CN115605976A - Charge state determination for single ion detection events - Google Patents

Abstract

Methods and systems for identifying or classifying charge states of detected ions. An example method for classifying charge states of detected ions may include generating a pulse for each ion of a plurality of ions detected by a detector, wherein each pulse has a pulse characteristic (e.g., pulse height, width, or area); generating a pulse-characteristic distribution for the generated pulses; and generating an identification of a charge state of one or more ions of the plurality of ions based on the pulse-signature distribution.

Description

Charge state determination for single ion detection events

Cross References to Related Cases

This application was filed as a PCT International Patent Application on May 14, 2021, and claims priority to U.S. Patent Application Serial No. 63/024,987, filed May 14, 2020, the entire disclosure of which is incorporated herein by reference in its entirety.

Background technique

As a general overview, mass spectrometry (MS) is an analytical technique for the detection and quantification of compounds based on the analysis of the mass-to-charge (m/z) values of the ions formed by 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 ionizing one or more compounds of interest from a sample, selecting one or more precursor ions of one or more compounds, Fragmentation of precursor ions into product ions, and mass analysis of product ions.

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

Mass spectrometry techniques often utilize the mass-to-charge ratio (m/z) of detected ions to generate mass spectral data. However, knowledge of the actual charge or mass of the detected ions is often not directly measurable. Therefore, some overlap of detected ions occurs in some scenarios. For example, a singly charged ion with a mass can appear in a mass spectrum to have the same mass-to-charge ratio as a doubly charged ion with twice the mass. This problem may generally be referred to as the peak overlapping problem.

In top-down mass spectrometry (MS) protein analysis, for example, overlapping 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 fragments or product ions are produced, including product ions that are 1-200 amino acids in length and have 1-50 different charge states. Product ion peaks heavily overlap each other in a single spectrum. Furthermore, 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 these overlapping peaks. Consequently, large product ions are often lost in top-down protein analysis, limiting sequence coverage of large proteins. International publications WO2020/157720, published 6 August 2020, and WO2019/197983, published 17 October 2019, both provide additional discussion of top-down MS protein analysis and related challenges.

Contents of the invention

In one aspect, the technology relates to a method of classifying the charge state of detected ions, the method comprising: generating a pulse for each of a plurality of ions detected by a detector, wherein each pulse has a pulse characteristic ; generating a pulse-characteristic distribution for the generated pulse; and generating an identification of a charge state of one or more ions of the plurality of ions based on the pulse-characteristic distribution.

In an example, the impulse-characteristic distribution is a plot of probability versus impulse characteristic. In another example, the pulse characteristic is at least one of pulse height, pulse width, or pulse area. In yet another example, the pulse characteristic is the pulse height, and the pulse height is the maximum voltage of the pulse. In yet another example, the detector is an electron multiplier detector and the detector is configured to detect primarily single ion events. In yet another example, generating the signature of the state of charge includes comparing the pulse-characteristic distribution to a reference pulse-characteristic distribution. In yet another example, the ions detected by the detector are generated by ionization of the sample, and the reference pulse-characteristic distribution is identified based on known characteristics of the sample.

In another example, the generated identification includes a probability of state of charge. In another example, the method further includes generating a deconvoluted mass spectrum for the detected ions based on the identification of the charge state, wherein one axis of the mass spectrum is mass rather than mass per charge (m/z). In yet another example, the m/z domain is used to group multiple ions into different groups, and for each group an identification of charge states based on pulse-characteristic distributions is performed. In yet another example, the step of grouping includes generating a mass spectrum based on a plurality of detected ions; identifying a first peak in the mass spectrum, wherein the first peak has a mass per charge (m/z) value; and based on the m/z value of the first peak The z value, which groups ions in the mass per charge (m/z) range. In yet another example, the step of grouping includes selecting a first subset of the plurality of ions into a first intensity band and selecting a second subset of the plurality of ions into a second intensity band; generating a second mass spectrum for a second intensity band; identifying a first peak in at least one of the mass spectra, wherein the first peak has a mass per charge (m/z) value; and based on the first peak The m/z value of , groups ions in each charge mass (m/z) range.

In another example, the method further includes generating a second pulse-characteristic distribution for ions in the m/z range, and generating the signature includes: determining that ions forming the first pulse-characteristic distribution have a first charge state; The ions of the second pulse-characteristic distribution have a second charge state. In another example, the method further includes determining one or more isotopes corresponding to the one or more ions forming the first peak based on the identification of the charge state. In yet another example, generating the signature of the state of charge includes comparing the first pulse-characteristic distribution to a reference pulse-characteristic distribution. In yet another example, ions detected by the detector are generated by ionization of the sample, and a reference pulse-characteristic distribution is identified based on known characteristics of the sample. In yet another example, the generated identification includes a probability of state of charge. In another example, the method is performed as part of a top-down protein analysis.

In another example, the method further includes identifying a second peak; determining a consistent m/z distance based at least on the first peak and the second peak; identifying the first peak and the second peak forming a signature; and wherein generating an identification of the charge state is based on consistent distances. In another example, the step of identifying peak-forming characteristics includes comparing impulse-characteristic distributions of said peaks and selecting peaks having substantially the same impulse-characteristic distribution. In yet another example, the comparison of pulse-characteristic distributions is performed by calculating a Euclidean distance between said pulse-characteristic distributions and comparing it to a predetermined threshold. In yet another example, the method further includes identifying missing peaks corresponding to the features based on the consistent distance. In yet another example, the method further includes generating a deconvoluted mass spectrum for the detected ion based on the identification of the ion's charge state, wherein one axis of the mass spectrum is mass rather than m/z. In another example, the pulse characteristic is the maximum voltage of the pulse.

In another aspect, the technology relates to mass analysis systems. The mass analysis system includes a detector configured to generate a pulse for each ion detected by the detector; a processor; and a memory storing instructions configured to cause the system to perform a set of operations when executed by the processor. The operations include generating a pulse for each of the plurality of ions striking the electron multiplier detector, wherein each pulse has a pulse characteristic; generating a pulse-characteristic distribution for the generated pulses; and based on the pulse-characteristic distribution, generating a plurality of An identification of the charge state of one or more ions in an ion. In an example, the detector is an electron multiplier detector. In another example, the mass analysis system also includes an ion source device, a dissociation device, and a mass analyzer.

In another aspect, the technology relates to a method for classifying the charge state of detected ions, the method comprising detecting, using a processor, an image of - transient time domain signal induced on a charge detector; converting the transient time domain signal into a plurality of frequency domain (FD) peaks corresponding to ions of the plurality of ions; generating FD-peaks for the generated pulses - a characteristic distribution; and, based on the FD-peak-characteristic distribution, generating an identification of the charge state of one or more ions of the plurality of ions.

In an example, the FD-peak-characteristic distribution is a plot of probability versus FD-peak-characteristic. In another example, the FD-peak-characteristic is peak intensity. In yet another example, generating the signature of the charge state includes comparing the FD-peak-characteristic distribution to a reference FD-peak-characteristic distribution. In another example, ions detected by the detector are generated by ionization of the sample, and a reference FD-peak-characteristic distribution is identified based on known characteristics of the sample. In yet another example, the generated identification includes a probability of state of charge. In yet another example, the method further includes generating a deconvoluted mass spectrum for the detected ion based on the identity of the charge state, wherein one axis of the mass spectrum is mass rather than mass per charge (m/z).

In another aspect, the technology relates to a method for classifying the charge state of detected ions. The method includes generating a pulse for each of a plurality of ions detected by a detector, where each pulse has a pulse signature; generating a pulse-characteristic distribution for the generated pulses; and based on the pulse-characteristic distribution, identifying a coarse charge state; identifying a peak pair of a first ion peak and a second ion peak such that the peaks have adjacent charge states; and based on the m/z value of the first ion peak, the m/z value of the second ion peak, and the charge state The mass of the charge carrier determines the refined charge state of the second ion peak.

In an example, the coarse charge state identification is accurate to a range of possible charge states for at least one peak forming a pair, and at least one charge state from the range is adjacent to the charge state identified for the second peak. In another example, the method further includes accepting the refined state of charge identification if the refined identified state of charge is an integer within a certain threshold. In yet another example, a third peak having an adjacent charge state is identified and forms a pair with at least one of the peaks, and the charge state identifications for the common peak match in both pairs. In yet another example, the method further includes determining the charge state of the first ion peak based on the determined charge state of the second ion peak. In yet another example, the method further includes obtaining the mass of the charge carriers based on known characteristics of the sample that was ionized to generate the plurality of ions.

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 features 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 which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.

Description of drawings

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

Figure 1A depicts an example system for performing mass spectrometry.

Figure 1B depicts an example diagram of ion pulses.

Figure 1C depicts an example mass spectrum separated into different bands or channels based on ion pulse strength.

Figure 2 depicts a graph of an example pulse-height distribution.

FIG. 3A depicts an example method for charge state assignment.

FIG. 3B depicts another example method for charge state assignment.

FIG. 3C depicts another example method for charge state assignment.

FIG. 3D depicts another example method for charge state assignment.

FIG. 3E depicts another example method for charge state assignment.

FIG. 3F depicts another example method for charge state assignment.

FIG. 4 depicts another example method for charge state assignment.

Figure 5 depicts an example zoom-in mass spectrum.

Figure 6 depicts an example peak detection operation for strip mass spectrometry.

FIG. 7 depicts example pulse-height distributions from FIG. 6 for selected peaks.

Figure 8 depicts an example pairwise similarity matrix.

Figure 9 depicts an example calculation for the most plausible state of charge.

10 depicts an example diagram for performing calculations for feature attributes.

Figure 11 depicts example plots for adjacent charge state peaks.

Figure 12 depicts an example graph of a transient time-domain signal measured by an image-charge detector.

Figure 13 depicts an example system including an image-charge detector.

detailed description

As discussed briefly above, peak overlap of detected ions is problematic for analysis of MS results. To address this peak overlap problem, one solution is to determine or infer the charge states of the ions that form the peaks. By determining the state of charge, the mass of the ions can then be resolved and ions from different species can be distinguished from each other. Additionally, multiple peaks in a mass spectrum can represent isotopic clusters or signatures. However, in some cases it may not be clear which peaks belong to which cluster. Charge state identification techniques can also address identification of the correct peaks for such clusters.

Analog-to-digital conversion (ADC) strip methods have previously been proposed for separating ions based on their intensity. One such example of the striping approach is disclosed in International Publication WO 2020/157720 published on August 6, 2020 (the '720 publication), which is incorporated herein by reference in its entirety. This ionic strength-based separation facilitates charge state separation, which increases peak capacity. However, the described method does not teach how to separate ions based on their charge state. This leads to two problems: firstly, the signal from the same species is diluted between multiple data channels, and secondly, there is no proposed way to construct a deconvoluted mass spectrum that facilitates subsequent data interpretation. Therefore, an improved method that can assign charge states to individual ion detection events is desired.

One such method has recently been published in Kafader et al., Multiplexed massspectrometry of individual ions improves measurement of proteoforms and their complexes, Nature Methods, Nature Methods Vol. 17, pp. 391-394 (2020). However, the method described in this paper is limited to mass spectrometers with detection systems in which the detected signal has a deterministic relationship to the measured charge (eg image-charge-sensitive detectors). Thus, among other things, the method described in this paper does not teach how to set up charge assignments for each individual ion measurement event of a mass spectrometer based detection system in which the measured signal has a probabilistic relationship to the measured charge (e.g., Electron multiplier based detection system).

In some of these new classes of acquisition strategies, direct identification of charge states is attempted before the corresponding signals are collectively added to the mass spectrum (see Kafader et al.). However, such strategies are not suitable for charge state assignment in electron multiplier detection systems. For systems based on electron multiplier detection systems, in many cases each charge state does not have a unique detector response, but instead has a unique pulse height distribution specific to each charge state and m/z value (more generally intensity distribution).

The present technique allows the charge state of ions to be determined or inferred from the characteristics of the pulses generated by the detector when the ions are detected. To this end, the present technique generates a distribution of pulse characteristics for multiple detected ions. Pulse characteristics may include pulse height, pulse width, or pulse area, among other possible characteristics. Depending on the charge state of the detected ions, the distribution of pulse characteristics forms a unique profile. Thus, the state of charge can be determined from the pulse-characteristic distribution. Once the charge state of an ion is determined, the mass of the ion can be determined based on its m/z and the ion can be distinguished from other ions. Ultimately, compounds analyzed by MS techniques can be identified based on the determined charge states of the detected ions. Thus, by identifying and/or assigning charge states of ions, the measurement capabilities of mass analytical instruments are improved. The accuracy of mass analysis instruments can be similarly improved.

FIG. 1A depicts an example mass analysis system 100 for performing mass spectrometry techniques. In some examples, system 100 may be a mass spectrometer. The example 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 memory 106 . For example, ion source device 101 may be an electrospray ion source (ESI) device. The ion source device 101 is shown as part of a mass spectrometer or may be a separate device. For example, the dissociation device 102 may be 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 may include, but is not limited to, electron capture dissociation (ECD) or electron transfer dissociation (ETD). CID is the most common dissociation technique used in tandem mass spectrometers. As mentioned above, in top-down and middle-down proteomics, intact or digested proteins are ionized and subjected to tandem mass spectrometry analysis. For example, ECD is a dissociation technique that preferentially dissociates peptides and protein backbones. Therefore, this technique is an ideal tool for analyzing peptide or protein sequences using top-down and middle-down proteomic approaches.

The mass analyzer 103 may be any type of mass analyzer for the desired technology, such as a time-of-flight (TOF), ion trap or quadrupole mass analyzer. Detector 104 may be a suitable detector for detecting ions and generating the signals discussed herein. For example, detector 104 may include an electron multiplying detector, which may include analog-to-digital conversion (ADC) circuitry. Detector 104 may also be an image charge-sensitive detector. Detector 104 generates detection pulses for detected ions.

Computing elements of system 100, such as processor 105 and memory 106, may be included in the mass spectrometer itself, located near the mass spectrometer, or located remotely from the mass spectrometer. In general, a computing element of the system may be in electronic communication with detector 104 such that the computing element can receive signals generated from detector 104 . Processor 105 may include a plurality of processors and may include any type of suitable processing component for processing signals and generating the results discussed herein. Depending on the exact configuration, memory 106 (in particular storing quality analysis programs and instructions to perform the operations disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or both. some combination of those. Other computing elements may also be included in system 100 . For example, 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 device(s) such as touch screen, keyboard, mouse, pen, voice input, etc., and/or output device(s) such as display, speakers, printer, etc. One or more communication links such as local area network (LAN), wide area network (WAN), point-to-point, Bluetooth, RF, etc. may also be incorporated into system 100 .

FIG. 1B depicts an example graph 110 of ion pulses generated from a detector, such as an electron multiplier detector. The y-axis represents intensity and the x-axis represents time. Intensity can be measured in 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.

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 the arrival of a different single ion to the detector. Pulses 111-113 can be digitized and peaks can be found from each digitized pulse. Intensity (or peak height) and arrival time pairs can be calculated and stored for each pulse. Rectangles 131, 132 and 133 represent the intensity or pulse height of the respective pulses.

Each pulse can be characterized by a pulse characteristic. Pulse characteristics may include characteristics such as pulse height, pulse width, and/or area under the pulse curve. The pulse height of each pulse is indicated by rectangles 131 , 132 and 132 . The pulse height may be a maximum pulse height for a corresponding pulse, and the pulse height may have a unit of voltage. The pulse width can be at any point in the pulse, but one measure of pulse width can be full width at half maximum (FWHM). Pulse width can have units of time. The area under the pulse curve can be generated by integrating the area under the corresponding pulse signal for each pulse.

The pulse feature can be used to separate the detected ions into different bands. Figure 1C depicts an example mass spectrum 150 that is 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 between 10-20 mV. A second mass spectrum 170 is generated for detected ions with a maximum pulse height between 20-30 mV. A third mass spectrum 180 is generated for ions detected with a maximum pulse height between 20-30 mV. Further details on this separation and banding are discussed further in the '720 publication. As discussed above, while there are benefits to separating ions into distinct bands, this separation does not allow identification of the charge state of a particular ion. As discussed further herein, pulse characteristics can be used to generate distribution profiles that allow charge state classification of ions.

FIG. 2 depicts a graph 200 of an example pulse-characteristic distribution. The pulse-characteristic distribution in graph 200 is based on the characteristics of the pulse height. Thus, the pulse-characteristic distribution may be referred to as pulse-height distribution or intensity distribution. In the graph, the x-axis represents the pulse height and the y-axis represents the probability or frequency of detection. For example, higher probability values indicate that ions with corresponding pulse heights are detected more frequently.

A first pulse-height distribution 202 and a second pulse-height distribution 204 are depicted in graph 200 . As can be seen from graph 200 , the pulse-height distributions overlap, but the first pulse-height distribution 202 has a different profile than the second pulse-height distribution 204 . The difference in profile shape is mainly due to the difference in the charge states of the detected ions forming the corresponding pulse-height distribution. For example, the detected ions forming the first pulse-height distribution 202 correspond to 3+ charged ions, while the detected ions forming the second pulse-height distribution 204 correspond to 7+ charged ions. Thus, once the various pulse-height distributions have been established or generated, it may be possible to determine the charge state of any individual detected ion by determining to which pulse-height distribution profile the corresponding ion pulse fits.

As some additional detail, the pulse-height distributions 202 , 204 are generated for product ions that have very similar m/z values at about 517 . Product ions were generated by top-down ECD analysis of carbonic anhydrase 2 (CA2). As discussed above, as seen in graph 200, pulse-height distributions originating from different charge states can overlap significantly. With such an overlap, a single intensity data point is not sufficient, and any charge state determination based on a single intensity data point will have a high chance of being incorrect.

However, for a collection of single-ion detection events originating from the same sample, it is sometimes possible to infer the charge state. This can be achieved either by comparing the pulse height distribution of such ensembles to that of known compounds of similar m/z and charge state, or using additional information usually available based on the properties of the sample being analyzed . An example of such additional information could be unique isotopic patterns that, if resolved by a mass spectrometer, encode charge state information into their relative positions in m/z space. Alternatively, charge state distributions can be used for this purpose, which also encode charge state information in relative positions in m/z space. In this disclosure, a method is described for charge state identification of a single detection event based on grouping of similar detection events in a set, assigning charge states to the set, and then assigning charge state information to individual events.

In an example, a data set containing pulse height distributions for different charge states and m/z is collected. Pulse-height distributions for groups of ions can be used to assign charge states to individual detection events. For detection events corresponding to unknown species, the set of detection events may be selected based on their relative proximity in m/z space. Pulse-height distributions can be calculated for such sets. This pulse-height distribution can then be matched against known pulse-height distributions from similar m/z and the "best" match selected. As will be appreciated, the term "best" may be used to identify a relatively determined optimal state, given the data acquired and the determination effort applied. The best matching charge state is then inferred for each detection event and/or corresponding ion from the collection. Alternatively or additionally, a model can be constructed based on well-characterized data of the intensity distribution for different m/z, and the charge state can be predicted based on this model. This can be achieved, for example, using machine learning techniques, where the training dataset can contain a priori collected annotated data.

In another example, in a first step, the data collected for the individual detection events may be summed to form a conventional mass spectrum or alternatively multiple mass spectra based on their intensities. In the second step, algorithms are employed for feature extraction and feature charge state assignment, where features are isotope clusters or charge state clusters corresponding to the same molecule. In a third step, the determination of the set of ion detection events corresponding to those features and subsequent inference of the charge state is performed.

In another example, a data acquisition and processing strategy is presented that combines information about the pulse-height distribution of individual ion groups with additional information about sample properties, such as isotope patterns or charge state distributions, to identify source A collection of detection events for self-similar ions and then assign charge states to individual ions.

FIG. 3A depicts an example method 300 for charge state assignment. At an operation 301, a pulse is generated for each ion of a plurality of ions detected by a detector. For example, when ions strike an electron multiplier detector, a pulse is generated. The pulses generated may be similar to those depicted in Figure IB and discussed above. Each pulse may have or be described by their corresponding pulse characteristics. The detected ions may be ions from ionization of a sample studied or analyzed by mass spectrometry techniques. At an operation 302, one or more pulse-characteristic distributions are generated based on the characteristics of the pulses. For example, the generated pulse-characteristic distribution can be generated for a specific pulse characteristic, such as pulse height. Thus, the pulse-to-characteristic distribution(s) generated in operation 302 may be a pulse-height distribution similar to the pulse-height distribution depicted in FIG. 2 and discussed above.

Based on the pulse-characteristic distribution(s) generated in operation 302 , an identification of the charge state of one or more ions of the plurality of ions may be generated at operation 303 . For example, the generated pulse-characteristic distribution may be compared to a set of reference pulse-characteristic distributions with known charge states to determine the closest match to the generated pulse-characteristic distribution. The charge states of the ions forming the generated pulse-characteristic distribution can then be assigned the charge states associated with the reference pulse-characteristic distribution. Based on external information or about known characteristics of the analyzed sample and/or m/z values of the ions forming the generated pulse-characteristic distribution, it is possible to limit or reduce the reference pulse-characteristics to which the generated pulse-characteristic distribution is compared The number of distributions. For example, for a particular m/z value or range, a subset of the reference pulse-characteristic distribution may exist, and/or a subset of the reference pulse-characteristic distribution may correspond to a particular isotope, compound, and/or sample.

In other examples, a machine learning model (eg, a neural network) can be trained based on a reference pulse-signature distribution with known charge states. The resulting impulse-characteristic distribution can be provided as input to a trained machine learning model. The trained machine learning model processes the pulse-characteristic distribution generated by the input, and the output of the trained machine learning model indicates the charge state or possible charge states corresponding to the generated pulse-characteristic distribution. For example, the output of the machine learning model may be an indication of the state of charge and/or an indication of a reference pulse-characteristic distribution that most closely matches the generated pulse-characteristic distribution. In some examples, the input to the trained machine learning model may also include m/z values or m/z ranges of ions forming the generated pulse-characteristic distribution. Additionally or alternatively, the input may also include external data about the sample, such as expected compounds or isotope types. In other examples, different machine learning models can be trained for different types of samples, and the machine learning model for the sample being analyzed can be selected for use in analyzing the generated impulse-characteristic distribution.

The identification or assignment of charge states may also or alternatively be based on grouping peaks together as features and then analyzing the relative distance between the grouped peaks. Additional details regarding this charge assignment process are discussed in more detail below with respect to method 3000 in FIG. 3D.

Returning to method 300 in FIG. 3A, at operation 304, a mass spectrum is generated for the detected ions. A mass spectrum may be generated based on the charge state(s) identified in operation 303 . For example, overlapping peaks can be resolved or otherwise indicated in the mass spectrum based on known charge states. For example, since the charge states of the ions used to form the mass spectrum are known or identified in operation 303, a deconvoluted mass spectrum for the detected ions may be generated. For deconvoluted mass spectra, one axis of the mass spectrum can be mass instead of mass per charge (m/z). At an operation 305, the compound or amount of the compound in the sample corresponding to the detected ion may be identified. The compound or amount of the compound can be identified from the mass spectrum generated in operation 304 and/or from the charge state(s) identified in operation 303 .

FIG. 3B depicts another example method 310 for charge state assignment. At an operation 311, a pulse is generated for each ion of the plurality of ions detected by the detector. Operation 311 may be substantially the same as or similar to operation 301 discussed above. Generated pulses can be stored in memory for subsequent analysis. At operation 312, a mass spectrum is generated based on the m/z values of the detected ions. At operation 313, peaks in the mass spectrum may be selected and/or identified. For example, peaks can be automatically identified by a peak-finding algorithm. In other examples, peaks may be identified and/or selected based on manual input received from a user. Identified peaks have associated m/z positions or values. The m/z position can be the center position of the peak and/or the centroid or weighted average m/z position on the peak.

At operation 314 , one or more pulse-characteristic distributions are generated for the ions forming the peak identified in operation 313 . The ions forming the peaks may be ions identified via a peak finding algorithm. The ions may also be ions within a particular m/z range of the m/z value of the identified peak. For example, the m/z range can be selected based on the characteristics of the peak and/or can be a preset range (eg, a fixed m/z value). As an example, the m/z range may be from a peak's start m/z value (ie, where the peak begins) to a peak's end m/z value (ie, where the peak ends).

For each ion forming a peak and/or in the m/z range, the corresponding pulses for those ions can be accessed. Pulses may be plotted or stored in a manner indicative of probability or frequency versus pulse characteristics (eg, pulse height). For example, the map can be similar to the map in Figure 2, or an array of pulse signatures and probability/frequency pairs can be generated and/or stored. One or more pulse-characteristic distributions can then be identified or generated for the pulse. For example, multiple pulse-characteristic distributions can be generated if peaks are formed by ions with different charge states.

At an operation 315 , based on the pulse-characteristic distribution generated in operation 314 , the charge state of one or more ions forming the identified peak is identified and/or assigned. Operation 315 may be similar to operation 303 in FIG. 3A and discussed above, and identifying charge states may be identified in a similar manner as discussed above. Based on the identified and/or assigned charge states, a mass spectrum can be generated and/or the compound or amount of the compound can be identified, as discussed above with reference to operations 304 and 305 .

FIG. 3C depicts another example method 320 for charge state assignment. At operation 321, a pulse is generated for each ion of the plurality of ions detected by the detector. Operation 321 may be the same as or similar to operation 301 discussed above. At operation 322, the detected ions are grouped according to their respective pulse characteristics. As an example, using the pulse characteristic of pulse heights, ions can be grouped into intensity bands based on their respective pulse heights. For example, based on the pulse characteristics of each ion, a first subset of the plurality of ions may be grouped into first intensity bands, and a second subset of the plurality of ions may be grouped into second intensity bands.

At operation 323, a mass spectrum for each intensity band may be generated. For example, where two intensity bands are used, a first mass spectrum may be generated for the first intensity band, and a second mass spectrum may be generated from the second intensity band. The mass spectrum can be similar to that depicted in Figure 1C and discussed above.

At operation 324 , one or more peaks are identified from the mass spectrum generated in operation 323 . Peak identification and/or selection may be performed in the same or similar manner as in operation 313 discussed above. By generating a mass spectrum prior to identifying the peak(s), background noise can be reduced or removed. For example, one or more of the intensity bands may exhibit a sharper signal and/or more clearly defined peaks than a single aggregated mass spectrum for all ions. As such, peaks can be more accurately identified.

At operation 325, ions that form the peak(s) identified in operation 323 and/or are within the m/z range of the m/z value of the identified peak(s) may be generated One or more pulse-characteristic distributions. For example, a first pulse-characteristic distribution corresponding to ions having a first charge state and a second pulse-characteristic distribution corresponding to ions having a second charge state may be generated in operation 314 . The pulse-to-characteristic distribution may be generated as discussed above, for example, with reference to operations 315 and 303 .

At an operation 326 , based on at least one of the pulse-characteristic distribution(s) generated in operation 325 , an identification of the charge state of the ions forming the identified peak(s) is generated. Identifying the charge state from the pulse-characteristic distribution(s) can be performed as discussed above. For example, operation 326 may be similar to operation 303 in FIG. 3A and as discussed above, and identifying charge states may be identified in a similar manner as discussed above. Based on the identified and/or assigned charge states, a mass spectrum can be generated and/or a compound can be identified, as discussed above with reference to operations 304 and 305 .

FIG. 3D depicts another example method 3000 for charge state assignment. FIG. 3D and the description of method 3000 below are the result of a description of an example set of step-by-step operations that may be employed for each task and corresponding steps for an example selected dataset. In this example, data from a top-down ECD experiment of carbonic anhydrase 2 (CA2) was used. FIG. 5 depicts an example zoom or data fragment of a mass spectrum 500 of data used in an experiment. More specifically, FIG. 5 depicts a scaled mass spectrum 500 for the m/z range 517-520 of a top-down ECD experiment for CA2.

Returning to Figure 3D, in step 3010, data from the detectors are recorded in such a way that for each detection event a pair of intensities and corresponding m/z values are recorded and stored. For example, for each pulse generated by the detector (due to a detected ion), at least one pulse characteristic (e.g., pulse height, pulse width, pulse area) and corresponding m/z value for the corresponding detected ion can be are stored as a pair. Data may be generated or recorded as discussed above and/or according to methods described in the '720 publication.

In step 3020, the recorded data is summed into a single spectrum or multiple mass spectra based on peak intensities, as described above and/or in the '720 publication. In this example, the data is summed to form multiple mass spectra corresponding to different intensity bands. Figure 6 depicts an example of such a band mass spectrum. For example, FIG. 6 depicts a plurality of banded mass spectra 600 including: (1) a first mass spectrum 602 for ions with corresponding pulse heights between 20-30 mV; Second mass spectrum 604 for ions with corresponding pulse heights; (3) third mass spectrum 606 for ions with corresponding pulse heights between 40-50 mV; and (4) for ions with corresponding pulse heights between 50-60 mV A fourth mass spectrum 608 of the ions.

Returning to Figure 3D, in step 3030, a peak detection operation is performed. Various algorithms may be employed for this step, as will be appreciated by those skilled in the art. For example, a continuous wavelet transform (CWT) algorithm may be used. The highlights/shades in Fig. 6 show the results of the peak detection process based on the continuous wavelet transform algorithm. For example, each peak detected by the peak detection algorithm is highlighted/shaded. A line through each peak depicted in Figure 6 indicates the m/z value of the corresponding peak. The shaded/highlighted outer borders may indicate the m/z range of the aforementioned peaks.

In step 3040, an impulse-characteristic distribution for each peak is calculated using detection events filtered by their proximity to the peak apex. For example, all pulses corresponding to ions in the highlighted region of a particular peak in Figure 6 can be used to generate a pulse-characteristic distribution. While multiple band spectra can be generated and used for peak identification (to increase the accuracy of the peak detection process), the pulses used to generate the pulse-characteristic distribution are taken from all band spectra. For example, if the peak selected to generate the pulse-characteristic distribution is the peak at approximately 518.0 in the 30-40 mV mass spectrum 604 in FIG. The m/z values of the peaks correspond to the pulses for all bands 602-608 of the ions.

The generated pulse-characteristic distribution may be a pulse-height distribution. An example of the calculated pulse-height distribution for multiple peaks in FIG. 6 is shown in FIG. 7 . As can be seen in Figure 7, two clusters of the pulse-height distribution are formed - a first cluster 702 and a second cluster 704. The first cluster 702 corresponds to ions having a first charge state, and the second cluster 704 corresponds to ions having a second charge state. Although the pulse-height distributions in each cluster are not identical, two distinct groupings of profiles or distributions are clearly visible from the plot in Fig. 7.

Returning to FIG. 3D, in step 3050, the pulse-characteristic distributions generated in operation 3040 may be compared to each other. Comparisons can be performed to group or cluster impulse-signature distributions with each other. Ions and/or peaks belonging to different isotopes can be grouped together based on grouping or clustering. One way to perform this comparison is to calculate the relative distance between pulse-characteristic distributions. For this, appropriate binning can be employed and the pulse-characteristic distribution can be represented as a vector containing the probabilities for each intensity range. The Euclidean distance or any other suitable norm can be calculated for each pair of vectors representing the intensity distribution. An example of calculated pairwise Euclidean distances is shown in table 800 depicted in FIG. 8 . Peaks of corresponding pulse-to-feature distributions with relative distances smaller than a predefined threshold can be grouped together to form a signature. In an exemplary analysis, peaks are grouped into features if the relative distance between each pulse-feature distribution within such a group is less than 0.1, forming two separate groups of peaks corresponding to two unique features. For example, two peaks identified in a mass spectrum may have corresponding pulse-characteristic distributions that are very similar to each other (ie, in the same cluster). This pulse-feature distribution similarity indicates that the peaks are likely formed by ions with the same charge state. Thus, peaks can be grouped together as features and considered to be part of an isotopic cluster.

Returning to Figure 3D, in step 3060, the charge state of the feature is identified under the assumption that the peaks are forming isotopic clusters. To achieve this, first, in operation 3050, peaks corresponding to or grouped into a feature may be sorted in ascending or descending order. Distances between adjacent peaks corresponding to features (eg, the m/z distance between a first peak and a second peak) can be calculated in m/z space. The consensus distance can then be chosen based on the most abundant (and thus most probable) distance for a particular feature, or on the basis of the smallest distance if the minimum distance can properly explain the other observed distances (e.g. other distances are multiples of the consensus distance) . The distance between adjacent isotopes in an isotopic cluster is inversely proportional to the characteristic charge, so the characteristic charge can be inferred from those distances.

An example of such a calculation is shown in FIG. 9 . Figure 9 depicts two signatures where peaks have been grouped based on the similarity of their corresponding pulse-feature distributions. For example, for the first feature, four peaks have been grouped together. For the second feature, three peaks have been grouped together. Peak positions and m/z distances between peaks are shown for each corresponding peak. Based on the distance, a predicted state of charge may be generated based on the inverse of the distance. For example, the reciprocal of 0.142 is 7.04 (ie, 1/0.142=7.04), and the reciprocal of 0.334 is 2.99 (ie, 1/0.334=2.99). The most frequently predicted charge or distance can then be used to identify or assign a charge state. For example, for the first feature, the most common predicted charge state is around 7 due to the m/z distance. Since the state of charge must be an integer, the consistent state of charge was determined to be 7. A similar calculation is performed on the second feature to determine a consistent charge state of 3. Notably, the distance between peaks 2 and 3 of the first feature differs from the consensus distance, and the treatment of this difference is discussed further below.

Returning to Figure 3D, method 3000 can continue to step 3070, where missing peaks can be calculated. Various strategies can be used to perform this step. For example, to find missing internal peaks, the algorithm first finds the distance between adjacent peaks that is greater than the consensus distance and essentially a multiple of the consensus distance. The multiplication factor N is then calculated as N=(measured distance)/(coincidence distance). Additional N-1 peaks can be inserted between those at their respective positions to form a complete isotopic cluster.

An example of using this algorithm can be demonstrated with reference to FIG. 9 . In Figure 9, the distance between peaks 2 and 3 in the first feature is 0.286, which is greater than the distances between other peaks in the first feature. The consistent distance for the first feature is about 0.143. Using the calculation above, N=0.286/0.143=2. Therefore, 1 (ie, N-1) peaks can be inserted between peaks 2 and 3. Additional peaks are provided after this step to account for isotopic clustering. The peak was placed at the m/z position of 517.994 m/z (this was calculated by averaging the m/z of the peaks labeled 2 and 3).

Another algorithm may alternatively or additionally be used to search for missing peaks, which is not limited to finding only internal peaks. This algorithm calculates the positions of possible adjacent peaks and then extracts the recorded signal corresponding to these positions. This signal is further processed to form a pulse-characteristic distribution, which can then be compared with one of the pulse-characteristic distributions (or alternatively averaged) calculated for the peaks in the group using a similar method as described in steps 3050-3060 Compare. The confirmed peaks are then added to the peak list of the feature.

Step 3080 may include performing an analysis to find overlapping peaks in multiple features. This step can be done by comparing the peak positions in each feature and addressing peaks at substantially the same position. For example, the newly found peak with m/z of 517.994 from the previous step 3070 is at substantially the same position as Peak 0 from Feature 2 (see FIG. 9 ) with mass 518.003. Overlapping peaks can be identified by looking for m/z differences or distances between peaks and comparing this to an overlap threshold. If the m/z distance is below a threshold, then peaks can be considered to overlap.

Once the overlapping peaks have been identified, an additional step of finding the contribution of each feature to the overlapping peaks can be performed. To do this, systems of linear equations can be written and approximately solved with or without constraints. Such constraints can include requiring each contribution to be non-negative. This step can be done, for example, using a non-negative least squares approximation algorithm.

At step 3090, detection events (eg, ions corresponding to pulses) can be assigned to features. For example, the following algorithm can be used to perform step 3090 . First, using the consistent distance from step 3070 and one or more additional instrument parameters such as instrument resolution, an m/z distribution can be modeled for each peak of the feature. Second, using the consistent pulse-characteristic distribution for the feature (which can be calculated as the average of the pulse-characteristic distributions of all non-overlapping peaks) and the m/z distribution from the first part of this step, one can calculate the reflected characteristic Two values with the probability of such a detection event. These values are the intersections of the m/z position and the calculated m/z distribution, and similar intersections of the detected event intensity and the consistent intensity distribution attributed to the signature. The product of those values is a score that can be used to attribute a detection event to a feature using a threshold.

An example of calculating such a score may be provided with reference to FIG. 10 , which depicts a modeled peak 1000 and a consistent pulse-characteristic distribution 1050 . The detected ion to be assigned has an m/z value of 517.994 and a pulse height of 34. Those values are indicated by the vertical lines in the graph. The m/z value of 517.994 intersects the modeled peak at an intensity of 0.31 (note that the intensity has been normalized to 1 for the modeled peak). A pulse height of 34 intersects the consistent pulse-characteristic distribution at 0.25. Therefore, the score can be calculated as 0.0775 (ie, 0.31*0.25=0.0775).

In the case of multiple overlapping features, the feature yielding the highest score is chosen. Additional constraints that balance the total contribution of the features to overlapping peaks can also be set and implemented. Additional or alternative algorithms may also be implemented for this step to determine the most appropriate signatures to assign detection events corresponding to detected ions. Such algorithms can estimate the probability of a detected event belonging to a certain feature, such as by using a Bayesian framework. In step 3010, a characteristic charge state is assigned to a detection event.

FIG. 3E depicts another example method 3200 for charge state assignment. At an operation 3202, a pulse is generated for each ion of the plurality of ions detected by the detector. For example, when ions strike an electron multiplier detector, a pulse is generated. The pulses generated may be similar to those depicted in Figure IB and discussed above. Each pulse may have or be described by their respective pulse characteristics. The detected ions may be ions from ionization of a sample studied or analyzed by mass spectrometry techniques. At operation 3204, one or more pulse-characteristic distributions are generated based on the characteristics of the pulses. For example, the generated pulse-characteristic distribution can be generated for a specific pulse characteristic, such as pulse height. Thus, the pulse-characteristic distribution(s) generated in operation 3204 may be a pulse height distribution similar to the pulse-height distribution depicted in FIG. 2 and discussed above.

At operation 3206, based on the pulse-to-character distribution, ion peaks with adjacent charge states are identified. Identifying peaks with adjacent charge states may include determining an estimated or rough charge state based on the pulse-characteristic distribution. The coarse charge state identification may only be accurate for the range of possible charge states of at least one peak forming a pair, and at least one charge state from this range is adjacent to the charge state identified for the second peak.

An example of ion peaks with adjacent charge states is shown in the example graph 1100 in FIG. 11 . The first peak is located at an m/z position of (m/z) ₁ and the second peak has an m/z position of (m/z) ₂ . The state of charge of those peaks may be estimated based on the pulse-characteristic distribution generated in operation 3204 .

At operation 3208, the charge state of the charge states of the ions forming the peak may be further determined based on the following equation:

Equation (1):

Equation (2):

Equation (3):

Equation 1 expresses the m/z position of the second peak ((m/z) ₂ ) in relation to the non-charge carrier mass (M) of the ion, the charge state of the second peak (z ₂ ) and the mass of the charge carriers (X) relationship. Equation 2 expresses the relationship between the m/z position of the first peak ((m/z) ₁ ) and the mass of the ion's non-charge carriers (M), the charge state of the second peak (z ₂ ) and the mass of the charge carriers (X) relationship. It is worth noting that Equation 2 assumes a charge state difference of 1 between the first peak and the second peak. Thus, in other examples where the estimated state of charge difference between the first peak and the second peak is not 1, 1 in Equation 2 is replaced by that value.

Equation 3 based on Equations 1 and 2 expresses the relationship between the charge state (z ₂ ) of the ion in the second peak and the m/z position of the second peak ((m/z) ₂ ), the m/z position of the first peak ( (m/z) ₁ ) and the mass (M) of the charge carrier. Each of these values is measured by a detector, or is known from the ionized sample and/or the sample preparation process. For example, a common charge carrier is the proton, which has a mass of about 1 atomic mass unit (AMU). Thus, Equation 3 can be used to determine the charge state (z ₂ ) of the ion forming the second peak. Based on the determined charge state (z ₂ ) of the ions forming the second peak, the charge state (z ₁ ) of the ions forming the first peak can be determined. The determined state of charge may be a refined state of charge compared to an initially estimated or determined coarse state of charge.

The refined state of charge should be an integer or close to an integer, such as within a threshold of integers. If not, then the coarse charge state identification or the fine charge state identification may be incorrect. Thus, coarse and/or refined charge state identifications may only be accepted if the refined identified charge states are integers within a certain threshold. If not, the coarse state of charge can be re-estimated and the method performed again with the revised coarse state of charge. Furthermore, to potentially increase confidence in the assignment, a third peak with an adjacent charge state could be identified. The third peak forms a pair with at least one of the first two peaks, and the charge state identification for the common peak matches in both pairs.

FIG. 3F depicts another example method 3300 for charge state assignment. Method 3300 utilizes an image-charge detector. Unlike ADC detectors, image-charge detectors detect the oscillations of ions in the mass analyzer. 12 depicts an example graph of a transient time-domain signal measured by an image-charge detector that includes components from each of a plurality of ions oscillating in a mass analyzer. In order to decompose the transient time domain signal measured by the image-charge detector into individual components, the transient time domain signal is converted into 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. Frequency domain peaks are converted to m/z peaks using well known formulas that depend on the particular type of mass analyzer to generate a mass spectrum.

Thus, for an image-charge detector, the intensity of the frequency-domain signal or peak is proportional to the charge state of the underlying ions, similar to how the pulse described above is proportional to the charge state. Thus, the intensity or other characteristics of the frequency domain (FD) peaks can be used to generate a distribution similar to the pulse-characteristic distribution discussed above. The distributions generated from the characteristics of the FD peaks can be referred to as FD-peak-characteristic distributions or FD-peak-intensity distributions, where the intensity of the FD peaks is used as the feature of interest. The FD-peak-characteristic distribution can then be used to determine the state of charge in substantially the same manner as the pulse-characteristic distribution.

Returning to Figure 3F, at operation 3302, a transient time-domain signal induced on an image charge detector of the mass analyzer by oscillations of the plurality of ions in the mass analyzer is detected. At operation 3304, the transient time domain signal is converted into a plurality of frequency domain (FD) peaks. Each frequency domain peak may correspond to one of the plurality of ions.

At operation 3306, one or more FD-peak-feature distributions are generated. For example, the generated FD-peak-characteristic distribution may be generated for a specific FD-peak-characteristic such as intensity. At an operation 3308 , based on the one or more FD-peak-feature distributions generated at operation 3306 , an identification of the charge state of one or more ions of the plurality of ions detected at operation 3302 is generated. Identifying the charge state based on the FD-peak-characteristic distribution can be performed using any of the methods described herein using the pulse-characteristic distribution. For example, the FD-peak-characteristic distribution can be used instead of the pulse-characteristic distribution.

At operation 3310, mass spectra are generated for the detected ions. In operation 3308, a mass spectrum may be generated based on the identified charge state(s). For example, using known charge states, overlapping peaks can be resolved or otherwise indicated in the mass spectrum. For example, since the charge states of the ions used to form the mass spectrum are known or identified in operation 3308, a deconvoluted mass spectrum for the detected ions may be generated. For deconvoluted mass spectra, one axis of the mass spectrum can be mass instead of mass per charge (m/z). At an operation 3312, the compound or amount of the compound in the sample corresponding to the detected ion can be identified. The compound or amount of the compound can be identified from the mass spectrum generated in operation 304 and/or from the charge state(s) identified in operation 3312 .

FIG. 13 depicts an example system 1300 including an image-to-charge detector 1318 . The system of FIG. 13 includes a mass spectrometer 1310 and computing components including memory and processor 1320 . Computational elements of the system, such as processor 1320 and memory, can be included in the mass spectrometer itself, located near the mass spectrometer, or located remotely from the mass spectrometer. In general, a computing element of the system may be in electronic communication with the detector 1318 such that the computing element can receive a signal generated from the detector 1318 . Processor 1320 may include a plurality of processors and may include any type of suitable processing component for processing signals and generating the results discussed herein.

Mass spectrometer 1310 includes mass analyzer 1317 . Mass analyzer 1317 includes image-charge detector 1318 . The image-charge detector 1318 generates an oscillating or transient time-domain signal for detected ions with an amplitude proportional to the charge state of the ions. Mass analyzer 1317 can be any type of mass analyzer that can detect ions using an image-charge detector, including but not limited to electrostatic linear ion trap (ELIT), FT-ICR or orbitrap mass analyzers. The mass analyzer 1317 is shown in Figure 13 as an ELIT, and the image-charge detector 1318 is shown as a pickup electrode of the ELIT.

The mass analyzer 1317 detects the transient time domain signal 1319 induced on the image charge detector 1318 by the oscillation of the plurality of ions in the mass analyzer 1317 . The plurality of ions are transmitted by mass spectrometer 1310 to mass analyzer 1317 . Processor 1320 converts transient time domain signal 1319 into a plurality of frequency domain pulses or peaks 1321 . Each frequency domain signal corresponds to one of the plurality of ions. For example, the processor 1320 converts the transient time domain signal 1319 into a plurality of frequency domain peaks 1321 using a Fourier transform.

Processor 1320 may compare the intensity of each frequency-domain peak in plurality of frequency-domain peaks 1321 to two or more different predetermined intensity ranges corresponding to two or more different state-of-charge ranges. Processor 1320 may store each frequency domain peak in one of two or more data sets 1322 corresponding to two or more predetermined intensity ranges based on the comparison. Processor 1320 can create a mass spectrum based on the frequency domain peaks and/or identified charge states discussed herein.

In various embodiments, during acquisition, the processor 1320 converts the transient time-domain signal 1319 into a plurality of frequency-domain peaks 1321, comparing the intensity of each frequency-domain peak to two or more different predetermined intensity ranges. A comparison is made and each frequency domain peak is stored in one of two or more data sets 1322. In an alternative embodiment, after acquisition, the processor 1320 converts the transient time-domain signal 1319 into a plurality of frequency-domain peaks 1321, comparing the intensity of each frequency-domain peak to two or more different predetermined intensity ranges. Compare, and store each frequency domain peak in one of two or more data sets 1322.

As discussed above, if multiple copies of the same ion are oscillating in the mass analyzer 1317 simultaneously, the measured intensity may not be proportional to the charge state. Thus, in various embodiments, mass spectrometer 1310 transmits ions to mass analyzer 1317 such that mass analyzer 1317 includes only a single ion with a particular m/z and charge state at any given time.

In various embodiments, the system of FIG. 13 also includes an ion source device 1311 . For example, ion source device 1311 may be an electrospray ion source (ESI) device. The ion source device 1311 is shown in Figure 13 as part of the mass spectrometer 1310, but could also be a separate device.

In addition, mass spectrometer 1310 also includes a dissociation device. A dissociation device may be, but is not limited to, an ExD device 1315 or a CID device 1313. Dissociation devices can be used, for example, for top-down protein analysis.

In top-down protein analysis, the ion source device 1311 ionizes the protein of the sample, producing multiple precursor ions for the protein in the ion beam. The dissociation device dissociates a plurality of precursor ions in the ion beam to produce a plurality of product ions of different charge states in the ion beam. As described above, mass spectrometer 1310 transmits the plurality of product ions to mass analyzer 1317 such that the plurality of product ions are the plurality of ions transmitted by mass spectrometer 1310 to mass analyzer 1317 .

In various embodiments, processor 1320 is used to control or provide instructions to ion source device 1311 and mass spectrometer 1310 and to analyze collected data. Processor 1320 controls or provides instructions by, for example, controlling one or more voltage, current or pressure sources (not shown).

FIG. 4 depicts another example method 400 for charge state assignment. Method 400 may be particularly useful if the isotopic distribution is not sufficiently resolved for at least a plurality of peaks. Operations 401-404 may be substantially the same as operations 3010-3040. At operation 405, the state of charge of the feature may be estimated. The feature may be formed from multiple peaks sharing a similar pulse-characteristic distribution, and the grouping of peaks to form the feature may be performed as discussed above with respect to operations 3050-3060. At operation 406, for each possible charge state estimated in operation 405, neighboring peaks may be identified and the likelihood of the neighboring peaks may be scored. At operation 407 , the most likely candidate is selected based on the scoring performed at operation 407 . The detection event may then be assigned to the feature in operation 408, and the feature charge state may be assigned to the detection event based on the charge state of the feature.

In another example, the methods in FIGS. 3A-3E may be combined with the method in FIG. 4 . In such an example, the strategy described in method 3000 of FIG. 3D can be used to process peaks with well-resolved isotopic patterns. For the remaining peaks whose isotopic patterns are largely unresolved, the strategy in Figure 4 can be used. In this combination, an additional step can be taken to define whether the peaks found are isotopically resolved. For example, this step can compare the peak widths of detected features with those of this mass spectrometer to obtain resolved and unresolved features;

For all these methods, three positive outcomes can generally be envisioned and their utility can be virtually identical. First, using information about each detected event (e.g., charge state), a deconvoluted d-mass spectrum can be generated using the formula (m/ _zmp )*Z, where Z is the determined charge state, where _mp is the proton d-mass. Second, this information can be used to construct a collection of spectra for each charge state covering the entire m/z range or a segment of the m/z range. Third, this information can be used to generate a list of features, each with m/z and z assigned to it. The compound and/or the amount of the compound present in the analyzed sample can then be determined or generated based on the identified features.

For all described embodiments, the step of assigning charge states to individual detection events may be associated with assigning a probability of said detection event originating from ions forming a feature and thus assigning said detection event to the charge state assigned to this feature Probability of replaces or includes it. For example, such probabilities can be calculated using a Bayesian framework. In a subsequent step of collating a representative mass spectrum (either in whole or in part), the proportional contribution from a detected event can then be distributed accordingly between the features it represents and their corresponding positions within the mass spectrum.

While the present teachings have been described in connection with various embodiments, the present teachings are not intended to be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications and equivalents, as will be recognized by those skilled in the art.

For example, aspects of the present 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 present disclosure. The functions/actions noted in the boxes may be assigned charge states for this function not appearing in the order shown in any flowchart. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Additionally, as used herein and in the claims, the phrase "at least one of element A, element B, or element C" is intended to convey any of the following: element A, element B, element C, elements A and B, element A and C, elements B and C, and elements A, B, and C.

The description and illustration of one or more aspects provided in this application are not intended to limit or limit the scope of the claimed disclosure in any way. The aspects, examples and details provided in this application are believed to be sufficient to convey ownership and to enable others to make and use the best mode of the claimed disclosure. The claimed disclosure should not be construed as limited to any aspect, example or detail provided in the application. Whether shown and described in combination or separately, various features (structures and methods) are intended to be selectively included or omitted to yield an embodiment having a particular set of features. Having provided the description and illustrations of the present application, those skilled in the art can conceive of the broader changes, modifications and substitutions that fall within the spirit of the broader aspects of the general inventive concept embodied in the application without departing from the claimed disclosure aspect.

Claims (40)

1. A method for classifying the charge state of detected ions, the method comprising: generating a pulse for each of the plurality of ions detected by the detector, wherein each pulse has pulse characteristics; generating a pulse-characteristic distribution for the generated pulses; and An indication of a charge state of one or more ions of the plurality of ions is generated based on the pulse-characteristic distribution. 2. The method of claim 1, wherein the pulse-characteristic distribution is a plot of probability versus pulse characteristic. 3. The method of any one of claims 1-2, wherein the pulse characteristic is at least one of pulse height, pulse width or pulse area. 4. The method of claim 3, wherein the pulse characteristic is the pulse height, and the pulse height is the maximum voltage of the pulse. 5. The method of any one of claims 1-4, wherein the detector is an electron multiplier detector and the detector is configured to detect primarily single ion events. 6. The method of any one of claims 1-5, generating an indication of the state of charge comprising comparing the pulse-characteristic distribution to a reference pulse-characteristic distribution. 7. The method of claim 6, wherein the ions detected by the detector are generated by ionization of the sample, and the reference pulse-characteristic distribution is identified based on known characteristics of the sample. 8. The method of any one of claims 1-7, wherein the generated signature comprises a probability of a state of charge. 9. The method of any one of claims 1-8, further comprising generating a deconvoluted mass spectrum for the detected ions based on the identity of the charge state, wherein one axis of the mass spectrum is mass rather than mass per charge (m/z). 10. The method of any one of claims 1-9, wherein the plurality of ions are grouped into distinct groups using the m/z domain, and charge state based on pulse-characteristic distribution is performed for each group logo. 11. The method of claim 10, wherein the step of grouping comprises: generating a mass spectrum based on the plurality of detected ions; identifying a first peak in the mass spectrum, wherein the first peak has a mass per charge (m/z) value; and The ions are grouped within each charge mass (m/z) range based on the m/z value of the first peak. 12. The method of claim 10, wherein the grouping step comprises: selecting a first subset of the plurality of ions into a first intensity band and selecting a second subset of the plurality of ions into a second intensity band; generating a first mass spectrum for the first intensity band; generating a second mass spectrum for the second intensity band; identifying a first peak in at least one of the mass spectra, wherein the first peak has a mass per charge (m/z) value; and The ions are grouped within each charge mass (m/z) range based on the m/z value of the first peak. 13. The method of claim 10, further comprising generating a second pulse-characteristic distribution for ions in the m/z range, and wherein generating the signature comprises: determining that ions forming the first pulse-characteristic distribution have a first charge state; and Ions forming the second pulse-characteristic distribution are determined to have a second charge state. 14. The method of claim 10, further comprising, based on the identification of the charge state, determining one or more isotopes corresponding to the one or more ions forming the first peak. 15. The method of any of claims 10-14, wherein generating the signature of the state of charge comprises comparing the first pulse-characteristic distribution with a reference pulse-characteristic distribution. 16. The method of claim 15, wherein the ions detected by the detector are generated by ionization of the sample, and the reference pulse-characteristic distribution is identified based on known characteristics of the sample. 17. A method as claimed in any of claims 10-16, wherein the generated signature comprises a probability of state of charge. 18. The method of any one of claims 1-17, wherein the method is performed as part of a top-down protein analysis. 19. The method of any one of claims 11-18, further comprising: Identify the second peak; determining a consistent m/z distance based at least on the first peak and the second peak; Identifying primary and secondary peak formation features; and Wherein generating the identification of the charge state is based on the consistent distance. 20. The method of claim 19, wherein the step of identifying peak-forming characteristics includes comparing pulse-characteristic distributions of the peaks and selecting peaks having substantially the same pulse-characteristic distribution. 21. The method of claim 20, wherein the comparison of pulse-characteristic distributions is performed by calculating a Euclidean distance between the pulse-characteristic distributions and comparing it to a predetermined threshold. 22. The method of any one of claims 19-21, further comprising, based on the consistent distance, identifying missing peaks corresponding to features. 23. The method of any one of claims 19-21, further comprising, based on the identification of the ion's charge state, generating a deconvoluted mass spectrum for the detected ion, wherein one axis of the mass spectrum is mass rather than m/z. 24. The method of any one of claims 1-23, wherein the pulse characteristic is the maximum voltage of the pulse. 25. A mass analysis system comprising: a detector configured to generate a pulse for each ion detected by the detector; processor; memory, storing instructions, a collection of instructions configured to cause the system to perform operations when executed by the processor, including: generating a pulse for each of the plurality of ions striking the electron multiplier detector, wherein each pulse has pulse characteristics; generating a pulse-characteristic distribution for the generated pulses; and An indication of a charge state of one or more ions of the plurality of ions is generated based on the pulse-characteristic distribution. 26. The mass analysis system of claim 25, wherein the detector is an electron multiplier detector. 27. The mass analysis system of any one of claims 25-26, wherein the mass analysis system further comprises an ion source device, a dissociation device, and a mass analyzer. 28. A method for classifying the charge state of detected ions, the method comprising: using a processor to detect a transient time-domain signal induced on an image-charge detector of a mass analyzer due to oscillations of multiple ions in the mass analyzer; converting the transient time domain signal into a plurality of frequency domain (FD) peaks corresponding to ions of the plurality of ions; generate FD-peak-characteristic distributions for the generated pulses; and Based on the FD-peak-feature distribution, an identification of the charge state of one or more ions of the plurality of ions is generated. 29. The method of claim 28, wherein the FD-peak-characteristic distribution is a plot of probability versus FD-peak-characteristic. 30. The method of any one of claims 28-29, wherein the FD-peak-characteristic is peak intensity. 31. The method of any of claims 28-30, generating an indication of the state of charge comprising comparing the FD-peak-characteristic distribution to a reference FD-peak-characteristic distribution. 32. The method of any one of claims 28-31, wherein the ions detected by the detector are generated by ionization of the sample, and the reference FD-peak-characteristic distribution is identified based on known characteristics of the sample . 33. A method as claimed in any of claims 28-32, wherein the generated signature comprises a probability of state of charge. 34. The method of any one of claims 28-33, further comprising, based on the identification of the charge state, generating a deconvoluted mass spectrum for the detected ions, wherein one axis of the mass spectrum is mass rather than per charge Mass (m/z). 35. A method for classifying the charge state of detected ions, the method comprising: generating a pulse for each of the plurality of ions detected by the detector, wherein each pulse has pulse characteristics; generating a pulse-characteristic distribution for the generated pulses; and Identify rough charge states based on pulse-characteristic distributions; identifying peak pairs of the first ion peak and the second ion peak such that the peaks have adjacent charge states; and Based on the m/z value of the first ion peak, the m/z value of the second ion peak and the mass of the charge carrier, a refined charge state of the second ion peak is determined. 36. The method of claim 35, wherein the rough charge state identification is accurate to a range of possible charge states for at least one peak forming a pair, and at least one charge state from the range is identical to that identified for the second peak The outgoing charge states are adjacent. 37. The method of any of claims 35-36, further comprising accepting the refined charge state identification if the refined identified charge state is an integer within a certain threshold. 38. The method of any one of claims 35-37, wherein a third peak with an adjacent charge state is identified and forms a pair with at least one of the peaks, and is used for the The charge state identities of the common peaks matched in both pairs. 39. The method of any one of claims 35-38, further comprising determining the charge state of the first ion peak based on the determined charge state of the second ion peak. 40. The method of any one of claims 35-39, further comprising obtaining the mass of the charge carriers based on known characteristics of the sample that was ionized to generate the plurality of ions.

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