Connection compilation

Abstract

A method for processing mass spectral data is provided. The mass spectral data comprise a variety of mass spectrometers. 1 -Mass spectra and a variety of MS N -Mass spectra, where each mass spectrum is assigned a corresponding retention time. The method involves identifying a group of features within the multitude of mass spectra. 1 -Mass spectra, wherein each feature of the group has a respective mass and wherein the features of the group have corresponding retention times. The procedure comprises, for each of the one or more features of the group: (i) submitting a corresponding MS N(1) send a mass spectrum to a mass spectrum search engine to obtain an identification result for that feature, and (2) determine a candidate ion type for the feature based on a mass difference between the mass associated with the feature and an expected mass from the identification result. The procedure includes identifying one or more compounds based on the feature group and the candidate ion type(s).

Description

FIELD OF THE INVENTION

[0001] The present invention relates to the field of mass spectrometry and, in particular, to methods for processing mass spectral data. STATE OF THE ART

[0002] It has been some time since the discovery of soft ionization techniques enabled the mass spectrometric analysis of intact small molecules, peptides, and proteins. Using these techniques, the acquired spectra are greatly simplified by very limited fragmentation within the source. However, individual compounds still manifest themselves as a series of mass-to-charge ratio (m / z) signals. These signals include, in addition to isotopes, solvent adducts, homo- or heterodimers, different charge states, and any fragments remaining in the source. Correctly "assembling" these multiple m / z signals is a critical step in both qualitative and quantitative analysis.

[0003] It is believed that there is potential for improvement in methods for processing spectral data. SUMMARY

[0004] A method is provided for processing mass spectral data of a sample. The mass spectral data comprises a plurality of MS 1 -Mass spectra and a variety of MS N -Mass spectra, where each mass spectrum of the plurality of MS 1 -Mass spectra and the variety of MS N -mass spectra are each assigned a retention time. The method comprises detecting a group of features in the plurality of MS 1 Mass spectra, wherein each feature of the group has a respective mass and wherein the features of the group have corresponding retention times. The method comprises identifying one or more compounds in the sample based on the group of features.

[0005] As described in more detail below, various embodiments provide improved methods for processing mass spectral data.

[0006] In embodiments, the mass spectral data is generated by an analytical instrument that includes a chromatographic separation device (such as a liquid chromatography (LC) separation device or a gas chromatography (GC) separation device) and a mass spectrometer. The chromatographic separation device can separate the sample in a chromatographic separation scan, and the mass spectrometer can acquire the mass spectral data during the chromatographic separation scan. Thus, each mass spectrum of the plurality of MS 1 -Mass spectra and the variety of MS N -Mass spectra were assigned a corresponding retention time, ie, each mass spectrum was obtained during the chromatographic separation scan at a corresponding chromatographic retention time.

[0007] As described below, in some embodiments, each MS N mass spectrum is an MS2 mass spectrum. However, any MS NInstead, it can also be a higher-order fragmentation spectrum, such as an MS3 spectrum. In general, N is an integer ≥ 2.

[0008] The mass spectral data may consist of (ie, be formed from) at least one sample file, each sample file being the data output of the mass spectrometer from a respective chromatographic separation scan. The mass spectral data of each sample file may contain multiple MS 1 -Mass spectra of the variety of MS 1 -Mass spectra and multiple MS N -Mass spectra of the variety of MS N -Mass spectra. The mass spectral data can consist of a single sample file or multiple sample files. If multiple sample files are present, each sample file can be from a chromatographic separation scan for a different portion of the same sample.

[0009] The method comprises detecting a group of features in the plurality of MS 1 -Mass spectra. Each feature of the group can be assigned a corresponding mass and a corresponding retention time. The features of the group can have corresponding (e.g., equal within a first tolerance) retention times, but different masses. The method can include the detection of one or more further feature groups in the plurality of MS 1 -Mass spectra, with each respective different group having a different retention time. Although only the processing of a single feature group is described in detail below, it should be understood that any additional feature group can be processed in a similar manner.

[0010] In some embodiments, each group of features is detected by first identifying features in the plurality of MS 1mass spectra are detected and then detected features are grouped that have corresponding (ie, equal within the first tolerance) retention times. Features can again be found in the multitude of MS 1 -Mass spectra are detected by first entering features per file into the MS 1 -Mass spectra of each sample file are detected and then features per file are grouped across sample files that have corresponding masses and retention times to form features.

[0011] The step of recognizing a group of features in the multitude of MS 1 -Mass spectra can therefore include: for each sample file: detecting a plurality of features per file in that sample file, each feature per file having a respective mass and a respective retention time; Forming a plurality of features from the features per file, each feature having a respective mass and a respective retention time; and forming a group of features by grouping features with corresponding retention times.

[0012] In these embodiments, each feature per file in a sample file can be detected by first generating a chromatogram for each unique mass-to-charge ratio (m / z) in the multiple MS 1-Mass spectra of the sample file are created, and a characteristic retention time is determined for each chromatogram. The characteristic retention time for a chromatogram can be the retention time at the center or apex of the chromatogram and can be determined, for example, using a peak detection algorithm or similar. The chromatograms can then be grouped according to their characteristic retention times. A deisotoping algorithm can be applied to each set of chromatograms to create a group of features per file.

[0013] Thus, each feature per file is a feature that is present in the deisotoped MS 1-Mass spectral data appears and has a unique combination of mass and retention time. Then, each feature per file of each group of features per file has a corresponding mass and retention time, and the features per file of each group have corresponding (i.e., the same within a second tolerance) retention times.

[0014] Similarly, the step of detecting a plurality of features per file in a sample file may include: Creating a variety of chromatograms from the multiple MS 1 -Mass spectra of the sample file, each chromatogram having a respective mass-to-charge ratio (m / z); Determining a characteristic retention time for each chromatogram; Grouping chromatograms with corresponding characteristic retention times into one or more chromatogram sets; and Apply a deisotoping algorithm to each set of chromatograms to form a group of features per file.

[0015] As described above, features within each feature group can have retention times that are equal within a first tolerance, while features within each file within each feature group can have retention times that are equal within a second tolerance. The second tolerance can be less than the first tolerance. This, as described below, ensures that differences in retention time between different sample files are properly accounted for.

[0016] Thus, the step of grouping features with corresponding retention times may comprise grouping features whose retention times are equal within a first tolerance; the step of grouping chromatograms with corresponding characteristic retention times may comprise grouping chromatograms with retention times that are equal to within a second tolerance; and the second tolerance may be lower than the first tolerance.

[0017] In some embodiments, the method comprises: for each of one or more features of a group of features: (i) submitting a corresponding MS Nmass spectrum to a mass spectrum search engine to obtain an identification result for that feature, and (ii) determining a candidate ion type (such as a candidate adduct type) for the feature based on any mass differences between the mass associated with the feature and an expected mass from the identification result. In this case, the step of identifying one or more compounds in the sample can be based on both the set of features and the candidate (adduct) ion types.

[0018] In these embodiments, steps (i) and (ii) may be performed for those features of the group for which corresponding MS N data are available in the mass spectral data. If multiple sample files are present, the corresponding MS N- Mass spectrum can be extracted from each of the sample files. Thus, an MS N-Mass spectrum corresponding to a feature, an MS N- Spectrum (from the multitude of MS N- mass spectra) for each of the features per file corresponding to the feature.

[0019] As mentioned above, it would be possible for the mass spectral data to consist of only a single sample file. In this case, each feature of the plurality of features would be formed from one feature per file of the plurality of features per file.

[0020] In these embodiments, the step of identifying one or more compounds based on the feature set may comprise: (i) determining one or more feature clusters, each feature cluster comprising one or more features of the group and possibly corresponding to a respective (individual) compound; (iii) determining one or more arrangements of the feature clusters for the feature group, each arrangement comprising one or more non-conflicting feature clusters; and (ii) selecting a preferred arrangement from the one or more arrangements of feature clusters for the feature group; and then: identifying one or more compounds based on the preferred arrangement of feature clusters.

[0021] However, in certain embodiments, the mass spectral data comprises a plurality of sample files, and each feature of the plurality of features is formed by grouping features per file across the sample files that have corresponding masses and corresponding retention times. In this case, for each group of features, a corresponding group of features per file is present in each sample file of the plurality of sample files.

[0022] The step of identifying one or more compounds based on the feature set may then include: for each group of features per file in a respective sample file: (i) determining one or more clusters of features per file, each cluster of features per file comprising one or more features per file of the group and possibly corresponding to a respective compound; (ii) determining one or more arrangements of the clusters of features per file for the group of features per file, each arrangement comprising one or more non-conflicting clusters of features per file; and (iii) selecting a preferred arrangement from the one or more arrangements of clusters of features per file for the group of features per file; and then, based on the preferred arrangements for the plurality of sample files: (iv) determining one or more arrangements of feature clusters for the feature group, each feature cluster comprising one or more features of the feature group and possibly corresponding to a respective compound, and each arrangement comprising one or more non-conflicting feature clusters; and (v) selecting a preferred arrangement from the one or more arrangements of feature clusters for the feature group; and then

[0023] Identifying one or more compounds based on the preferred arrangement of feature clusters.

[0024] As described in more detail below, this two-step process of first determining a preferred arrangement for each sample file and then resolving any conflicts between the preferred arrangements from different sample files increases the reliability and consistency of compound identification when multiple sample files are present.

[0025] In some embodiments, the step of determining one or more clusters of features per file may comprise, for each group of features per file in a respective sample file: Assign one or more candidate ion types to each feature per file in the group; Determining one or more candidate relationships between features per file of the group; and Resolve any conflicts between the candidate ion types and the candidate relationships for the group.

[0026] In these embodiments, the step of assigning one or more candidate ion types to each feature per file of the group may comprise assigning one or more candidate ion types from a plurality of different categories to each feature per file of the group. The categories of candidate ion types may include, for example, (i) identified ion types; (ii) user-defined base ion types; (iii) standard ion types; and (iv) fragment ion types within the source.

[0027] Similarly, the step of determining one or more candidate relationships (or "transitions") between features per file of the group may involve determining one or more candidate relationships from several different relationship categories. The relationship categories may include, for example: (i) relationships of fragments within the source; and (ii) adduct relationships.

[0028] Thus, for example, each feature per file of the group may have a corresponding charge, and the step of assigning one or more candidate ion types to each feature per file of the group may comprise: Assigning an identified ion type to each feature per file of the group that corresponds to a feature for which an identification result was obtained; and / or Assign a custom base ion type or a standard ion type to each feature per file in the group based on the respective charge of the feature per file.

[0029] Additionally or alternatively, the step of assigning one or more candidate ion types to each feature per file of the group may comprise: assigning a fragment ion type within the source to each feature per file of the group that has a mass equal to the mass of an expected fragment within the source of another feature per file in the group.

[0030] In this case, the step of determining one or more candidate relationships between per-file features of the group may comprise: determining a fragment relationship within the source between a per-file feature of the group and another per-file feature of the group if the per-file feature has a mass equal to the mass of an expected fragment within the source of the other per-file feature (or, equivalently, if the other per-file feature has a mass equal to the mass of an expected fragment within the source of the per-file feature).

[0031] In these embodiments, the mass of one or more expected fragments within the source of a feature per file may be determined from an MS N -Mass spectrum (from the variety of MS N -mass spectra) corresponding to this feature per file.

[0032] Alternatively, the method may comprise providing the mass of one or more expected fragments within the source of that feature as part of the identification result for a feature. In this case, the masses of the one or more expected fragments within the source of a feature per file may be derived from the provided masses.

[0033] In addition, the masses provided as part of the identification result can be from one or more MS N mass spectra configured to simulate fragmentation in the source. As described in more detail below, the use of custom MS N -Mass spectra in this way the identification of fragmentation in the source can be significantly improved.

[0034] In some embodiments, the step of determining one or more candidate relationships between features per file of the group may comprise determining one or more candidate adduct relationships between features per file of the group based on allowable mass shifts between the masses of the features per file of the group.

[0035] In embodiments, all conflicts are resolved once all possible candidate ion types and candidate relationships for the set of features per file have been determined.

[0036] In some embodiments, the step of resolving any conflicts between the candidate ion types and the candidate relationships may comprise: Removing all candidate relationships that conflict with a feature assigned as an identified ion type per file (e.g., removing all candidate adduct relationships and / or removing all candidate fragment relationships in the source that conflict with a feature assigned as an identified ion type per file); and / or removing all invalid candidate fragment relationships within the source.

[0037] As mentioned above, in some embodiments, the method includes determining one or more clusters of features per file for each group of features per file in a respective sample file. Each of these determined clusters may potentially correspond to a respective compound.

[0038] Therefore, in embodiments, the step of determining one or more clusters of features per file may comprise: Determine most or all possible clusters of features per file; and then Removing all invalid clusters; and / or Remove all clusters that do not contain a per-file feature assigned as a custom base ion.

[0039] As also mentioned above, the method may include determining one or more arrangements of clusters (of features per file or of features) for each group and then selecting one of the arrangements as the preferred arrangement. Each of these arrangements includes one or more clusters that do not conflict with each other. Any cluster that does not have a conflicting cluster may be used immediately in the preferred arrangement without further processing.

[0040] The step of determining one or more arrangements of clusters (of features per file or of features) may therefore include: Determine whether a cluster conflicts with one or more other clusters; and If a cluster is determined not to conflict with another cluster, that cluster is used in the preferred cluster arrangement for the group.

[0041] On the contrary: Where conflicting clusters exist, they must be resolved. In some embodiments, this is done by identifying several different cluster arrangements, assigning a score to each arrangement, and selecting the arrangement with the highest score as the preferred arrangement.

[0042] The step of selecting a preferred arrangement of clusters (of features per file or of features) from one or more arrangements of clusters may therefore comprise: Determining a score for each cluster arrangement; and Select the arrangement with the highest score.

[0043] The step of determining a score for each cluster arrangement may include: Determining a cluster score for each cluster in the array by: (i) assigning a weighting factor to each candidate ion type assignment of the cluster, (ii) assigning a relationship score to each candidate relationship of the cluster, and (iii) calculating a cluster score for the cluster by dividing the sum of the weighting factors and relationship scores by the number of features or features per file in the cluster; and Determine a score for each array by: dividing the sum of the cluster scores for the array by the number of clusters in the array.

[0044] Another aspect relates to a method for mass spectrometry, comprising: Analyzing a sample to obtain mass spectral data that includes a variety of MS 1 -Mass spectra and a variety of MSN -mass spectra, each mass spectrum being associated with a respective retention time; and Process the mass spectral data using the methods described above.

[0045] Another aspect provides a non-transitory computer-readable storage medium storing computer software code that, when executed on a processor, performs the method(s) described above.

[0046] Another aspect provides a control system for an analytical instrument, such as a mass spectrometer, wherein the control system is configured to cause the analytical instrument to perform the method(s) described above.

[0047] Another aspect provides an analytical instrument, such as a mass spectrometer, comprising the control system described above. DESCRIPTION OF THE DRAWINGS

[0048] Various embodiments will now be described in more detail with reference to the accompanying figures, in which: Fig. 1 schematically shows a mass spectrometer that can be operated according to embodiments; Fig. 2 schematically shows a method according to embodiments; Fig. Figure 3 shows a representation of a main relation diagram comprising a plurality of features; Fig. Figure 4 shows a representation of a per-file relationship diagram including a plurality of features per file; Fig. Figure 5A shows a representation of one feature per file with an identified ion type, Fig. Figure 5B shows a representation of one feature per file that has been labeled as one of two different user-defined base ion types, Fig. Figure 5C shows a representation of one feature per file that has been marked as a standard ion type, Fig. Figure 5D shows a representation of one feature per file that has been labeled as one of the two different user-defined base ion types or as a fragment ion in the source; Fig. Figure 6 shows a representation of a relationship diagram per file, comprising two features per file and including various possible ion types and transitions; Fig. 7A and Fig. 7B illustrates a process for removing invalid transitions from an initial relationship graph per file. Fig. Figure 8 shows a representation of a relationship diagram per file, comprising eight features per file and including various possible ion types and transitions; Fig. Figure 9 shows a representation of different possible clusters resulting from the features per file of Fig. 8 were formed; Fig. 10 illustrates a process for removing invalid clusters from an initial set of possible clusters; Fig. Figure 11 illustrates a process for removing invalid orphan clusters from an initial set of possible clusters. Fig. Figure 12 shows a representation of different possible clusters resulting from the features per file of Fig. 8 were formed after invalid clusters were removed; Fig. Figure 13 illustrates a process for determining different possible explanations for the group of features per file from the different possible clusters of Fig. 12; Fig. 14 illustrates a final explanation for the set of features per file as determined according to embodiments; Fig. Figure 15 illustrates a process for dissolving fragments. DETAILED DESCRIPTION

[0049] Fig. Figure 1 schematically illustrates an analytical instrument, such as a mass spectrometer, that can be used in conjunction with the methods described herein. As in Fig. 10, the instrument 1 includes an ion source 10, a mass filter 20, a fragmentation device 30, and a mass analyzer 40.

[0050] The ion source 10 is configured to generate ions from a sample. The ion source 10 can be coupled to a chromatographic separation device (not shown), such as a liquid chromatography (LC) separator, a gas chromatography (GC) separator, or a capillary electrophoresis separator, so that the sample ionized in the ion source 10 comes from the separation device. The ion source 10 can be any suitable ion source, such as an electrospray ionization (ESI) source, an atmospheric pressure ionization (API) source, a chemical ionization source, an electron impact (EI) ion source, or the like.

[0051] The mass filter 20 is arranged downstream of the ion source 10 and is configured to receive ions from the ion source 10. The mass filter 20 is configured to filter the received ions according to their mass-to-charge ratio (m / z). The mass filter 20 can be configured such that received ions having an m / z within an m / z transmission window of the mass filter are passed through the mass filter, while received ions having an m / z outside the m / z transmission window are attenuated by the mass filter, e.g., are not passed through the mass filter. The m / z of the width and / or center of the transmission window can be controllable (variable), e.g., by appropriately controlling the RF and / or DC voltage(s) applied to the electrodes of the mass filter 20.For example, the mass filter 20 can be operated in a transmission mode, where most or all ions within a relatively wide m / z window are passed through the mass filter 20, and in a filtration mode, where only ions within a relatively narrow m / z window (centered on a desired m / z) are passed through the mass filter 20. The mass filter 20 can be any suitable type of mass filter, such as a quadrupole mass filter.

[0052] The fragmentation device 30 is arranged downstream of the mass filter 20 and is configured to receive most or all of the ions transmitted by the mass filter 20. The fragmentation device 30 can be configured to selectively fragment some or all of the received ions, i.e., to generate fragment ions. The fragmentation device 30 can be operated in a fragmentation mode of operation, in which most or all of the received ions are fragmented to generate fragment ions (which can then be forwarded by the fragmentation device 30), and in a non-fragmentation mode of operation, in which most or all of the received ions are forwarded without being (intentionally) fragmented. It would also be possible to implement a non-fragmentation mode of operation by causing ions to bypass the fragmentation device 30.The fragmentation device 30 can also be operated in one or more intermediate modes, e.g., where the degree of fragmentation is controllable (variable). The fragmentation device 30 can also be operated in higher-order fragmentation modes (MS). N ), e.g., wherein the fragment ions are further fragmented one or more times by the fragmentation device 30.

[0053] The fragmentation device 30 may be any suitable type of fragmentation device, such as a collision-induced dissociation (CID) fragmentation device, an electron-induced dissociation (EID) fragmentation device, a photodissociation fragmentation device, and so on. Numerous other types of fragmentation are possible.

[0054] In embodiments, the fragmentation device 30 is a collision-induced dissociation (CID) fragmentation device. Therefore, the fragmentation device may include a collision cell that may be filled with a collision gas, e.g., maintained at a relatively high pressure. Ions may be selectively fragmented in the collision cell by controlling (varying) the kinetic energy with which ions are caused to enter the collision cell. In a fragmentation mode of operation, ions may be accelerated so that they enter the collision cell with a relatively high kinetic energy, which may cause most or all of the accelerated ions to be fragmented.In a non-fragmentation mode of operation, ions can be induced to enter the collision cell with relatively low kinetic energy, which may not be sufficient to cause most or all of the ions to fragment. In intermediate modes, ions with intermediate kinetic energy may enter the collision cell.

[0055] The mass analyzer 40 is arranged downstream of the fragmentation device 30 and is configured to receive ions from the fragmentation device 30. Thus, the mass analyzer 40 can receive unfragmented precursor ions or fragment ions, depending on the operating mode of the fragmentation device 30. The mass analyzer 40 is configured to analyze the received ions to determine their mass-to-charge ratio (m / z) and / or their mass, i.e., to create a mass spectrum of the ions. The mass analyzer 40 can be any suitable type of mass analyzer, such as an ion trap mass analyzer, an electrostatic orbital trap mass analyzer (such as a Thermo Fisher Scientific Orbitrap™ FT mass analyzer), or a time-of-flight (ToF) mass analyzer, such as a multi-reflection time-of-flight (MR-ToF) mass analyzer.

[0056] It should be noted that Fig. 1 is merely schematic and that the instrument may, and in embodiments does, include any number of one or more additional components. For example, the instrument may comprise one or more ion transfer stages disposed between each of the illustrated components, including, for example, an air pressure interface and / or one or more ion guides, lenses, and / or other ion optical devices configured to allow some or all of the ions to be suitably transferred through the instrument. The ion transfer stage(s) may include any number and configuration of ion optical devices, for example, optionally including one or more ion guides, lenses, and / or other ion optical devices.

[0057] In some embodiments, the instrument may include more than one mass analyzer. For example, the instrument may be a hybrid mass spectrometer with two mass analyzers of the type described in EP 3,410,463, the contents of which are incorporated herein by reference.

[0058] As in Fig. As shown in Figure 1, the instrument is controlled by a control unit 50, such as an appropriately programmed computer, which controls the operation of various components of the instrument and, for example, adjusts the voltages to be applied to the various components of the instrument. The control unit 50 may also receive and process data from various components that include the analyzer(s) according to various embodiments.

[0059] The instrument can operate in different modes. For example, the instrument can be a tandem mass spectrometer that can operate in an MS1 ​​mode and an MS2 mode.

[0060] In the MS1 mode of operation (or "full mass scan"), the mass filter 20 is operated in its transmission mode of operation and the fragmentation device 30 is operated in its non-fragmentation mode of operation, so that, for example, a broad m / z range (e.g., the entire mass range) of non-fragmented ("precursor" or "parent") ions are analyzed by the analyzer 40 to generate an MS1 ​​spectrum.

[0061] In the MS2 mode of operation, the mass filter 20 is operated in its filtering mode of operation and the fragmentation device 30 is operated in its fragmentation mode of operation, such that, for example, a selected narrow m / z range of precursor ions is fragmented and the resulting fragment ions (“product ions” or “daughter ions”) are analyzed by the analyzer 40 to generate an MS2 spectrum.

[0062] The instrument can also be operated in one or more higher-order fragmentation modes, such as an MS3 mode of operation, in which precursor ions are fragmented, at least some of the resulting fragment ions are themselves fragmented, and the second-generation fragment ions ("grandchild ions") are analyzed by the analyzer 40 to generate an MS3 spectrum. In general, the instrument can be operated in any order of fragmentation mode, i.e., in an MS N -Operating mode with N≥2.

[0063] A method for operating the analyzer includes supplying a sample to a chromatographic (e.g., LC or GC) separation device so that the sample is chromatographically separated, ionizing the eluent from the chromatographic separation device in the ion source 10, and analyzing the resulting ions. Different compounds in the sample are subject to different retention times (RT) in the chromatographic separation device and are therefore eluted (and ionized) from the chromatographic separation device at different times. The chromatographic separation device typically requires several tens of seconds or several minutes to complete each chromatographic separation scan.

[0064] During each chromatographic separation scan, several MS2 spectra (or more generally several MS Nspectra) by sequentially changing the center of the (narrow) m / z window of the mass filter between each of a plurality of different m / z values, for example to sequentially select (and fragment) each of a plurality of different precursor ions with respective different m / z.

[0065] In a data-dependent acquisition (DDA) mode of operation, the plurality of different m / z values ​​may correspond to a plurality of different precursor ions identified from corresponding MS1 data (i.e., a full mass scan). A typical data-dependent acquisition (DDA) procedure therefore involves repeatedly performing the following steps during a chromatographic separation scan: (i) obtaining an MS1 ​​spectrum over an m / z range of interest; (ii) identifying one or more precursor ions of interest in the MS1 spectrum; and (iii) obtaining an MS2 (or MS N-) spectrum with respect to each of the identified precursor ions of interest. Step (iii) comprises, for each of the identified precursor ions: isolating the precursor ion using the mass filter 20, fragmenting the isolated precursor ions in the fragmentation device 30, and mass analyzing the fragment ions using the mass analyzer 40.

[0066] In a data-independent acquisition (DIA) MS2 (or MS N -) In this operating mode, the plurality of different m / z values ​​can be taken from a predetermined (fixed) list, i.e., without reference to MS1 data. For example, a narrow m / z isolation window can be traversed sequentially over the entire m / z range of interest, as described, for example, in EP 3,410,463.

[0067] In any case, each chromatographic separation scan performed by the analytical instrument generates a sample file. Each sample file contains data related to several MS1 spectra, each with an associated retention time; and data related to several MS2 (or MS N -) Spectra, each with an associated retention time and mass filter isolation window or precursor ion m / z. A prepared sample can be fractionated, and a chromatographic separation scan can be performed for each fraction to create multiple such sample files related to the same sample.

[0068] The repetition rate of the DDA / DIA method can be fast enough to sample each chromatographically separated compound of interest multiple times during its chromatographic elution. Therefore, for each sample file, a chromatogram trace can be created for each individual m / z of interest from the multiple MS1 spectra. Such chromatograms typically manifest as a peak corresponding to the chromatographic elution peak of each chromatographically separated compound.

[0069] The central (e.g., apex) retention time (RT) of each chromatogram can then be determined, e.g., using a suitable peak detection algorithm or similar. Chromatograms with the same (e.g., within a certain tolerance) central retention time (RT) can be pooled, resulting in one or more sets with multiple m / z values, where the same retention time (RT) has been determined for all m / z values ​​within a set.

[0070] Each of these sets of multiple m / z values ​​can result from several different compounds co-eluting from the chromatographic separation device and can be very complex and difficult to interpret. Therefore, there is a need to "cluster" each set of multiple m / z values ​​into one or more clusters, where each cluster is a subset of m / z values ​​from the set (or, where appropriate, the full set) of multiple m / z values ​​for which the same RT was determined, and all m / z values ​​in a cluster belong to the same single compound. In other words, each individual chromatographically separated compound can result in a cluster of multiple m / z values ​​in the MS1 data (with the same RT), leading to complexity in the MS1 data when multiple co-eluting compounds are present.

[0071] A single compound can (at the same RT) result in several different m / z values ​​in MS1, for example, due to different charge states (z) and different isotopes. A set of isotopes appears in MS1 ​​data as a characteristic series of peaks separated by 1 m / z for singly charged species, 1 / 2 m / z for doubly charged species, 1 / 3 m / z for triply charged species, and so on. This understanding means that the MS1 data can be "deisotopized," e.g., by combining those m / z values ​​(in a set of multiple m / z values ​​for which the same RT was determined) that correspond to a set of isotopes into a so-called "feature per file," where each feature per file has a single characteristic m / z (corresponding, for example, to the lightest isotope).This understanding also means that the correct charge state for each of these features can be determined per file (from the m / z separation between isotopic peaks). (Any "singlets" (i.e., peaks that appear without corresponding isotopes—typically low-abundance peaks where the corresponding isotopes are below the detection limit) appearing in the MS1 data can be assumed to be singly charged (or have a different standard charge, depending on the sample type, etc.)). Knowledge of the charge state (z) and mass-to-charge ratio (m / z) in turn means that a single characteristic mass (m) can be determined for each feature per file. Algorithms for such deisotopization and charge state determination are well known in the art.

[0072] However, despite such deisotoping algorithms, multiple features per file may still be present for each compound. In other words, once the isotopes have been removed from each set of multiple m / z values, a group of multiple features per file may remain (where each such group of multiple features per file may result from a single compound or from several different co-eluting compounds). Therefore, there is a need to further group each group of multiple features per file into one or more clusters, with all features per file in a cluster belonging to the same single compound.

[0073] The presence of multiple attributes per file for a single connection can have various causes. In particular, this can be due to the following reasons: (i) Solvent adducts; i.e., adduction during the ionization process can lead to peaks in MS 1 with a characteristic mass difference relative to the neutral mass M of a compound. For example, the protonated ion [M+H] + a mass difference of ~1, while the ammonia adduct [M+NH4] + has a mass difference of ~18 and so on. (ii) Homo- or heterodimers; ie, two proteins linked together. These could, in turn, manifest as characteristic mass differences in MS 1. (iii) Fragments within the source; i.e., unintentional fragmentation of ions may result in fragment ions appearing in MS1 ​​spectra (not only in MS2 spectra as desired), complicating the interpretation of MS1 spectra.

[0074] Similarly, if multiple sample files exist for the same sample, multiple "features" may exist for each compound (where a feature is the set of multiple corresponding features per file from the multiple sample files). Ideally, all corresponding features per file of a feature would be identical in terms of m / z and RT; however, in practice, RT may vary slightly between different chromatographic separation runs. Therefore, there is a need to group each group of multiple features into one or more clusters, with all features in a cluster belonging to the same single compound.

[0075] Various embodiments contemplate methods for clustering such features or per-file features, that is, for determining which features or per-file features relate to the same compound.

[0076] Known methods for performing such link compilation rely exclusively on expected mass shifts between features per file. However, the inventors have now recognized that these approaches pose several problems: (i)(a) Compounds with intrinsic charge cannot be correctly identified. Some compounds have an intrinsic charge and typically do not form additional adducts. Therefore, no mass shift (between at least two different ions) is detectable in MS1 ​​that would provide an indication of their correct assignment (i.e., it cannot be determined from MS1 data alone whether an ion is, for example, a protonated ion [M+H] + or an ammonia adduction [M+NH4] +etc., which must be known in order to reliably determine the neutral mass M of the compound). The known procedure can lead to incorrect assignment of these “orphan ions”, e.g., by using a standard ion definition (e.g., the protonated ion [M+H] + in electrospray ionization). As described below, various embodiments solve this problem by performing a preliminary identification of the compound by using its fragmentation data (e.g., MS2 or MS N ) in a database using standards (such as the mzCloud™ database). (i)(b) In this context, compounds that do not form (de)protonated ions are often misidentified. Some compounds preferentially produce a type of ion that may be different from the standard ion (e.g., different from the protonated ion [M+H] +in electrospray ionization). For example, some lipids tend to undergo exclusive sodium adduction [M+Na] + This creates the same problem as above. As described below, various embodiments address this problem again by performing a preliminary identification of the compound by comparing its fragmentation data in a database with standards. (ii) Compound-specific fragments within the source are not identified. Many compounds undergo minor fragmentation during ionization (or elsewhere in the instrument during an MS1 ​​scan), which is why their fragments appear in MS1 ​​spectra. These fragment ions may then be mistakenly treated by the assembly algorithm as additional MS1 features, leading to false positive hits. An example of this is the loss of one or more EG (ethylene glycol) units to PEG (polyethylene glycol). Upon analysis, the sample may appear to contain both PEGn6 and PEGn5, when in fact, the latter is an instrument-generated fragment. As described below, various embodiments address this problem by using reference MS2 (or MS N -) Retrieve data for each characteristic (e.g. from the MS2 or MS Ndata in the sample file(s) or by the identification step (e.g. mzCloud™) using a standard MS2 or MS N -Spectrum from a collection of MS2 or MS N -low energy spectra (which can be configured to simulate fragmentation in the source) and then search the MS 1 data for fragments that match the fragments in the reference MS2 or MS N -Data match. (iii) Inconsistent adduct type assignment across multiple sample files. There are several reasons why different fractions of the same sample may yield different adduct type assignment results. For example, this may be due to intensity / abundance variations between fractions that result in one or more adducts being below the detection limit, which in turn results in a different assignment of the entire adduct cluster. As described below, embodiments address this problem by placing multiple, potentially competing individual fraction explanations (or "assemblies") into a final diagram that is resolved at the end of the process.

[0077] Fig. 2 illustrates a method according to embodiments. As in Fig. As shown in Figure 2, in a first step (step 100), features are identified per file in individual sample files (in the manner described above), and these are then grouped into features across the multiple sample files by matching their RT and m / z values. These consolidated features are inserted into a master relation diagram 510, for example, as nodes.

[0078] Fig. 3 shows an example representation of such a main relation diagram 510. In Fig. 3, each shaded circle (node) represents an identified feature.

[0079] Back to Fig. 2: If MS2- (or MS N -) Data for one of the detected features are available (from the MS2 or MS N-data in one of the sample files for the same sample), this data is submitted to a database search algorithm for identification. Any suitable database search algorithm can be used, such as the mzCloud spectral library. From the returned results (which include the neutral mass M of the precursor ion), the possible adduct type assignments are determined. In particular, mass shifts between the measured mass and the determined identification mass can be used to calculate the ion type of the feature. For example, if the mass measured in MS1 ​​is (M+18), the feature is assigned as an ammonia adduct [M+NH4] + identified and this information is inserted into the main relation diagram 510 as a possible identification of the ion type.

[0080] This process not only enables the reliable assignment of a feature's ion type but also provides a method for correctly identifying compounds with intrinsic charge. Possible ion types can be taken directly from the identification metadata or from one or more predefined lists, or automatically generated for a specific charge state, e.g., by (de)protonation.

[0081] Therefore, in embodiments, a library search 200 is performed prior to the final compound compilation in step 500. The results of the search 200 are not treated (as is usual) as a definitive identification of the feature, but rather as a high probability possibility that must later be resolved in light of any other competing explanations and identifications (e.g., from other sample files).

[0082] Steps 300 and 400 of Fig. 2 are executed to group the features per file for each group of features per file (at the same RT) into possible clusters of related features per file. To solve problem (iii) above, this process operates on a file-by-file basis. Subsequently, all possible explanations are added to the main graph 510, and any conflicts between explanations from different sample files are resolved in step 500 (described below).

[0083] In step 300, the features per file in each sample file are grouped by matching RT values ​​(e.g., as described above) and optionally by one or more other properties such as peak shape, etc., in step 310. Because this process operates on a file-by-file basis at this stage, a very tight tolerance can be used to group the features per file by RT. As shown, mapping to the features occurs in step 320.

[0084] Fig. Figure 4 shows an example representation of a resulting relationship diagram per file. In this file context, each shaded circle in Fig. 4 represents one feature per file in a group of features per file for which the same RT was determined.

[0085] In step 400 of Fig. 2, the grouped features per file are analyzed to determine all possible relationships between the different features per file in each group.

[0086] First, all possible ion types are provisionally assigned to each feature per file of the group. Fig. 5A-D illustrate various possible categories of ion types, including identified ions ( Fig. 5A), base ions ( Fig. 5B), standard ions ( Fig. 5C) and fragment ions ( Fig. 5D). During this phase, each feature per file may have multiple possible preliminary ion type assignments that must be resolved later in the process.

[0087] Ion type identification information (from step 200) is added to the features per file for which the corresponding feature was identified in step 200. For example, Fig. 5A representative of one feature per file of a feature identified in step 200 as a protonated ion [M+H] + was identified.

[0088] Each feature per file is also provisionally assigned one or more user-defined base ions ( Fig. 5B), depending on the known charge of the feature per file (where the charge of each feature per file is known from the deisotoping algorithm as described above (or from other preprocessing of the data)), or a standard ion is assigned ( Fig. 5C) if the user has not defined a base ion for ions with the known charge of this feature per file. The user-defined base ions can be set by the user as desired, e.g., depending on the sample chemistry and / or the ion source configuration, etc. As described further below, in some embodiments, at least one user-defined base ion may be required to be present in a cluster for that cluster to be considered valid. The standard ion may be the most frequently formed ion (e.g., by (de-)protonation) during ionization for ions with the charge of the feature per file. For example, in electrospray ionization, the standard ion may be defined as a protonated ion [M+H] + for singly positively charged ions, [M+2H] 2+ for doubly positively charged ions, etc.

[0089] For example, Fig. 5B represents one singly positively charged feature per file, which has been provisionally assigned one of two user-defined base ions, namely either a protonated ion [M+H] + or a sodium adduct [M+Na] + . Fig. Figure 5C represents one doubly positively charged feature per file, which is tentatively assigned to the standard doubly positively charged ion, namely [M+2H] 2+ , was assigned.

[0090] Let us return again to Fig. 2. In step 410, the per-file features in the group are analyzed to determine whether any of the per-file features could be a fragment within the source. For those per-file features of the group for which MS2 (or MS N -) data are available for the corresponding characteristic (i.e. the same characteristics as determined in step 200), representative MS2 (or MS N -) Data retrieved.

[0091] In the representative MS2 (or MSN -) data can be the corresponding MS2 (or MS N -) data from the sample file(s). However, a standard MS2 (or MS N -) Spectrum from a library of low-energy collision spectra. Such a spectrum can be returned along with the identification (e.g., from mzCloud™) and configured to accurately simulate the fragmentation in the source. Thus, in embodiments, new functionality is added to the search engine (e.g., to mzCloud™) that returns masses from low-energy collision spectra (e.g., HCD 10) as part of each identification hit.

[0092] If identification 200 was not successful and / or if no low-energy collision data are available, acquired MS2 (or MS N -) Spectra from the raw file as representative MS2 (or MS N-) spectra are used. However, assignment confidence is higher when using curated fragmentation data based on authentic standards.

[0093] The representative MS2 (or MS N -) data are then compared with the other per-file features in the group to determine whether any of the other per-file features could be a fragment within the source of the per-file feature in question. This is done, for example, by searching for per-file features in the group whose mass matches the mass of a fragment within the source in the representative MS2 (or MS N -) data matches.

[0094] All features per file in the group that have such a matching mass are marked as potential fragments within the source. For example, Fig. 5D represents a singly positively charged feature per file, which is provisionally assigned either one of the two user-defined base ions ([M+H] + or [M+Na] + ) or a fragment ion within the source. Since the ion type of a fragment within the source is generally unknown, a generic ion type assignment (e.g., [Me] + ) be used.

[0095] For possible fragments within the source, the diagram also records the relationship between the possible fragment within the source and the parent ion of that fragment within the source. For example, in Fig. 6 this potential relationship is indicated by a dotted line connection between the nodes (where the feature labelled ‘P’ per file is possibly the precursor ion of the fragment within source ‘F’).

[0096] To return to Fig. 2: In a next step (step 420), possible adduct relationships between the features per file in the group are determined by searching for allowable mass shifts between the masses of the features per file. A list of predefined ion types (e.g., adducts, common neutral losses, simple multimers, and charge states) is used to generate expected mass shifts. These expected mass shifts are applied to each group to find all possible relationships between the features per file of the group. So, for example, if two features per file have a mass difference of 7 (=18-1), these two features could contain a protonated ion [M+H] + and its ammonia adduct [M+NH4] + If the difference is 22(=23-1), the features per file could be [M+H] + and [M+Na] + be etc.

[0097] All possible relationships are recorded in the diagram. For example, in Fig. 6 a possible adduct relationship is indicated by a solid line between the nodes, where the line is labeled according to the possible adduct relationship (ie, [M+H] + ↔ [M+NH4] + in this example).

[0098] Fig. Figure 6 shows a simple example of a group of two features per file labeled with different possible relationships. In particular, the first feature (left) was identified as either an ion of type [M+H] + or as a fragment within the source [Me] + The second feature (right) was designated as one of two different base ions, namely either [M+H] + or [M+NH4] + . In addition, Fig. 6 the solid line labeled [M+H] + ↔ [M+NH4] +representative of a possible adduct transition, while the dotted line represents a possible fragment relationship within the source (where the feature labeled “P” is the precursor ion of the fragment “F” within the source).

[0099] As can be seen from the example in Fig. 6, the process described above may result in several possible, potentially conflicting, relationships between the features per file in a group. Next, these conflicts are resolved in step 430 of Fig. 2 solved.

[0100] To do this, any conflicts between the ion type assignments (loops) and the transitions (connections between nodes) are first resolved. This process is described in Fig. 7 and includes (i) removing all transitions that conflict with identified ions ( Fig. 7A); and (ii) removing invalid fragment transitions ( Fig. 7B).

[0101] Fig. 7A shows again the example from Fig. 6, but with an additional solid line labeled [M+H-NH3] + ↔[M+H] + , which represents a second possible adduct transition (in addition to the [M+H] transition + ↔[M+NH4] + ). As in Fig. 7A, the fragment designation within the source [Me] + for the first feature per file, as it is related to the identified ion type [M+H] + for the first feature per file. Then the second possible adduct transition ([M+H-NH3] + ↔[M+H] + ) because the [M+H-NH3] required for this transition + also with the identified ion type [M+H] + for the first feature per file.

[0102] Fig. Figure 7B shows an example in which an illegal fragment transition is removed. In particular, the fragment transition [M+H]+ ↔ [M+Na] + is removed because [M+Na] + -Predecessor no [M+H] + fragment can be created (i.e., fragment transitions must not "create" new elements). In contrast, the possible fragment transition [M+H] + ↔ [M+NH4] + received, since the [M+H] + -fragment of a [M+NH4] + -precursor can be created.

[0103] Fig. Figure 8 shows a simplified example of a per-file diagram that can result from the process described above, with all remaining possible ion type assignments and transitions indicated by the various loops and connections between the nodes. Several different explanations for assembling the per-file features are still possible. From the diagram in Fig. 8, all possible clusters (where each cluster corresponds to a single compound M) can be determined.

[0104] Fig. Figure 9 shows the results of this determination. In Fig. 9, each possible cluster is represented as a shaded box. The leftmost box 431 represents a cluster for which there is no other competing explanation (i.e., whose features per file do not appear in any other possible cluster). This thus represents a successful identification of a cluster that does not require further processing (at the file level). This cluster 431 is added to the main graph 510 (to be later resolved in step 500 using all other competing explanations from other sample files).

[0105] Each remaining cluster is a possible cluster that conflicts with at least one other possible cluster. These different conflicts are indicated by the vertical dashed lines in Fig. 9. Next, these conflicts are resolved.

[0106] As in Fig. 10, in embodiments where the user has specified one or more base ion types (as described above), all possible clusters that do not contain one or more features per file that may be associated with one or more base ion types are rejected. (If the user has not specified a required base ion type, this step can be skipped.) Therefore, as shown in Fig. 10A, a cluster with a base ion is retained. As shown in Fig. 10B, a cluster with only one identified ion can be rejected. As shown in Fig. 10C, a cluster with only one standard ion can be rejected. As shown in Fig. As shown in Figure 10D, a cluster without base ions can be rejected.

[0107] With further reference to Fig. 9, in the example shown, the possible cluster labeled 432 is removed because only two transitions were found, without a possible assignment (loops) of a base adductor or an identified adductor to any of the nodes within cluster 432. Cluster 432 is therefore considered invalid and excluded from further consideration.

[0108] Next, as in Fig. 11, after rejecting all invalid ion clusters, all remaining orphan clusters (i.e., single features per file without transitions to other features per file) are considered. As shown in Fig. 11A, the assignment is retained for all orphan clusters that have a valid ion type assignment (namely, base ion, identified ion, or standard ion assignments). As shown in Fig. As shown in Figure 11B, for orphan clusters marked as potential fragments within the source, the ion type assignment is retained only in the case of identified ions and generic fragment ions (while the ion type assignment is removed in the case of base ions and standard ions).

[0109] Thus, in embodiments, step 430 includes (i) removing ion type assignments with a mismatched charge; (ii) removing ion type assignments that conflict with a pre-identified ion type; (iii) removing any clusters that lack one or more user-defined base ion types; and (iv) removing any invalid fragment-precursor relationships (e.g., where additional atoms would need to be added to the fragment).

[0110] Fig. 12 shows the resulting diagram after processing the diagram from Fig. 9. As in Fig. 12, clusters 431 and 432 were formed from Fig. 9 (for the reasons stated above). In addition, the orphaned node 433 from Fig. 9 after removing cluster 432, it is no longer in conflict with any other node. It is therefore considered resolved and has been removed from the diagram in Fig. 12 away.

[0111] As in Fig. As can be seen in Figure 12, there may still be several conflicting potential clusters at this stage. However, the diagram has been split into two independent sub-diagrams, each of which can be processed separately since they do not conflict with each other.

[0112] The remaining conflicts are then resolved using a points system. This process is Fig. 13, which illustrates the process for the leftmost subgraph in Fig. 12. A similar process would be performed separately for the other subgraph in Fig. 12 carried out.

[0113] In the evaluation process, the largest of the possible clusters is initially selected and assumed to be correct (“activated”). Therefore, as in Fig. 13 under A, the cluster labeled 434 is activated first. All other clusters that conflict with this largest cluster are then deactivated. Subsequently, the next largest possible cluster (which has not yet been activated or deactivated) is activated, and so on, until a first declaration (or "assignment" or "arrangement") is reached. Therefore, in the example of Fig. 13 under A the lower image represents this initial explanation, in which the group of features per file is explained by two clusters 434, 435.

[0114] This declaration is then provided with an assignment score. To calculate the assignment score, each ion type assignment (loop) is assigned a relative weighting factor. Any suitable weighting factors can be used. For example, the highest relative weight can be used for the most abundant adducts (e.g., [M+H] or [MH]), a slightly lower weight can be used for common adducts (e.g., [M+Na] or [M+K]), and an even lower weight can be used for the rest (e.g., rare adducts). The relative size of the different weighting factors can be configured as desired, e.g., to reflect the probability of occurrence of a particular adduct under the particular experimental conditions / sample chemistry used for data acquisition, and can be set by the user.

[0115] Furthermore, each transition is evaluated with twice the sum of its two ion weight factors. Here, the factor of two is used because a transition explains two nodes, while a loop assignment explains only one node. Since the default assignment (loop) is typically the adduct with the highest weight, the algorithm would otherwise strongly favor orphans assigned as default adducts, which is undesirable.

[0116] Each cluster is then assigned a cluster score, which is the sum of all loop and transition scores for the cluster divided by the number of nodes in the cluster.

[0117] Finally, an assignment score is calculated for the explanation, which is the sum of all cluster scores for all activated clusters in the explanation divided by the number of clusters. The assignment score is designed to achieve a high score in situations with many similar clusters (and, for example, to avoid explanations that contain only one large cluster and many orphaned elements).

[0118] Let us return again to Fig. 13. Next, one or more other possible explanations are tested. As in Fig. 13 under B, one of the clusters is activated which was selected in the first step (i.e. Fig. 13 under A), and the above process is repeated to create a competing statement. For example, in Fig. 13 under B, the right image represents this competing explanation, in which the group of features per file is now explained by three clusters: 436, 437, and 438. This explanation is again evaluated using the same scoring system.

[0119] Here, too, another possible explanation is tested by activating another of the clusters deactivated in the first step. The above process is then repeated to obtain another competing explanation. For example, in Fig. 13 under C, the right image presents this further competing explanation, in which the group of features per file is now explained by two clusters, 437 and 439. This explanation is again evaluated using the same scoring system.

[0120] This process can be repeated, for example, until all possible explanations have been evaluated. The explanation with the highest assignment score can then be selected as the final explanation for the output.

[0121] Therefore, the possible clusters are evaluated recursively to achieve the best possible assignment score, with the design of the score reflecting the nature of the samples analyzed, e.g., that under certain chromatographic conditions, most compounds behave similarly and should therefore form similar ion clusters.

[0122] Fig. 14 shows an example final declaration in which the group of features per file consists of Fig. 9 is explained by five clusters 431, 433, 437, 439, 440. This last explanation is added to the main diagram 510.

[0123] If multiple sample files exist, a similar process is performed for each sample file, and one explanation per sample file is added to the main diagram 510 (and any duplicates are removed). This may result in the main diagram 510 containing some conflicting explanations (i.e., similar to Fig. 12).

[0124] So if we Fig. 2, all conflicting explanations in the main diagram 510 are resolved in step 500. This process is performed using the same algorithm described above (with reference to Fig. 12 to 14). This step ensures that the information from all sample files is taken into account and a consistent mapping is achieved across the files.

[0125] Each resulting subcluster represents a unique connection. Thus, a connection can finally be identified from each cluster in the final main diagram (step 520 of Fig. 2).

[0126] Although various particular embodiments have been described above, various alternatives and additions are possible.

[0127] A possible next step in the processing pipeline could, for example, be a quantitative analysis of the results. This requires knowing which features within a cluster can be used and which should be neglected (e.g., to summarize their areas for quantification). Typically, any fragments are ignored, and only those ions classified as meaningful adducts are used. However, it is possible that a fragment should be used for quantification, e.g., if it relates to a true adduct. A common fragment of [M+NH4], for example, is [M+H]. Therefore, an additional step can be applied to mark true adducts for further processing, while all generic fragments should be ignored.

[0128] This process is Fig. 15. As shown in Fig. 15 under A, it is possible that some fragments are shared by several precursors. If this is the case, as in Fig. 15 under B, and the relationship to one of the precursors is also a valid adduct transition, the relationship to any other precursor is rejected. This allows the fragment to be stored for later use and avoids multiple uses. As shown in Fig. 15 under C, if there are other adduct transitions of features for a fragment where the fragment-precursor relationship was not recognized, these assignments are rejected and the fragment is assigned as a generic fragment ion.

[0129] The methods described herein are generally applicable to mass spectrometric analysis, e.g., of small molecules, such as in the analysis of metabolic pathways, degradation products, forensic analysis, etc.

[0130] Tables 1 and 2 show example results from an artificial sample of eight compounds mixed in known ratios with their respective

[13] C-labeled analogues. The unique labeling made it possible to identify the compounds together with all possible analogues and to evaluate the method.

[0131] Table 1 illustrates experimental results using a conventional assembly procedure. As shown in Table 1, three additional compounds were clearly reported using the conventional technique. These compounds exhibited the expected isotopic exchange profile, but their retention time closely matched one of the expected compounds. Consideration of the assigned molecular formulas, and in particular the formula differences between compounds with the same retention time, suggested an incorrect interpretation.

[0132] Table 2 illustrates experimental results using the assembly method of various embodiments. As shown in Table 2, the same data were analyzed using the compound assembly method according to embodiments. In this case, all three additional compounds were correctly interpreted as fragments within the source and appended to their respective precursors.

[0133] The compound assembly method of various embodiments enables the complete and consistent assembly of multiple distinct forms of ions originating from a single compound within multiple samples. In contrast to conventional techniques based solely on expected mass shifts within MS1 ​​spectra, embodiments additionally utilize MS2 fragmentation spectra and online identification tools for initial adduct assignment as well as for the detection of untargeted fragments in the source. The results of these steps are consolidated into unique ion clusters using a set of chemical and heuristic rules. This strategy significantly reduces the number of false positive identifications.

[0134] This is done according to embodiments in particular by: (i) Applying a two-stage clustering mechanism to consolidate potentially related features across multiple sample files. This enables consistent ion assignment across multiple sample files. (ii) Use the acquired MS2 data for pre-identification of compounds to provide highly confident candidates for adduct assignment based on matches in the spectral library. This avoids further misassignment of ions of identifiable compounds. (iii) Leveraging curated low-energy collision spectra of already identified compounds in the data to then search for specific candidate fragments within the source, thereby reducing the number of false positive unique compounds. (iv) Use of the acquired MS2 spectra to search for matching fragment candidates in the source. This reduces the number of false positive unique compounds in cases where low-energy collision spectra are not available. (v) Applying a set of rules to the network of all possible ion relationships to remove low-confidence assignments, thereby reducing the probability of incorrect adduct assignment. (vi) Applying a user-defined scoring and evaluation mechanism to the consolidated network of ion relationships to resolve potentially conflicting assignments, resulting in individual ion clusters corresponding to unique compounds.

[0135] Although the present invention has been described with reference to various embodiments, it will be understood that various changes may be made without departing from the scope of the invention as set forth in the appended claims. 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. Cited patent literature

[0000] EP 3,410,463 [0057, 0066]

Claims

[1] A method for processing mass spectral data, wherein the mass spectral data comprises a plurality of MS 1 -Mass spectra and a variety of MS N (N ≥ 2) mass spectra, each mass spectrum being assigned a corresponding retention time and the method comprising: Recognizing a group of features in the multitude of MS 1 -mass spectra, wherein each feature of the group has a respective mass and wherein the features of the group have corresponding retention times; for each or more characteristics of the group: (i) submitting a corresponding MS N -mass spectrum to a mass spectrum search engine, to obtain an identification result for that feature, and (ii) determining a candidate ion type for the feature based on a mass difference between the mass associated with the feature and an expected mass from the identification result; and then identifying one or more compounds based on the group of features and the candidate ion type(s). [2] The method of claim 1, wherein step (ii) of determining a candidate ion type for the feature comprises: determining a candidate adduct type for the feature based on the mass difference between the mass of the feature and the expected mass from the identification result. [3] The method of claim 1 or 2, wherein the mass spectral data comprises at least one sample file, each sample file corresponding to a respective chromatographic separation scan and comprising a plurality of MS 1 -Mass spectra and multiple MS Nmass spectra, and wherein the step of detecting a group of features in the plurality of MS 1 -Mass spectra include: for each sample file: detecting a plurality of features per file in that sample file, each feature per file having a respective mass and a respective retention time; Forming a plurality of features from the features per file, each feature having a respective mass and a respective retention time; and Forming a group of features by grouping features with corresponding retention times. [4] The method of claim 3, wherein the step of detecting a plurality of features per file in a sample file comprises: Creating a variety of chromatograms from the multiple MS 1 -Mass spectra of the sample file, each chromatogram having a respective mass-to-charge ratio (m / z); Determining a characteristic retention time for each chromatogram; Grouping chromatograms with corresponding characteristic retention times into one or more chromatogram sets; and Apply a deisotoping algorithm to each set of chromatograms to form a group of features per file. [5] The method of claim 4, wherein: the step of grouping features with corresponding retention times comprises grouping features with retention times that are equal within a first tolerance; the step of grouping chromatograms with corresponding characteristic retention times comprises grouping chromatograms with retention times that are equal within a second tolerance; and the second tolerance is smaller than the first tolerance. [6] A method according to claim 3, 4 or 5, wherein the mass spectral data comprises a plurality of sample files and each feature of the plurality of features is formed by grouping features per file having corresponding masses and corresponding retention times. [7] The method of claim 6, wherein each feature group is formed from a corresponding group of features per file in each sample file, and wherein the step of identifying one or more compounds based on the feature group comprises: for each group of features per file in a respective sample file: (i) determining one or more clusters of features per file, each cluster of features per file comprising one or more features per file of the group and possibly corresponding to a respective compound; (ii) determining one or more arrangements of the clusters of features per file for the group of features per file, each arrangement comprising one or more non-conflicting clusters of features per file; and (iii) selecting a preferred arrangement from the one or more arrangements of clusters of features per file for the group of features per file; and then, based on the preferred arrangements for the plurality of sample files: (iv) determining one or more arrangements of feature clusters for the feature group, each feature cluster comprising one or more features of the feature group and possibly corresponding to a respective compound, and each arrangement comprising one or more non-conflicting feature clusters; and (v) selecting a preferred arrangement from the one or more arrangements of feature clusters for the feature group; and then identifying one or more compounds based on the preferred arrangement of feature clusters. [8] A method for processing mass spectral data, wherein the mass spectral data comprises a plurality of sample files, each sample file containing a plurality of MS 1 -Mass spectra and multiple MS N (N ≥ 2) mass spectra, each mass spectrum being assigned a corresponding retention time, the method comprising: for each sample file: Detect a variety of features per file in the MS 1 -Mass spectra of this sample file, with each feature per file having a respective mass and a respective retention time; Forming a plurality of features from the features per file by grouping features per file that have corresponding masses and corresponding retention times; Forming a group of features by grouping features with corresponding retention times and forming a corresponding group of features per file in each sample file; and then for each group of features per file in a respective sample file: (i) determining one or more clusters of features per file, each cluster of features per file comprising one or more features per file of the group and possibly corresponding to a respective compound; (ii) determining one or more arrangements of the clusters of features per file for the group of features per file, each arrangement comprising one or more non-conflicting clusters of features per file; and (iii) selecting a preferred arrangement from the one or more arrangements of clusters of features per file for the group of features per file; and then, based on the preferred arrangements for the plurality of sample files: (iv) determining one or more arrangements of feature clusters for the feature group, each feature cluster comprising one or more features of the feature group and possibly corresponding to a respective compound, and each arrangement comprising one or more non-conflicting feature clusters; and (v) selecting a preferred arrangement from the one or more arrangements of feature clusters for the feature group; and then identifying one or more compounds based on the preferred arrangement of feature clusters. [9] A method according to claim 7 or 8, wherein the step of determining one or more clusters of features per file comprises, for each group of features per file in a corresponding sample file: Assign one or more candidate ion types to each feature per file in the group; Determining one or more candidate relationships between features per file of the group; and Resolve any conflicts between candidate ion types and candidate relationships. [10] The method of claim 9, wherein each feature per file has a corresponding charge, and wherein the step of assigning one or more candidate ion types to each feature per file of the group comprises: Assigning an identified ion type to each feature per file of the group that corresponds to a feature for which an identification result was obtained; and / or Assign a custom base ion type or a standard ion type to each feature per file in the group based on the respective charge of the feature per file. [11] A method according to claim 9 or 10, wherein: the step of assigning one or more candidate ion types to each per-file feature of the group comprises: assigning a fragment ion type within the source to each per-file feature of the group that has a mass corresponding to the mass of an expected fragment within the source of another per-file feature in the group; and / or the step of determining one or more candidate relationships between per-file features of the group comprises: determining a fragment relationship within the source between a per-file feature of the group and another per-file feature of the group if the per-file feature has a mass corresponding to the mass of an expected fragment within the source of the other per-file feature. [12] The method of claim 11, further comprising obtaining the mass of an expected fragment within the source of a feature per file by: Determine the mass of an expected fragment within the source of a feature per file from an MS N -Mass spectrum corresponding to the feature per file. [13] A method according to claim 11 or 12, further comprising obtaining the mass of an expected fragment within the source of a feature per file by: Providing the mass of one or more expected fragments within the source of the feature as part of the identification result for a feature. [14] A method for processing mass spectral data, wherein the mass spectral data comprises a plurality of MS 1 -Mass spectra and a variety of MS N (N ≥ 2) mass spectra, each mass spectrum being assigned a corresponding retention time and the method comprising: Recognizing a group of features in the multitude of MS 1-mass spectra, wherein each feature of the group has a respective mass and wherein the features of the group have corresponding retention times; for each or more characteristics of the group: (i) submitting a corresponding MS N -mass spectrum to a mass spectrum search engine to obtain an identification result for that feature, and (ii) providing the mass of one or more expected fragments within the source of the feature as part of the identification result; Assigning a fragment ion type within the source to each feature of the group that has a mass equal to the mass of an expected source fragment of another feature in the group; and Identify one or more compounds based on the set of features and the fragment ion type(s) within the source. [15] A method according to claim 13 or 14, wherein the mass(es) provided as part of the identification result consist of one or more MS N -Mass spectra are determined that are configured to simulate fragmentation within the source. [16] A method according to any one of claims 9 to 15, wherein the step of determining one or more candidate relationships between features per file of the group comprises: Determining one or more candidate adduct relationships between features per file of the group based on allowable mass moves between features per file of the group. [17] A method according to any one of claims 7 to 16, wherein the step of selecting a preferred cluster arrangement from the one or more cluster arrangements comprises: Determining a score for each cluster arrangement; and Select the arrangement with the highest score. [18] The method of claim 17, wherein the step of determining a score for each arrangement of clusters comprises: Determining a cluster score for each cluster in the array by: (i) assigning a weighting factor to each candidate ion type assignment of the cluster, (ii) assigning a relationship score to each candidate relationship of the cluster, and (iii) calculating a cluster score for the cluster by dividing the sum of the weighting factors and relationship scores by the number of features or features per file in the cluster; and Determine a score for each array by: dividing the sum of the cluster scores for the array by the number of clusters in the array. [19] Mass spectrometry method comprising: Analyzing a sample to obtain mass spectral data that includes a variety of MS 1 -Mass spectra and a variety of MS N-mass spectra, each mass spectrum being associated with a respective retention time; and Analyzing the mass spectral data using the method according to any one of the preceding claims. [20] A non-transitory computer-readable storage medium storing computer program code which, when executed on a processor, performs the method of any preceding claim. [21] A control system for an analytical instrument, the control system being configured to cause the analytical instrument to perform the method according to any one of claims 1 to 19. [22] An analysis instrument comprising the control system of claim 21.

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