Systems and methods for charge state assignment in mass spectrometry - Patents.com

Abstract

A method for assigning charge states in mass spectrometry includes receiving detector response signals corresponding to multiple ion arrival events. The detector response signals include information related to individual ion responses generated by the detector for each ion arrival event. Detector response profiles are generated for mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival events based on the detector response signals. The m / z bins are grouped into multiple groups based on similarities in the detector response profiles of the m / z bins. Charge states are assigned to one or more features based on the grouping of the m / z bins.

Description

[Background technology]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application was filed as a PCT international patent application on August 6, 2021, and claims the benefit of priority to U.S. patent application Ser. No. 63 / 062,231, filed August 6, 2020, the entire disclosure of which is incorporated herein by reference in its entirety.

[0002] In general, mass spectrometry (MS) is an analytical technique for the detection and quantification of chemical compounds based on the analysis of the mass / charge (m / z) values ​​of ions formed from those compounds. MS involves ionization of one or more compounds of interest from a sample, generation of precursor ions, and mass analysis of the precursor ions. Tandem mass spectrometry or mass spectrometry / mass spectrometry (MS / MS) involves ionization of one or more compounds of interest from a sample, selection of one or more precursor ions of the one or more compounds, fragmentation of the one or more precursor ions into product ions, and mass analysis of the product ions.

[0003] Both MS and MS / MS can provide qualitative and quantitative information. The measured precursor or product ion spectra can be used to identify the molecule of interest. The intensities of the precursor and product ions can also be used to quantify the amount of the compound present in the sample.

[0004] Mass spectrometry techniques often utilize the mass / charge ratio (m / z) of detected ions to generate mass spectrum data. However, knowledge of the actual charge or mass of detected ions is often not directly measurable. As a result, some degree of overlap of detected ions may occur in certain scenarios. For example, a singly charged ion with a certain mass may appear in a mass spectrum as having the same mass / charge ratio as a doubly charged ion with twice the mass. This problem may be generally referred to as the peak overlap problem.

[0005] In top-down mass spectrometry (MS) protein analysis, for example, overlap of mass or mass / charge (m / z) peaks in mass spectra is a significant problem. In this type of analysis, a very wide range of different fragment or product ions are generated, including product ions with lengths ranging from 1 to 200 amino acids and with 1 to 50 different charge states. The product ion peaks are significantly overlapped with each other in a single spectrum. In addition, the overlap can be so extensive that even mass spectrometers with the highest mass resolution (Fourier transform ion cyclotron resonance (FT-ICR) or Orbitrap) cannot deconvolute such overlapped peaks. As a result, abundant product ions are often lost in top-down protein analysis, limiting sequence coverage of abundant proteins. Both International Publication No. WO2020 / 157720 ('720 Publication), published on August 6, 2020 (Patent Document 1), and International Publication No. WO2019 / 197983, published on October 17, 2019 (Patent Document 2), provide additional discussion of and associated challenges of top-down MS protein analysis. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication No. 2020 / 157720 [Patent Document 2] International Publication No. 2019 / 197983 Summary of the Invention [Means for solving the problem]

[0007] In one aspect, the technique relates to a method of assigning charge states in mass spectrometry, the method including receiving detector response signals corresponding to a plurality of ion arrival events, the detector response signals including information related to individual ion responses generated by a detector for each ion arrival event, generating detector response profiles for mass / charge (m / z) bins of a mass spectrum generated from the ion arrival events based on the detector response signals, grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins, and assigning charge states to one or more features based on the groups of m / z bins. In an example, the method further includes generating a simplified mass spectrum based on the groups of m / z bins, the groups of m / z bins being shown in the simplified mass spectrum. In another example, the method further includes calculating masses corresponding to the ion arrival events based on the assigned charge states. In yet another example, the grouping of m / z bins is further based on additional separation domain data. In yet another example, the additional separation domain data includes at least one of a dwell time, a drift time, or a compensation voltage for ion mobility.

[0008] In another example of the above aspect, grouping the m / z bins is performed using a principal component analysis (PCA), a k-means clustering algorithm, or a principal component variable grouping (PCVG) algorithm. In an example, the method further includes generating a representation of the ion arrival events, the representation having an m / z dimension, a detector response dimension, and an ion count or probability dimension. In another example, the representation is a heat map. In yet another example, grouping the m / z bins is performed at least in part by applying a pattern recognition algorithm to the representation.

[0009] In another aspect, the technology relates to a system for assigning charge states in mass spectrometry, the system including a detector configured to generate a detector response signal for each ion arrival event, a processor, and a memory storing instructions that, when executed by the processor, are configured to cause the system to perform a set of operations including: receiving detector response signals corresponding to a plurality of ion arrival events from the detector, the detector response signals including information related to individual ion responses generated by the detector for each ion arrival event; generating detector response profiles for mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival events based on the detector response signals; grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins; and assigning charge states to one or more features based on the groups of m / z bins. In an example, the operations further include generating a simplified mass spectrum based on the groups of m / z bins, the groups of m / z bins being depicted in the simplified mass spectrum. In another example, the grouping of the m / z bins is further based on additional separation domain data. In yet another example, the additional separation domain data includes at least one of a dwell time, a drift time, or a compensation voltage for ion mobility. In yet another example, the grouping of m / z bins is performed using a principal component analysis (PCA), a k-means clustering algorithm, or a principal component variable grouping (PCVG) algorithm.

[0010] In another example of the above aspect, the system further includes generating a representation of the ion arrival events, the representation having an m / z dimension, a detector response dimension, and an ion population or probability dimension. In an example, the system further includes generating a confidence score for the group, and the assigning of the charge state is further based on at least one of the confidence scores.

[0011] In another aspect, the technique relates to a method of assigning charge states in mass spectrometry, the method including receiving detector response signals corresponding to a plurality of ion arrival events, the detector response signals including information related to individual ion responses generated by the detector for each ion arrival event, generating detector response profiles for mass / charge (m / z) bins of a mass spectrum generated from the ion arrival events based on the detector response signals, grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins, identifying m / z bins that represent single ion arrival events, and assigning charge states to one or more features based on the groups of m / z bins identified as having single ion arrival events. In an example, the method further includes generating a confidence score for the group, and the assigning of the charge state is further based on at least one of the confidence scores. In another example, the grouping of the m / z bins is further based on additional separation domain data including at least one of residence time, drift time, or compensation voltage for ion mobility. In yet another example, identifying an m / z bin that represents a single ion arrival event is based on the frequency of detection events observed in the m / z bin.

[0012] In another aspect, the technology relates to a method of analyzing data in mass spectrometry, the method including receiving a detector response signal corresponding to a plurality of ion arrival events, the detector response signal comprising information related to an individual ion response generated by the detector for each ion arrival event, generating a data representation consisting of at least a detector response profile generated from the ion arrival events and mass / charge (m / z) bins of a mass spectrum based on the detector response signal, and utilizing the data representation for at least one of compound identification and species identification. In an example, the compound identification includes compound quantification. In another example, the compound identification includes assigning a charge to at least one detected group of ions. In yet another example, the assigning the charge is based at least in part on the detector response profile. In yet another example, the method further includes simplifying the data representation.

[0013] In another example of the above aspect, the simplified data representation includes at least one higher ranked tensor data. In an example, simplifying the data representation includes generating one or more spectra in the m / z domain. In another example, simplifying the data representation includes generating one or more spectra in the mass domain. In yet another example, simplifying the data representation is based at least in part on a detector response profile. In yet another example, simplifying the data representation includes grouping m / z bins with similar detector response profiles.

[0014] In another example of the above aspect, the method further includes calculating a mass corresponding to the ion arrival event based on the assigned charge state. In an example, simplifying the data representation is based on additional separate domain data. In another example, the additional separate domain data includes at least one of a residence time, a drift time, and a compensation voltage for ion mobility. In yet another example, simplifying the data representation is performed at least in part by using multivariate analysis. In yet another example, simplifying the data representation is performed at least in part based by applying a non-negative factorization algorithm.

[0015] In another example of the above aspect, the multivariate analysis includes at least one of a principal component analysis (PCA), a k-means clustering algorithm, a t-SNE algorithm, and a principal component variable grouping (PCVG) algorithm. In an example, simplifying the data representation is performed at least in part based on using pattern recognition or machine learning. In another example, simplifying the data representation is performed at least in part using a statistical method to match observed detector responses to a catalog of predetermined detector response distributions. In yet another example, the catalog is generated a priori. In yet another example, the catalog is generated from a simplified data representation.

[0016] In another example of the above aspect, compound or species identification includes generating a compound or species library and matching the compound or species to the library using an algorithm. In an example, the detection system is one of an electron multiplier based detection system or a mirror image charge based detection system.

[0017] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additional aspects, features, and / or advantages of the examples will be set forth in part in the description that follows, or will be obvious from the description, or may be learned by practice of the disclosure. [Brief description of the drawings]

[0018] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0019] [Figure 1A] FIG. 1A depicts an exemplary system for performing mass spectrometry.

[0020] [Figure 1B] FIG. 1B depicts an exemplary plot of an ion pulse.

[0021] [Figure 1C] FIG. 1C depicts an exemplary mass spectrum separated into different bands or channels based on ion pulse intensity.

[0022] [Diagram 2] FIG. 2 illustrates an example plot of a transient time domain signal measured by an image charge detector.

[0023] [Diagram 3] FIG. 3 depicts an exemplary system including an image charge detector.

[0024] [Figure 4] FIG. 4 depicts an example spectrum with overlapping features.

[0025] [Diagram 5] FIG. 5 depicts a plot of an exemplary pulse height distribution.

[0026] [Figure 6] FIG. 6 is a plot illustrating an example of a mass spectrum divided into m / z bins.

[0027] [Figure 7] FIG. 7 depicts an exemplary heat map.

[0028] [Figure 8] FIG. 8 is a plot showing an exemplary detector response profile.

[0029] [Figure 9] FIG. 9 depicts an exemplary method for charge state assignment using detector response profiles.

[0030] [Figure 10] FIG. 10 depicts an exemplary method for charge state assignment using detector response profiles.

[0031] [Figure 11] FIG. 11 depicts an exemplary method for charge state assignment using detector response profiles.

[0032] [Figure 12] FIG. 12 depicts an exemplary method for charge state assignment using detector response profiles. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] As briefly discussed above, peak overlap of detected ions is problematic for the analysis of MS results, and distinguishing mass signals generated for ions with similar mass / charge (m / z) can be a challenging problem in mass spectrometry. For compound identification, mass spectra are usually converted into a list of monoisotopic masses corresponding to different compounds. To find such masses, the following strategy is often adopted: first, each peak in the mass spectrum is assigned to a corresponding isotopic cluster with a certain charge state. Following this, the lowest m / z peak is found for each cluster, which is the peak that corresponds to the monoisotopic mass. Once the cluster charges are known, the monoisotopic peaks of each cluster can be converted into a zero-charge list of monoisotopic masses, which can then be used in subsequent algorithms that attribute mass spectrum peaks to chemical compounds. Alternatively, instead of the monoisotopic m / z, the average m / z of the isotopic cluster and its corresponding charge state can be used as well. The method is advantageous when certain peaks from isotopic clusters are below the detection limit, especially when the monoisotopic m / z is below the detection limit. In practice, correct charge state assignment to features (isotopic clusters) in a mass spectrum can be an important step towards compound identification.

[0034] Traditionally, charge deconvolution algorithms in the m / z domain are used for charge state discrimination. However, this approach is challenging when severe spectral overlaps, including interdigitated peaks and peak overlaps, exist. This is often the case for composite spectra of mixtures such as top-down analysis spectra or product ion spectra of large amounts of biopolymers. Figure 4 depicts an exemplary plot illustrating an example of multiple overlapping features in an ECD top-down spectrum of carbonic anhydrase 2 (CA2) where traditional algorithms are prone to errors.

[0035] It is recognized that the detection response for both electron multipliers or image charge detection systems can be proportional to the charge state of the measured ion (see, e.g., the discussion in the references in the '720 patent, which is incorporated herein by reference in its entirety). Thus, in theory, the charge state can be determined based on careful examination of such intensities. Interestingly, there have been few attempts to exploit the phenomenon of charge state estimation, as this is technically difficult.

[0036] First, detection events from multiple acquisitions are conventionally combined into a single spectrum to compress the data. However, such compression prevents any further analysis of the detector response of each individual ion event, making it impossible to infer the charge state. Thus, a complete record of each ion detection event intensity and its mass spectral characteristics (e.g., time of flight or oscillation frequency) is preferred for such analysis. Alternative data compression strategies can also be utilized to retain some information of the individual detector responses while still maintaining data compression. For example, each detection event can be added together into multiple spectra that form a detector response band similar to the approach described in the '720 publication.

[0037] Second, multiple co-detected ions can generate a detector response that is substantially the sum of the detector responses generated by each co-arriving ion. Thus, it is not always possible to infer the charge state of an ion using only the detector response intensity of the detected signal. In general, a sufficiently low ion flux is preferred for charge state determination using the detector response such that the number of detection events involving co-arriving ions is minimized.

[0038] Third, another challenge with such methods is that the detector response distribution for each particular type of ion is wide and often overlaps for different species. Figure 5 is a plot 500 illustrating an example of a detector response distribution when detecting an ion with a 3+ charge compared to an ion with a 7+ charge. As shown, the pulse height distribution 502 for an ion with a 3+ charge is different from the pulse height distribution 504 for an ion with a 7+ charge. As a result, the pulse height distributions as observed by the detector for ions of the same m / z (in this example, an m / z value of 517) are wide and overlapping.

[0039] Such a broad pulse height distribution renders any conventional charge state assignment approach based on detector response intensity inferior due to the difficulty in distinguishing ions of the same m / z but different charge.

[0040] The problem of wide intensity distribution for direct identification of charge states using the detection response intensity has been recognized, and few strategies have been proposed to address it for mass spectrometers employing image charge-based detectors. In such systems, the wide distribution can be mainly due to collisions with residual neutral molecules during the measurement, which suppresses the coherent oscillation of the ion of interest and effectively stops the detection of its signal, making its contribution dependent on the actual ion measurement time. It has therefore been proposed to filter detection events due to ions experiencing collisions during acquisition (Kafader et. al. Anal. Chem. 2019,91,4,2776-2783). However, this approach leads to many ions being discarded, thus significantly increasing the time to obtain good ion statistics. In addition, approaches to reduce the base pressure and decrease the ion velocity have also been proposed, however, they have a detrimental effect on the mass analyzer performance. Finally, it was proposed to employ advanced data processing techniques to detect the exact time of the impact and thus increase or decrease the measured signal strength according to the actual detection time (Kafader et. al. J.Am. Soc. Mass. Spectrom. 2019,11,2200-2203).

[0041] For mass spectrometers using electron multiplier detectors, the average number of secondary emission electrons is well defined for each ion with a particular m / z and charge, but the exact number of emitted primary electrons that defines the magnitude of the observed response is a probabilistic quantity. Both the secondary emission yield and collisions with the buffer gas are described by Poisson statistics, but the physics underlying the processes are very different. Thus, none of the techniques proposed to address the wide distribution for mass spectrometers employing image charge-induced detectors are applicable to mass spectrometers using electron multiplier-based detection systems. Thus, there is a need for techniques to at least address this problem.

[0042] In many cases, the detector response profile may be insufficient for accurate determination of the charge state, and the detector response profile may be very useful for separating signals arising from different compounds. This, combined with the fact that accurate charge state information is encoded in the m / z domain, allows for substantially improved performance when traditional charge determination algorithms are coupled with the detector response domain for charge state determination.

[0043] Importantly, because separation occurs at the last step of mass spectrometry analysis, this method may be applicable in some cases where alternative approaches would not work. Specifically, liquid chromatography (LC) methods may provide separation of compounds. However, they are less informative in terms of separation of product ions arising from the same precursor, while fragmentation from the same precursor may still overlap substantially. Similarly, ion mobility separations traditionally performed before fragmentation (e.g., differential mobility separation) require significant modification to set up after fragmentation separation.

[0044] One approach to improve the performance of conventional charge determination algorithms is to exploit detector response profiles for grouping data. Often the same chemical compound has multiple isotopes that form isotope clusters, which may or may not be resolved in the m / z domain. The m / z bins that correspond to the locations of those isotopes under certain circumstances may have similar detector response profiles. For example, detector response profiles may be similar if at least two conditions are satisfied. First, the signals do not overlap (i.e., the m / z bins do not contain signals from multiple different species); second, for all m / z bins that contain signals from those isotopes, the signals are obtained primarily under single ion arrival conditions. This similarity allows for grouping of m / z bins that contain information from the same compound (effectively splitting the signals between multiple channels). This results in substantially simplified spectra for subsequent charge detection analysis by conventional algorithms. Instead of charge detection, such simplified spectra may be used for direct spectrum matching or other algorithmic identification of compounds or species that do not perform a charge detection step. For example, the species may be a microorganism and corresponding data may be generated in MS and MS / MS (tandem MS) systems. In some instances, a library of simplified compound or species spectra may be generated and used for the direct spectral matching.

[0045] FIG. 1A depicts an exemplary mass spectrometry system 100 for performing mass spectrometry techniques. In some examples, the system 100 can be a mass spectrometer. The exemplary system 100 includes an ion source device 101, a dissociation device 102, a mass analyzer 103, a detector 104, and computing elements such as a processor 105 and a memory 106. The ion source device 101 can be, for example, an electrospray ion source (ESI) device. The ion source device 101 is shown as part of a mass spectrometer or can be a separate device. The dissociation device 102 can be, for example, an electron-based dissociation (ExD) device or a collision-induced dissociation (CID) device. Electron-based dissociation (ExD), ultraviolet photodissociation (UVPD), infrared photodissociation (IRMPD), and collision-induced dissociation (CID) are often used as fragmentation techniques for tandem mass spectrometry (MS / MS). ExD can include, but is not limited to, electron capture dissociation (ECD) or electron transfer dissociation (ETD). CID is the most conventional technique for dissociation in tandem mass spectrometers. As described above, in top-down and middle-down proteomics, intact or digested proteins are ionized and subjected to tandem mass spectrometry. For example, ECD is a dissociation technique that preferentially dissociates peptides and protein backbones. As a result, this technique is an ideal tool for analyzing peptide or protein sequences using top-down and middle-down proteomics approaches.

[0046] Mass analyzer 103 can be any type of mass analyzer used for the desired technique, such as a time-of-flight (TOF), ion trap, or quadrupole mass analyzer. Detector 104 can be any suitable detector for detecting ions and generating signals as discussed herein. For example, detector 104 can include an electron multiplier detector that can include analog-to-digital conversion (ADC) circuitry. Detector 104 can produce a detection pulse for the detected ions. Detector 104 can also be an image charge-induced detector.

[0047] The computing elements of the system 100, such as the processor 105 and memory 106, may be included in the mass spectrometer itself, may be located adjacent to the mass spectrometer, or may be located remotely from the mass spectrometer. In general, the computing elements of the system may be in electronic communication with the detector 104 such that the computing elements are able to receive signals generated from the detector 104. The processor 105 may include multiple processors and may include any type of suitable processing components to process the signals and generate the results discussed herein. Depending on the exact configuration, the memory 106 (which stores, among other things, the mass spectrometry programs and instructions to perform the operations disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. Other computing elements may also be included in the system 100. For example, the system 100 may include storage devices (removable and / or non-removable), including, but not limited to, solid state devices, magnetic or optical disks, or tape. System 100 may also have input devices such as a touch screen, keyboard, mouse, pen, voice input, etc., and / or output devices such as a display, speakers, printer, etc. One or more communication connections such as a local area network (LAN), a wide area network (WAN), point-to-point, Bluetooth, RF, etc. may also be incorporated into system 100.

[0048] 1B depicts an example plot 110 of an ion pulse generated from a detector, such as an electron multiplier detector. The y-axis represents intensity and the x-axis represents time. Intensity may be in units of voltage. For example, for an electron multiplier detector, the output of the detector may be a voltage (often expressed in millivolts (mV)) based on the detected electrons.

[0049] In FIG. 1B, three pulses are depicted: a first pulse 111, a second pulse 112, and a third pulse 113. Each of the pulses 111-113 represents a different single ion arriving at the detector. The pulses 111-113 can be digitized and a peak can be found from each digitized pulse. An intensity (or peak height) and arrival time pair can be calculated and stored for each pulse. Rectangles 131, 132, and 133 represent the intensity or pulse height of the respective pulse.

[0050] Each of the pulses may be characterized by pulse characteristics. The pulse characteristics may include characteristics such as pulse height, pulse width, and / or area under the curve of the pulse. The pulse height of each pulse is shown by rectangles 131, 132, and 132. The pulse height may be the maximum pulse height for the respective pulse, and the pulse height may have units of voltage. The pulse width may be at any point of the pulse, but one measure of the pulse width may be full width at half maximum (FWHM). The pulse width may have units of time. The area under the pulse curve may be generated by integrating the area under the respective pulse signal for each pulse.

[0051] Pulse characteristics may be used to separate detected ions into different bands. FIG. 1C depicts an exemplary mass spectrum 150 separated into different bands or channels based on ion pulse characteristics, specifically, maximum pulse height. A first mass spectrum 160 is generated for detected ions having a maximum pulse height of 10-20 mV. A second mass spectrum 170 is generated for detected ions having a maximum pulse height of 20-30 mV. A third mass spectrum 180 is generated for detected ions having a maximum pulse height of 20-30 mV. Additional details regarding such separation and banding are discussed further in the '720 publication. As discussed above, separating ions into different bands has benefits, but separation does not allow for identification of the charge state of a particular ion. As discussed further herein, pulse characteristics may be utilized to generate detector response profiles that allow for m / z bin grouping and ultimately charge sorting.

[0052] FIG. 2 depicts an example plot 200 of a transient time domain signal measured by an image charge detector, the transient time domain signal including components from each of a plurality of ions oscillating in a mass analyzer. To resolve the transient time domain signal measured by the image charge detector into individual components, the transient time domain signal is converted to a frequency domain signal. Transformation methods include, but are not limited to, Fourier transform or wavelet transform. Peaks in the frequency domain signal correspond to individual ions of the plurality of ions oscillating in the mass analyzer. The frequency domain peaks are converted to m / z peaks using well-known formulas that depend on the particular type of mass analyzer to generate the mass spectrum.

[0053] For image charge detectors, the intensity of the frequency domain signal or peak is therefore proportional to the charge state of the underlying ion, similar to how the pulse described above is proportional to the charge state. Thus, the intensity or other characteristics of the frequency domain (FD) peak can be used to generate a distribution similar to the pulse characteristic distribution discussed above. A distribution generated from the characteristics of the FD peak can be referred to as an FD peak characteristic distribution, or, if the intensity of the FD peak is used as the characteristic of interest, as an FD peak intensity distribution. The FD peak characteristic distribution can then be used in substantially the same manner as the pulse characteristic distribution to determine the charge state.

[0054] 3 depicts an exemplary system 300 including an image charge detector 318. The system of FIG. 3 includes a mass spectrometer 310 and computing components including memory and a processor 320. The computing elements of the system, such as the processor 320 and memory, may be included in the mass spectrometer itself, may be located adjacent to the mass spectrometer, or may be located remotely from the mass spectrometer. In general, the computing elements of the system may be in electronic communication with the detector 318 such that the computing elements are able to receive signals generated from the detector 318. The processor 320 may include multiple processors and may include any type of suitable processing components to process the signals and generate the results discussed herein.

[0055] The mass analyzer 310 includes a mass analyzer 317. The mass analyzer 317 includes an image charge detector 318. The image charge detector 318 generates an oscillatory or transient time domain signal for detected ions with an amplitude proportional to the ion charge state. The mass analyzer 317 can be any type of mass analyzer that can detect ions using an image charge detector, including, but not limited to, an electrostatic linear ion trap (ELIT), an FT-ICR, or an Orbitrap mass analyzer. The mass analyzer 317 is shown in FIG. 3 as an ELIT, and the image charge detector 318 is shown as the pick-up electrode of the ELIT.

[0056] The mass analyzer 317 detects a transient time domain signal 319 induced on an image charge detector 318 by the oscillation of the plurality of ions in the mass analyzer 317. The plurality of ions are transmitted to the mass analyzer 317 by the mass analyzer 310. The processor 320 converts the transient time domain signal 319 into a plurality of frequency domain pulses or peaks 321, each frequency domain signal corresponding to an ion of the plurality of ions. The processor 320 converts the transient time domain signal 319 into a plurality of frequency domain peaks 321, for example, using a Fourier transform.

[0057] The processor 320 may compare the intensity of each frequency domain peak of the plurality of frequency domain peaks 321 to two or more different predetermined intensity ranges corresponding to two or more different charge state ranges. The processor 320 may store each frequency domain peak in one of two or more data sets 1322 corresponding to the two or more predetermined intensity ranges based on the comparison. A mass spectrum 1623 may then be generated from the data sets 322. The processor 320 may create a mass spectrum based on the frequency domain peaks and / or the identified charge states discussed herein.

[0058] In various embodiments, the processor 320 transforms the transient time domain signal 319 into a plurality of frequency domain peaks 321, compares the intensity of each frequency domain peak to two or more different predetermined intensity ranges, and stores each frequency domain peak in one of two or more data sets 322 during acquisition. In an alternative embodiment, the processor 320 transforms the transient time domain signal 319 into a plurality of frequency domain peaks 321, compares the intensity of each frequency domain peak to two or more different predetermined intensity ranges, and stores each frequency domain peak in one of two or more data sets 322 after acquisition.

[0059] As explained above, when multiple copies of the same ion oscillate simultaneously in the mass analyzer 317, the measured intensity may not be proportional to the charge state. As a result, in various embodiments, the mass analyzer 310 transmits ions to the mass analyzer 317 such that the mass analyzer 317 contains only a single ion of a particular m / z and charge state at any given time.

[0060] In various embodiments, the system of FIG. 3 further includes an ion source device 311. The ion source device 311 can be, for example, an electrospray ion source (ESI) device. The ion source device 311 is shown in FIG. 3 as part of the mass spectrometer 310, but can also be a separate device. In addition, the mass spectrometer 310 further includes a dissociation device. The dissociation device can be, but is not limited to, an ExD device 315 or a CID device 316. The dissociation device can be used, for example, for top-down protein analysis.

[0061] In top-down protein analysis, the ion source device 311 ionizes proteins of a sample and generates multiple precursor ions for the proteins in the ion beam. The dissociation device dissociates the multiple precursor ions in the ion beam and generates multiple product ions with different charge states in the ion beam. The mass analyzer 310 transmits the multiple product ions to the mass analyzer 317 such that the multiple product ions are multiple ions transmitted by the mass analyzer 310 to the mass analyzer 317 as described above.

[0062] In various embodiments, the processor 320 is used to control or provide instructions to the ion source device 311 and the mass spectrometer 310, and to analyze the data that is collected. The processor 320 controls or provides instructions by controlling, for example, one or more voltage, current, or pressure sources (not shown).

[0063] FIG. 4 depicts an exemplary plot illustrating an example of multiple overlapping features in an ECD top-down spectrum of carbonic anhydrase 2 (CA2). FIG. 5 depicts an exemplary pulse characteristic distribution plot 500. The pulse characteristic distribution is based on the analysis performed to generate the spectrum in FIG. 4. The pulse characteristic distribution in plot 500 is based on pulse height characteristics. Thus, the pulse characteristic distribution may be referred to as a pulse height distribution or intensity distribution. In the plot, the x-axis represents pulse height and the y-axis indicates the probability or frequency of detection. For example, a higher probability value indicates that an ion having the corresponding pulse height was detected more frequently.

[0064] A first pulse characteristic distribution 502 and a second pulse characteristic distribution 504 are depicted in the plot 500. As can be seen from the plot 500, the pulse characteristic distributions overlap, but the first pulse characteristic distribution 502 has a different profile than the profile of the second pulse characteristic distribution 504. The difference in profile shape is mainly due to the difference in the charge state of the detected ions forming the respective pulse height distributions. For example, the detected ions forming the first pulse characteristic distribution 502 correspond to 3+ charged ions, and the detected ions forming the second pulse characteristic distribution 504 correspond to 7+ charged ions. Thus, once various pulse height distributions are established or generated, it may be possible to determine the charge state of any single detected ion by determining the pulse height distribution profile to which the corresponding ion pulse fits. The pulse characteristic distribution may be considered to be a detector response profile.

[0065] As some additional detail, pulse characteristic distributions 502, 504 were generated for product ions having a very similar m / z value of about 517. The product ions were generated from a top-down ECD analysis of carbonic anhydrase 2 (CA2), such as the spectrum depicted in FIG. 4. As discussed above, as can be seen from plot 500, the pulse height distributions resulting from different charge states can overlap significantly. In the case of such overlap, a single intensity data point is insufficient, and any charge state determination based on a single intensity data point will have a significant chance of being incorrect.

[0066] As discussed above, one approach to improve the performance of conventional charge determination algorithms is to exploit pulse characteristic distributions to group data. Often the same chemical compound has multiple isotopes that form isotopic clusters, which may or may not be resolved in the m / z domain. The m / z bins that correspond to the positions of those isotopes under certain circumstances have similar detection response profiles. This similarity allows for the grouping of m / z bins that contain information from the same compound (effectively splitting the signal between multiple channels). This results in substantially simplified spectra for subsequent charge detection analysis by conventional algorithms.

[0067] FIG. 6 is a plot 600 illustrating an example of a mass spectrum divided into m / z "bins." Each m / z "bin" represents an m / z range and includes a portion of the mass spectrum (e.g., the ion count for ions in the m / z range). The various m / z bins are then grouped based on the similarity of their detector response profiles, such as the pulse characteristic distributions discussed above. By grouping the m / z bins in such a manner, a substantially simplified spectrum may be formed for each group. These grouped spectra may be shown in the spectrum as different colors (e.g., "red," "black," "dark blue," "light blue") or distinguished in other manners or formats. In other examples, separate spectra for each of the grouped bins may be generated, displayed, and / or analyzed.

[0068] In the depicted exemplary plot 600, four distinct groups are highlighted, including a first group of m / z bins 602, a second group of m / z bins 604, a third group of m / z bins 606, and a fourth group of m / z bins 606. The corresponding detector response profile for each group is also shown, with arrows connecting the detector response profile to the associated group of m / z bins. For example, a first set of detector response profiles 612 is shown corresponding to the first group 602. This first group of m / z bins 602 share a similar detector response profile (as shown in the first set of detector response profiles 612) and therefore have been grouped together. Similarly, other sets of detector response profiles 614-618 are also depicted corresponding to their respective groups of m / z bins.

[0069] If the mass spectrometry system includes additional separation domains (e.g., residence time for LC separation, drift time, compensation voltage for ion mobility domain, etc.), one or more of the separation domains may also be used, alone or in combination, to group the signals. In some aspects, a combination of separation domains may be utilized to group the signals into multiple specific subgroups. For example, m / z bin groups may be first subgrouped based on detector response profiles and then further subgrouped based on such additional separation domain data. In other examples, m / z bin groups may be first subgrouped based on additional separation domain data and then based on detector response profiles. In other examples, m / z grouping may be based on similarity of both detector response profiles and additional separation domain data.

[0070] The grouping of m / z bins can be performed by various multivariate analysis algorithms, such as, for example, Principal Component Analysis (PCA), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE), or other known grouping algorithms known in the art. In the depicted example, the Principal Component Variable Grouping (PCVG) algorithm was used to group the m / z bins.

[0071] The use of detector response profiles to group or relate different m / z bins to each other can also be done in different ways and through different representations. For example, FIG. 7 depicts an exemplary heat map, which represents m / z on the x-axis, detector response on the y-axis, and the ion count or probability of the corresponding detector response as the intensity of the color in the heat map. For example, the darker the color, the more ions are counted at the corresponding m / z position and detector response position. The detector response axis can have units corresponding to the type of detector response profile utilized. For example, the detector response axis can represent pulse height.

[0072] Visualizations or representations capable of showing three quantities (such as a three-dimensional plot with probability or ion count as the depth axis) may also be utilized, for example a representation having a first dimension of detector response, a second dimension of m / z position, and a third dimension of probability or ion count.

[0073] By generating such visualizations or representations, the detector response profile data and m / z position information can be processed in a single algorithm. For example, these data representations can be subjected to various pattern recognition algorithms that can allow patterns to be classified and / or detected. These pattern recognition algorithms can be, for example, machine learning algorithms or image recognition algorithms.

[0074] In some embodiments, additional steps may be performed, including, for example, generating a library of detector response profiles and their associated charge states using well-characterized compounds. The library may be a generic library applicable to several instruments, or alternatively, the library may be a custom library generated for a specific instrument. The library of detector response profiles and associated charge states for each of the well-characterized compounds providing a reference template or detector response distribution may be stored and then later accessed for comparison in a subsequent analysis. For example, in the subsequent analysis, the captured detector response profile is compared to the stored detector response profiles in the library of detector response profiles to identify the corresponding stored detector response profile, thereby identifying the associated charge state for the captured detector response profile.

[0075] Optionally, the m / z positions of the compound may be stored in a library of detector response profiles and associated charge states associated with the compound of interest. In this embodiment, a step of charge state assignment is performed based on the similarity between the captured detector response profile generated from the captured mass spectrometry data captured for the compound of interest and the stored detector response profile in the library associated with the compound. The m / z positions that define one or more m / z bins are attributed to the corresponding one or more adjacent charge states for the compound. In a subsequent step, the defined one or more m / z bins may then be extracted together from the captured mass spectrometry data for subsequent analysis. Such a library of detector responses may be synonymously referred to as a catalog of detector responses.

[0076] In some cases, when a single m / z bin contains signals from multiple different species arriving at the detector, the overlap feature has not only peaks combined with each other, but also overlapping peaks. In some cases, such signals can be precisely attributed to those overlap features. Indeed, in the absence of events detected together, the total signal is the sum of the respective contributions arising from the different species, and can therefore be decomposed into individual contributions using, for example, a conventional linear algebra algorithm, such as the non-negative least squares (NNLS) algorithm, among other decomposition techniques. It is convenient to refer to a detector response profile arising from a single species and recorded under a single ion arrival condition as a basic detector response profile. In some aspects, multiple detector response profiles can be captured. Each of the multiple detector response profiles corresponds to its own basic detector response profile or an associated combination of basic detector response profiles. In either case, each detector response profile corresponding to an overlapping peak can be decomposed into its basic detector response profile.

[0077] If the condition for single ion arrival could not be satisfied for all ions, there would be arrival events where m / z bins containing signals from the same type of ion would have different detector response distributions depending on the number of ions arriving during that event. In effect, signals with multiple ions arriving simultaneously, effectively bunched together on the detector, would lead to a right shift in intensity of the detector response distribution.

[0078] 8 is a plot showing exemplary detector response profiles (e.g., distributions) in response to the same ion type arriving at different rates per acquisition cycle. In the example of FIG. 8, the ion delivery rates correspond to an average number=0.2 ions per TOF push (predominantly single ion arrivals per acquisition cycle) and an average number=6 ions per push (predominantly multiple ion arrivals per acquisition cycle). A first detector response profile (e.g., distribution) 802 corresponds to a lower ion delivery rate with primarily single ion arrivals, and a second detector response profile (e.g., distribution) 804 corresponds to a higher ion delivery rate with primarily multiple ion arrivals.

[0079] As shown, multiple ion arrivals can prevent efficient grouping of such ions. In some cases, it is difficult to satisfy the condition of a single ion arrival for each type of ion acquired. This is especially problematic when there is a large disparity in the total number of different ion species. In this case, very long acquisition times would be required to acquire data with sufficient statistics for the few ions while satisfying the condition of a single ion arrival for the many ions. It is therefore desirable to have a strategy that can tolerate a certain degree of multiplicity for ion arrivals.

[0080] Importantly, single and multiple ion arrivals can be distinguished by simple inspection of the frequency of observed detection events in an m / z bin. This process can be modeled using a Poisson distribution with a simple calculation of the occurrence of "no detection" for a particular m / z bin, and it is possible to calculate the frequency of each ion multiplicity in the same bin. Such frequencies can then be used as input to a grouping algorithm to help assign ions with different multiplicities to the same group of ions. In the case of overlapping features at higher multiplicities, they can be resolved using Bayesian structures or other suitable techniques. Based on the building blocks, several different embodiments are possible that combine the m / z and detector response domains to address the charge state determination problem.

[0081] FIG. 9 is an exemplary method 900 for charge state assignment using detector response profiles. In operation 902 of the exemplary method 900 of FIG. 9, data is acquired in a raw mode (retaining information about each detection event). For example, pulse characteristics or frequency domain information from the detector is retained so that a detector response profile can be generated. As an example, the raw data can be received as detector response signals corresponding to multiple ion arrival events. The detector response signals include information related to individual ion responses (e.g., pulses) generated by the detector for each ion arrival event. In operation 904, the data can be combined to form detector response bands and spectra, such as banded spectra as illustrated and discussed above with respect to FIG. 1C and described in the 720' publication. Operations 902 and 904 can be combined and performed during data acquisition. In some examples, operation 904 can be omitted and non-banded spectra can be used.

[0082] Following operations 902 and 904, each m / z bin (with a non-zero ion count) is grouped according to its detector response profile in operation 906. Grouping of m / z bins may include generating a list of m / z bins with similar detector response profiles. This step may be performed, for example, using a grouping algorithm such as PCA or a K-nearest neighbor algorithm that receives the detector response profile for the m / z bins as input. Grouping of m / z bins may also be based on applying a pattern recognition algorithm to a representation such as the heat map of FIG. 7 or other types of representations discussed in connection with FIG. 7. Such pattern recognition algorithms may include, for example, machine learning algorithms or image recognition algorithms. As discussed above, the grouping operation may also be based on additional separation domain data associated with the detected events / ions in the m / z bins (e.g., residence time for LC separation, drift time, compensation voltage for ion mobility domain, etc.).

[0083] Operation 906 may also include generating a detector response profile for each of the m / z bins. Generating the detector response profile may be generated during acquisition based on the pulse characteristics (or frequency domain characteristics) received for each detection event. Thus, in some examples, each pulse characteristic need not be stored as corresponding to each detected ion. Rather, the detector response (e.g., pulse characteristics) is stored as associated with the m / z bin to enable creation of a detector response profile for the m / z bin.

[0084] Based on the list and / or grouping of m / z bins generated in operation 906, a substantially simplified mass spectrum may be formed. Multiple simplified mass spectra may be generated based on the groups. For example, a different spectrum for each group or list of m / z bins may be generated. The simplified spectrum and / or spectra may then be subjected to a charge state assignment algorithm that may be applied to the m / z domain in operation 908. This operation may be performed using a charge deconvolution algorithm in m / z space, as would be understood by one of skill in the art. Application of the charge state assignment algorithm results in an assignment of charge states of the features formed by the m / z bins of a particular group. For example, each grouping of m / z bins may be assigned a charge state. Optionally, signals representing the features (e.g., signals formed by the corresponding group of m / z bins) may be converted to zero charge signals (e.g., multiplying the assigned charge by the m / z value) and added together to form a mass spectrum as part of operation 908. With the charge states identified, the specific analytes and / or the quantities of the specific analytes can be more accurately determined from the resulting mass spectra.

[0085] FIG. 10 is another exemplary method 1000 for charge state assignment using detector response profiles. In the exemplary method 1000 of FIG. 10, operations 1002-1006 are similar to operations 902-906 of method 900 depicted in FIG. 9 and described above. Operation 1008 in method 1000 adds an operation of determining or assigning a confidence score to the group or list generated in operation 1006. The confidence score may be a score of grouping quality, which represents a quantitative degree of similarity of an m / z bin to a group or groups. For example, a detector response profile of one m / z bin may partially match several other detector response profiles of other m / z bins. However, the grouping is ultimately based on the best match of the partial maps. The strength of the match may be represented by a confidence score. The grouping / list generated in operation 1006 and the confidence score output generated in operation 1008 may then be used as input to a charge state determination algorithm in m / z space / domain in operation 1010, similar to operation 908 in method 900 of Figure 9. In some examples, the charge state determination algorithm may be based on a Bayesian structure, such as the UniDec algorithm (Marty et. al Anal. Chem. 2015,87,8,4370-4376), which benefits from the additional confidence scores generated in operation 1008.

[0086] 11 is another exemplary method 1100 for charge state assignment using detector response profiles. In the exemplary method 1100 of FIG. 11, operations 1102-1106 are similar to operations 902-906 of method 900 depicted in FIG. 9 and described above. In operation 1108, m / z bins with signals due to the elementary detector response profile are identified. One exemplary method for this is to examine the m / z bins included in each group for resemblance to full or partial isotope clusters to which at least two isotopes belong. The corresponding detector responses from those m / z bins in the group are due to the elementary detector response.

[0087] In operation 1110, m / z bins with overlapping detector response profiles are found (or identified). This operation may be completed by identifying all non-zero m / z bins not identified in operation 1108. These identified m / z bins with overlapping detector response profiles may be due to overlapping signals. In operation 1112, the overlapping signals for each of the m / z bins identified in operation 1110 are decomposed into elementary signals using known algorithms such as the Non-Negative Least Squares (NNLS) algorithm. In operation 1114, a group / list of m / z bins is completed using the decomposed signals generated in operation 1112. For example, some of the decomposed elementary signals are then grouped / listed, whereby the corresponding signal (e.g., ion count) amounts from each contributing detector response profile are added to the correct group. In operation 1116, the group / list of m / z bins is subjected to a charge state assignment algorithm in the m / z domain similar to the operations discussed above. For example, discrimination may be based on the relative distance of peaks forming, for example, an isotopic cluster.

[0088] FIG. 12 is another exemplary method 1200 for charge state assignment using detector response profiles. In the exemplary method 1200 of FIG. 12, operations 1202-1206 are similar to operations 902-906 of method 900 depicted in FIG. 9 and described above. In operation 1208, a determination is made as to whether the group / list represents a single ion arrival. For example, a group of m / z bins that represent a single ion arrival event may be identified. Such a determination or identification may be made by analyzing characteristics of the corresponding detector response profile and determining whether the detector response profile has characteristics more similar to a single arrival event (e.g., detector response profile 802 in FIG. 8) or to multiple ion arrivals (e.g., detector response profile 804 in FIG. 8). Single ion arrivals and multiple ion arrivals are also distinguished by a simple examination of the frequency of detection events observed in an m / z bin. This process can be modeled using a simple calculation of the occurrence of "no detections" for a particular m / z bin using a Poisson distribution, and it is possible to calculate the frequency for each ion multiplicity in the same bin. Such frequencies can then be used as input to a grouping algorithm to help assign ions with different multiplicities to the same group of ions.

[0089] In operation 1210, the grouped m / z bins may be filtered or further grouped based on the multiplicity of the ion detection events. For example, m / z bins with single ion events may be retained or filtered into the single ion event category. m / z bins with multiple ion events may then be removed or filtered into the multiple ion event category. Notably, m / z bins with multiple ion events may initially be grouped together because their respective detector response profiles most closely match the detector response profiles of other m / z bins with multiple ion events.

[0090] In some examples, a base detector response profile (e.g., a detector response profile when a single-ion condition occurs) may be generated or simulated for an m / z bin with multiple ion events. Such generation of the base response profile may be from a stored library or database storing correlations with multiple ion response profiles. Other techniques may also be implemented to generate the base response profile. The generated base response profile may then be used for grouping / listing of m / z bins. Thus, contributions from m / z bins with multiple ion events may still be used with m / z bins with single-ion events.

[0091] Acts 1212-1218 are then similar to acts 1110-1116 of method 1100 of Figure 11, discussed above. In the case of overlapping features at higher multiplicities, they may be decomposed using a Bayesian structure or other suitable techniques.

[0092] The operations of the methods described above in Figures 9-12 (among other operations described herein) may be implemented in systems and / or system components discussed herein, such as the systems in Figures 1-3. For example, the operations may be performed by one or more processors according to instructions stored in memory.

[0093] While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those skilled in the art.

[0094] For example, aspects of the disclosure are described above with reference to block diagrams and / or operational illustrations of methods, systems, and computer program products according to aspects of the disclosure. The functions / acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially in parallel or the blocks may sometimes be executed in the reverse order, depending on the functionality / acts involved. Furthermore, as used in this specification and claims, the phrase "at least one of element A, element B, or element C" is intended to convey any of element A, element B, element C, elements A and B, elements A and C, elements B and C, and elements A, B, and C.

[0095] The summary and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the claimed disclosure in any way. The aspects, examples, and details provided in this application are deemed sufficient to convey ownership and enable others to make and use the claimed disclosure in its best form. The claimed disclosure should not be interpreted as being limited to any aspect, example, or detail provided in this application. Various features (both structural and methodological), whether shown and described in combination or separately, are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Given the summary and illustrations of this application, one skilled in the art may envision variations, modifications, and alternative aspects that fall within the spirit of the general inventive concept of the broader aspects embodied in this application without departing from the broader scope of the claimed disclosure.

Claims

1. 1. A method for assigning charge states in mass spectrometry, the method comprising: receiving a detector response signal corresponding to a plurality of ion arrival events, the detector response signal comprising information relating to individual ion responses generated by the detector for each ion arrival event; generating a detector response profile for mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival event based on the detector response signal; grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins; assigning a charge state to one or more features based on the group of m / z bins; A method comprising:

2. 2. The method of claim 1, further comprising generating a simplified mass spectrum based on the groups of m / z bins, the groups of m / z bins being represented in the simplified mass spectrum.

3. The method of any one of claims 1-2, further comprising calculating a mass corresponding to said ion arrival event based on said assigned charge states.

4. The method of any one of claims 1-3, wherein grouping the m / z bins is further based on additional separation domain data.

5. The method of claim 4 , wherein the additional separation domain data includes at least one of a residence time, a drift time, or a compensation voltage for an ion mobility.

6. The method of any one of claims 1-5, wherein grouping the m / z bins is performed using Principal Component Analysis (PCA), a k-means clustering algorithm, or a Principal Component Variable Grouping (PCVG) algorithm.

7. The method of any one of claims 1-6, further comprising generating a representation of the ion arrival events, the representation having an m / z dimension, a detector response dimension, and an ion count or probability dimension.

8. The method of claim 7 , wherein the representation is a heat map.

9. The method of any one of claims 7-8, wherein grouping the m / z bins is performed at least in part by applying a pattern recognition algorithm to the representation.

10. 1. A system for assigning charge states in mass spectrometry, the system comprising: a detector configured to generate a detector response signal for each ion arrival event; A processor; A memory that stores instructions Equipped with The instructions, when executed by a processor, receiving, from the detector, a detector response signal corresponding to the plurality of ion arrival events, the detector response signal comprising information relating to an individual ion response generated by the detector for each ion arrival event; generating a detector response profile for mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival event based on the detector response signal; grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins; assigning a charge state to one or more features based on the group of m / z bins; The system is configured to cause the system to perform a set of operations including:

11. 11. The system of claim 10, wherein the operations further include generating a simplified mass spectrum based on the group of m / z bins, the group of m / z bins being represented in the simplified mass spectrum.

12. The system of any one of claims 10-11, wherein grouping the m / z bins is further based on additional separation domain data.

13. The system of claim 12 , wherein the additional separation domain data includes at least one of a residence time, a drift time, or a compensation voltage for an ion mobility.

14. The system of any one of claims 10-13, wherein grouping the m / z bins is performed using Principal Component Analysis (PCA), a k-means clustering algorithm, or a Principal Component Variable Grouping (PCVG) algorithm.

15. A system according to any one of claims 10-14, further comprising generating a representation of the ion arrival events, the representation having an m / z dimension, a detector response dimension and an ion count or probability dimension.

16. The system of any one of claims 10-14, further comprising generating confidence scores for the groups, wherein assigning the charge states is further based on at least one of the confidence scores.

17. 1. A method for assigning charge states in mass spectrometry, the method comprising: receiving a detector response signal corresponding to a plurality of ion arrival events, the detector response signal comprising information relating to individual ion responses generated by the detector for each ion arrival event; generating a detector response profile for mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival event based on the detector response signal; grouping the m / z bins into a plurality of groups based on similarity of the detector response profiles of the m / z bins; identifying m / z bins that represent single ion arrival events; assigning a charge state to one or more features based on the group of m / z bins identified as having a single ion arrival event; A method comprising:

18. The method of claim 17 , further comprising generating confidence scores for the groups, and wherein assigning the charge states is further based on at least one of the confidence scores.

19. 19. The method of claim 17 or 18, wherein grouping the m / z bins is further based on additional separation domain data including at least one of residence time, drift time, or compensation voltage for ion mobility.

20. The method of any one of claims 17-19, wherein identifying an m / z bin representing a single ion arrival event is based on the frequency of detection events observed in that m / z bin.

21. 1. A method for analysing data in mass spectrometry, the method comprising: receiving a detector response signal corresponding to a plurality of ion arrival events, the detector response signal comprising information relating to individual ion responses generated by the detector for each ion arrival event; generating a data representation based on the detector response signal, the data representation consisting of at least a detector response profile and mass-to-charge (m / z) bins of a mass spectrum generated from the ion arrival events; utilizing said data representation for at least one of compound identification and species identification; A method comprising:

22. 22. The method of claim 21, wherein the compound identification comprises compound quantification.

23. The method of any of claims 21-22, wherein said compound identification comprises assigning a charge to at least one detected group of ions.

24. The method of claim 23 , wherein the assigning of charge is based at least in part on the detector response profile.

25. The method of any of claims 21-24, further comprising simplifying the data representation.

26. 26. The method of claim 25, wherein the simplified data representation comprises at least one higher ranked tensor data.

27. 26. The method of claim 25, wherein simplifying the data representation comprises generating one or more spectra in the m / z domain.

28. A method according to any of claims 25-26, wherein simplifying the data representation comprises generating one or more spectra in the mass domain.

29. The method of any of claims 25-28, wherein simplifying the data representation is based at least in part on the detector response profile.

30. The method of any of claims 25-29, wherein simplifying the data representation comprises grouping the m / z bins with similar detector response profiles.

31. The method of any of claims 21-30, further comprising calculating a mass corresponding to said ion arrival event based on an assigned charge state.

32. The method of any of claims 25-31, wherein the simplifying of the data representation is based on additional separate domain data.

33. 33. The method of claim 32, wherein the additional separation domain data comprises at least one of a dwell time, a drift time, and a compensation voltage for ion mobility.

34. The method of any of claims 25-33, wherein simplifying the data representation is performed at least in part by using multivariate analysis.

35. The method of any of claims 25-33, wherein simplifying the data representation is performed based at least in part by applying a non-negative factorization algorithm.

36. 35. The method of claim 34, wherein the multivariate analysis comprises at least one of a principal component analysis (PCA), a k-means clustering algorithm, a t-SNE algorithm, and a principal component variable grouping (PCVG) algorithm.

37. The method of any of claims 25-33, wherein simplifying the data representation is performed at least in part based on using pattern recognition or machine learning.

38. A method according to any of claims 25-33, wherein simplifying the data representation is performed using statistical methods for at least partially matching observed detector responses to a catalogue of predetermined detector response distributions.

39. 40. The method of claim 38, wherein the catalog is generated a priori.

40. 40. The method of claim 38, wherein the catalog is generated from the simplified data representation.

41. 22. The method of claim 21, wherein the compound identification or the species identification comprises generating a compound or species library and using an algorithm to match compounds or species to the library.

42. A detection system for carrying out the method of any of claims 1-9 or 17-41, said detection system being one of an electron multiplier based detection system or an image charge based detection system.

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