Identification of mass spectrometer peaks

Abstract

Method and system for analyzing data generated by an ion analyzer, comprising receiving data generated by the ion analyzer, identifying a region in the data generated by the ion analyzer that contains at least one feature, and processing the identified region by: If one or more properties of the identified area meet one or more criteria, use a first algorithm to identify one or more individual peaks within the identified area, and a second algorithm to determine properties of the one or more individual peaks within the identified area. If the identified area does not meet one or more criteria, use a third algorithm to determine properties of one or more individual peaks within the identified area.

Description

Field of invention

[0001] The present invention relates to a system and a method for identifying peaks in the data of a mass analyzer and spectrometry and in particular for more accurately resolving individual components. Background of the invention

[0002] Mass spectrometers generate graphical data that includes features and peaks corresponding to the individual components in a sample. Computational techniques can be used to resolve peaks in the data and measure the properties of any ion peaks that may be present. If the ion masses or mass-to-charge (m / z) ratios for different components are similar, their peaks will lie close together and may overlap. This can occur particularly with mass spectrometers of relatively low resolution and with time-of-flight (ToF) mass spectrometers.

[0003] Although different species can form overlapping peaks in the data, other physical effects can cause individual features in a mass spectrum to split into more than one peak, which could be mistaken for different components in the sample.

[0004] Fig. Figure 1A illustrates a portion of the mass spectrum of a sample containing two distinct components with different m / z, resulting in different ion flight times (in a ToF mass spectrometer). In this example data, the two corresponding peaks in the spectrum overlap. The overlapping peaks must be separated to estimate the arrival time and / or other parameters for each of the two individual ion species.

[0005] Fig. Figure 1B illustrates a portion of a mass spectrum from another sample containing a single ion species. This data includes a multimodal peak, which should not be interpreted as a signal resulting from different ion species (as in the one in Fig. (case 1A shown), but should instead be treated as a signal originating from only a single ion species. As in Fig. As shown in Figure 1B, a multimodal signal is very likely to be generated when only a few ions of a species are detected. Averaging many such signals could result in a unimodal peak with a shape similar to the arrival time distribution. However, averaging can be time-consuming and inefficient. Similarly, a larger number of ions and a better signal-to-noise ratio could reduce the probability of misleading multimodal peaks, but this is not always achievable.

[0006] GB2617318A describes a method for resolving individual components in ToF mass spectrometer data. However, this method can still result in features of ion species with a single m / z being split into several different peaks.

[0007] Therefore, a procedure and a system are needed to eliminate these problems. Brief description of the invention

[0008] A mass analyzer or mass spectrometer, such as a time-of-flight mass spectrometer (ToF mass spectrometer) (i.e., with one or more mass analyzers), generates data that includes features related to one or more analyzed ion species. The ion masses or mass-to-charge ratio (m / z) values ​​for different components should produce distinct features or peaks in the data (when graphically represented). However, features generated by species with individual ion masses may produce split peaks. Different techniques and algorithms can be used to process the features or segments of the data.While these techniques can be very effective at resolving multiple peaks within a single feature, they can lead to split peaks being incorrectly identified as having different m / z values ​​if they originate from species with the same ion mass. To avoid this, the properties of each feature in the mass spectrum data are determined. For example, the amplitude or peak height property (e.g., the maximum count value within a feature) can be determined. Other properties can also be included.

[0009] The determined property or properties of the characteristic are compared with one or more criteria. These criteria can, for example, be one or more threshold values.

[0010] If one or more criteria are met for a particular characteristic, one type of processing (a first type) can take place. If one or more criteria are not met, another type of processing (a second type) can be carried out. For example, if the maximum count value of the characteristic is below a threshold (e.g., a predefined or dynamically calculated threshold), the first type of processing can be carried out, and if the maximum count value is above the threshold, the second type of processing is carried out.

[0011] The first type of processing may include a step that is not performed in the second type of processing, while other steps are common to both. Alternatively, the two types of processing may be completely different and have few or no functions in common.

[0012] If necessary, the first type of processing includes a step or algorithm to unfold the two or more peaks within the feature under consideration (i.e., to identify whether more than one true peak is present, and / or to resolve that single peak into two or more peaks relating to different m / z values). Thus, meeting the criteria could indicate that a region containing a feature under consideration may consist of two or more ion species and that applying a deconvolution algorithm is unlikely to cause the splitting of a single peak (due to a single ion species). For example, if the signal, amplitude, or maximum number of counts in the feature is low (below a threshold), this may indicate that the ion concentration is insufficient to cause space charge effects that could lead to feature splitting.Thus, for these peaks with lower amplitude (below the threshold), the deconvolution or resolution algorithm can be performed (e.g., to determine whether two or more different ion species or m / z values ​​are actually present).

[0013] If the signal, amplitude, or maximum number of counts in the feature is high (above the threshold), this may indicate that the ion concentration is sufficient to cause space charge effects to split the feature. Therefore, the deconvolution or resolution algorithm is not executed for these higher-amplitude (above the threshold) peaks to avoid results indicating different ion species when only one is present in the identified feature.

[0014] In both cases, the properties of any individual peaks within the identified range can be determined (e.g., using curve-fitting techniques). These techniques can generate data about the peaks (e.g., amplitude, height, maximum value, width, full width at half maximum [FWHM], etc.). This data can be determined using the same or different techniques, regardless of whether one or more of the original criteria are met.

[0015] The method can be implemented as part of the process for obtaining mass spectrum data. For example, the method can be implemented in a control system for a mass analyzer or mass spectrometer. This provides an improved operating mass analyzer or mass spectrometer that delivers more accurate results. Alternatively, the method can be implemented in a computer system outside the mass analyzer (including using data stored some time after the mass analyzer has finished operating). Such a computer system can generate data that describe real-world results for samples that might otherwise be difficult or impossible to obtain.

[0016] When processing the region of data generated by an ion analyzer, the first algorithm is used to identify one or more individual peaks if the region meets one or more criteria (e.g., performed for lower-intensity signals). Data regions with a maximum intensity value at or above a certain threshold (e.g., a first threshold) are not subjected to the first algorithm (e.g., a deconvolution algorithm) because they may exhibit artificial peak splitting (e.g., because space charge effects may be detectable in higher-signal samples). The properties of one or more individual peaks within such a higher-signal region are obtained directly using a third algorithm, e.g., without using a deconvolution algorithm to identify peaks.

[0017] The third algorithm can be the same as the second algorithm, but it does not include the first. Preferably, the third algorithm does not include steps for identifying and / or separating peaks and only determines the properties of peaks within the data range.

[0018] According to a first aspect, a method for analyzing data generated by an ion analyzer is provided, the method comprising the following: Receiving data generated by the ion analyzer; Identifying a range in the data generated by the ion analyzer that contains at least one feature; and Processing the identified area by: If one or more properties of the identified area meet one or more criteria, use a first algorithm to identify one or more individual peaks within the identified area, and a second algorithm to determine properties of the one or more individual peaks within the identified area; and / or If the identified area does not meet one or more of the criteria, use a third algorithm to determine properties of one or more individual peaks within the identified area. where one or more criteria include that the maximum intensity value or the maximum area of ​​the data within the identified range is below a first threshold. Thus, the most appropriate data processing technique can be used for each feature or data range. This prevents two or more different peaks (from different ion species) from going unresolved. although they should be resolved, and it also avoids resolving, unfolding, or creating as an artifact two or more separate peaks, thereby splitting a single peak (due to a single ion species) into separate peaks.

[0019] In some datasets, peaks may be located close to, but in adjacent, regions. This can lead to some overlap or leakage of a portion of one peak into another. The second algorithm may include processing or steps to identify such a situation and either completely eliminate such a contribution from neighboring peaks to the one or more individual peaks within the region, or alternatively, compensate for such a situation.

[0020] The second and third algorithms may be identical. In this case, if one or more criteria are met, a more complex procedure with additional steps can be used to process the feature, and if one or more criteria are not met, a simplified procedure is applied to the feature.

[0021] Optionally, the second and / or third algorithms can be centroidal algorithms for calculating a midpoint value of one or more individual peaks. For example, the centroidal algorithm (i.e., a process for determining the midpoint, time, or m / z value for a peak) can split the peak in half so that the area on the left and right sides is equal, and specify the time (corresponding to the m / z value in a ToF spectrometer) at which this split is performed. Other algorithms can also be used.

[0022] If desired, the centroidal algorithm can provide an output that includes m / z, arrival time or flight time positions and intensities of one or more individual peaks.

[0023] Optionally, the centroidal algorithm can provide output that further includes a resolution of one or more individual peaks. Other data can also be provided.

[0024] The first threshold may correspond to an intensity level at which a space charge-induced bifurcation occurs. This first threshold intensity level can correspond to a concentration of ions (or the specific ion under investigation) that causes significant space charge effects and results in a splitting of the peak. Such an intensity level can be determined, for example, by performing a series of tests using increasing ion concentrations for different samples until a bifurcation occurs.

[0025] Optionally, one or more criteria may include the requirement that the maximum intensity value of the data within the range exceeds a second threshold. This second threshold may be a value corresponding to a noise level that results in multiple erroneous peaks. In other words, applying this threshold can filter out peaks likely attributable to noise in the data, thus speeding up processing and reducing erroneous data.

[0026] Optionally, the intensity value can be any of the following: number of counts, number of ions, or number of arbitrary units. Other units can also be used.

[0027] If necessary, the threshold (e.g., first and / or second threshold) can be set based on an m / z value or arrival time (e.g., in a ToF mass analyzer) of a peak within the identified range. The thresholds can also be set by other techniques or predefined over a range of m / z values ​​or first arrival times for ions in a ToF mass analyzer.

[0028] If necessary, the threshold (e.g., first and / or second threshold) can be based on a calibration curve that includes the m / z value or the arrival time (e.g., for a ToF mass analyzer). The calibration curve can be specific to a mass analyzer and / or a sample type. The calibration curve or the thresholds can be derived in advance by conducting experiments to determine intensity levels that cause splitting of individual peaks, or calculated based on the probability that space charge effects are significant and likely to cause artifacts in the data.

[0029] Optionally, the first algorithm can be a deconvolution algorithm. The first algorithm can be configured to identify multiple individual peaks within the identified area (and thus identify multiple individual peaks if multiple individual peaks are present within the identified area, but identify a single peak if only a single peak is present within the identified area).

[0030] The deconvolution algorithm may include the following: (i) Receiving a first data segment generated by the ion analyzer, wherein the first data segment comprises data associated with a first arrival time range; (ii) Applying a filter to the first data segment to produce a filtered version of the first data segment, optionally configuring a width associated with the filter to depend on the width of an expected ion arrival time distribution for the ion analyzer for arrival times within the first arrival time range; and subsequently (iii) Identifying one or more ion peaks in the filtered version of the first data segment. This deconvolution algorithm is described in more detail in GB2617318, and these details and steps can be used within the scope of the present exemplary implementation. Other deconvolution algorithms can also be used.

[0031] If necessary, the third algorithm may include the following steps: Identifying a local minimum between two areas with a local maximum of higher intensity within the identified area; If a difference or percentage difference between the local minimum and at least one of the two local maximum regions exceeds a threshold, identify the two local maximum regions as separate ion peaks; and Determining the midpoint positions and / or widths of the two regions with local maximums. This third algorithm can be used when there is a high probability that two further true peaks (due to different ion species) are present in the feature, or at least when it is unlikely that a splitting of the peaks has occurred (e.g., because the intensity is below a threshold that is likely to cause splitting-inducing space charge effects).

[0032] If necessary, the third algorithm may include the following steps: Identifying a local minimum between two areas with a local maximum of higher intensity within the identified area; If the intensity of the local minimum is below a threshold, identify the two areas with local maximum as separate ion peaks; and Determining the center positions and / or widths of the two areas with a local maximum.

[0033] If necessary, the steps for processing the identified area can be repeated for multiple identified areas until all areas in the data generated by the ion analyzer have been processed. Thus, the method can be used to process a complete dataset or a complete mass spectrum. However, the method can also be used with only one feature or with a subset of features in the dataset.

[0034] The ion analyzer can optionally be a time-of-flight mass analyzer (ToF mass analyzer) (or mass spectrometer). Other ion analyzers can also be used. Different ion analyzers may use different criteria and / or algorithms to process the data.

[0035] Optionally, the step of identifying the region further includes restricting the width of the identified region to a first m / z or arrival time range. The method and the first algorithm can be used in the context of ion mobility spectrometry.

[0036] If necessary, the step of identifying the area in the data generated by the ion analyzer, which contains at least one feature, may further include the following step: Selecting the area in the data that completely surrounds the feature without including any neighboring features. This can be used to effectively separate peaks.

[0037] According to a second aspect, a device is provided which includes the following: a processor; and a memory in which computer-executable instructions are stored which, when executed by the processor, cause the device to perform the procedure as described above.

[0038] The device may optionally be a mass spectrometer.

[0039] According to a third aspect, a control system is provided for an analyzer or a mass spectrometer, wherein the control system is configured to cause the analyzer to perform the procedure as described above.

[0040] The methods described above can be implemented as a computer program that includes program instructions for operating a computer. The computer program can be stored on a computer-readable medium, including non-volatile computer-readable media.

[0041] The computer system can include one or more processors (e.g., local, virtual, or cloud-based), such as a central processing unit (CPU), and / or a single or multiple graphics processing units (GPUs). The processor can execute logic in the form of a software program. The computer system can include memory, which may consist of volatile and non-volatile storage media. A computer-readable medium (CRM) can be included for storing the logic or program instructions. For example, embodiments can include a non-volatile computer-readable medium (CRM) on which software is stored, comprising instructions executable by one or more computers, which, when executed, cause the one or more computers to perform the disclosed methods.Non-volatile CRM can refer to a CRM that stores data for short periods or while a power supply is available, such as a storage device or random access memory (RAM). For example, a non-volatile computer-readable medium can include storage components such as a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state drive), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cassette, and / or magnetic tape. The different parts of the system can be interconnected using a network (e.g., wireless and wired networks). The computer system can include one or more interfaces. The computer system can include a suitable operating system such as UNIX™, Windows™, or Linux™.

[0042] It should be noted that each of the features described above can be used with any specific aspects or embodiments. Brief description of the characters

[0043] The present disclosure can be implemented in practice in a variety of ways, and embodiments are now described, merely as examples and with reference to the accompanying drawings, wherein: Fig. 1 graphical data showing problems with known data processing techniques; Fig. 2 graphical data showing the advantages of data processing techniques; and Fig. 3 graphical data showing that indicate space charge effects in mass spectrometry data; Fig. Figure 4 shows a flowchart of a procedure for analyzing data from a mass analyzer; Fig. 5 a system for implementing the procedure of Fig. 4 shows; Fig. Figure 6 shows a flowchart of an exemplary procedure for analyzing data from a mass analyzer; Fig. 7 graphical data shows which indicate how calibration data can be used in the procedure of Fig. 4 or Fig. 6 can be generated; Fig. 8 further graphical data are shown, indicating space charge effects in mass spectrometry data; Fig. Figure 9 shows graphical data indicating how different parameters can be used to generate calibration data for use in the procedure of Fig. 4 or Fig. 6 to generate; and Fig. 10 simulated graphical data shows a peak splitting due to space charge effects.

[0044] Please note that the figures are shown for clarity and are not necessarily drawn to scale. Identical features are marked with the same reference symbols. Detailed description of embodiments

[0045] Mass spectrometers with relatively low resolution can exhibit overlapping peaks, requiring peak deconvolution or peak splitting algorithms. With high-resolution analyzers, such as the Thermo Fisher Scientific™ Orbitrap™ Astral™ mass spectrometer, such techniques may not be necessary, at least for MS / MS analysis, where the Astral™ analyzer excels, due to the absence of overlapping features in a typical spectrum. Nevertheless, samples can still generate peak doublets. For example, tandem mass tag reporterions (TMT reporterions) can produce isobaric doublets that may slightly overlap even at relatively high resolution (70,000+). This has been observed particularly with strong peaks, which can lead to a decrease in resolution due to space charge effects.

[0046] Experiments with liquid chromatography with mass spectrometry (LC-MS) have shown that significant improvements can be achieved by implementing peak splitting algorithms across the entire mass range, even with ion analyzers with a resolution of approximately 100,000 and more. Fig. Figure 2 shows a diagram with exemplary experimental data, including a series of peptides identified by the Orbitrap™ Astral-MS from an LC-MS analysis of 100 ng HeLa digest for a range of LC gradient lengths and maximum ion accumulation times. The application of a peak splitting algorithm has a significant impact on the depth of analysis, particularly in the short 5-minute experiments with a long ion accumulation time of 20 ms, which produce spectra with larger populations and thus crowded spectra. If the analyzer has a smaller population, whether due to a short ion accumulation time of 3 ms, a long LC gradient of 60 minutes, or the use of FAIMS (Field Asymmetric Waveform Ion Mobility Spectrometry) to improve selectivity, the improvement may be smaller, but still beneficial.

[0047] However, a problem arises when applying peak deconvolution algorithms to data generated by this type of analyzer and other relatively slow, highly sensitive ToF analyzers. Most ToF analyzers emit ion packets with relatively small populations, but rapidly, at 5–10 kHz, and construct spectra by averaging many such events. Some analyzers, instead, collect a large number of ions in an ion trap placed at the entrance to the flight path before performing pulsed extraction at a much lower frequency (~200 Hz). This extraction ion trap is far more efficient than conventional orthogonal extractors in terms of delivering ions to the ion analyzer, but the sensitivity advantage is diminished by the high ion load per pulse, which can include thousands of ions at a single m / z in a single pulse.

[0048] Such strong pulses present (largely resolved) challenges to the dynamic range of the detection, but can generate strong space charge effects that counteract the focusing fields of the analyzer and reduce the resolution, typically to ~50,000 when >1,000 ions are flying together at a single m / z. Although this can usually be tolerated, a dangerous effect on the peak shape has been observed, where very strong peaks can exhibit bifurcation or even trifurcation instead of simple broadening. This is evident in the diagrams of the Fig. 3a, Fig. 3b and Fig. Figure 3c illustrates the consequence of an incorrect fitting of two peaks to a single feature, even though only one species with a single m / z is present, by a peak splitting or deconvolution algorithm. This can have serious implications for mass accuracy and quantification.

[0049] The Fig. 3a, Fig. 3b and Fig. Figure 3c illustrates a peak of an ion profile with an m / z of 1522 with a calculated centroid at a) 500 ions in the peak, b) 5000 ions in the peak with deactivated deconvolution of overlapping peaks, and c) 5000 ions in the peak (shows the effect of a deconvolution algorithm on the feature).

[0050] It has been found that a more primitive centroid determination for features folded in this way surprisingly provides more effective and accurate results, while maintaining mass accuracy and quantification. Therefore, it is advantageous to use feature properties to determine different techniques or algorithms that should be used to process these features in order to obtain more optimal results and more accurate analyses. Thus, a selection of the processing technique can be based on properties of the features identified in the mass data. This can be done automatically by applying the most appropriate technique or algorithm to each feature. There may be two or more different techniques or algorithms to choose from, but two such examples are described below.

[0051] Fig. Figure 4 shows a flowchart of a procedure 300 used to process data from an ion analyzer, such as a time-of-flight mass analyzer (ToF mass analyzer) or spectrometer. In step 310, data is acquired from the ion analyzer. This data can come directly from the ion analyzer (e.g., a ToF mass spectrometer) or be retrieved from a data store or database containing previously acquired, stored, and retrieved data.

[0052] In step 320, a feature is identified in the data. This can be done by identifying peaks with an intensity greater than a noise threshold or by another suitable method. In certain embodiments, the signal and / or the collection of digital samples is divided into a plurality of segments depending on the data. One or more segments can be generated when the ion intensity exceeds a threshold. For example, a segment or feature can begin when the intensity of a sample exceeds a first feature threshold and end when the intensity of a sample falls below a second feature threshold. The first and second feature thresholds can be the same or different. If the intensity falls only briefly below the second feature threshold (e.g., below a predefined value), the segment or feature can continue.This can be achieved, for example, by configuring the digitizer so that a segment or feature is only terminated if the signal remains below the second feature threshold for a specific number of samples. It would also be possible to start a segment or feature for a specific number of samples before the intensity of a sample exceeds the first feature threshold, and / or terminate for a specific number of samples after the signal has fallen below the second feature threshold (which is defined, for example, by a dynamic or static parameter).

[0053] Regardless of how the feature is identified, its properties are determined in step 330. For example, a maximum intensity (e.g., amplitude, data counts, ion count, etc.) or area within the data constituting the feature can be determined. Other features can also be determined, including, but not limited to, peak width, peak shape, full width at half maximum (FWHM), etc.

[0054] In step 340, the feature property(ies) is / are compared with one or more criteria. In a simple example, the maximum intensity identified in step 330 can be compared with one or more threshold values. The one or more criteria can be met if the maximum intensity is below a threshold value, above a threshold value, and / or between two different threshold values.

[0055] Features that exhibit properties fulfilling one or more of the criteria are processed according to the "Yes" step flow (350, 360, 370). Step 350 applies a first algorithm to the feature's data. The first algorithm can unfold, extract, or identify individual peaks within the single feature. The output of the first algorithm (e.g., time or m / z values ​​of individual peaks and feature data) is processed according to the second algorithm (step 360). The second algorithm can generate parameters that describe the individual peaks, and step 370 provides output (e.g., from the second algorithm) indicating the properties of each peak.

[0056] Features that do not meet one or more criteria are processed according to the "no" step sequence (380, 370). In this case, only a single algorithm is applied, and no complex deconvolution algorithm (first algorithm) is used. Thus, the third algorithm produces only an output (e.g., parameters) that describes the individual peak(s) in the data. This output is provided at step 370. Procedure 300 can be repeated from step 320 to step 370 until all identified features have been sorted and processed. Procedure 300 can be implemented by a separate computer system or by a computer or control system used to operate a mass analyzer or mass spectrometer.

[0057] It should be noted that even if one or more criteria for a particular feature are not met, more than one true peak (i.e., with respect to different ion species) may still be present. However, this feature (with more than one peak) does not meet the criteria for deconvolution. The third algorithm, using a crude peak-separation technique (at least compared to the first algorithm), may still be able to identify individual peaks (if they are sufficiently separated).

[0058] As in Fig. As shown in Figure 5, the computer system 400 (e.g., a control unit of a mass spectrometer) comprises a number of components, including communication interfaces 420, system circuitry 430, input / output (I / O) circuitry 140, display circuitry and interfaces 450, and a data storage device 470. The system circuitry 420 can include one or more processors or CPUs 480 and a memory device 490. The system circuitry 430 can include any combination of hardware, software, firmware, and / or other circuitry. The system circuitry 430 can be implemented with one or more systems on a chip (SoCs), application-specific integrated circuits (ASICs), microprocessors, and / or analog and digital circuitry.

[0059] The display circuit can provide one or more graphical user interfaces (GUIs) 460, and the I / O interface circuit 440 can include touch-sensitive or non-touch-sensitive displays, sound, voice, or other recognition inputs, buttons, switches, speakers, buzzers, and other user interface elements. The I / O interface circuit 440 can include microphones, cameras, headset and microphone input / output ports, USB (Universal Serial Bus) ports, and SD or other memory card slots. The I / O interface circuit 440 can further include data carrier interfaces (e.g., a CD-ROM or DVD drive) and other bus and display interfaces.

[0060] Memory 490 can include volatile (RAM) or non-volatile memory (e.g., ROM or flash memory). The operating system 492 of the computer system 400, applications or software 494, dynamic data 496, and / or static data 498 can be stored in the memory. Data storage or data source 470 can, for example, include one or more databases 472, 474, and / or a file storage or file system.

[0061] The second and / or the third algorithm (steps 360 and steps 380 in Fig. 4) The algorithms can be the same or different. In an example implementation, they can both be a type of centroidal algorithm that provides data indicating a time or m / z value for one or more peaks within a feature of the mass analyzer data. This can be based on determining a peak's maximum value and the corresponding m / z value at that maximum. A mass center calculation can also be used to determine an intensity-weighted average. Other algorithms can include smoothing techniques (e.g., the Savitzky-Golay method) or fitting a peak to a function before calculating the peak's midpoint value (e.g., extracted from the parameters of the fitted curve).

[0062] Another example of calculating the parameter(s) of the peaks is fitting a suitable (single) peak model to the data within the feature. Any suitable peak model can be used, such as a Gaussian distribution or an asymmetric Gaussian distribution.

[0063] In another example, the second and / or third algorithm (e.g., the centroidal algorithm) can slice a peak in half (perpendicular to the time or m / z axis) so that the area on the left and right sides is equal. The algorithm can then specify the time (the m / z value) at which this slice is made. Furthermore, it may be possible to use only samples between the left half and the right half of the full width at half maximum (FWHM). This provides a more robust exclusion of noise and other unwanted anomalies in the tail regions of elongated tailing peaks.

[0064] Features can be identified from the ion analyzer data using any suitable technique (step 320). For example, after applying a noise-removal threshold, a series of isolated spectral features may be obtained that form a spectrum. These features can be successively identified within the parameters described in procedure 300. Fig. The 4 loops shown are processed.

[0065] A time-of-flight spectrum can be acquired using a digitizer (where noise is excluded or removed using a noise threshold). These thresholds can be preset, but the detector can also be calibrated so that a fixed number of ions (at a fixed m / z) produces a fixed signal. This ensures that the number of ions in each peak is known, or at least that a signal range for the peak is consistent. This can be pre-programmed or calibrated to set a limit for the number of ions (or peak intensity). The threshold can be calculated or adjusted for each specific m / z value using one or more calibration curves. For example, if parameters are used to generate an intensity threshold for a peak (e.g.,(including parameters for both m / z and trap HF amplitude), a calculation or threshold can be set after the feature is detected. For example, different calibration curves can be defined for different sample types, concentrations, mass analyzers, and / or mass analyzer settings or parameters.

[0066] Existing deconvolution algorithms may include low-intensity signal thresholds below which deconvolution is not applied, thus preventing the processing system from being overloaded by dealing with minute signals or residual noise. However, it has been found that for particularly high-intensity signal peaks, it is advantageous to prevent the application of deconvolution algorithms that would otherwise erroneously produce peaks with bifurcation (or further splitting). This requires the application of a high-intensity threshold (in addition to or instead of the low-intensity threshold to prevent noise, and possibly both) above which the deconvolution algorithm is not applied.This ensures that only features below the intensity required to induce an unwanted bifurcation (e.g., due to space charge or other effects) are considered for the deconvolution algorithm.

[0067] This value can be preset or dynamically determined based on the properties of the device and / or sample.

[0068] There are several types of deconvolution algorithms (or first algorithms) that can be used. In one example implementation, mass-dependent smoothing is applied, and then a spectral feature can be truncated at local minima of the smoothed signal. The centroid and the area under the spectral feature can be calculated independently within these segments. Additional processing can be applied to account for signal leakage from one segment to another. Simplified algorithms may be preferable for speeding up processing, especially when the available processing time is limited (e.g., to process thousands of peaks at 200 Hz).

[0069] Fig. Figure 6 shows a flowchart of an exemplary implementation of Procedure 500. In this exemplary implementation, the criteria for selecting the first or second set of algorithms to process each feature in a dataset are based on (selected, calculated, dynamic, or predefined) thresholds. Individual features within a captured spectrum (e.g., from a ToF mass analyzer) are selected. Upper and lower thresholds (if used) can be calculated (in this example). A determination is made as to whether the feature property to be compared with the threshold(s), e.g., intensity, ion count, width, etc., is above and / or below the respective threshold(s) (e.g., within a band). If the criteria are met, a peak deconvolution algorithm is applied. If the criteria are not met (e.g.,(If the intensity is above the upper threshold), the deconvolution algorithm is not executed.

[0070] Signal processing can iterate through each feature or segment of the data until a determination is made for each one, i.e., which features are unfolded and which are not. In this example, after all features have been compared to the threshold(s) in this way, a centroidal algorithm is executed for each feature to generate accurate m / z positions, intensities, or other attributes (i.e., the third algorithm in Procedure 300, which refers to Fig. 4 was described).

[0071] In an exemplary implementation, the procedure 300, 500 can be implemented on data generated by the Orbitrap™ Astral™ MS. This instrument is described in US10699888B2. In this exemplary implementation, the instrument combines the Orbitrap and Astral™ mass analyzers with quadrupole isolation and an electrospray ion source. In a typical MS / MS analysis, narrow regions of analyte ion are isolated from the quadrupole electrosprayed sample, fragmented in an ion processing unit, and then subjected to mass analysis by the Astral™ analyzer. Although the time that ions can accumulate in the ion processing unit can be varied to control the overall ion population, the distribution of ions with different m / z ratios is not so easily adjusted, and individual strong peaks can occur.The dynamic range of the Astral™ analyzer can keep pace with thousands of ions in a single peak against a background of tens of thousands of ions that have accumulated in the trap.

[0072] When determining a value to be set for the upper threshold for peak splitting (i.e., the threshold above which no deconvolution or first algorithm is applied), it is preferable to refer to measurements that show where distortion of the peak shape (e.g., visible) begins to occur. Fig. Figure 7 shows a graph of such measurements for a wide m / z range of isolated ions (in this example, Pierce™ FlexMix™ calibration solution was used) with respect to peak height in volts and ion count, calculated from a peak area, with a correction for m / z. It should be noted that distortion of the peak shape may be visible before the bifurcation becomes so pronounced that the peak splitting (deconvolution) algorithm produces two separate peaks from a single ion. As shown in Fig. As shown in Figure 7, the peak height decreases significantly with increasing m / z. This is because, for a given number of charges, the peak height decreases with increasing m / z in ToF analyzers. While peak height may not be an ideal measure for determining bifurcation, it is very easy to measure and quick to calculate, which is advantageous when processing hundreds of thousands of features per second.

[0073] As described above, space charge effects generally depend on the number of ions. The number of ions can be determined and used to calculate the high or upper threshold, which serves as at least one criterion for running the first algorithm. This high or upper threshold can be calculated during sample analysis or at another time point. The ion number trend in the example data appears nearly flat for the ions with the lowest m / z (where no bifurcation is discernible) and then increases almost linearly (where bifurcation occurs).One reason for the step at low m / z values ​​could be that the RF amplitude of the trap is able to increase with the m / z, thus maintaining the capture power up to approximately m / z 250. After this point, the depth of the pseudopotential well decreased with increasing m / z, and the charge density within the trap declined, thereby increasing the detectable tolerance to space charge of the ions in flight. In the [reference]... Fig. The results shown in section 7 indicate the doubly charged MRFA ion at m / z 262 (in Fig. 7 marked with a cross) also shows a significant decrease in tolerance to space charge, which is due to the deeper pseudopotential well for m / z 262 relative to its heat energy propagation.

[0074] In an exemplary implementation, a fixed high threshold can be based on the selected ion number, such that below this high threshold no bifurcation occurs for any probable ion m / z and charge state. In the Fig. In the example shown, this high threshold can be around 1,000 ions (however, other numbers can also be used, including 500, 1,500, 2,000, 3,000, etc.). Mode dependencies or knowledge of the charge state of a precursor could modify the threshold, as these allow for prediction of the likely charge states of fragments. The threshold could, of course, also be made dependent on the m / z ratio. The threshold can also be based on a capture parameter q or a pseudopotential well depth to better link the threshold to the source of the dependency (e.g., the initial ion capture conditions).

[0075] Another factor to consider when determining one or more criteria is that measurements of isolated ions may not be representative of the states of the majority of the analyzed ions. Typically, the trap contains a mixture of ions with different m / z ratios, which, under space charge, push ions with higher m / z ratios to the trap's edges. This space charge effect in the trap reduces the charge density for most species and, paradoxically, increases the tolerance to resonant space charge effects occurring in the ToF analyzer. Fig. Figure 8 shows graphical data that includes a strong bifurcation feature (bottom diagram) generated by 5,000 isolated ions with m / z 524, which collapses back into a single peak (top diagram) when the ions are captured together with 100,000 ions covering the mass range m / z 150-2,000 (e.g., from the FlexMix™), including lower mass species with m / z 195 and 262.

[0076] It is possible to account for the global space charge effect when calculating the high or upper threshold. Species with the lowest m / z in the distribution tend to behave more like isolated ions, while species with higher m / z become more tolerant of space charge. However, calculating thresholds under such conditions may require creating a distribution map, for example, by binning features into m / z bands, to simplify the calculation.

[0077] Fig. Figure 9 shows graphical results illustrating parameters that can be used to calculate the high or upper threshold. Height, area, and number of ions are interrelated properties and can be scaled by m / z, ToF, and feature width, which are also interrelated. Feature width (whether baseline, 10%, or full width at half maximum, etc.) can be useful as an additional parameter to the existing scaling, as a means of determining whether a feature is too wide for a bifurcation, and thus as a means of distinguishing a bifurcation of multiple analyte species within large features.

[0078] Certain regions of a spectrum may contain known features with well-understood behavior. For example, TMT reporter ions in the region of the spectrum around m / z 120 are well-defined and known, and the positions of complementary TMTc ions can be deduced from the mass and charge state of an isolated precursor. Isobaric reporter ions occur in doublets (or quadruplets), and consequently, the upper threshold for splitting these features can be relaxed, raised, or removed.

[0079] Although the technique and procedure 300, 500 reduce the risk of single-ion species data undergoing splitting or bifurcation, there may still be situations and samples exhibiting closely spaced (true) peaks. These data, showing superimposed isobaric features of multiply charged ions (where peak splitting is desirable), must be distinguished from an overloaded peak at a single m / z, where peak splitting should be avoided. As can be seen, an intensity threshold may not be helpful in differentiating these results. While the threshold could easily be removed when analyzing multiply charged precursors (if their time of flight or m / z is known), top-down MS / MS experiments can generate mixtures of charge states, and it is helpful to observe strongly singly charged fragments.The feature width (or analogously the ratio of height to area) could be used to determine how to apply an upper intensity filter, since features of strong multiply charged ions are on average wider than features of strong singly charged ions.

[0080] Although the concept of the high or upper threshold is described for certain types of analyzers (e.g., Astral™ analyzer), it can be applied to any time-of-flight analyzer where space charge effects occur.

[0081] Fig. Figure 10 shows a graphical simulation of a spectral feature with a bifurcated peak under strong space charge adjacent to a weaker peak belonging to a different ion species. This simulated peak can be above the upper or higher threshold (regardless of how it is determined). This illustrates that it is possible for peaks from different ions to be present, attached with high intensity to a bifurcated pulse, as in Fig.Figure 10 illustrates how, to detect such peaks in a high-intensity pulse (i.e., one that would otherwise meet the criteria for non-execution of the first or deconvolution algorithm), a local minimum can be determined (indicated by diamond markings in this figure) at which splitting would occur if the first algorithm were applied. Splitting can be permitted for local minima below a threshold dependent on the flight time. This threshold could also depend on the highest intensity in the feature. Thus, the third algorithm could be adapted to include such a test.

[0082] For example, in the case of a space-charge-induced bifurcation, it is unlikely that the minimum at the splitting point will be more than about 25% below the maximum, yet most of the desired splitting occurs at a lower level. Therefore, splitting should only be applied if the ratio of the local minimum to the height of the maximum falls below a certain value, such as 0.5 or 0.25. Another approach could be to always skip splitting at the largest local minimum and only perform splitting at lower local minima if their height falls below a (further) threshold compared to the highest sample or the largest local minimum. Similar procedures could be performed based on the areas of the peaks after splitting or the ion numbers, as described above.

[0083] The method and the system can be implemented in hardware, software, or a combination of both. The method and the system can be implemented either as a server comprising a single computer system or as a distributed network of servers interconnected via a network. Any type of computer system or other electronic device can be designed to perform the described methods.

[0084] Within the context of this document, including the claims, unless the context indicates otherwise, singular forms of terms are to be interpreted as including the plural form, and vice versa. For example, unless the context indicates otherwise, a singular reference in this document, including the claims, such as "a" or "an" (such as an ion multipole device) means "one or more" (such as one or more ion multipole devices). In the description and claims of this disclosure, the words "comprise," "including," "having," and "contain," as well as variations of these words, such as "comprising" and "includes" or similar, mean "including, but not limited to," and are not intended to exclude (and do not exclude) other components.Furthermore, the use of "or" is inclusive, so the phrase "A or B" is true if "A" is true, "B" is true, or both "A" and "B" are true.

[0085] The use of all examples or illustrative expressions provided in this document (“for example”, “such as”, “for instance”, and similar expressions) serves only to enhance the clarity of the disclosure and does not indicate any limitation of the scope of the disclosure, unless claimed otherwise. No wording in the description is to be interpreted as referring to any unclaimed element essential to the implementation of the disclosure.

[0086] The terms "first" and "second" can be interchanged without changing the scope of the revelation. That is, an element designated as "first" can instead be designated as "second," and an element designated as "second" can instead be considered "first."

[0087] All steps described in this description can be performed in any order or simultaneously, unless otherwise specified or the context requires otherwise. Furthermore, where it is described that one step is performed after another, this does not preclude the execution of intermediate steps.

[0088] It is also understood that for each of the components or embodiments described herein, any possible candidates or alternatives listed for that component may generally be used individually or in combination with one another, unless otherwise implicitly or explicitly understood or stated. It is understood that any enumeration of such candidates or alternatives serves only for illustration and not as a limitation, unless otherwise implicitly or explicitly understood or stated.

[0089] Unless otherwise specified, all technical and scientific terms used have the meaning generally understood by a person skilled in the art in the field to which the various embodiments described herein belong.

[0090] As is obvious to the person skilled in the art, details of the foregoing embodiment can be varied without deviating from the scope of the present invention as defined by the attached claims.

[0091] For example, although the above methods and system have been described in relation to a ToF mass analyzer, the technique can also be used with other mass analyzers.

[0092] Many combinations, modifications, or changes to the features of the foregoing embodiments are readily apparent to a person skilled in the art and are intended to form part of the invention. Any features specifically described in relation to one embodiment or example can also be used in any other embodiment by appropriate modifications. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] GB 2617318A

[0006] GB 2617318

[0030] US 10699888B2

[0071]

Claims

[1] Method for analyzing data generated by an ion analyzer, the method comprising: Receiving data generated by the ion analyzer; Identifying a range in the data generated by the ion analyzer that contains at least one feature; and Processing the identified area by: If one or more properties of the identified area meet one or more criteria, use a first algorithm to identify one or more individual peaks within the identified area, and a second algorithm to determine properties of the one or more individual peaks within the identified area; and / or If the identified area does not meet one or more of the criteria, use a third algorithm to determine properties of one or more individual peaks within the identified area. where one or more criteria include that the maximum intensity value or the maximum area of ​​the data within the identified area is below a first threshold. [2] Method according to claim 1, wherein the second and the third algorithm are the same. [3] Method according to claim 1 or claim 2, wherein the second and / or the third algorithm are centroid algorithms for calculating a midpoint value of one or more individual peaks. [4] Method according to claim 3, wherein the centroidal algorithm provides an output that includes m / z positions or arrival time and intensities of one or more individual peaks. [5] Method according to claim 4, wherein the centroidal algorithm provides an output which further comprises a resolution of one or more individual peaks. [6] Method according to any of the preceding claims, wherein the first threshold corresponds to an intensity level at which a space charge-induced bifurcation occurs. [7] Method according to any of the preceding claims, wherein one or more criteria comprise that the maximum intensity value of the data within the range is above a second threshold. [8] Method according to any of the preceding claims, wherein the intensity value is one of the following: number of counts, number of ions or number of arbitrary units. [9] Method according to any of the preceding claims, wherein the threshold is determined based on an m / z value or arrival time of a peak within the identified area. [10] Method according to claim 9, wherein the threshold is based on a calibration curve that includes the m / z value or the arrival time. [11] Method according to any of the preceding claims, wherein the first algorithm is a deconvolution algorithm. [12] Method according to claim 11, wherein the deconvolution algorithm comprises: (i) Receiving a first data segment generated by the ion analyzer, wherein the first data segment comprises data associated with a first arrival time range; (ii) Applying a filter to the first data segment to produce a filtered version of the first data segment, wherein a width associated with the filter is configured to depend on a width of an expected ion arrival time distribution for the ion analyzer for arrival times within the first arrival time range; and subsequently (iii) Identifying one or more ion peaks in the filtered version of the first data segment. [13] Method according to any of the preceding claims, wherein the third algorithm comprises the following steps: Identifying a local minimum between two areas with a local maximum of higher intensity within the identified area; If a difference or percentage difference between the local minimum and at least one of the two local maximum regions exceeds a threshold, identify the two local maximum regions as separate ion peaks; and Determining the center positions and / or widths of the two areas with a local maximum. [14] Method according to any of the preceding claims, wherein the third algorithm comprises the following steps: Identifying a local minimum between two areas with a local maximum of higher intensity within the identified area; If the intensity of the local minimum is below a threshold, identify the two areas with local maximum as separate ion peaks; and Determining the center positions and / or widths of the two areas with a local maximum. [15] Method according to any of the preceding claims, wherein the steps of processing the identified area are repeated for a plurality of identified areas until all areas in the data generated by the ion analyzer have been processed. [16] Method according to any of the preceding claims, wherein the ion analyzer is a time-of-flight mass analyzer (ToF mass analyzer). [17] Method according to claim 16, wherein the step of identifying the area further comprises limiting the width of the identified area to a first arrival time range. [18] Method according to any of the preceding claims, wherein the step of identifying the area in the data generated by the ion analyzer which contains at least one feature further comprises the following step: Selecting the area in the data that completely surrounds the feature without including any adjacent features. [19] Device comprising: a processor; and a memory in which computer-executable instructions are stored which, when executed by the processor, cause the device to perform the method according to any of the preceding claims. [20] Device according to claim 19, wherein the device is a mass spectrometer. [21] Control system for an analyzer, wherein the control system is configured to cause the analyzer to perform the method according to any one of claims 1 to 18.

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