Quantification of a substance present in a sample

Abstract

A sample is separated into its constituent analytes by a first separator using a time parameter. The constituent analytes are then analyzed using a mass spectrometer, which provides intensity measurements for each analyte as a function of the mass-to-charge ratio. The relationship between the measured intensity at a mass-to-charge ratio and the time parameter for each analyte defines a corresponding peak. A peak quality factor can be determined for each of the peaks, and / or a common peak position with respect to the time parameter can be determined for at least some of the peaks. A specific range for a time parameter, to be used for substance quantification, can be based on the peak quality factor (or factors).Alternatively, based on the specific peak quality factors and / or the specific common peak position, a subset of the peaks can be selected to quantify the substance.

Description

Technical field of the disclosure

[0001] The disclosure relates to the quantification of a substance present in a sample, wherein the sample is separated into its constituent analytes over a time parameter by a first separator (e.g., a chromatographic or ion mobility separator), and the constituent analytes are then further analyzed by a mass spectrometer. This can be implemented in a method, a computer program, a controller for a mass spectrometry system, and / or a mass spectrometry system. Background of the Revelation

[0002] Software analysis of mass spectrometry data is becoming increasingly useful for identifying and quantifying the substances present in a sample. Analysis of MS1 data is generally considered easier than analysis of MS nData is considered a separate issue, and the analysis of Data Independent Acquisition (DIA) data can be even more complex. Mass analysis may be followed by a first separation step, for example, using a chromatographic separator, including gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS), or an ion mobility separator, commonly known as ion mobility spectrometry-mass spectrometry (IMS-MS). This simply increases the complexity of the analysis workflow.

[0003] Traditionally, there are two main approaches to DIA data analysis. The first approach utilizes database-driven search engines, which are commonly used to analyze data-dependent acquisition (DDA) data. A second approach is targeted analysis, also known as SWATH-MS (Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra). These data analysis algorithms primarily focus on sample identification, but quantification is also considered.

[0004] WO-2009 / 146345 discusses the assignment of a precursor ion to one or more related product ions. Data sets containing information about the precursor ion are obtained from multiple injections and then normalized according to a single retention time. By determining which product ions are within a given retention time window with respect to the individual retention time, the presence of such product ions can be used to determine their relationship to the precursor ion.

[0005] WO-2012 / 035412 concerns the use of multiple product ions to characterize an unknown sample compound. The chromatographic peak retention time can be correlated across the product ions to improve identification.

[0006] Typical existing approaches to quantification are based on a single raw data sample (or a single event). For example, Barkovits, Katalin, et al., “Reproducibility, specificity, and accuracy of relative quantification using spectral library-based data-independent acquisition” (Molecular & Cellular Proteomics 19.1 (2020): 181–197) discuss the selection of appropriate spectral libraries for peptide identification and quantification. Searle, Brian C., et al., “Generating high-quality libraries for DIA MS with empirically corrected peptide predictions” (Nature communications 11.1 (2020): 1–10) describe library generation using empirical data, such as fragmentation and retention time prediction. Demichev, Vadim, et al., “DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput” (Nature methods 17.1 (2020): 41-44) describe the removal of interfering product ions by selecting the best correlated chromatographic product ion peaks with their respective product ion partners.

[0007] Therefore, it is desirable to improve the analysis of mass spectrometry data to enable more accurate quantification of a substance in a sample. This is especially the case when the sample is first separated into its individual constituent analytes, which are then subjected to mass analysis. Brief description of Revelation

[0008] Against this background, methods for quantifying a substance present in a sample according to claim 1 and claim 9, a computer program according to claim 23, a controller for a mass spectrometry system as defined in claim 24, and a mass spectrometry system according to claim 25 are provided. Preferred and / or optional features are disclosed in the dependent claims.

[0009] The present disclosure concerns the quantification of a substance present in a sample. The sample is separated into its constituent analytes by a first separator using a time parameter. For example, the first separator may be a chromatographic separator (such as GC or LC). In this case, the sample is typically a liquid or gaseous sample. The time parameter then refers to a retention time, and the constituent analytes are separated components from the chromatographic separator at different retention times. In another example, the first separator is an ion mobility separator. The sample is then a mixture of sample ions. In this case, the time parameter refers to a drift time or a retention time, and the constituent analytes are constituent ions of the mixture of sample ions.

[0010] Aspects according to the disclosure may be embodied in software (as a computer program that may be stored on a computer-readable medium, optionally non-transitory), in a controller for a mass spectrometry system, and / or a mass spectrometry system. Additionally or alternatively, these aspects may be implemented as a process.

[0011] In either case, the constituent analytes are further analyzed using a mass spectrometer (where the first separator is a chromatographic separator, each constituent analyte is first ionized). For example, analysis of the constituent analytes in the mass spectrometer may involve processing the ions; and mass analyzing the processed ions. In some cases, no fragmentation occurs during processing (although mass filtering or selection and / or cooling may be incorporated), and mass analysis may involve non-fragmented ions. Alternatively, the ions may be precursor ions, and processing the ions may involve fragmenting the precursor ions to generate fragment ions, so mass analysis of the fragment ions is performed. In that case, mass analysis of the fragment ions may involve Data Independent Acquisition (DIA)-MS. n -analysis.

[0012] For each of the one or more constituent analytes, measurements of intensity versus mass-to-charge ratio are obtained from the mass spectrometer. Several such measurements are performed over the time parameter. For each constituent analyte, a peak is defined by a relationship of the measured intensity versus the time parameter for a selected mass-to-charge ratio (or mass-to-charge ratio range). Preferably, each peak comprises at least a minimum number of intensity measurements, each for a different value of the time parameter (e.g., at least 5 measurements).

[0013] In some cases, ions of the substance to be quantified exhibit one of the selected mass-to-charge ratios, which may enable the application of the disclosed techniques to the analysis of full MS1 scans, typically analyzing multiple ions within a single scan (rather than quantifying a single peak). More commonly, each of the selected mass-to-charge ratios relates to a corresponding ion fragment of the substance to be quantified. Quantification may involve identifying the substance and / or identifying a chemical composition corresponding to one or more peaks (such that the selected mass-to-charge ratio or the region of the peak(s) corresponds to the identified chemical composition).

[0014] In a first aspect, there may be a single peak (i.e., only data for a single mass-to-charge ratio or range across the time parameter are provided), but more typically, multiple peaks are obtained. For each peak, a peak quality is determined, from which a specific range for the time parameter is determined. The determined specific range of the time parameter can then be used to quantify the substance. It is particularly noted that the peak quality factor is dependent on a range for the time parameter used to determine the peak quality factor.

[0015] Additionally, a flatness detection algorithm may be used (e.g., as an algorithm or one of a variety of algorithms for determining a peak quality factor) to determine the specific range for the time parameter.

[0016] The specific range for the time parameter can be determined iteratively. For example, a first range of the time parameter can be used to determine a first peak quality factor for each peak. Subsequently, a second range of the time parameter is identified, which can be narrower or wider than the first range of the time parameter. For example, the second range can be based on a predetermined increase or decrease in the time parameter compared to the first range (at one or both ends of the range). Optionally, the second range can be based on the first peak quality factor (or first peak quality factors), for example by comparing them to a criterion (or criteria, for example one or more thresholds). The identified second range of the time parameter can be used to determine a second peak quality factor for each peak.The specific range for the time parameter can thus be based on the first peak quality factor (or factors) and the second peak quality factor (or factors). This procedure can continue until the peak quality factor (or factors) meets the criterion (or criteria). For example, the range of the time parameter can become increasingly broader until the criterion (or criteria) is (are) met.

[0017] The initially evaluated intensity versus mass-to-charge ratio measurements may refer to the first range of the time parameter. If the second range of the time parameter is broader than the first range, additional intensity versus mass-to-charge ratio measurements may be evaluated for the constituent analyte(s). In this case, the initial intensity measurements and additional intensity measurements together may cover the second range of the time parameter. The second peak quality factor(s) may thus be based on the initial intensity measurements and the additional intensity measurements.

[0018] The substance is advantageously quantified based on the intensity measurements for each of the one or more peaks that fall within the determined specific range. The determined specific range for the time parameter can be used not only to quantify the measured sample, but also the same substance from a different sample. Alternatively, a parallel evaluation of a specific range of the time parameter can be performed for each of the multiple samples. In this case, a specific range for the time parameter can be determined for the evaluated samples.

[0019] In a second aspect, multiple peaks of the intensity measurement are obtained over the time parameter (each peak relates to a respective mass-to-charge ratio or range). For each peak, a corresponding peak quality factor can be determined (wherein the peak quality factor depends on a range for the time parameter) and / or for at least some of the peaks, a common peak position with respect to the time parameter can be determined. Subsequently, a subset of the peaks is selected for quantification of the substance based on the determined peak quality factors and / or the determined common peak position.

[0020] In one approach, the subset of peaks can be selected by evaluating a peak position for each of the peaks against the determined common peak position. For example, only peaks whose peak position lies within a specified distance from the determined common peak position can be selected for the subset.

[0021] In another approach (which can be used in conjunction with the approach discussed above), the subset of peaks can be selected by evaluating the respective peak quality factor for each of the peaks against a criterion (e.g., greater than, at least, equal to, not greater than, or less than a threshold). The subset of peaks can then be selected based on whether the criterion is met for each peak (i.e., selected peaks can be those that meet the criterion). Optionally, multiple criteria can be present.

[0022] Each of the selected subsets of peaks can then be quantified. The substance can then be quantified based on the quantification of the selected subset of peaks.

[0023] For the processes according to the disclosure, no correlation of peak intensities among product ions or a correlation with a precursor mass need be used.

[0024] It should be noted that the various aspects of the disclosure may be combined. According to each aspect, the mass spectrometer may be controlled to perform mass analysis of each of the constituent analytes and provide intensity measurements versus mass-to-charge ratio for each of the plurality of components. Brief description of the drawings

[0025] The disclosure may be put into practice in various ways and preferred embodiments will now be described by way of example only with reference to the accompanying drawings in which: Fig. 1 schematically illustrates a mass spectrometry system for use in accordance with the disclosure; Fig. Figure 2 shows the measured intensity versus time peaks for three exemplary mass-to-charge ratio ranges; Fig. 3 shows a block diagram of a first procedure according to the disclosure; Fig. 4 shows a block diagram of a second procedure according to the disclosure; and Fig. 5a to 5d each illustrate a plot of measured intensity versus time peaks and show the effect of the procedures according to the disclosure. Detailed description of preferred embodiments

[0026] With reference first to Fig. 1, a mass spectrometry system for use in accordance with the disclosure is schematically illustrated. The mass spectrometry system 10 receives a sample 5 and includes: a first separator 20; a mass spectrometer 30; and a controller 40. The mass spectrometer 30 includes: at least one ion optics device 32 (e.g., an ion guide, an ion trap, and / or an ion selector or filter); a collision cell 34; a mass analyzer 36; and a detector 38. Optionally, the mass analyzer 36 and the detector 38 can be combined into a single device.

[0027] The first separator 20 separates the sample 5 into component analytes. This separation occurs over a specific period of time, with the different component analytes being output from the first separator 20 at different times. Thus, a time parameter is associated with the first separator 20, with each component analyte having a different corresponding value (or range) for the time parameter.

[0028] Preferably, the first separator 20 is a chromatographic separator, for example, a gas chromatograph (GC) or a liquid chromatograph (LC) instrument, so that the time parameter can be a retention time (RT). Then, the mass spectrometry system 10 can be a GC-MS or LC-MS system. In such cases (and optionally with other types of separators), the mass spectrometer 30 further comprises an ion source 31, which is advantageously configured to receive separated analytes (effluent or eluate) from the first separator 20 and to generate ions from the received separated analytes.

[0029] Alternatively, the first separator 20 may be an ion mobility spectrometer (IMS), so that the time parameter may be a drift time (for example, in the case of a drift tube or traveling wave ion mobility separation) or a retention time (for example, in the case of a separation based on the mobility of trapped ions, TIMS). Then, the mass spectrometry system 10 may be an IMS-MS system. The first separator 20 may then comprise an ion source (not shown), and the output of the first separator 20 in this case comprises ions. Therefore, the mass spectrometer 30 does not need to comprise an ion source 31, and the ion source 31 is therefore Fig. 1 shown as optional.

[0030] The controller 40 is configured to control the operation of the first separator 20 and the mass spectrometer 30. Furthermore, the controller 40 receives mass spectral data from the detector 38. Optionally, the controller 40 may also receive data from the first separator 20. Thus, the controller may cause the mass spectrometry system 10 to perform experiments and then receive the results of the experiment for analysis. The analysis may be performed by the controller or by an external processor or processing system (not shown). The controller 40 is typically a single device, but it may include multiple parts that typically work together.

[0031] As discussed above, approaches according to the disclosure are particularly advantageous for the analysis of mass spectral data from DIA-MS n -experiments (typically MS 2-experiments). In this case (and controlled by the controller 40), the mass spectrometry system 10 is operated to separate the sample 5 into one or more constituent analytes, each having an associated time parameter range. Thus, a single constituent analyte may be output from the first separator 20 over a time parameter range, resulting in multiple constituent analyte outputs from the first separator 20. Each constituent analyte output with an associated time parameter is then provided to the mass spectrometer 30, where it is fragmented in the collision cell 34 and analyzed by the mass analyzer 36, with the mass spectral data being detected by the detector 38. The mass spectral data is then communicated to the controller 40.Each mass analysis typically indicates multiple fragments, with each fragment having a different mass-to-charge ratio (m / z) and a corresponding detected intensity. This is referred to as an MS. n -mass spectrum. As a result, the controller 40 receives several MS n -Mass spectra, where each MS n mass spectrum relates to one or more output constituent analytes from the first separator 20 (and thus has an associated time parameter). Typically, for each individual output constituent analyte, several MS n mass spectra are obtained. In other words, during the output of each constituent analyte, several MS n -Mass spectra obtained.

[0032] Optionally, multiple MS1 mass spectra can also be obtained for each individual output constituent analyte, i.e., multiple MS1 mass spectra can be obtained during the output of each constituent analyte.

[0033] Quantification is typically performed by first applying a DIA identification tool to the mass spectra to perform an initial identification of the constituent analytes (or compounds). Each compound identification provides a point in the time parameter (e.g., a retention time point) at which the compound was detected, as well as a list of the detected product (i.e., fragment) ions. This list can be comprehensive or simply represent a selection of the most intense or accurate product (fragment) ions detected.

[0034] Subsequently, the recovery of peaks from the MS nAnalysis is possible by targeting specific m / z and time parameter values ​​of the set of product (fragment) ions. For example, intensities can be obtained for the set of product (fragment) ions at their specific m / z values ​​(optionally subject to a user-specified or automatically estimated tolerance) for the point of the time parameter determined by the DIA identification tool. Subsequently, intensities for the same m / z values ​​(again optionally subject to a user-specified or automatically estimated tolerance) can be retrieved for spectra at neighboring values ​​of the time parameter. This retrieval process can then be repeated until a minimum number of spectra, min_s (typically min_s = 5), is reached. In this way, it is possible to correlate or match intensity measurements for the same fragment (based on m / z value or range) across the time parameter.

[0035] This data can be displayed graphically. In relation to Fig. Figure 2 shows peaks of measured intensity versus time for three example fragments (each fragment corresponding to a respective m / z range) from a DIA analysis. A first peak 61, corresponding to a first fragment, is relatively low (intensity approximately 3500) and has a peak position at approximately 31.58 on the time axis. A second peak 62, corresponding to a second fragment, is relatively high (intensity approximately 32000) and has a peak position at approximately 31.57 on the time axis. A third peak 63, corresponding to a third fragment, has an intermediate intensity (approximately 18000) with a peak position at approximately 31.64 on the time axis. All three peaks lie in approximately the same range of retention times.

[0036] It is desirable to use the information from these three peaks to quantify the substance from which the three fragments appear to originate. Quantification may be possible by summing the peak intensities for the identified product ions over a specific time parameter range. Advantageously, the same range is used for all peaks. The identification points to a relatively arbitrary point in the time parameter range (e.g., a specific time point of compound elution), which may not be the peak retention or may not even indicate a suitable range.

[0037] Referring again to Fig. Figure 2 shows a selected range 65 from the time parameter from approximately 31.53 to approximately 31.62. The data points of the first peak 61, the second peak 62, and the third peak 63 that lie within the selected range 65 are highlighted. Therefore, improved selection of the range 65 is desirable. The second peak 62 begins slightly earlier than the first peak 61. The third peak 63 begins significantly later than the first peak 61 and the second peak 62. It can also be seen that the peak position of the third peak 63 is slightly different from the peak position of the first peak 61 and the second peak 62 (in other words, the third peak 63 is offset from the other two). It is therefore unclear whether the fragment corresponding to the third peak 63 originates from the same substance (parent or precursor ion) as the respective fragments corresponding to the first peak 61 and the second peak 62.

[0038] It has been recognized that these problems can be solved with a mathematical approach. In particular, a Peak Quality Factor (PQF) algorithm can be used to objectively and quantitatively evaluate the quality of each peak over the selected time parameter range. More typically, multiple PQF algorithms can be used, as discussed, for example, in "MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data," Chetnik et al., Metabolomics 16 (2020): 1–13. Selecting the time parameter range that maximizes the result of the PQF algorithms can therefore enable improved peak and compound quantification.

[0039] The quantification algorithm can initially only be provided with data from a limited range of the time parameter. The algorithm can therefore desirably retrieve ion product intensities from mass spectra of neighboring time parameter points. The PQF algorithms can also inform in which direction additional data should be retrieved (up and / or down with respect to the time parameter) and when to stop retrieving. This process can be performed simultaneously for all identifications of the same compound in all samples of the experiment; this can ensure that the same set of product ions is used for all samples. This allows a product ion to be discarded from all samples if the output of the PQF algorithms determines that the peak for the product ion does not have sufficient (user-defined) quality for any of the samples.This also allows to define a fixed range (or at least a width) of the time parameter for all samples if desired by the user (in other words, the same width or range can be used for multiple samples).

[0040] With reference next to Fig. 3 shows a block diagram of a first procedure according to the disclosure. This procedure is particularly for range selection for the time parameter for substance quantification purposes. In an initial step 100, the mass spectrometer provides (or a controller retrieves from the mass spectrometer) data comprising intensity measurements versus mass-to-charge ratio for each of the one or more constituent analytes. As noted above, a relationship of the measured intensity at each selected mass-to-charge ratio over the time parameter defines a respective peak (as in Fig. 2). Typically, the data are obtained from a DIA experiment in which several MS 2Spectra were acquired as a function of a time parameter, such as a retention time (RT) for GC-MS or LC-MS. Therefore, there are typically multiple peaks (each for a different m / z value or range), with each peak corresponding to a specific fragment of one of the constituent analytes.

[0041] Advantageously, an optional identification step 105 is first performed. This step uses a (known) identification algorithm that processes the received data to determine the identity of the compounds in the data. Each identification may preferably comprise a list of the m / z ratios of multiple (n) product or fragment ions of the identified parent ion. Known algorithms typically allow each identification to be performed within any (but usually only a single) MS 2 spectrum is performed. The MS used 2spectrum does not necessarily have to be the one corresponding to the peak of the intensity peak over time. Typically, identification in the MS 2 spectrum is performed at an arbitrarily chosen value for the time parameter, which can be referred to as the base time t0. It is therefore assumed that the identified parent ion (and its corresponding parent compound) contains all fragments identified at this value of the time parameter.

[0042] In the PQF step 110, one or more PQF algorithms are applied to the retrieved data aggregated over a selected range of the time parameter to determine a PQF result. The selected range desirably contains a minimum number of data points, generally at least 5 data points. To achieve this, the selected range is typically set to include the following: the base time t0; two data points before the base time t0, i.e., at t -1 and t -2 ; and two data points after the base time t0, i.e., at t1 and t2. All peaks whose intensity is below a predefined threshold for this first minimum number of data points (e.g., 5) are preferentially discarded, since a minimum number of data points is generally required to evaluate the peak properties for a fragment.

[0043] The PQF result is then compared with at least one PQF criterion in verification step 120. The one or more PQF algorithms can thus determine whether each peak shape is good enough or whether a product does not produce a good peak for quantification. A flatness detection algorithm can be part of the PQF algorithms to determine when a peak boundary is reached in one or both directions. Thus, the at least one PQF criterion can include determining whether a certain threshold of a minimum number of peaks, min_peaks, of sufficient quality (corresponding to the PQF result) is available for quantification. This threshold can be user-defined or automatically defined. Another criterion can concern the optimization (e.g., maximization) of the PQF result.

[0044] If the at least one PQF criterion is not met, the range change path 124 is selected to the range change step 130. In the range change step 130, it is determined how the selected range of the time parameter is to be changed. For example, the selected range of the time parameter can be extended at one or both ends of the range (for example, by t -3and / or t3), be reduced at one or both ends of the range, or be expanded at one end and reduced at the other. The use of multiple PQF algorithms can enable such decisions to be made. The selected range of the time parameter is then updated accordingly. As mentioned above, a minimum number of data points is required to evaluate the peak properties for a fragment. Therefore, if the change in the selected range results in a fragment not having enough data points above the threshold to reach the minimum, the fragment is preferentially excluded.

[0045] Based on the updated selected range of the time parameter, additional data may be retrieved. This occurs in retrieval step 135. It should be understood that this is not always necessary, and therefore this step is optional (indicated by a dashed outline).

[0046] The process then continues by repeating PQF step 110, but now with the updated selected range of the time parameter. It is understood that this loop may repeat multiple times if at least one PQF criterion considered in verification step 120 is not met. It is expected that the selected range can be adjusted both forward and backward in time with respect to the base time t0.

[0047] If at least one PQF criterion considered in verification step 120 is met (e.g., the PQF result is optimized and / or one or more other criteria are met), quantification path 126 is selected. In quantification step 140, the substance is quantified based on the selected range of the time parameter (updated, if necessary, as in the immediately preceding range change step 130). This can be achieved by summing the intensities of the remaining fragments between the boundaries defined by the updated selected time range.

[0048] The above approach focuses on determining the range of the time parameter to be used in quantification. This also takes into account the fragments selected for quantification. However, this is not the focus of this approach. Another approach, preferably combined with the approach discussed above, focuses on which fragments should be included in quantification.

[0049] In general, this can be a method for quantifying a substance present in a sample. The sample is separated into its component analytes by a first separator over a time parameter. The component analytes are then further analyzed by a mass spectrometer. In practice, a component analyte can be provided (output) by the first separator over a range of the time parameter (a time range). Thus, the mass spectrometer can provide multiple mass spectra for each component analyte.

[0050] In one example, the sample comprises (or is) a liquid or gaseous sample. Then, the first separator can be a chromatographic separator, and the time parameter refers to a retention time. In this case, the constituent analytes from the chromatographic separator are separated components at different retention times, and the constituent analytes are further analyzed with the mass spectrometer by ionizing each analyte to provide ions. The constituent analytes, which are ionized to provide the ions, are then analyzed in the mass spectrometer by: processing the ions, in particular by fragmentation; and mass analyzing the processed (fragmented) ions.

[0051] Alternatively, the sample comprises (or is) a mixture of sample ions, and the first separator is an ion mobility separator. The time parameter then refers to a drift time or a retention time, and the constituent analytes are constituent ions of the mixture of sample ions. These can also be processed (e.g., by fragmentation), and the processed (fragmented) ions can then be subjected to mass analysis.

[0052] In any case, each of the peaks can typically be assigned to a corresponding fragment ion from the DIA-MS n -analysis (e.g. MS 2 analysis) of the constituent analytes.

[0053] In a method according to a first aspect, intensity measurements versus mass-to-charge ratio for each of the one or more constituent analytes are received by the mass spectrometer. A relationship of the measured intensity at each of one or more selected mass-to-charge ratios versus the time parameter for each constituent analyte defines a respective peak. In embodiments, the selected mass-to-charge ratios (which may be a range of mass-to-charge ratios) may be determined based on the fragments. Then, a specific range for the time parameter to be used to quantify the substance is determined based on a respective peak quality factor for each of the one or more peaks, wherein the peak quality factor is dependent on a range for the time parameter.Typically, several peak quality factors can be used, each of which can provide a statistical measure of the peak, for example, in terms of peak definition, symmetry, or other shape features.

[0054] Preferably (after obtaining the measurement data), the substance and / or a chemical composition corresponding to at least one peak (and typically each peak) is identified. In the latter case, the selected mass-to-charge ratio(s) of the at least one peak may correspond to the identified chemical composition. Identifying the peaks and / or the substance can help determine a starting point and / or an initial range for the time parameter of each peak.

[0055] Advantageously, each peak comprises at least a specified number of intensity measurements, typically at least 5 intensity measurements (but optionally 7, 9, or 11). Peaks with fewer than the specified number of intensity measurements may not be considered.

[0056] Optionally, the specific range for the time parameter can be further determined using a flatness detection algorithm. This may determine whether the full extent of each peak is captured by the selected range for the time parameter.

[0057] In some implementations, determining the specific range for the time parameter comprises: determining a respective first peak quality factor for each of the one or more peaks with respect to a first range of the time parameter; and determining a respective second peak quality factor for each of the one or more peaks with respect to a second range of the time parameter, wherein the second range of the time parameter is narrower or wider than the first range of the time parameter. Subsequently, the specific range for the time parameter to be used to quantify the substance can be determined based on the one or more first peak quality factors and the one or more second peak quality factors.For example, the change between the one or more second peak quality factors and the one or more first peak quality factors may indicate whether the specific range for the time parameter should be the first range of the time parameter, the second range of the time parameter, lower than the first range of the time parameter, higher than the second range of the time parameter, or between the first range of the time parameter and the second range of the time parameter.

[0058] Advantageously, the second range of the time parameter can be selected based on the one or more first peak quality factors (e.g., increasing the second range of the time parameter compared to the first range of the time parameter if the one or more first peak quality factors are too low) and / or based on a predetermined difference factor (e.g., a stepwise increase or decrease of the second range of the time parameter compared to the first range of the time parameter).

[0059] It may not be possible to initially evaluate all of the data required to determine the one or more quality factors for the second peak. For example, the received intensity measurements versus mass-to-charge ratio for each of the one or more constituent analytes may be initial intensity measurements for the first range of the time parameter. If the second range of the time parameter is broader than the first range of the time parameter, then additional intensity measurements versus mass-to-charge ratio for the one or more constituent analytes may be evaluated. In this case, the initial intensity measurements and additional intensity measurements together may cover the second range of the time parameter.Then, the respective second peak quality factor can be determined for each of the one or more peaks based on the initial intensity measurements and the additional intensity measurements.

[0060] In preferred embodiments, the specific range for the time parameter is determined iteratively by repeating the step of determining a respective second peak quality factor for each of the one or more peaks relative to a second range of the time parameter. Preferably, each second range of the time parameter becomes progressively wider (such that the range of the time parameter is iteratively increased until it is determined that the peak quality factor meets the specified criterion(s).

[0061] The specific range for the time parameter can be determined by evaluating the respective peak quality factor for each of the one or more peaks against a criterion (or multiple criteria). The specific range for the time parameter can be determined based on whether the criterion is met.

[0062] Preferably, the substance is quantified based on (summing of) the intensity measurements for each of the one or more peaks falling within the determined specific range.

[0063] Optionally, the determined specific range for the time parameter can be used to quantify the same substance from a different sample.

[0064] Referring now to Fig. Figure 4 shows a block diagram of a second procedure in this direction. This procedure serves, in particular, to determine the fragments to be included for the purpose of substance quantification. In an initial step 200, the mass spectrometer provides data (or a controller retrieves it from the mass spectrometer) comprising intensity measurements versus the mass-to-charge ratio for each of the one or more constituent analytes. In this case, there are necessarily multiple peaks (each for a different m / z value or range). This will typically be the case when fragments (e.g., from DIA MS 2 ) of one of the constituent analytes, but there may also be other situations where multiple peaks are considered to quantify a substance.

[0065] Advantageously, an optional identification step 205 is first carried out. This was described above with respect to the identification step 105 in Fig. 3 and is therefore not repeated here for brevity. Either based on the initial step 200 or based on the optional identification step 205, an initial set of peaks is identified for quantification.

[0066] In evaluation step 210, the peaks identified for quantification are evaluated. There are two options for this evaluation: A first option involves running one or more PQF algorithms on the retrieved data aggregated over a range of the time parameter. A second option involves determining a common peak position. This can be determined by a statistical measurement, for example, by determining an average, which can include a mean, a median, or a mode of the peak positions. Both options can be used together.

[0067] The evaluation result is then compared with at least one criterion in verification step 220. For example, the results of the PQF algorithm (or PQF algorithms) for each peak can be compared with a threshold value. Additionally or alternatively, the peak positions of each of the peaks can be compared with the determined common peak position. A threshold value can be specified for the distance between a peak position and the determined common peak position, so that one criterion can be that the distance exceeds the threshold value. For example, one of the peaks can be an interfering peak originating from a different parent ion. The separation in the time parameter range can be sufficient to indicate that the peak should be excluded from quantification.

[0068] If the at least one criterion is not met, peak reselection path 224 is selected to peak removal step 230. In peak removal step 230, at least one of the peaks previously identified for quantification is selected to be excluded. This is typically determined based on evaluation step 210. For example, if a peak does not meet a PQF-based criterion (a threshold) and / or has a peak position that is at least (or more than) one threshold away from the determined common peak position, the peak is selected to be excluded from the set of peaks identified for quantification.

[0069] The process then continues by repeating evaluation step 210, but now with the updated set of peaks identified for quantification. It is understood that this loop may repeat several times if at least one criterion considered in verification step 220 is not met.

[0070] If the at least one criterion considered in verification step 220 is met (e.g., all peaks have a sufficient PQF and / or the peak position of all peaks is not further (or further) than a threshold value from the determined common peak position), quantification path 226 is selected. In quantification step 240, the substance is quantified based on the peaks updated during the process and identified for quantification. This can be achieved by summing the intensities of the fragments in the set of peaks identified for quantification, in particular between boundaries defined by a selected time range.

[0071] Advantageously, both processes can be performed simultaneously or sequentially. As a result, both a selected time range and a selected set of peaks can be determined for quantification purposes.

[0072] With either or both approaches, the result (selected time range and / or selected set of peaks) used to quantify one sample can be advantageously used to quantify the same substance in another sample. This can be the case, for example, when multiple sample replicates are processed in multiple experimental runs. This can ensure consistent quantification between runs.

[0073] Returning to the general concepts discussed above, another aspect may be considered (which may be combined with any other aspect disclosed herein). In this aspect, a method for quantifying a substance present in a sample may also be considered. The same considerations as discussed above may apply to this aspect.

[0074] In the method, intensity measurements versus the mass-to-charge ratio for each of one or more constituent analytes are received by the mass spectrometer. A relationship of the measured intensity at each of a plurality of selected mass-to-charge ratios versus the time parameter for each constituent analyte defines a respective peak. In other words, peaks are received with respect to a plurality of selected mass-to-charge ratios. These can, for example, each be fragments of the same substance (e.g., in DIA-MS n -experiments).

[0075] For each of the peaks, a corresponding peak quality factor can be determined. The peak quality factor depends on a range for the time parameter. Preferably, multiple peak quality factors can be determined for each of the peaks. Further details regarding optional aspects and / or the implementation of the peak quality factor determination were discussed above and apply equally here. Additionally or alternatively, a common peak position with respect to the time parameter is determined for at least some of the peaks. Subsequently, a subset of the peaks is selected for quantification of the substance based on the determined peak quality factors and / or the determined common peak position.

[0076] In this way, the peaks selected for substance quantification are determined based on their properties, in particular the quality of the peak and / or whether the peaks are aligned by a common (maximum) position. The common peak position can be determined statistically, for example, using an averaging, weighted averaging, or heuristic algorithm. Poor-quality and / or offset peaks may indicate interference, and omitting such peaks from substance quantification can improve accuracy.

[0077] Optionally, selecting the subset of peaks may comprise evaluating a peak position for each of the peaks in comparison with the determined common peak position (e.g., determining an offset). The subset of peaks is then advantageously selected based on the evaluation (e.g., by disregarding any peaks whose peak position is at least or more than a threshold away from the determined common peak position).

[0078] Advantageously, selecting the subset of peaks comprises evaluating the respective peak quality factor (or factors) for each of the peaks against a criterion (e.g., whether one or more thresholds are met or exceeded; for example, each threshold may apply to a respective peak quality factor). The subset of peaks may then be selected based on whether the criterion is met for each peak. There may be multiple criteria, each of which may apply to a corresponding peak quality factor.

[0079] Each of the selected peak subsets is preferably quantified (by summing the measured intensities over a selected range for the time parameter). Subsequently, the substance can be quantified based on the quantification of the selected peak subset (e.g., by summing them).

[0080] Preferably (after obtaining the measurement data), the substance and / or chemical compound corresponding to at least one peak (and typically every peak) is identified. This can also help determine the set of peaks from which the subset is to be selected.

[0081] As above, each peak advantageously comprises at least a predetermined number of intensity measurements, typically at least 5 intensity measurements (but optionally 7, 9, or 11). Peaks with fewer than the predetermined number of intensity measurements may not be considered.

[0082] Regarding the Fig. Figures 5a to 5d each show a plot of measured intensity versus temporal peaks, illustrating the effect of different PQF algorithms when the time domain is changed. These algorithms use the same data as in Fig. 2, as described above, with three peaks. Three example PQF algorithms are used: ZigZagld (a smaller number is considered better); Symmetry (-1 is optimal, and the closer to this value, the better); and trigPeakSim (1 is optimal, and the closer to this value, the better).

[0083] For the sake of completeness, a brief description of these example PQF algorithms is now provided (more details are included in “MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data,” Chetnik et al., see above). ZigZagld is a shorthand for the zigzag index. A zigzag index (ZZ) captures shape quality by measuring the normalized variance between a point and its immediate neighbor on either side. A normalized average of all zigzag index values ​​(a zigzag index value can be calculated for each point of the peak except the two extremes) is the value used as the quality factor for the peak. Where ZZ is the zigzag index of the peak and I n the nth intensity measurement (there are N measurements in the peak); this can be expressed mathematically as follows: ZZ=ZZ1 / ZZ2; ZZ1=sum((2ln−ln-1−ln+1)2),from n = 2 to n = N - 1; ZZ2=N*EPl2; and EPI = I A - avg(I1 + I2 + I N-1 + I N ), where I A is the value of the maximum intensity.

[0084] Symmetry (SY) measures the correlation between the left and right halves of a peak. This can be expressed mathematically as follows: SY=cor([l1,…,IN / 2],[IN / 2,…,IN]), range: [−1,1].

[0085] TrigPeakSim (TPASR) is short for triangular peak area symmetry or similarity ratio. This peak quality factor estimates shape quality by comparing the peak area to the area of ​​the triangle formed by the apex and boundaries. This can be expressed mathematically as follows: TPASR = abs(triangle_area - AUC) / max(triangle_area, AUC), where AUC is the area under the curve of the peak (or simply the peak area).

[0086] In Fig. 5a five data points are selected as highlighted, in Fig. 5b the time range is extended to seven data points, in Fig. 5c nine data points are used and in Fig.5d, eleven data points are selected. For both the first peak (with the lowest peak intensity) and the second peak (with the highest peak intensity), it can be seen that the ZigZagld value and the trigPeakSim value decrease with increasing number of data points. The symmetry value for the first and second peaks changes, but not significantly. For the third peak (with intermediate peak intensity compared to the other two peaks and slightly offset peak position), the ZigZagld value decreases as the number of data points increases. However, the symmetry value is indeterminate until the number of data points reaches 11. In contrast, the trigPeakSim value appears optimal when the number of data points is 5, 7, or 9, but lower when the number of data points reaches 11.

[0087] It is therefore understandable that a combination of PQF algorithms is advantageously used to evaluate the peaks and determine the number of data points (the range selected for the time parameter).

[0088] Returning to the general terms discussed above (in each aspect), it will be understood that the mass spectrometer can be controlled to perform mass analysis of each of the constituent analytes and provide intensity measurements versus mass-to-charge ratio for each of the plurality of components.

[0089] Aspects according to the disclosure may be embodied as a computer program (optionally on a computer-readable medium, which may be non-transitory), as a controller for a mass spectrometry system (in hardware and / or software), or as a mass spectrometry system comprising such a controller. It is understood that such a mass spectrometry system may further comprise: a first separator configured to separate a sample into its constituent analytes over a time parameter; and a mass spectrometer configured to receive and analyze the constituent analytes.

[0090] Processes, methods, or implementations according to the disclosure may require minimal RAM memory consumption by retrieving only the minimum number of intensities that may be required or desired for reliable quantification. Such processes may be performed for multiple samples simultaneously. This may potentially control the consistency of the product ion set used for quantification and similar peak boundaries for all samples.

[0091] Separating DIA identification and DIA quantification can enable a second validation step that improves quantification and even discards the quantification of unreliable compounds (in case none of the product ions from the common set of product ions in a sample has generated a reliable peak). Furthermore, more consistent quantification can be provided, as the same set of product ions can be used for all samples. Furthermore, a product ion that has not generated a reliable peak can be removed from the common set of product ions for all samples. Separating identification and quantification can, in particular, provide the opportunity to identify the best and most abundant product ion set for each identified compound from all samples involved.

[0092] Although embodiments according to the disclosure have been described with reference to particular types of devices and applications (particularly mass spectrometry), and the embodiments have particular advantages in such a case, as discussed herein, approaches according to the disclosure may be applied to other types of devices and / or applications. In particular, the devices according to the invention may also be used for other applications. Although the specific structure, arrangement, and operating details (e.g., parameters) of the described processes are potentially advantageous (particularly in view of known configurations and capabilities), they may be varied significantly to achieve modes of operation with similar or equivalent performance. Separation types other than those disclosed herein may also be contemplated. Certain features may be omitted or substituted, for example, as indicated herein.Unless otherwise stated, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent, or similar purpose. Thus, unless otherwise stated, each disclosed feature represents only one example of a generic set of equivalent or similar features.

[0093] It has been suggested above that a retention time range (or equivalently, drift or another suitable time range) can be determined based on measurements from one sample and then used for another sample, particularly when multiple sample replicates are processed in multiple experimental runs. Alternative approaches are possible. For example, parallel evaluation of the retention time range can be performed for all (or at least several) samples. This allows a consensual (i.e., a common or statistically determined, e.g., average or weighted mean) retention time range to be determined for the evaluated samples. When parallel analysis of multiple samples is used, peaks with fewer than a predetermined number of intensity measurements do not need to be ignored but can be integrated into the quantification measurement.The determined time range can then be used to quantify all samples (possibly including other samples that were not used to determine the range).

[0094] A variation of the methods described above is to apply the same algorithm to full MS1 scans, searching only for the expected (precursor) mass for each identified compound. In this case, the term "fragment" used above can be replaced with "identified compound," and the process can be used in the same way.

[0095] In the general terms discussed above, it can optionally be considered that ions of the substance to be quantified have one of the selected mass-to-charge ratios. Then, the quantification of a peak can correspond to the quantification of the substance.

[0096] In this detailed description of the various embodiments, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one skilled in the art will recognize that these various embodiments may be practiced with or without these specific details. Moreover, one skilled in the art can readily appreciate that the specific sequences in which methods are presented and performed are illustrative, and it is contemplated that the sequences may be varied and still remain within the scope of the various embodiments disclosed herein.

[0097] As used herein, including the claims, singular forms of terms should be construed to include the plural form and vice versa, unless the context indicates otherwise. For example, unless the context indicates otherwise, throughout this document, including the claims, a singular reference such as "a" or "an" (such as an ion multipole device) means "one or more" (such as one or more ion multipole devices). Throughout the description and claims of the present disclosure, the words "comprise," "include," "have," and "contain," and variations of these words, such as "comprising" and "comprises," or the like, mean "including, but not limited to," and are not intended to exclude (and do not exclude) further components.Furthermore, the use of “or” is inclusive, such that the expression “A or B” is true if “A” is true, “B” is true, or both “A” and “B” are true.

[0098] The use of any and all examples provided herein, or of exemplary language ("for example," "such as," "such as," and the like), is intended only to better illustrate the disclosure and does not imply any limitation on the scope of the disclosure unless otherwise claimed. Language in the description should in no way be construed as indicating that any unclaimed element is essential to the practice of the disclosure.

[0099] The terms "first" and "second" may be interchanged without changing the scope of the disclosure. That is, an element referred to as a "first" element may instead be referred to as a "second" element, and an element referred to as a "second" element may instead be considered a "first" element.

[0100] All steps described in this patent specification may be performed in any order or concurrently, unless otherwise stated or unless the context requires otherwise. Furthermore, the description of a step as being performed after another step does not preclude the performance of intervening steps.

[0101] It is also understood that for any given component or embodiment described herein, any of the possible candidates or alternatives listed for those components may generally be used individually or in combination with one another, unless otherwise implicitly or explicitly understood or stated. It is understood that any list of such candidates or alternatives is illustrative only and not limiting, unless otherwise implicitly or explicitly understood or stated.

[0102] All literature and similar materials cited in this disclosure, including but not limited to patents, patent applications, articles, books, contracts, and Internet websites, are expressly incorporated by reference in their entirety for all purposes. Unless otherwise described, all technical and scientific terms used herein have a meaning as commonly understood by one of ordinary skill in the art to which the various embodiments described herein belong. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Zitierte Patentliteratur

[0000] WO-2009 / 146345

[0004] WO-2012 / 035412

[0005] Zitierte Nicht-Patentliteratur

[0000] Barkovits, Katalin, et al. „Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition“ (Molecular & Cellular Proteomics 19.1 (2020): 181-197

[0006] Searle, Brian C., et al. „Generating high quality libraries for DIA MS with empirically corrected peptide predictions“ (Nature communications 11.1 (2020): 1-10

[0006] Demichev, Vadim, et al. „DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput“ (Nature methods 17.1 (2020): 41-44

[0006] MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data“, Chetnik et al., Metabolomics 16 (2020): 1-13

[0038] MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data“, Chetnik

[0083]

Claims

[1] A method for quantifying a substance present in a sample, wherein the sample is separated into its component analytes by a first separator over a time parameter and the component analytes are then further analyzed by a mass spectrometer, the method comprising: Receiving intensity measurements versus mass-to-charge ratio for each of one or more constituent analytes from the mass spectrometer, wherein a relationship of the measured intensity at each of one or more selected mass-to-charge ratios over the time parameter defines a respective peak for each constituent analyte; and Determining a specific range for the time parameter to be used to quantify the substance based on a respective peak quality factor for each of the one or more peaks, wherein the peak quality factor is dependent on a range for the time parameter. [2] The method of claim 1, wherein the specific range for the time parameter is further determined using a flatness detection algorithm. [3] A method according to claim 1 or claim 2, wherein determining the specific range for the time parameter comprises: determining a respective first peak quality factor for each of the one or more peaks with respect to a first range of the time parameter; Determining a respective second peak quality factor for each of the one or more peaks with respect to a second range of the time parameter, wherein the second range of the time parameter is narrower or wider than the first range of the time parameter; and wherein the specific range for the time parameter to be used to quantify the substance is determined based on the one or more first peak quality factors and the one or more second peak quality factors. [4] The method of claim 3, wherein the second range of the time parameter is selected based on the one or more first peak quality factors and / or a predetermined derivative factor. [5] A method according to claim 3 or claim 4, wherein the received intensity measurements versus mass-to-charge ratio for each of the one or more constituent analytes are initial intensity measurements for the first range of the time parameter and the second range of the time parameter is wider than the first range of the time parameter, the method further comprising: Receiving additional intensity measurements versus the mass-to-charge ratio for the one or more constituent analytes, wherein the initial intensity measurements and the additional intensity measurements together cover the second range of the time parameter; and wherein the respective second peak quality factor is determined for each of the one or more peaks based on the initial intensity measurements and the additional intensity measurements. [6] A method according to any one of claims 3 to 5, wherein the specific range for the time parameter is determined iteratively by repeating the step of determining a respective second peak quality factor for each of the one or more peaks with respect to a second range of the time parameter, each second range of the time parameter becoming progressively wider. [7] A method according to any one of the preceding claims, further comprising: Using the determined specific range for the time parameter to quantify the same substance from a different sample; and / or Quantifying the substance based on the intensity measurements for each of the one or more peaks that lie within the determined specific range. [8] The method of any preceding claim, wherein the step of determining the specific range for the time parameter comprises: Evaluating the respective peak quality factor for each of the one or more peaks against a criterion, wherein the specific range for the time parameter is determined based on whether the criterion is met. [9] A method for quantifying a substance present in a sample, wherein the sample is separated into its component analytes by a first separator over a time parameter and the component analytes are then further analyzed by a mass spectrometer, the method comprising: Receiving intensity measurements versus mass-to-charge ratio for each of one or more constituent analytes from the mass spectrometer, wherein a relationship of the measured intensity at each of the plurality of selected mass-to-charge ratios versus the time parameter defines a respective peak for each constituent analyte; Determining a respective peak quality factor for each of the peaks, wherein the peak quality factor is dependent on a range for the time parameter, and / or determining a common peak position with respect to the time parameter for at least some of the peaks; and Selecting the subset of peaks to quantify the substance based on the determined peak quality factors and / or the determined common peak position. [10] The method of claim 9, wherein selecting the subset of peaks comprises: Evaluating a peak position for each of the peaks against the determined common peak position, selecting the subset of peaks based on the evaluation. [11] The method of claim 9 or 10, wherein selecting the subset of peaks comprises: Evaluate the respective peak quality factor for each of the peaks against a criterion, whereby the subset of peaks is selected based on whether the criterion is met for each peak. [12] A method according to any one of claims 9 to 11, further comprising: Quantifying each of the selected subset of peaks; and Quantification of the substance based on the quantification of the selected subset of peaks. [13] A method according to any one of claims 9 to 12, further comprising the method according to any one of claims 1 to 8. [14] A method according to any one of claims 1 to 11 or 13, wherein ions of the quantifiable substance have one of the selected mass-to-charge ratios. [15] A method according to any one of the preceding claims, wherein the constituent analytes are analyzed in the mass spectrometer as ions by: Processing the ions; and mass analyzing the processed ions. [16] The method of claim 15, wherein the sample is a liquid or gaseous sample, the first separator is a chromatographic separator, the time parameter relates to a retention time, the constituent analytes are components separated from the chromatographic separator with different retention times, and the constituent analytes are further analyzed by the mass spectrometer by ionizing each analyte to provide the ions. [17] The method of claim 15, wherein the sample is a mixture of sample ions, the first separator is an ion mobility separator, the time parameter refers to a drift time or a retention time, and the constituent analytes are constituent ions of the mixture of sample ions. [18] A method according to any one of claims 15 to 17, wherein the ions are precursor ions and processing the ions comprises fragmenting the precursor ions to produce fragment ions such that the fragment ions are subjected to mass analysis. [19] The method of claim 18, wherein each of the peaks corresponds to a respective fragment ion from the Data Independent Acquisition (DIA)-MS n -analysis of the constituent analytes. [20] A method according to any one of the preceding claims, further comprising: Identify the substance; and / or Identifying a chemical composition corresponding to at least one peak, wherein the selected mass-to-charge ratio or mass-to-charge ratios of the at least one peak correspond to the identified chemical composition. [21] A method according to any one of the preceding claims, wherein each peak comprises at least a predetermined number of intensity measurements. [22] A method according to any one of the preceding claims, further comprising: Controlling the mass spectrometer to perform a mass analysis of each of the constituent analytes and to provide the intensity measurements versus the mass-to-charge ratio for each of the plurality of components. [23] A computer program which, when operated by a processor, is configured to carry out the method according to any one of the preceding claims. [24] A controller for a mass spectrometry system configured to cause the mass spectrometry system to perform the method of any one of claims 1 to 22. [25] Mass spectrometry system comprising: a first separator configured to separate a sample into its constituent analytes over a time parameter; a mass spectrometer configured to receive and analyze the constituent analytes; and the control according to claim 24.

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