow, the present disclosure proposes various extensions to the original "BoxCar" method. FIG. 1A shows a flowchart illustrating the general concept of performing HDR scanning. In particular, FIG. 1A illustrates the basic workflow of high dynamic range (HDR) scanning. The sequence of recording sub-scan steps (#i, #M+i, #2×M+i, ...) is similar to the BoxCar sequence shown in FIG. 1A of WO 2018 / 134346.

[0037] Automatic scan range segmentation Embodiments of the present disclosure automatically partition a scan range of interest into multiple m / z subranges based on the TIC distribution across the entire m / z range, thereby enabling the selection of m / z subranges of variable position and width and dynamically adapting these m / z subranges to the time-dependent composition of the sample. The present disclosure provides an algorithm that can be used to partition a given m / z range into a set of typically overlapping m / z subranges, as shown in FIG. 1A, and then sequentially inject ions from these m / z subranges in two or more multiplexed subscans (partial scans). The HDR algorithm accepts the following input parameters:

[0038] [Table 1]

[0039] The present algorithm provides a method for acquiring mass spectral data of a sample across at least a portion of an m / z range. The method begins by receiving mass spectral data of the sample across the m / z range. First, the algorithm divides the (full scan range) mass spectrum (FM-LM) into m / z bins of size min_width / 2 and calculates the TIC as the sum of the peak intensities in each bin. Starting with the highest TIC bin and proceeding in descending order of TIC value, adjacent bins with similarly sized TICs are clustered together. The purpose of the partitioning procedure is to separate high-intensity regions from low-intensity regions so that more time can be spent unraveling the low-intensity regions. Thus, in general terms, the present method partitions the m / z range into one or more sets of m / z subranges, each set containing one or more m / z subranges. The partitioning is achieved by dividing the m / z range into a plurality of m / z bins, determining an ion abundance indication for each m / z bin based on the mass spectral data, and forming one or more sets of m / z subranges of m / z subranges by assigning m / z bins having ion abundances corresponding to at least a threshold degree to the formed m / z subranges.

[0040] In particular, partitioning the m / z range may include (i) identifying an initial m / z bin (e.g., having the highest ion abundance) among the multiple m / z bins; (ii) determining that one or more m / z bins adjacent to the initial m / z bin (either directly adjacent or, optionally, with one or more intervening bins that are not directly adjacent) have ion abundances that correspond to the ion abundance of the initial m / z bin to at least a threshold degree; and (iii) assigning the initial m / z bin and one or more m / z bins adjacent to the initial m / z bin to the formed m / z subrange.

[0041] This process can form clusters of m / z bins with corresponding ion abundances. Once this is completed for the first cluster, the process can be repeated for the remainder of the m / z range to form multiple distinct clusters. The multiple distinct clusters can be stored as a list L of clusters. Thus, the method can further include forming a complement of the formed m / z subrange. The complement of the formed m / z subrange is the set of m / z values ​​for the entire m / z range excluding the formed m / z subrange. That is, if the m / z range spans m / z1 to m / z4, the formed m / z subrange spans m / z2 to m / z3, and m / z4 > m / z3 > m / z2 > m / z1, the complement of the formed m / z subrange consists of the set of m / z values ​​spanning m / z1 to m / z2 and the set of m / z values ​​spanning m / z3 to m / z4, but the complement does not include the set of m / z values ​​spanning m / z2 to m / z3. Steps (i), (ii), and (iii) can be repeated for the complement of the formed m / z subranges, thereby forming additional m / z subranges of one or more sets of m / z subranges. This can be repeated by iteratively forming the complement of the formed m / z ranges and repeating steps (i), (ii), and (iii) for each successive complement of the formed m / z subranges, thereby forming multiple additional m / z subranges of one or more sets of m / z subranges.

[0042] In this algorithm, a threshold T of half the TIC of the currently processed bin is used (T = 0.5 × start_tic), meaning that all neighboring bins with TIC ≥ T are added to the cluster. That is, there can be a threshold degree of agreement between m / z bins that is a predetermined ratio of the ion abundance of the less abundant m / z bin (which can be inferred, for example, from the total ion current) to the ion abundance of the more abundant m / z bin. This predetermined ratio is preferably at least 0.5 to ensure that an acceptable degree of agreement is obtained.

[0043] Furthermore, a single bin that does not exceed T but is between two compatible bins is also added to the cluster to avoid over-partitioning of the spectrum into subranges that do not meet the minimum width requirement. In particular, the methods described herein may advantageously include determining that a first m / z bin and a second m / z bin have ion abundances that correspond to at least a threshold degree, determining that a third m / z bin between (e.g., directly between) the first and second m / z bins has an ion abundance that does not correspond to the ion abundances of the first and second m / z bins, to at least a threshold degree, and assigning the first, second, and third m / z bins to a single m / z subrange.

[0044] The reason for using half width instead of the full minimum width min_width for the bin size is that half width increases the flexibility for the clustering step, since bins of similar TIC size on either side of the currently processed bin can be added to the cluster to reach the minimum width. If the cluster does not reach the minimum width (i.e., contains only one bin), the cluster is either extended symmetrically by min_width / 4 on either side, or asymmetrically by using the intensity-weighted m / z centroids of the bins and extending them by a total of min_width / 4.

[0045] Other bin sizes (min_width / N, where N=an integer ≧1) can also be used. An integer of 2 is preferred to provide more flexibility when clustering adjacent bins together (i.e., joining adjacent bins on the left or right side) while at the same time preventing over-segmentation of the spectrum, which results in many windows that do not meet the min_width requirement. Each of the multiple m / z bins can have a width that is configurable by the user, and / or each of the multiple m / z bins has a width that is half a predefined (e.g., by the user) minimum width.

[0046] FIG. 1B illustrates how a set of clusters can be processed. Starting with the highest TIC cluster and proceeding in descending order of TIC value, the available clusters in list L are used to partition the (full) scan range into m / z subranges, primarily with the goal of separating high-TIC m / z regions from regions with sparse signal. When selecting TIC clusters and calculating the partitioned scan range, the selected clusters are stored in list S, and a set of partitioned m / z subranges covering the entire scan range is stored in list P. At each iteration, a new cluster from list L that does not overlap with an existing cluster in S is selected from L and added to S. The clusters in S are iteratively processed in ascending m / z order to (re)partition the scan range. As a result, a new list of m / z subranges P is generated each time S is extended with a new cluster.

[0047] In each iteration, a cluster adds at least one and at most three subranges (including the cluster itself) to P. If the cluster touches one or two existing boundaries, i.e., if the cluster starts and / or ends on an existing subrange of P or FM or LM, one (if it touches two boundaries) or two (if it touches one boundary) subranges are added to P. If the cluster does not touch existing boundaries, three subranges are added because two complementary windows along with the cluster itself are required to cover the entire m / z scan range.

[0048] That is, having constructed a list of clusters L (based on the TICs of the m / z bins, as described above), the algorithm selects the highest TIC cluster from L and adds it to the list of selected clusters S (which in the first iteration contains only one cluster). A (re)partitioning step is then performed using only the clusters in S (i.e., only one cluster initially, but more clusters are included in subsequent iterations) by processing the clusters in ascending m / z order, to generate a partitioned scan range P. Following the principle that in each iteration, a cluster adds at least one and at most three subranges (a maximum of three subranges can only be added once), if a single cluster is somewhere in the middle of the scan range (i.e., the cluster does not coincide with the upper or lower bound of the full scan range), then the first version of P contains three m / z windows, i.e., n=3.

[0049] Next, if more than four windows are required (n < N), the next highest TIC cluster that does not overlap with any of the clusters of S is selected from L, and the process is repeated by adding that cluster to the list of selected clusters S (which includes two clusters). Next, the (re)partitioning step is performed again from the beginning (i.e., P from the previous iteration is discarded), and a new version of P is created. According to the above rules, up to five m / z windows can exist within P (i.e., n = 5), or fewer if two clusters within S are in contact with each other or with the boundaries of the scan range. The entire process is repeated until n = N or until n > N (in which case, the previous version of P that gives n < N is used). Thus, the available clusters are processed until the desired number of sub-ranges N in P is reached. If N cannot be reached exactly, the best solution that does not exceed N is selected (i.e., by using the previous version of P). That is, the method may include repeatedly forming m / z sub-ranges until the total number of formed m / z sub-ranges is less than or equal to a predefined total number (N, fixed, user-defined, or dynamically variable) of m / z sub-ranges in one or more sets of m / z sub-ranges. The m / z sub-ranges included in P are used to set the HDR sub-scan.

[0050] Additional criteria other than those shown in FIG. 1B may be applied. For example, one criterion that is not shown in FIG. 1B and can be applied after the partitioning procedure is as follows: namely, the number of windows n must be greater than or equal to the number of HDR sub-scans (otherwise, HDR acquisition cannot be performed). If this criterion is not met, for example, the partitioning procedure can be repeated using an increased threshold for clustering m / z bins of similar TIC magnitudes (e.g., increasing the threshold from 0.5 to 0.75 or 0.9), thereby attempting to implement a larger number of distinct clusters.

[0051] The m / z subranges in lists L, S, and P (used to partition the m / z range) may be referred to as preliminary subranges. Any m / z subranges used in the process of partitioning the complete m / z range may be described as preliminary m / z subranges. The preliminary subranges may, in some cases, be identical to the final m / z subranges. However, in some cases, the preliminary m / z subranges may be determined based on the above algorithm, and then the preliminary m / z subranges may be adjusted before forming the final set of m / z subranges used for mass analysis.

[0052] As mentioned above, the partitioning procedure described herein can form up to three m / z subranges from a single cluster of m / z bins. Generalized, forming one or more m / z subranges of one or more sets of m / z subranges based on respective preliminary m / z subranges (e.g., subranges in list S) can include assigning each preliminary m / z subrange to one or more sets of m / z subranges and assigning one or two m / z subranges adjacent to (e.g., bordering on either side of) each preliminary m / z subrange to the one or more sets of m / z subranges, each of the one or two m / z subranges adjacent to each preliminary m / z subrange extending from one end of the respective preliminary m / z subrange to one end of a further preliminary m / z subrange. This process can be used to provide a list of m / z subranges spanning an m / z range while separating high-abundance regions from low-abundance regions. Preferably, the method further comprises increasing the width of at least one of the one or two m / z subranges adjacent to each preliminary m / z subrange, which can ensure that overlapping m / z subranges are obtained, as discussed in more detail below.

[0053] In the partitioning process, the m / z subrange size is preferably automatically adjusted to account for the overlap of desired subranges, which is generally calculated by the linear relationship overlap_offset+overlap_factor×window_width. Essentially, first, non-overlapping clusters are formed, and then this step determines the desired degree of overlap for the non-overlapping clusters found in the previous step. When an m / z subrange is added to the list, the algorithm determines whether the current m / z subrange or the previous m / z subrange (i.e., the adjacent m / z subrange to the left) is adjusted.

[0054] If both subranges have the same size, the subrange with the higher TIC is adjusted (by decreasing the lower boundary of the current subrange or by increasing the upper boundary of the previous subrange).

[0055] If the current subrange is larger than the previous subrange, the upper boundary of the previous subrange is adjusted (increased) using the overlap calculated for the current subrange.

[0056] If the previous subrange is larger than the current subrange, the lower boundary of the current subrange is adjusted (decreased) using the overlap calculated for the previous subrange.

[0057] The partitioning process may be described as including first assigning m / z bins having ion abundances corresponding to at least a threshold degree to a first preliminary m / z subrange (e.g., an initial cluster) and assigning m / z bins having ion abundances corresponding to at least a threshold degree to a second preliminary m / z subrange (e.g., a second cluster). The method may then include determining that the first preliminary m / z subrange overlaps with the second preliminary m / z subrange and discarding the second preliminary m / z subrange without assigning each m / z bin to an m / z subrange of one or more sets of m / z subranges. This ensures that non-overlapping preliminary m / z subranges are obtained. These non-overlapping preliminary m / z subranges can then be processed as described above to obtain the desired degree of overlap.

[0058] The disclosed method may include allocating an initial m / z bin and one or more m / z bins adjacent to the initial m / z bin to form a first preliminary m / z subrange. Forming the m / z subranges may include at least one of forming m / z subranges by increasing the width of the first preliminary m / z subrange and / or forming m / z subranges by increasing the width of a second preliminary m / z subrange adjacent to the first preliminary m / z subrange. This procedure may ensure that overlapping windows are initially formed from non-overlapping windows.

[0059] The method may include determining that a first preliminary m / z subrange and a second preliminary m / z subrange adjacent to the first preliminary m / z subrange have the same width, determining (e.g., based on TIC) which of the first preliminary m / z subrange and the second preliminary m / z subrange is associated with a higher ion abundance, and increasing the width of one of the first preliminary m / z subrange and the second preliminary m / z subrange associated with the higher ion abundance. Increasing the width of at least one of the preliminary m / z subranges may be performed to at least partially overlap the formed m / z subranges. For example, due to the way the m / z subranges are distributed among the different sets of m / z subranges, one m / z subrange in the first set of m / z subranges may at least partially overlap another m / z subrange in the different set of m / z subranges.

[0060] The disclosed methods may further include increasing the width of the second preliminary m / z subrange based on determining that the first preliminary m / z subrange is wider than the second preliminary m / z subrange. Alternatively, the width of the preliminary m / z subrange may be increased based on determining that the first preliminary m / z subrange is narrower than the second preliminary m / z subrange. This may help ensure that the formed subranges have widths that allow for effective acquisition of mass spectral data.

[0061] Forming the m / z subranges of one or more sets of m / z subranges may include forming one or more preliminary m / z subranges by assigning m / z bins having ion abundances corresponding to at least a threshold degree to individual preliminary m / z subranges, and forming one or more m / z subranges of the one or more sets of m / z subranges based on the individual preliminary m / z subranges. The preliminary m / z subranges may be described as clusters of m / z bins. In this way, clusters of m / z bins having similar ion abundances are formed, which can then be used to form m / z subranges.

[0062] In any case, the first and second preliminary m / z subranges may advantageously overlap by an amount that includes an offset proportional to the width of the first or second preliminary m / z subrange and / or that includes a constant offset. For example, a linear relationship overlap_offset+overlap_factor×window_width may be used, or some variation of this relationship may be used.

[0063] Considering a pair of adjacent subranges, another option is to always adjust the size of the higher TIC subrange, regardless of the actual subrange width, to ensure that the lower TIC subrange does not contain any high-intensity signal from the overlap region shared with the higher TIC subrange, which reduces dynamic range. However, as overlap increases with subrange width, the resulting overlap for a wide high-TIC subrange may exceed the width of the adjacent narrower low-TIC subrange and even overlap with the next subrange. This contradicts the concept of using non-overlapping subranges in each HDR subscan. Overlapping m / z subranges within a single scan should be avoided because overlapping regions are injected twice into the analyzer, distorting peak intensities and complicating signal processing. Because the overlap width depends on the window size (if overlap_factor > 0), the overlap calculated for a large window may actually exceed the width of the adjacent smaller window and even extend into each subsequent window, complicating the HDR workflow. For example, considering window ABCD, if the overlap of A extends into C, then a subscan covering A and C does not meet the requirement of non-overlapping windows.

[0064] Furthermore, high TIC subranges are typically narrower than low TIC subranges, so size-based adjustments as outlined above are typically consistent with the goal of preserving dynamic range for the lower TIC subranges.

[0065] When characterizing the transmission profile of a filter, a 95% threshold may be appropriate for defining a high transmission region. Other methods of characterizing the response profile may be used. For example, a relatively high transmission region of the first response profile and / or the second response profile may be a region having at least 90% ion transmission, at least 95% ion transmission, or at least 99% ion transmission.

[0066] In practice, the flank parameters can be determined by first recording a set of transmission profiles across a given m / z range, using multiple separation widths, and then fitting a trapezoidal function to the raw data. This can be used to directly obtain the flank widths without applying a predetermined high transmission threshold. In any case, each response profile is preferably substantially trapezoidal and may have a relatively high transmission region between multiple relatively low transmission regions (e.g., two low transmission flows on either side of a high transmission region). For example, determining the first and second sets of m / z subranges may include determining a first trapezoidal fit of the first response profile and a second trapezoidal fit of the second response profile based on mass spectral data obtained using a first mass filter and a second mass filter, and determining a relatively high transmission region of the first response profile and a relatively high transmission region of the second response profile based on the first and second trapezoidal fits.

[0067] Once the exact m / z boundaries of the subranges, including all overlaps, and therefore the final subrange widths, are known, the width of the low-transmission flank can be calculated as lt_width = lt_offset + lt_factor × final_window_width. These parameters can easily be determined in advance for the quadrupole model used in the instrument and then hard-coded in the software. However, if the individual (sample-to-sample) variations in these parameters are too large, they can also be calibrated individually for each instrument (either during manufacturing or during a regular system calibration run by the customer).

[0068] To avoid the need to rescale the intensity of peaks located on the quadrupole low-transmission flanks, the subrange overlap must be significantly larger than the flank region. Then, in the stitching procedure, peaks in the flank region of a subrange can be derived from the corresponding high-transmission region of an adjacent subrange. For example, the overlap parameters (offset and coefficient) can be 5 Th and 15%, and the low-transmission parameters can be 1 Th and 4%. This results in an overlap of 20 Th and a low-transmission width of 5 Th for a subrange size of 100 Th. An example of quadrupole flank width is shown in Figure 1A, which can be well-described by the parameters lt_offset 1 Da and lt_factor 4%. In particular, Figure 1A shows the quadrupole separation profile flank width depending on the separation subrange width.

[0069] As indicated by the formula lt_width=lt_offset+lt_factor×final_window_width, determining the first and second sets of m / z subranges may include determining a degree of overlap for the first and second response profiles based on the width of at least one of the first and / or second response profiles. For example, the width of the relatively low transmission region may be taken into account. Using the fact that the width of the sides of a trapezoid typically increases with the separation width to set the degree of overlap can ensure that data from the high transmission region of the response profiles is always available. Specifically, the relatively high transmission region of the first response profile preferably overlaps with the relatively high transmission region of the second response profile by an amount greater than the width of the relatively low transmission region of the first response profile and / or the width of the relatively low transmission region of the second response profile.

[0070] The above-described automatic segmentation of the m / z scan range can be performed once, for example, at the beginning of sample analysis when the sample composition does not change significantly over the course of the analysis. Alternatively, segmentation can be performed at regular intervals during the analysis, depending on how quickly the sample composition changes over time. It is also possible to perform the procedure after each HDR scan.

[0071] For example, the method may include: partitioning an m / z range into a plurality of first sets of m / z subranges, each first set including one or more m / z subranges; performing a first mass analysis on the sample for each first set of m / z subranges, thereby obtaining a plurality of first partial mass spectral data sets; and partitioning the m / z range into a plurality of second sets of m / z subranges based on ion abundances indicated by the plurality of first partial mass spectral data sets, each second set including one or more m / z subranges; and performing a second mass analysis on the sample for each second set of m / z subranges, thereby obtaining a plurality of second partial mass spectral data sets. The automated partitioning can be repeated as many times as necessary during an experiment. For example, if a sample has a time-dependent composition, partitioning performed once may not be optimal for a sample after a certain period of time has passed. Therefore, the partitioning method of the present disclosure can be repeated at multiple different time points based on ion abundances from previous scans.

[0072] In embodiments of the present disclosure, a preliminary mass analysis (e.g., standard full MS) is performed on a sample across an m / z range to obtain a preliminary mass spectral data set. 1There may be a step of performing a preliminary scan (although a BoxCar type scan may also be a preliminary scan). The step of partitioning the m / z range into a plurality of first sets of m / z subranges may be performed based on the ion abundances indicated by the preliminary mass spectral dataset. The disclosed method may iteratively partition the m / z range into a plurality of further sets of m / z subranges, each further set of m / z subranges being determined based on the ion abundances indicated by at least one previously obtained partial mass spectral dataset. Further mass analyses on the sample may be performed for each further set of m / z subranges, thereby obtaining a plurality of further partial mass spectral datasets. Thus, the partitioning may be iteratively performed based on the time-dependent composition of the sample. At least one, and preferably each, mass analysis is performed using an MS 1 It can be mass spectrometry. 1 and M.S. 2 or MS N A combination of analyses may also be used.

[0073] As shown in FIG. 1A , partitioning the m / z range into one or more sets of m / z subranges preferably includes forming M sets of m / z subranges, each containing W m / z subranges. In this implementation, the m / z subranges are numbered in m / z order (e.g., ascending m / z order, but may also be descending m / z order). The i-th set of m / z subranges includes m / z subrange numbers i, M+i, 2M+i, ..., (W-1)M+i, for each value of i=1, ..., M. This method of distributing m / z subranges among subscans helps ensure that a high dynamic range is achievable across the entire m / z range. It will be understood that the total number of m / z subranges may not be exactly divisible by M and W. For example, if M=3 and W=3 (i.e., M×W=9), but 10 subranges are desired, then one set of m / z subranges can include additional (i.e., 4) m / z subranges. In cases where the total number of subranges is not divisible by M and / or W, the same scheme for distributing m / z subranges into different sets (i.e., i, M+i, 2M+i, ..., (W-1)M+i) is used, with the simple addition of additional m / z subranges to (at least) one set.

[0074] In some embodiments, the m / z subranges may be distributed among the subscans, for example, so that each subscan gets an approximately equal share of the low and high intensity m / z subranges.

[0075] In some alternative examples, each m / z subrange in a separate set may have an ion abundance that corresponds, at least to a threshold degree, to the ion abundance of the formed m / z subrange. This may mean that a subscan performed on each set of m / z subranges is unlikely to be performed on a mixture of very high abundance ions and very low abundance ions. For example, partitioning the m / z ranges may include any one or more of assigning one or more m / z subranges associated with relatively high ion abundances in the sample to a first set of m / z subranges of one or more sets of m / z subranges, and / or assigning one or more m / z subranges associated with relatively low ion abundances in the sample to a second set of m / z subranges of one or more sets of m / z subranges, and / or assigning one or more m / z subranges associated with intermediate ion abundances in the sample to a third set of m / z subranges of one or more sets of m / z subranges. Each set of m / z subranges can include m / z subranges having ion abundances corresponding to at least a threshold degree (e.g., some predetermined percentage), which can be indicated by a preliminary mass spectral data set, such as a prescan or a previous HDR scan.

[0076] equidistant subranges As an alternative to automatic segmentation, the scan range from FM to LM can be segmented into N equidistant subranges (also called windows). The subrange or window widths are: equidistant subranges

number

[0077] The size of the subrange overlap is given by overlap_size = overlap_offset + overlap_factor × window_width. Taking overlap into account, the lower limit of the m / z subrange is given by FM + (i-1) × (window_size-overlap_size), where i is the subrange index (i=1...N).

[0078] Custom Subranges Because the composition of the sample injected into the MS, and therefore the useful mass range, typically changes over time during a chromatographically supported proteomics experiment, it may be advantageous to allow the user to define multiple time-dependent sets of m / z subranges, or, if the m / z region of interest varies linearly, to define an "m / z gradient." The instrument can then select a set of subranges based on the retention time (RT) of the experiment.

[0079] Another application of custom subranges is when there may be strict requirements for quantification of results from one LC run to another, e.g., label-free quantification. In such cases, allowing the instrument to dynamically adjust subranges can result in discrepancies in peak intensity comparisons between two LC runs of the same study. This is because dynamically created different m / z subrange settings can alter ion intensities and introduce intensity variations caused by the HDR method (i.e., solely by the instrument) and not by chemical / biological differences between the samples analyzed in the two LC runs. To avoid this effect, a first reference LC run based on a single sample or a mixture of samples can be performed fully dynamically (as described above for automatic segmentation) to optimize the HDR / LC / MS method. Thereafter, a fixed set of custom m / z subranges and LC retention times for all measurements can be used for all further runs when the actual samples of interest are measured.

[0080] Thus, some embodiments include receiving a sample from a chromatograph, such as a liquid chromatograph or a gas chromatograph, and the method may include repeating the methods described herein (e.g., partitioning and scanning) one or more times on one or more samples obtained from the chromatograph to obtain time-dependent mass spectral data for the sample.

[0081] Combination of custom, equal interval and / or automatic segmentation of subranges Either two or all three m / z subrange partitioning methods can be used simultaneously in a single LC run. For example, custom subranges can be applied to cover the desired m / z and / or RT range based on a priori knowledge of the sample or knowledge gained during HDR / LC / MS method optimization. However, beyond these known ranges, measurements can still be performed in automatic partitioning mode. In this way, a combination of custom and automatic partitioning subrange algorithms can be achieved in a single LC / MS scan, or even in part of an MS scan. In practice, this can ensure that measurements generate data for peaks of interest (target mode) while simultaneously improving the overall dynamic range of the measurement, which may be of interest for standard data processing or for later retrospective analysis.

[0082] Examples of automatic segmentation As previously mentioned, HDR scans measure at least two subsets of non-overlapping windows to obtain at least two separate subscans, and then partition the scan range into a set of overlapping m / z windows or subranges before stitching the subscans together to obtain the HDR full scan. With regard to distributing m / z windows to subscans, one can consider, for example, a scan range partitioned into 12 m / z windows (designated A-L) of any size, and how these windows are distributed among the available subscans (two or three in the following example).

[0083] Window:ABCDEFGHIJKL 1) Two subscans Subscan #1: ACEGIK Subscan #2: BDFHJL 2) Three subscans Subscan #1: ADGJ Subscan #2: BEHK Subscan #3: CFIL

[0084] In a preferred embodiment, the partitioning process, i.e. how windows A to L in the example are obtained, involves the following basic steps: 1) Partition the scan range into equidistant bins (preferably half the minimum window width) and calculate their TICs. 2) Clustering bins of similar TIC size to form TIC clusters. 3) Iteratively convert the TIC clusters into partitions of the entire scan range, proceeding in descending order of the cluster TICs, until the desired number of windows is reached.

[0085] Example: Scan range m / z 100~1000 1) Process the cluster m / z 200-300 (not bordering any existing boundaries) (C = window resulting from the selected cluster, F = window interpolated to fill the entire scan range). a. Window #1: 100~200(F) b. Window #2: 200~300(C) c. Window #3: 300~1000(F) 2) Process the cluster m / z 100-150 (touching the lower end of the scan range) a.Window #1: 100~150(C) b. Window #2: 150-200 (F) c. Window #3: 200~300(C) d. Window #4: 300~1000(F) 3) Processing the cluster m / z 750-770 (not bordering an existing boundary) a.Window #1: 100~150(C) b. Window #2: 150-200 (F) c. Window #3: 200~300(C) d. Window #5: 300~750(F) e. Window #6: 750~770(C) f. Window #7: 770~1000(F) 4) Processing of cluster m / z 280-320: overlap with window #3, skip cluster 5)...

[0086] This procedure is independent of the distribution of m / z windows to subscans. It seeks to optimize the window size, taking into account parameters such as the minimum window width and the desired number of windows (total or per subscan) to separate m / z regions based on the TIC pattern. These parameters may be user-defined. Once the windows are determined, calculations of the overlap and quadrupole transmission profile (both given by scaling factors for the absolute m / z width and window width) can proceed as described in more detail below.

[0087] Figures 3A-3G show an example of a process for automatically partitioning the m / z range of actual mass spectral data, which can be implemented using the method shown in Figures 1A and 1B. Figure 3A shows an example HeLa spectrum, scan range m / z 350-1650, partitioned into 26 bins of size 50 Th (corresponding to a minimum window width of 100 Th). As shown in Figure 3B, after determining the TIC value for each bin, bins of similar TIC magnitude are clustered together, starting with the highest TIC bin between m / z 550 and 600. Four adjacent bins between m / z 600 and 800 are added to form the first cluster in the m / z 550-800 range. The resulting TIC clusters were sorted in descending order by their TIC value. In Figure 3C, the top 10 clusters are labeled (1 = highest TIC cluster).

[0088] As shown in Figure 3D, the segmentation procedure begins with the highest TIC cluster at m / z 550–800 (shown within the solid-line border). To fully cover the entire scan range, two windows are interpolated (shown by dashed lines), resulting in three windows: m / z 350–550, m / z 550–800, and m / z 800–1650. In Figure 3E, a second cluster (m / z 800–900) is adjacent to the first cluster, resulting in four m / z windows. The dimensions of the right interpolated window are adjusted accordingly (m / z 900–1650). In Figure 3F, the third cluster (m / z 1100–1150) is elongated to meet the required minimum width of 100 sq m, resulting in m / z 1087.5–1187.5. Since it does not border any of the existing cluster windows, a third complementary window between m / z 900 and 1087.5 is added, resulting in six windows. This process is repeated iteratively.

[0089] In Figure 3G, a fully segmented spectrum is shown, containing nine overlapping m / z windows (five cluster windows and four complementary windows). The window overlap can be calculated during or after the segmentation procedure. The windows shown within the solid boundary are to be measured in the first HDR subscan, and the windows shown within the dashed boundary are to be measured in the second HDR subscan. The first HDR subscan provides a partial mass spectral data set for a first set of m / z subranges, and the second HDR subscan provides a partial mass spectral data set for a second set of m / z subranges.

[0090] In a preferred embodiment of the present disclosure, the m / z window can be adapted to the time-dependent composition of the sample. For example, in the first HDR cycle, the partitioning can be based on a standard (non-HDR) full scan, as shown in Figure 3. Thereafter, a standard (i.e., non-HDR scan) can be used, but the previous HDR scan can be used as the basis for subsequent partitioning.

[0091] AGC improvement 1) Redistribution of injection time To overcome the problem that the maximum available injection time (max IT) may not be fully utilized, the AGC algorithm of existing systems is enhanced with a redistribution function. The redistribution of injection time is performed after periodic determination of the injection time and can be implemented in a manner that does not interfere with the established AGC algorithm on the Exploris™ instrument. The method of this embodiment provides a method for acquiring mass spectral data of a sample over at least a portion of an m / z range, where the m / z range includes a set of one or more m / z subranges (e.g., HDR subscans).

[0092] First, injection times are calculated for m / z subranges without imposing any upper limit to ensure that the maximum IT is fully utilized by all subranges. That is, the method begins by determining an initial distribution of injection times, including an initial injection time for each m / z subrange of a set of one or more m / z subranges. Once the injection times for the m / z subranges are determined by the AGC based on the previous analytical scan or AGC prescan, the total injection time is calculated as the sum of the individual injection times of the subranges. If the sum exceeds the overall maximum IT, the injection times are redistributed as follows:

[0093] 1. Determine the injection time that corresponds to an equal distribution of the total maximum IT available for scanning. equal_IT=overall_max_IT / N 2. Calculate sum_exceeding, the sum of the infusion times exceeding equal_IT, and sum_remaining, the total remaining infusion time obtained by subtracting infusion times equal_IT or less (sum_remaining≦overall_max_IT). 3. Sort in ascending order by injection times greater than equal_IT.

[0094] 4. Process the sorted injection times from step 3, starting with the smallest. a. Calculate the new injection time from the currently allocated time, old_IT.

number

[0095] Thus, the method includes determining an adjusted distribution of injection times, including adjusted injection times for each m / z subrange, based on determining that the total time of the initial distribution of injection times exceeds the total available injection time for acquiring mass spectral data. Determining the adjusted distribution of injection times may include significantly shortening one or more relatively long initial injection times relative to one or more relatively short initial injection times. For example, the longer initial injection times may be preferentially significantly shortened.

[0096] In the redistribution algorithm outlined above, equal_IT serves as a threshold. Step 4a uses a dynamic coefficient to adjust the currently allocated injection times of m / z subranges for a particular subrange based on the threshold equal_IT. Processing injection times in ascending order ensures that lower injection times are not reduced beyond equal_IT and that the final sum does not exceed overall_max_IT.

[0097] As an example, consider an HDR experiment with N=10 subranges and an overall maximum IT of 100 ms, resulting in equally distributed injection times of 10 ms, as shown in Table 2. Table 1 shows the injection times (IT) (ms) for 10 m / z subranges from the existing AGC algorithm ("Old IT") and the IT values ​​derived from the redistribution algorithm ("New IT").

[0098] [Table 2]

[0099] Using a maximum IT setting of 100 ms for each subrange for the initial AGC calculation, five subranges are assigned injection times ≤10 ms by the AGC, while the remaining five subranges exceed the equal injection time (>10 ms). The remaining time of 80.9 ms (i.e., 0.1 ms subtracted from 100 ms: 1, 3 ms, 5 ms, and 10 ms) is then redistributed among the five subranges (#6-#10) that exceed the equal setting. Using steps 4a-4c described above, injection time #6 is first reduced to 10 ms, then injection time #7 is reduced to approximately 11 ms, and finally injection time #10 is reduced to approximately 23 ms, resulting in a total injection time of 100 ms. Therefore, the unused injection time from subranges #1-#4 can be distributed among four of the five subranges (#7-#10). It can be seen that the adjusted injection times (new IT) are the same as the initial injection times (old IT). Specifically, the adjusted distribution of injection times includes a plurality of injection times (#1 to #5) that do not change from the values ​​in the initial distribution.

[0100] Thus, it can be seen that determining an adjusted distribution of injection times includes reducing at least one of the initial injection times for each m / z subrange such that the total time of the adjusted distribution of injection times for a set of one or more m / z subranges is less than or equal to the total available injection time for acquiring mass spectral data. In this case, the injection times for subranges #6 through #10 are reduced. The longest initial injection times are reduced to approximately 22.51% of their initial values, while window #6 is reduced to 40% of its initial value and windows #1 through #15 are not reduced at all. Thus, the relatively long initial injection times can be reduced to a greater extent (e.g., in absolute terms or in terms of percentage) than one or more relatively short initial injection times.

[0101] Determining the adjusted distribution of injection times may include reducing at least one, and optionally each, of the initial injection times that exceed a threshold injection time. In some embodiments, the method includes reducing a plurality (e.g., each) of the initial injection times that exceed the threshold injection time by a scaling factor (which may be a static scaling factor or may be a dynamic scaling factor that is iteratively calculated, such as in step 4 of the above algorithm). To avoid excessive reduction of relatively short injection times, the disclosed method may include setting the threshold injection time as the adjusted injection time for each m / z subrange in which the initial injection time reduced by the scaling factor is less than the threshold injection time.

[0102] Determining the adjusted distribution of injection times may include determining a total preliminary injection time by summing the difference between the initial injection time and the threshold injection time for each m / z subrange whose initial injection time is less than the threshold injection time, and setting adjusted injection times for one or more m / z subranges whose initial injection time exceeds the threshold injection time by distributing the total preliminary injection, thereby increasing the initial injection time for one or more (e.g., some or each) m / z subranges whose initial injection time exceeds the threshold injection time. Thus, an efficient redistribution of "preparatory" injection time can be achieved. The threshold injection time is equal to the total available injection time (equal_IT) divided equally among one or more m / z subranges.

[0103] As an alternative to the distribution method described above, the injection times calculated by the AGC can simply be scaled equally by a scaling factor given by the ratio of the overall maximum IT to the sum of the calculated injection times. In the example outlined in Table 2, the scaling factor is 100 / 369.1 = 27%, so subranges #1 and #10 are assigned 0.027 ms and 27 ms, respectively. To prevent the scaling factor from being dominated by very long injection times, an upper limit can be applied to the calculated injection times. Therefore, determining the adjusted distribution of injection times can include reducing the initial injection time for each m / z subrange. Determining the adjusted distribution of injection times can include reducing the initial injection time for each m / z subrange by a scaling factor (e.g., all can be reduced by the same scaling factor, or different scaling factors can be used).

[0104] Furthermore, m / z subranges in spectral regions that are of greater interest for post-processing analysis than other spectral regions may be preferred and given higher injection times (or AGC targets, see below). Such priority regions may be pre-specified by the user and taken into account by the instrument when (re)distributing AGC targets and / or injection times.

[0105] Thus, the method may include receiving an indication (which may be user input or may be automatically determined) that an m / z subrange is an m / z subrange of interest, and setting a relatively high adjusted injection time for the m / z subrange of interest. In particular, the algorithm may allocate a longer injection time than would be allocated using an equal partitioning algorithm.

[0106] 2) Use subrange-specific AGC targets In the current AGC implementation, the total AGC target specified by the user for the full scan is divided equally among the subranges by default. For example, for a full scan with an AGC target of 1e6 and 10 scans per HDR subscan, each subrange is assigned a target value of 1e5 by default. However, this even distribution can be detrimental under certain circumstances. For example, if the TIC of a narrow subrange is dominated by a single peak, its AGC target can be reduced to avoid mass deviations caused by space charge effects. The procedure for detecting TIC dominance and reducing the target accordingly can be automatically performed by the instrument for each subrange. Furthermore, the distribution of AGC targets can be based on spectral preferences for post-processing analysis.

[0107] Thus, determining the adjusted distribution of injection times may include adjusting (e.g., reducing) initial injection times for m / z subranges based on an indication of the ion abundance for each m / z subrange. For example, determining the adjusted distribution of injection times may include reducing initial injection times for m / z subranges based on an indication that the ion abundance for each m / z subrange is caused by a single m / z peak. For example, if a certain percentage of the TIC (or another measure of abundance) is attributable to one particular m / z value (or a very narrow m / z range, such as an m / z range covering an isotopic cluster), this may be taken as an indication that the ion abundance for a particular m / z subrange is substantially caused by a single m / z peak.

[0108] m / z partial range stitching In the final step of the HDR scan workflow shown in FIG. 1A, mass spectral data from m / z subranges from the HDR subscans are combined to generate a full-scan spectrum, which can be further treated and processed like a standard full-scan. For each subrange, the MS peak centroid and profile data are copied to the resulting full-scan spectrum. The overlap between adjacent subranges increases flexibility, since the included data is essentially available twice, insofar as the start and end m / z values ​​of the copying operation can be determined individually within the overlap region. Thus, in general terms, the method includes determining first and second sets of m / z subranges by setting the first and second sets of m / z subranges such that a relatively high transmission region of a first response profile at least partially overlaps a relatively high transmission region of a second response profile. The method may include obtaining multiple partial mass spectral datasets using the methods described herein and combining (e.g., stitching) the multiple partial mass spectral datasets into a single mass spectral dataset.

[0109] The method preferably includes actively determining the subranges to ensure that they overlap. Thus, there may be the steps of: determining whether a relatively high transmission region of a first response profile at least partially overlaps with a relatively high transmission region of a second response profile; and, based on determining that the relatively high transmission region of the first response profile does not at least partially overlap with the relatively high transmission region of the second response profile, adjusting the first and / or second sets of m / z subranges so that the relatively high transmission region of the first response profile at least partially overlaps with the relatively high transmission region of the second response profile. Each m / z subrange in a given set of m / z subranges preferably at least partially overlaps with an m / z subrange in a different set of m / z subranges. Thus, the m / z subranges in the different sets may collectively span the m / z range. Each set of m / z subranges may include multiple spaced apart m / z subranges. For example, the m / z subranges in each of the multiple sets of m / z subranges may be interleaved along the m / z axis. Each m / z subrange in the first set of m / z subranges is contiguous with (eg, directly borders) an m / z subrange in the second set of m / z subranges.

[0110] It will be understood that when multiple subscans are performed on multiple sets of m / z subranges, each set of m / z subranges including multiple m / z subranges, there will be multiple m / z subranges that can be expanded to ensure they overlap. Thus, for example, a first set of m / z subranges may include a first plurality of m / z subranges, and a first mass filter may have a plurality of response profiles, each including a relatively high transmission region and one or more relatively low transmission regions for each m / z subrange of the first set. That is, the m / z subranges of the first subscan can be associated with a plurality of response profiles spaced apart along the m / z axis. Similarly, a second set of m / z subranges may include a second plurality of m / z subranges, and a second mass filter may have a plurality of response profiles, each including a relatively high transmission region and one or more relatively low transmission regions for each m / z subrange of the second set. In such a case, the step of determining the first and second sets of m / z sub-ranges may advantageously comprise setting the first and second sets of m / z sub-ranges such that each relatively high transmission region of the response profile of the respective first mass filter at least partially overlaps with a relatively high transmission region of the response profile of the respective second mass filter, and thus a plurality of overlapping m / z sub-ranges may be formed.

[0111] It will be understood that this method can be extended to any number of subscans and is not limited to two sets of m / z subranges. For example, if a third set of m / z subranges is desired, the response profile associated with the third set may overlap, at least in part, with the response profile of the second mass filter on the left and with the response profile of the first mass filter on the right. The present disclosure can also be extended to a fourth set of m / z subranges. In such a case, the response profile associated with the first set may overlap with the response profile associated with the fourth set and the second set; the response profile associated with the second set may overlap with the response profile associated with the first set and the third set; the response profile associated with the third set may overlap with the response profile associated with the second set and the fourth set; and the response profile associated with the fourth set may overlap with the response profile associated with the third set and the first set. This pattern can be repeated for any number of sets of m / z subranges.

[0112] The presence of overlapping regions allows data from the quadrupole side regions of a subrange to be omitted, which can be replaced with data from the high-transmission region of an adjacent subrange. If the overlap between subranges is too small to extend beyond the side regions, a correction for peak intensities at the subrange edges is necessary. This intensity correction (e.g., as described by F. Meier et al.) typically requires a standard full scan acquired along with the HDR scan during the entire experiment, and an additional post-processing step to determine a correction factor by comparing the peak intensities of the HDR scan with those of the standard full scan. However, if the overlap is large enough, the periodic acquisition of full scans and the additional correction step can be avoided, saving time.

[0113] The stitching procedure is i(i=1...N), where each subrange is associated with a subscan j=1...m. In a typical setting of m=2 subscans, subranges #1, #3, #5, ... come from subscan #1, and subranges #2, #4, #6, ... come from subscan #2. The subranges are stitched consecutively, starting from the lowest m / z subrange w1. Due to data redundancy in the overlap region shared with the adjacent subrange w2, the end m / z value of the copy operation for w1 can be freely chosen within the overlap region, taking into account the following aspects: The end m / z should be outside the low transmission regions of both w1 and w2. The end m / z should be chosen to preserve the isotope distribution in both subranges. Cutting the isotope distribution so that one part comes from subscan #1 and the other from subscan #2 should be avoided to keep the intensities consistent at the molecular species level.

[0114] In general terms, the methods described herein may include determining an end m / z value that lies within the intersection of a first m / z subrange of a first set of m / z subranges and a second m / z subrange of a second set of m / z subranges, and including mass spectral data from between the end m / z value and the endpoint of the first m / z subrange and mass spectral data from between the end m / z value and the endpoint of the second m / z subrange into a single mass spectral dataset. Thus, data from overlapping windows may be stored with the end m / z value acting as a cutoff point where the m / z data of the single dataset switches from being obtained from the first window to being obtained from the second window. The intersection of the first m / z subrange and the second m / z subrange may include at least a portion of a relatively high transmission region of the first response profile and at least a portion of a relatively high transmission region of the second response profile. Furthermore, the end m / z value may be determined based on the distribution of isotopes within the first and / or second m / z subranges (e.g., determined to avoid splitting of isotope peak clusters).

[0115] If it is not possible to select the end m / z such that all found isotope distributions are copied only from one or another subrange, i.e., if all possible end m / z values ​​cut off at least one isotope distribution, all peaks in the overlap region are copied exclusively from one of the subranges. If the noise-weighted TIC of the overlap region of the first subrange is higher than the noise-weighted TIC of the second subrange, peaks are copied from w1 until the low-transmission region of w1 (right flank) is reached. Otherwise, peaks are copied from w1 until the high-transmission region of w2 (left flank) is reached. Detailed procedures are outlined below. For example, the method may include determining which of the first m / z subrange and the second m / z subrange is associated with a higher ion abundance (e.g., the noise-weighted TIC of the overlap region). Combining the multiple partial mass spectral data sets into a single mass spectral data set can include including mass spectral data from one of a first m / z subrange and a second m / z subrange associated with higher ion abundances in the single mass spectral data set.

[0116] In Figure 4A, overlapping windows w1 and w2 are shown schematically. w1 and w2 overlap, and this overlap includes the low transmission sides of w1 and w2. The overlap region also includes the intersection of the high transmission regions of each window, from which the selected end m / z value is selected.

[0117] FIG. 4B shows a set of response profiles for different m / z subranges spaced apart along the m / z axis. A single mass filter can have different response profiles in different m / z subranges. Three different response profiles are shown: a first response profile between m / z1 and m / z2, a second response profile between m / z3 and m / z4, and a third response profile between m / z5 and m / z6. In some embodiments of the present disclosure, multiple such sets of m / z subranges collectively span the entire m / z scan range of interest. Embodiments of the present disclosure compensate for the trapezoidal nature of the response profiles by ensuring that sets of m / z subranges (such as those shown in FIG. 4B) are formed to overlap, as shown in FIG. 4A.

[0118] Once the overlapping m / z subranges have been determined, the sample can be mass filtered to isolate ions within the first and second m / z subranges using first and second mass filters having first and second response profiles corresponding to the first and second m / z subranges. The first and second response profiles each have a relatively high transmission region and one or more relatively low transmission regions. Partial mass spectral data sets are then obtained by performing mass analysis on the sample across the first and second m / z subranges. These partial mass spectral data sets can then be stitched together (although they can simply be stored as separate partial mass spectra).

[0119] Subrange or window w i To stitch the high-transparency overlap region w shared with the adjacent subrange on the right i+1 But, w i and w i+1 The boundaries of the "raw" overlapping regions are first analyzed with respect to the TIC and isotopic distribution contained in each region. i+1 Starting m / z and w i The end m / z is given by, i.e., w i+1,start From w i,end, or ranges from o1 to o2. Due to the trapezoidal shape of the mass filter (quadrupole in preferred embodiment) separation, the overlap region is narrowed by the low transmission sides, making the high transmission overlap region o1' o2'. Table 3 shows exemplary m / z dimensions and overlaps for the first HDR subrange and its vicinity.

[0120] [Table 3]

[0121] The TIC in the high transmittance overlap region is the window w i and w i+1 (hereafter referred to as windows 1 and 2 for simplicity) respectively, and the resulting value is the average noise value N av Divided by (TIC / N av )1 and (TIC / N av The different signal-to-noise levels of the sub-scans are taken into account to yield the TIC / N av )1≧(TIC / N av For 2, it is preferable to use the signal from subscan 1 ((TIC / N av )1<(TIC / N av )2 and vice versa). For both windows, the distribution of isotopic clusters in the overlap region is evaluated. For window / subscan 1, determine the rightmost isotope distribution that is still within the available high transmission window, i.e., whose highest m / z peak is closest to the right boundary o2'. The highest m / z peak of this isotope distribution marks the m / z threshold for subscan 1, scan1_thresh. If no isotope distribution meets the criteria, scan1_thresh is set to o2'. For window / subscan 2, determine the leftmost isotope distribution that spans the window, i.e., its lowest m / z peak is closest to the left boundary o1' and its highest m / z peak exceeds the right boundary o2'. The lowest m / z peak of this isotope distribution marks the m / z threshold for subscan 2, scan2_thresh. If no isotope distribution meets the criteria, scan2_thresh is set to o2'.

[0122] The isotope distribution can be determined for each subscan in the previous step by applying a charge state detection / deconvolution algorithm to the subscan, such as the APD algorithm described in U.S. Patent No. 10,593,530, which is incorporated herein by reference. For example, the methods disclosed herein can include identifying peaks with spacing and / or intensity similar to isotopic clusters.

[0123] The m / z threshold copy_thresh for copying signals from subscans 1 and 2 is determined based on the m / z thresholds scan1_thresh and scan2_thresh. Signals with m / z values ​​less than copy_thresh are copied from subscan 1 to the resulting HDR scan. The copy operation for window 2 in the next cycle starts at copy_thresh. If scan1_thresh and scan2_thresh are equal, copy_thresh is set to scan1_thresh. Otherwise, copy_thresh is determined as follows: If scan2_thresh>scan1_thresh, set copy_thresh to scan2_thresh. In this case, the inference is that window 1 has a better overall S / N, so more signal from subscan 1 will be used (and vice versa if subscan 2 has a better overall S / N). If scan2_thresh≦scan1_thresh, the isotope distributions (ISD) found in the previous step are evaluated with respect to their S / N values. If the ISD found for window 1 has a better S / N than the ISD found for window 2, copy_thresh is set to scan1_thresh (otherwise scan2_thresh).

[0124] The last copied peak from subscan 1 (if copy_thresh=scan1_thresh) or the first copied peak from subscan 2 (if copy_thresh=scan2_thresh) may occur as a slightly shifted duplicate in other subscans. Therefore, other subscans may be checked for duplicates of the last or first included peak, and the threshold may be adjusted accordingly (if necessary). That is, there may be a step to determine whether the first partial mass spectral data set matches the second partial mass spectral data set (e.g., in the overlap region). If the sets do not match, corrective action can be taken. For example, the m / z threshold for copying signals from subscans can be adjusted. Alternatively, a warning may be issued when the data do not match.

[0125] The peak centroids, peak profiles, and noise data are copied from subscan 1 to the HDR fullscan until copy_thresh is reached. i+1 and w i+2 The next pair of windows and subscans corresponding to w are processed, i+1 The copy operation for the last window w starts from the lowest m / z above copy_thresh. N Ha, w N-1 are copied as is, starting from the previous copy_thresh calculated for

[0126] In some cases, the methods described herein may include determining which of the first m / z subrange and the second m / z subrange is associated with a higher signal-to-noise ratio, and combining the multiple partial mass spectral datasets into a single mass spectral dataset may include including mass spectral data from one of the first m / z subrange and the second m / z subrange that is associated with a higher signal-to-noise ratio in the single mass spectral dataset. Thus, data that exhibits a good signal-to-noise ratio may be used in the stitching procedures of the present disclosure.

[0127] Hybrid HDR Scan As an alternative to the above-described method in which multiple HDR subscans are stitched together to obtain an HDR full scan, a "hybrid" approach can be used, involving a standard full scan and a single HDR "zoom" scan (or a small number of subscans) consisting of selected non-overlapping m / z subranges. This has the advantage of maintaining the standard full scan as the reference for quantification, while the single HDR "zoom" scan provides deeper insight into sparse regions of the full scan. These regions in the standard full scan can be replaced with the corresponding m / z subranges from the HDR scan (subscan) to obtain a hybrid HDR full scan. When this hybrid HDR workflow involves two scan events, the scan rate performance is comparable to that of an approach using two HDR subscans.

[0128] The sparse regions in the full scan can be determined using the procedure described for automatic segmentation of the step scan range. From the resulting m / z subranges, only those with non-overlapping low TIC values ​​are analyzed in the HDR scan, while those with high TIC values ​​are not analyzed separately but are simply taken from the full scan.

[0129] To obtain a hybrid HDR scan, a spectral "section" from a standard full scan can be stitched with an HDR subrange as described above. A "section" from a full scan can be treated similarly to an HDR subrange, with the difference being that the "section" does not exhibit the low-transmission side of the quadrupole splitting, which allows more flexibility in the selection of stitching boundaries.

[0130] Some applications require the fastest possible method with an increased dynamic range. Simply put, it should be possible to perform a standard full MS scan at a frequency lower than the "zoom" scan to maximize the increase in dynamic range while minimizing the increase in total analysis time caused by the additional full MS scan. Stitching may also be performed at a reduced frequency only if the standard and "zoom" subscans are measured consecutively. The reduced frequency criterion may be based on the LC peak width. For example, if the average duration of compound elution from the LC is approximately 30 seconds, stitching with a standard full MS scan and a "zoom" subscan may be performed only every 10 seconds. At the same time, the "zoom" scan may still be measured independently every 1 or 2 seconds (which is the typical duration of a complete MS scan in DDA). In this way, the number of extra MS scans can be reduced by approximately 10 times, minimizing the loss of information / peaks. The lost peaks are expected to be primarily in the middle of the measured overall dynamic range and eluting in close proximity to abundant peaks in the RT and m / z domains. Such peaks are injected into the instrument along with the more abundant peaks, and as a result, only the upper part of their LC elution profile can be detected by MS, thus significantly reducing retention times. Such a mode can be used in applications where the total analysis time should be as short as possible, but still require the highest possible dynamic range of the MS analysis, e.g., large cohort studies.

[0131] Additional possible modifications to the HDR "zoom" mode can be applied where altered ion optics settings (DC and RF voltages) may be advantageous. Such alternative ion optics settings may be applied only to some m / z windows / ranges, subscans / scans, rather than all subscans / scans, i.e. - for a selected m / z window only (targeted approach), - It can be applied to one or a few subscans (for example only a "zoom" subscan, or alternatively only the full MS scan).

[0132] For example, gentle trap settings can be applied to reduce unstable ion fragmentation. However, this mode increases injection times and may decrease instrument stable operation time because a larger fraction of ions reach the ion optics elements, leading to faster contamination and resulting ion charging (deteriorating instrument robustness). Alternatively, it is possible to apply "gentle trap" settings only to selected m / z windows (targeted approach), or only to "zoom" subscans (to improve signal intensity of unstable low-abundance ions), or only to full MS scans (to reduce the impact on instrument robustness). When different ion optics settings (DC and RF voltages) are used, additional control of the ion optics can be obtained, unlike the typical operation of the instrument. GB Patent No. 2,585,372 describes methods by which ion optics can be controlled and is incorporated herein by reference. GB Patent No. 2108949.5 describes optimizing ion optics for better transmission of unstable ions, and methods for controlling ion optics are also incorporated herein by reference.

[0133] Generally, in the disclosed methods, at least one mass analysis may be performed using different instrument parameters (e.g., ion optics settings such as DC and RF). The instrument parameters may be determined based on various factors, such as the m / z of the ions being analyzed, or based on the abundance of the ions in the sample. The method may include performing a mass analysis for one or more m / z subranges of each set using ion optics settings determined based on the abundance of the ions in the sample (e.g., based on data from a previous prescan or a previous HDR scan).

[0134] In a hybrid approach, full-range mass analysis may be performed on a sample across the m / z range, and the mass spectral data of at least one partial mass spectral data set may be adjusted based on the full-range mass analysis of the sample across the m / z range. For example, a full scan may be used to normalize data from one or more partial scans. This same process of using a standard scan as a quantitative baseline can also be implemented for HDR subscans that span the full m / z range and is not exclusively applicable to hybrid standard / HDR scans.

[0135] Automatic optimization of the number of scans M and / or the number of m / z windows N (NxM optimization) The equidistant and automatic partitioning m / z window algorithm can receive as input the total number N of desired m / z windows. During an LC / MS experiment, the composition of a mass spectrum can vary significantly, from a very sparse spectrum to a very dense spectrum, from a relatively even distribution of intensity across all peaks to a concentration of the majority of the signal in only one to three of the most abundant peaks. As a result, the best-performing HDR scan can be observed using different numbers of m / z windows at different times during an LC / MS experiment. Therefore, in some embodiments, automatic determination and selection of the optimal number of desired m / z windows N during an LC / MS experiment can be implemented in real time. Criteria and constraints can be defined for this optimization.

[0136] For example, the number of m / z subranges (N) in each set of m / z subranges can be at least one of fixed, configurable by a user, and / or determined based on mass spectral data of the sample (e.g., obtained from a supplemental scan or a previous HDR scan). Additionally or alternatively, the number of sets of m / z subranges (M) in the multiple sets of m / z subranges can be at least one of fixed, configurable by a user, and / or dynamically determined based on mass spectral data of the sample (e.g., obtained from a supplemental scan or a previous HDR scan). N and / or M can be continuously varied throughout the experiment. N and / or M can be determined according to an optimization procedure described below.

[0137] Thus, in general terms, the methods of the present disclosure can perform an optimization procedure on the number of m / z subranges in each set of m / z subranges and / or the number of sets of m / z subranges in multiple sets of m / z subranges. The optimization procedure may be based on at least one of the dynamic range of the mass analysis and / or the total time available to perform the mass analysis.

[0138] Optional starting conditions and constraints for NxM optimization: Scan and m / z partitioning methods: automatic, equidistant or custom, or a combination of different methods, or hybrid HDR scan - Fixed number of subscans M or allowable number of subscans range: min_M~max_M - Maximum duration of one cycle of additional subscans (including all injection times and technical switching times between m / z windows and subscans) - Fixed number N of m / z windows or tolerance range: min_N to max_N. max_N can be defined as the ratio of the total mass range (delta between LM and FM) to min_width (from Table 1)

[0139] The parameters N and M can be optimized at this stage to add very little additional time, for example by selecting the minimum allowable number of subscans and introducing the additional constraint that the sum of injection time and technical time should not exceed the total duration of the additional subscans.

[0140] Optional optimization strategies: The parameters N and M can be optimized to achieve the following: -Maximum dynamic range - Maximum ratio between dynamic range gain and total duration of extra subscans (i.e., best win with minimal increase in total measurement time)

[0141] This optimization may be performed iteratively during the experiment to determine a new set of m / z subranges as the composition of the sample evolves.

[0142] Optional NxM optimization algorithm: The following optimization algorithm can be applied each time a segmentation of an m / z subrange is initiated: Start with a pair of values ​​for M and N. 1. Repeat the following steps for all allowed subscan numbers M: 2. Repeat the m / z window partitioning process for the selected N and given starting conditions and constraints 3. Once the found window has been partitioned and spanned over a given M, calculate one or more optimization criteria and add them all as one record to the optimization history. 4. Choose the next window N based on all optimization history for a given M. 5. Evaluate whether the optimization of N for a given M is complete based on the optimization method used. The evaluation can be done using a gradient method, by simple iteration over the entire range N, or using another method. If the optimization of N is not complete, go to step 2. 6. If optimization for N is finished, save the final results of the N optimizations for a given M and proceed to step 1 with another M from the tolerance range. If no more M are available from the tolerance range, proceed to the next. 7. Compare all discovered N optimization results against all allowed M and select the one that best satisfies one or more optimization criteria.

[0143] The main goals of NxM optimization: Over-segmentation of the scan range should be avoided. This occurs when the dynamic range achieved decreases as the number of m / z windows increases. Over-segmentation can be caused by a decrease in the duty cycle of sample usage per m / z window. 1. Before the last segmentation step, the average size window with low abundance peaks is assigned 2.5x injection time, considering that the AGC target is not achieved. 2. The partitioning algorithm without extra NxM optimization finishes in 1 × injection time per half window size, with 0.5 × injection time lost due to technical time to switch between windows. 3. That is, in this example, if the segmentation algorithm is stopped before the last step, a signal gain of 2.5 times can be achieved for each peak.

[0144] If experimental time is strictly limited, it may be important to stop segmentation when a reasonable dynamic range gain is reached. Furthermore, it may be important to take into account the technical switching time between windows (approximately 6 ms on the Exploris™). If the total additional subscan duration per cycle is limited, at some point in the segmentation, further separating the window into smaller parts may lead to an actual loss of signal due to the increased burden of technical switching time.

[0145] How to implement HDR scanning on your device In a standard implementation on an Exploris™ instrument, ions from different m / z windows emerging from the ion source are filtered by the quadrupole and collected in an ion storage device (C-trap) before being injected into the orbital trap mass analyzer.

[0146] However, the following equipment can be used to obtain HDR scans: Parallel filling by storing all ions in a trapping device, periodically releasing them, separating them by arrival time according to any type of ion mobility or time of flight, gating the desired window, collecting them in a final storage device and then injecting them into the analyzer (e.g., as described in U.S. Pat. Nos. 7,829,842, 7,999,223, 9,064,679, 9,293,316, 9,812,310, 10,199,208, 10,224,193). As above, but with ions continuously scanned out of the first trapping device, only the desired non-overlapping window is allowed to pass to the final storage device (e.g., as described in US Pat. No. 7,157,698 / US Pat. No. 7,342,224). Separation of ions by m / z or mobility into an array of storage devices (e.g., as described in U.S. Pat. No. 9,147,563, U.S. Pat. No. 9,293,316 / U.S. Pat. No. 9,812,310), followed by ejection of ions from some of them at desired times and selection of non-overlapping windows for transfer to storage devices for subsequent injection into an analyzer.

[0147] These alternative methods can use a fast switching quadrupole or any other mass filter in addition to sharpening the shape of the final window that reaches the final storage device (C-trap).

[0148] Figure 5 shows a preferred mass spectrometry system for implementing the methods described herein. The mass spectrometry system is a Thermo Scientific Orbitrap Exploris™ 480 mass analyzer modified to perform the methods described herein. The mass analyzer system includes a large-capacity transfer tube 501, an electrodynamic ion funnel 502, an EASY-IC internal calibrant source 503, an advanced active beam guide (AABG) 504, an advanced quadrupole technology (AQT) 505, an independent charge detector 506, a C-trap 507, an ion-routing multipole 508, and an orbital trap mass analyzer 509. The AQT 505 is configured to filter ions into segmented m / z subranges as described above. Ions are trapped in the C-trap 507 based on injection times calculated according to the methods described above. The orbital trap mass analyzer 509 then acquires mass spectral data for the sample after the ions have been filtered. Although FIG. 5 is a preferred hardware configuration, various other types of mass spectrometry systems can be used.

[0149] It will be understood that the methods described above can be implemented as one or more corresponding modules in hardware and / or software. For example, the functionality described above can be implemented as one or more software components for execution by a processor of a mass spectrometry system. Alternatively, the functionality can be implemented as hardware, such as one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), and / or other hardware configurations. Method steps implemented in the flowcharts contained herein or as described above can each be implemented by a corresponding respective module. Furthermore, multiple method steps implemented in the flowcharts contained herein or as described above can be implemented together by a single module. Such modules and hardware can be integrated into a mass spectrometry system.

[0150] To the extent that embodiments of the present disclosure are implemented by a computer program, it will be understood that storage media and transmission media bearing the computer program also form aspects of the present disclosure. A computer program may have one or more program instructions, or program code, that, when executed by a computer, causes embodiments of the present disclosure to be performed. As used herein, the term "program" may refer to a set of instructions designed to run on a computer system, and may include subroutines, functions, procedures, modules, object methods, object implementations, executable applications, applets, servlets, source code, object code, shared libraries, dynamic link libraries, and / or other sets of instructions designed to run on a computer system. A storage medium may be a magnetic disk (such as a hard drive or floppy disk), an optical disk (such as a CD-ROM, DVD-ROM, or Blu-ray disk), or memory (such as a ROM, RAM, EEPROM, EPROM, flash memory, or portable / removable memory device), etc. A transmission medium may be a communication signal, a data broadcast, a communication link between two or more computing devices, etc.

[0151] Each feature disclosed in this specification, unless stated otherwise, may be replaced by alternative features serving the same, equivalent, or similar purpose. Thus, unless stated otherwise, each feature disclosed is only an example of a generic series of equivalent or similar features.

[0152] Furthermore, several variations can be made to the described embodiments and will be apparent to those skilled in the art upon reading this specification. For example, although orbital trap mass analyzers are primarily described, the mass analyzers described herein may be any one or more of orbital trap mass analyzers or trap-based time-of-flight (ToF) mass analyzers, where ions enter a trap and are ejected therefrom into a ToF mass analyzer.

[0153] As used herein, including in the claims, unless the context indicates otherwise, the singular forms of terms herein are to be construed as including the plural, and vice versa where the context allows. For example, unless the context requires otherwise, references to the singular forms herein, including in the claims, such as "a" or "an" (e.g., an ion or m / z subrange) mean "one or more" (e.g., one or more ions, or one or more m / z subranges). Throughout the description and claims of this disclosure, the words "comprise," "including," "having," and "contain," as well as variations of words such as "comprising" and "comprises," or the like, mean that the described features include additional features that follow and are not intended to (and do not) exclude the presence of other components. Furthermore, when a first characteristic is described as being "based on" a second characteristic, this may mean that the first characteristic is based entirely on the second characteristic, or that the first characteristic is based at least in part on the second characteristic.

[0154] The use of any and all examples or exemplary language (such as "for instance," "such as," "for example," and similar language) provided herein is intended merely to better illustrate the invention and does not pose a limitation on the scope of the disclosure unless specifically claimed. No language in the specification should be construed as indicating any element not claimed as essential to the practice of the disclosure.

[0155] Any steps described herein may be performed in any order, or simultaneously, unless otherwise stated or otherwise required by context. Furthermore, if a step is described as being performed after another step, this does not exclude intervening steps from being performed.

[0156] All aspects and / or features disclosed herein may be combined in any combination, except combinations in which at least some of such features and / or steps are mutually exclusive. In particular, preferred features of the present disclosure are applicable to all aspects and embodiments of the present disclosure and may be used in any combination. Similarly, features described in non-essential combinations may be used separately (rather than in combination).

[0157] The following numbered clauses illustrate further advantageous embodiments of the present invention. 1. A method for acquiring mass spectral data for a sample over at least a portion of an m / z range, comprising: receiving mass spectral data for the sample over an m / z range; Dividing the m / z range into multiple m / z bins; determining an ion abundance index for each m / z bin based on the mass spectral data; forming one or more sets of m / z subranges by assigning m / z bins having ion abundances corresponding to at least a threshold degree to the formed m / z subranges, wherein each set includes one or more m / z subranges; performing mass spectrometry on the sample for each set of m / z subranges, thereby obtaining one or more partial mass spectral data sets. 2. Partitioning the m / z range (i) identifying an initial m / z bin from a plurality of m / z bins; (ii) determining that one or more m / z bins adjacent to the initial m / z bin have ion abundances that correspond to the ion abundances of the initial m / z bin to at least a threshold degree; (iii) assigning the initial m / z bin and one or more m / z bins adjacent to the initial m / z bin to the formed m / z subrange. 3. The method of clause 2, wherein the initial m / z bin is an m / z bin of the plurality of m / z bins having the highest ion abundance. 4. The method of clause 2 or clause 3, comprising forming a complement of the formed m / z subranges. 5. The method of clause 4, comprising repeating steps (i), (ii), and (iii) for the complement of the formed m / z subranges, thereby forming one or more further m / z subranges of the one or more sets of m / z subranges. 6. The method of clause 4 or clause 5, comprising iteratively forming complements of the formed m / z ranges and repeating steps (i), (ii), and (iii) for each successive complement, thereby forming a plurality of further m / z subranges of the one or more sets of m / z subranges. 7. The method of any one of the preceding clauses, wherein partitioning the m / z range into one or more sets of m / z subranges comprises repeatedly forming m / z subranges until the total number of m / z subranges formed is less than or equal to a predetermined total number of m / z subranges in the one or more sets of m / z subranges. 8. The method of any one of the preceding clauses, wherein partitioning the m / z range into one or more sets of m / z subranges includes forming M sets of m / z subranges each containing W m / z subranges, the m / z subranges being numbered in order of m / z, and the i-th set of m / z subranges includes m / z subrange numbers i, M+i, 2M+i, ..., (W-1)M+i, for each value of i=1, ..., M. 9. Partitioning the m / z range determining that a first m / z bin and a second m / z bin have ion abundances corresponding to at least a threshold degree; determining that a third m / z bin between the first m / z bin and the second m / z bin has an ion abundance that does not correspond to the ion abundances of the first and second m / z bins, to at least a threshold degree; and assigning the first, second, and third m / z bins to a single m / z subrange. 10. Partitioning the m / z range assigning m / z bins having ion abundances corresponding to at least a threshold degree to a first preliminary m / z subrange; assigning m / z bins having ion abundances corresponding to at least the threshold degree to a second preliminary m / z subrange; determining that a first preliminary m / z subrange overlaps with a second preliminary m / z subrange; 4. The method of any one of the preceding clauses, comprising discarding the second preliminary m / z subranges without assigning each m / z bin to an m / z subrange of the one or more sets of m / z subranges. 11. Partitioning the m / z range forming one or more preliminary m / z subranges by assigning m / z bins having ion abundances corresponding to at least a threshold degree to each preliminary m / z subrange; and forming one or more m / z subranges of the one or more sets of m / z subranges based on the respective preliminary m / z subranges. 12. Forming one or more m / z subranges of one or more sets of m / z subranges based on each preliminary m / z subrange; assigning each preliminary m / z subrange to one or more sets of m / z subranges; assigning to the one or more sets of m / z subranges one or two m / z subranges adjacent to a respective preliminary m / z subrange, each of the one or two m / z subranges adjacent to a respective preliminary m / z subrange extending from one end of the respective preliminary m / z subrange to one end of a further preliminary m / z subrange; 12. The method of claim 11, wherein the method further comprises increasing the width of at least one of the one or two m / z subranges adjacent to each preliminary m / z subrange. 13. Partitioning the m / z range includes assigning an initial m / z bin and one or more m / z bins adjacent to the initial m / z bin to form a first preliminary m / z subrange, and forming the m / z subrange includes: forming m / z subranges by increasing the width of a first preliminary m / z subrange, and / or The method of any one of the preceding clauses, comprising at least one of forming m / z subranges by increasing the width of an existing second preliminary m / z subrange adjacent to the first preliminary m / z subrange. 14. Determining that a first preliminary m / z subrange and a second preliminary m / z subrange adjacent to the first preliminary m / z subrange have the same width; determining which of the first preliminary m / z subrange and the second preliminary m / z subrange is associated with a higher ion abundance; increasing a width of one of the first preliminary m / z subrange and one of the second preliminary m / z subranges associated with higher ion abundances. 15. increasing the width of the second preliminary m / z subrange based on determining that the first preliminary m / z subrange is wider than a second preliminary m / z subrange adjacent to the first preliminary m / z subrange; or 15. The method of claim 13 or 14, comprising increasing a width of the first preliminary m / z subrange based on determining that the first preliminary m / z subrange is narrower than the second preliminary m / z subrange. 16. The method of any one of clauses 13-15, wherein increasing the width of at least one of the first preliminary m / z subrange and the second preliminary m / z subrange causes the first and second preliminary m / z subranges to at least partially overlap. 17. The first and second preliminary m / z subranges are: including an offset proportional to the width of the first or second preliminary m / z subrange; and / or 17. The method of clause 16, overlapping by an amount that includes a constant offset. 18. Partitioning the m / z range into a plurality of first sets of m / z subranges, each first set including one or more m / z subranges; performing a first mass analysis on the sample for each first set of m / z subranges, thereby obtaining a plurality of first partial mass spectral data sets; partitioning the m / z range into a plurality of second sets of m / z subranges based on ion abundances indicated by the plurality of first partial mass spectral data sets, each second set including one or more m / z subranges; performing a second mass analysis on the sample for each of a second set of m / z subranges, thereby obtaining a plurality of second partial mass spectral data sets. 19. The method of clause 18, comprising further partitioning the m / z range one or more times and performing one or more further mass analyses to obtain a plurality of respective further partial mass spectral data sets. 20. Each of the multiple m / z bins has a width that is configurable by the user; and / or 10. The method of any one of the preceding clauses, wherein each of the plurality of m / z bins has a width that is half the predetermined minimum width. 21. The method of any one of the preceding clauses, wherein the threshold match between m / z bins is a predetermined ratio of the ion abundance in the low abundance m / z bin to the ion abundance in the high abundance m / z bin, preferably the predetermined ratio is at least 0.5. 22. The method of any one of the preceding clauses, wherein the measure of ion abundance is total ion current (TIC). 23. A method for acquiring mass spectral data of a sample over at least a portion of an m / z range, wherein the m / z range comprises a set of one or more m / z subranges, the method comprising: determining an initial distribution of injection times, the distribution including an initial injection time for each m / z subrange of a set of one or more m / z subranges; determining an adjusted distribution of injection times, including adjusted injection times for each m / z subrange, based on determining that the total time of the initial distribution of injection times exceeded the total available injection time for acquiring mass spectral data; performing mass analysis for each m / z subrange according to the adjusted injection time distribution to obtain a partial mass spectral data set; The method, wherein determining the adjusted distribution of injection times includes reducing at least one of the initial injection times for each m / z subrange such that the total time of the adjusted distribution of injection times for the set of one or more m / z subranges is less than or equal to the total available injection time for acquiring mass spectral data. 24. The method of clause 23, wherein determining the adjusted distribution of infusion times includes shortening one or more relatively long initial infusion times to a greater extent than one or more relatively short initial infusion times. 25. The method of clause 23 or clause 24, wherein determining the adjusted distribution of infusion times comprises reducing at least one, and preferably each, initial infusion time above a threshold infusion time. 26. The method of any one of clauses 23-25, wherein determining the adjusted distribution of injection times includes reducing a plurality of initial injection times that exceed a threshold injection time by a scaling factor. 27. The method of clause 26, wherein determining the adjusted distribution of injection times includes setting a threshold injection time as the adjusted injection time for each m / z subrange in which the initial injection time reduced by a scaling factor is less than the threshold injection time. 28. Determining an adjusted distribution of infusion times is determining a total pre-injection time by summing the difference between the initial injection time and the threshold injection time for each m / z subrange whose initial injection time is less than the threshold injection time; 28. The method of any one of clauses 23 to 27, comprising: setting adjusted injection times for one or more m / z subranges whose initial injection times exceed a threshold injection time by distributing the total pre-injection, thereby increasing the initial injection times for one or more m / z subranges whose initial injection times exceed the threshold injection time. 29. Determining an adjusted distribution of infusion times is determining the sum of each initial infusion time exceeding the threshold infusion time, sum_exceeding, where the threshold infusion time is equal to or less than the IT Thr and determining Total available infusion time from IT Thr determining the sum sum_remaining by subtracting each initial injection time that is less than or equal to Iteratively, in ascending order, for each initial injection time that exceeds the threshold injection time: From each initial injection time old_IT,

[0158]

number

Claims

1. 1. A method for acquiring mass spectral data for a sample over at least a portion of an m / z range, wherein the m / z range includes a plurality of sets of m / z subranges, each set including one or more m / z subranges; determining a first set of m / z subranges of the plurality of sets of m / z subranges and determining a second set of m / z subranges of the plurality of sets of m / z subranges, the first set including a first m / z subrange and the second set including a second m / z subrange; mass filtering the sample using a first mass filter to isolate ions in the first set of m / z subranges; and performing mass analysis on the sample across the first set of m / z subranges to obtain a first partial mass spectral data set, the first mass filter having a first response profile corresponding to the first m / z subrange, the first response profile being substantially trapezoidal and having relatively high transmission regions between a plurality of relatively low transmission regions, the relatively high transmission regions being regions having higher ion transmission than the relatively low transmission regions; mass filtering the sample using a second mass filter to isolate ions in the second set of m / z subranges, and performing mass analysis on the sample across the second set of m / z subranges to obtain a second partial mass spectral data set, the second mass filter having a second response profile corresponding to the second m / z subranges, the second response profile being substantially trapezoidal and having relatively high transmission regions between a plurality of relatively low transmission regions; determining the first and second sets of m / z subranges includes setting the first and second sets of m / z subranges such that the relatively high transmission region of the first response profile at least partially overlaps with the relatively high transmission region of the second response profile; Determining the first and second sets of m / z subranges comprises: determining whether the relatively high transmission region of the first response profile at least partially overlaps with the relatively high transmission region of the second response profile; and adjusting the first and / or second sets of m / z subranges so that the relatively high transmission region of the first response profile at least partially overlaps with the relatively high transmission region of the second response profile based on determining that the relatively high transmission region of the first response profile does not at least partially overlap with the relatively high transmission region of the second response profile.

2. the first set of m / z subranges comprises a first plurality of m / z subranges, and the first mass filter has a plurality of response profiles, each including a relatively high transmission region and one or more relatively low transmission regions, for each m / z subrange of the first set; the second set of m / z subranges includes a second plurality of m / z subranges, and the second mass filter has a plurality of response profiles, each including a relatively high transmission region and one or more relatively low transmission regions, for each m / z subrange of the second set; 2. The method of claim 1, wherein said step of determining said first and second sets of m / z subranges comprises setting said first and second sets of m / z subranges such that each relatively high transmission region of each response profile of said first mass filter at least partially overlaps with a relatively high transmission region of a response profile of said second mass filter.

3. The method of claim 1 , wherein the first mass filter and the second mass filter are the same mass filter.

4. The method of claim 1 , wherein the first mass filter is a quadrupole and / or the second mass filter is a quadrupole.

5. 2. The method of claim 1, wherein the relatively high transmission region of the first response profile and / or the second response profile is a region having at least 90% ion transmission.

6. Determining the first and second sets of m / z subranges comprises: determining a first trapezoidal fit of the first response profile and / or a second trapezoidal fit of the second response profile based on mass spectral data obtained using the first mass filter and the second mass filter; and determining the relatively high transmission region of the first response profile and the relatively high transmission region of the second response profile based on the first and / or second trapezoidal fits.

7. 2. The method of claim 1 , wherein determining the first and second sets of m / z subranges comprises determining a degree of overlap for the first and second response profiles based on a width of at least one of the first and / or second response profiles.

8. 2. The method of claim 1 , wherein determining the first and second sets of m / z subranges comprises determining a degree of overlap for the first and second response profiles based on a width of a relatively low transmission region of at least one of the first and / or second response profiles.

9. 2. The method of claim 1, wherein the relatively high transmission region of the first response profile overlaps with the relatively high transmission region of the second response profile by an amount greater than a width of the relatively low transmission region of the first response profile and / or a width of the relatively low transmission region of the second response profile.

10. 2. The method of claim 1 , wherein each set of m / z subranges includes multiple m / z subranges, and each m / z subrange in a given set of m / z subranges at least partially overlaps with an m / z subrange in a different set of m / z subranges.

11. 10. The method of claim 1, comprising performing mass analysis for one or more m / z subranges of each set using different ion optics settings.

12. Each mass spectrometry 1 The method of claim 1, wherein the method is mass spectrometry.

13. The method of claim 1 , wherein the m / z subranges of each of the sets of m / z subranges collectively span the m / z range.

14. The method of claim 1 , wherein each set of m / z subranges comprises a plurality of spaced apart m / z subranges.

15. 10. The method of claim 1, comprising receiving the sample from a chromatograph, and repeating the method of claim 1 one or more times for one or more samples obtained from the chromatograph to obtain time-dependent mass spectral data for the sample.

16. 2. The method of claim 1, comprising: performing full range mass analysis on the sample across the m / z range; and adjusting mass spectral data of at least one partial mass spectral dataset based on the full range mass analysis on the sample across the m / z range.

17. 1. A method for acquiring mass spectral data for a sample over at least a portion of an m / z range, the method comprising: Obtaining a plurality of partial mass spectral data sets using the method of claim 1; combining the plurality of partial mass spectral data sets into a single mass spectral data set.

18. combining the plurality of partial mass spectral data sets determining an end m / z value that is within an intersection of a first m / z subrange of the first set of m / z subranges and a second m / z subrange of the second set of m / z subranges; the single mass spectral data set; mass spectral data from between the end m / z value and the endpoint of the first m / z subrange; and including mass spectral data from between the end m / z value and an end point of the second m / z subrange; the method includes determining the end m / z value based on a distribution of isotopes within the first and / or second m / z subranges; and / or 18. The method of claim 17, wherein the intersection of the first m / z subrange and the second m / z subrange comprises at least a portion of the relatively high transmission region of the first response profile and at least a portion of the relatively high transmission region of the second response profile.

19. determining which of the first m / z subrange and the second m / z subrange are associated with higher ion abundances, wherein combining the plurality of partial mass spectral data sets into a single mass spectral data set comprises including the mass spectral data from one of the first m / z subrange and the second m / z subrange associated with the higher ion abundances in the single mass spectral data set; and / or 19. The method of claim 17 or 18, comprising determining which of the first m / z subrange and the second m / z subrange is associated with a higher signal-to-noise ratio, wherein combining the plurality of partial mass spectral datasets into a single mass spectral dataset comprises including the mass spectral data from the one of the first m / z subrange and the second m / z subrange associated with the higher signal-to-noise ratio in the single mass spectral dataset.

20. 10. A mass spectrometry system comprising a mass analyzer, a processor, and one or more mass filters configured to perform the method of claim 1.

21. 21. A computer program comprising instructions that, when executed by the processor of a mass spectrometry system according to claim 20, cause the mass spectrometry system to perform the method of claim 1.

22. 22. A computer readable medium storing the computer program of claim 21.

Images