Charge detection mass spectrometry using multiple cumulative events

Abstract

Charge detection mass spectrometry using multiple cumulative events. The invention discloses a system for charge detection mass spectrometry (CDMS). The system performs a process including: subdividing an m / z range of interest into a plurality of m / z windows; determining an ion population control parameter of each m / z window; accumulating ion populations derived from the sample in the ion memory according to one or more accumulation events; transferring the ion population to a mass analyzer; and performing mass analysis on the ion population to acquire a CDMS spectrum of the ion population. Each cumulative event corresponds to a different m / z window of the plurality of m / z windows. The ion population control parameter for each m / z window adjusts the amount of ions accumulated in the ion memory during the accumulation event. During each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion memory based on the ion population control parameter for the corresponding m / z window.

Description

Background Art

[0001] In the early days of mass spectrometry, all ion sources produced only singly charged ions. Although mass spectrometers measure the mass-to-charge ratio (m / z) of ions, the determination of mass was simple because the charge (z) was always equal to one (1). When mass spectrometry was combined with ion sources that produced multiply charged ions (e.g., electrospray ionization sources), the determination of mass became more challenging. If the charge on the ion is ambiguous, so is the calculated mass. The original solution to this problem was to use the m / z spacing between adjacent charge state peaks of the same mass. With the development of high-resolution mass analyzers, it became more common to determine charge by measuring the m / z spacing between adjacent isotopic peaks because these peaks are known to be one (1) Dalton apart. Both of these methods of determining charge are challenged by the highly complex spectra in which neither clear charge states nor isotopic distributions can be easily discerned.

[0002] One solution to address charge ambiguity is to use charge detection mass spectrometry (CDMS). In some embodiments of CDMS, one ion is analyzed at a time using a detector that can simultaneously sense both the m / z and charge z of each ion. Traditionally, this has been performed using an electrostatic linear ion trap (ELIT), in which an image current detector is placed between a pair of ion mirrors, allowing the ion to pass through the detector multiple times, thereby improving sensitivity and charge assignment accuracy. ELIT operates optimally with only a single ion, as m / z resolution is typically poor and ion-ion interactions can disrupt trajectories, limiting ion lifetimes.

[0003] For ions with sufficient charge to generate a detectable signal in a single pass, the ELIT can be operated in a triggered mode, where an optical gate between the source and the ELIT opens to admit ions and closes once the first ion passes the detector. However, most ions do not carry sufficient charge to be detected in a single pass. In these cases, the gate open time is set to the time that most often results in the capture of one ion, on average. This is somewhat inefficient because ion arrival times are randomly distributed, so the probability of seeing zero or two ions is just as high as the probability of seeing one. As a result, on average, a single ion is detected in less than 50% of the spectrum.

[0004] Recently, the orbital electrostatic trap mass analyzer (Orbitrap TMMass analyzers in the form of ion analyzers (manufactured and sold by Thermo Fisher Scientific, Inc., Waltham, MA) have been used for CDMS. Some of these embodiments of CDMS are referred to as single ion mass spectrometry (I2MS) or Direct Mass Technology. TM (DMT, Thermo Fisher Scientific, Waltham, MA). Due to the nature of the orbital electrostatic trap mass analyzer, which has both high resolution and high spatial charge tolerance, hundreds of individual ions can be detected simultaneously, and this multiplexing significantly increases the rate at which statistically significant data can be collected, even for complex analyte mixtures. A specific embodiment of the CDMS involves processing the transient signal generated by the axial oscillations of the ion population to produce a Selective Time Overview (STORI) plot of a set of resonating ions. The slope of each STORI plot is proportional to the charge of the different ion species in the ion population being analyzed, and the charge of the ions can be determined by a slope-charge calibration function. Using the determined charge z and m / z for each ion species, a true mass spectrum can be generated that plots the detected signal as a function of mass (m).

[0005] Because it is possible to utilize the orbital electrostatic trap mass analyzer to carry out multiple ion measurement, it is possible to observe too many ions in each spectrum. In the orbital electrostatic trap mass spectrometer in which the CDMS method is implemented, the ions generated by the ion source are transferred to the ion trap (alternatively referred to as ion storage) via ion optical device before the mass analysis in the orbital electrostatic trap mass analyzer. The duration of ion accumulation in the ion trap (commonly referred to as ion injection time, accumulation time, ion accumulation time or ion filling time) determines the number of ions measured by the orbital electrostatic trap mass analyzer. In CDMS, each signal detected in the spectrum is distinguished from each other signal in the spectrum along the m / z domain. If the signals overlap on m / z, it is difficult to determine whether there are multiple individual ions that contribute to the signal, or there is an ion in the charge sum of the individual ions. For example, when two or more ions of the same m / z are detected at the same time, the charge state becomes unclear because only the net charge of all ions at the m / z place is known, rather than the charge on each ion or even the actual number of ions. In less severe cases, resolved but adjacent m / z ions can reduce the accuracy of charge determinations because the signals will constructively and destructively interfere with each other, which can affect the determination of signal intensity and make proper assignment of charge difficult.

[0006] To minimize overlap and interference between signals in the m / z domain, the ion population measured in each spectrum should be controlled to achieve single ion resolution, where the mass analyzer is not loaded with ions of the same or similar m / z values. However, using a limited ion population increases the number of spectra required for representative sampling and, thus, the time required to perform CDMS on a sample. Thus, under ideal conditions, a large number of ions will be observed in each spectrum, allowing the acquisition of a complete data set in the shortest possible time, but the number of ions in any one spectrum will not be so large as to cause interference between adjacent or overlapping signals.

[0007] Automatic gain control (AGC) is a technique that has been used to balance the spectral dynamic range and space charge effects in trapped ion mass spectrometers. However, AGC is not ideal for CDMS because AGC measures the total signal (e.g., total ion current (TIC)) in the collected spectrum to adjust the subsequent accumulation time to reach the target total signal. In CDMS, the total signal is proportional to the sum of the charge states on all ions. If the charge state of the analyte ion is unknown before the experiment, it is impossible to select a suitable target signal. Therefore, in the field of mass spectrometry, there is still a clear need for an automatic ion accumulation time selection technique that is suitable for CDMS data acquisition in a single ion system. Summary of the Invention

[0008] The following description presents a simplified overview of one or more aspects of the methods and systems described herein to provide a basic understanding of such aspects. This summary is not an extensive overview of all covered aspects and is neither intended to identify key or critical elements of all aspects nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects of the methods and systems described herein in a simplified form as a prelude to the more detailed description presented below.

[0009] In some exemplary embodiments, a system for charge detection mass spectrometry (CDMS) includes: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause a computing device to direct a mass spectrometer to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in an ion storage during an accumulation event; accumulating ion populations from a sample in the ion storage according to one or more accumulation events that each correspond to a different m / z window in the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window; transferring the accumulated ion population to a mass analyzer; and mass analyzing the ion population to acquire a CDMS spectrum of the ion population.

[0010] In some exemplary embodiments, a non-transitory computer-readable medium stores instructions that, when executed, direct at least one processor of a computing device for charge detection mass spectrometry to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in an ion storage during an accumulation event; directing accumulation of ion populations from a sample in the ion storage according to one or more accumulation events that each correspond to a different m / z window in the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window; directing transfer of the accumulated ion population to a mass analyzer; and directing mass analysis of the ion population to acquire a CDMS spectrum of the ion population.

[0011] In some exemplary embodiments, a system for charge detection mass spectrometry (CDMS) includes: an ion store that accumulates ion populations from a sample according to one or more accumulation events; a mass analyzer that acquires a mass spectrum of the accumulated ion populations by the CDMS after the accumulated ion populations are transferred to the mass analyzer; a mass filter that selectively transmits ions from the sample based on their m / z; and a computing device configured to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, wherein each of the one or more accumulation events corresponds to a different m / z window among the plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in the ion storage during the corresponding accumulation event; and directing accumulation of ions originating from the sample in the ion storage according to the one or more accumulation events, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate various embodiments and are part of the specification. The illustrated embodiments are examples only and do not limit the scope of the present disclosure. Throughout the drawings, the same or similar reference numerals represent the same or similar elements.

[0013] Figure 1 An exemplary embodiment of a mass spectrometer is shown.

[0014] Figure 2 An exemplary automatic ion control system is shown.

[0015] Figure 3 An exemplary method of performing automatic ion control is shown.

[0016] Figure 4 An exemplary method for performing automatic ion control according to the peak spacing method is shown.

[0017] Figure 5 An exemplary method for performing automatic ion control according to the sum intensity method is shown.

[0018] Figure 6 An exemplary method for performing CDMS using automated ion control is shown.

[0019] Figure 7A An exemplary m / z profile is shown, which plots the intensity of ions in a hypothetical incident ion beam as a function of the m / z of the ions.

[0020] Figure 7BAn exemplary CDMS spectrum acquired by mass analysis of the ion population accumulated in a single accumulation event is shown.

[0021] Figure 7C shows that the cumulative time is Figure 7B An exemplary CDMS spectrum acquired for mass analysis of the accumulated ion populations in a single accumulation event with twice the exemplary accumulation time.

[0022] Figure 8 A functional diagram of an exemplary mass spectrometer that can be used for CDMS with a multiple accumulation event approach is shown.

[0023] Figure 9 A functional diagram of an exemplary mass spectrometer including an electrostatic trap mass analyzer is shown.

[0024] Figure 10 A flow chart illustrating an exemplary method of implementing a CDMS using a multiple accumulation event approach is shown.

[0025] Figure 11 shows a graph subdivided into multiple m / z windows for multiple accumulation events. Figure 7A m / z distribution plot of and exemplary CDMS spectra acquired by CDMS using multiple accumulation events.

[0026] Figure 12 An exemplary computing device is shown that may be specifically configured to perform one or more of the operations, methods, and processes described herein. DETAILED DESCRIPTION

[0027] Described herein are methods, systems, and apparatus for performing CDMS having improved sample throughput and improved data quality compared to conventional CDMS techniques. The CDMS techniques described herein subdivide the m / z range of interest into a plurality of different m / z windows and accumulate ion populations in an ion store in a plurality of different accumulation events to achieve a more uniform signal density across the m / z space of an incident ion beam. Each accumulation event corresponds to a different m / z window, and ion population control parameters are independently determined for each m / z window to accumulate a desired number of ions during each accumulation event. The ion population control parameters regulate the ion population analyzed by the mass analyzer during an acquisition event. In some examples, the ion population control parameters for each m / z window are independently determined by the automatic ion control (AIC) technique described herein.

[0028] By adjusting the ion accumulation in a collection of multiple different accumulation events covering the m / z range of interest, the probability of detecting ions in lower-density m / z regions of the incident ion beam is increased, while the probability of multiple ion events and interference in higher-density m / z regions is reduced. As a result, ion accumulation in the ion storage is not constrained by the densest m / z region, and more ions can be analyzed per unit time, thereby increasing sample throughput without compromising data quality. In addition, data quality can be improved compared to conventional CDMS techniques because the CDMS technique using multiple accumulation events as described herein better characterizes regions of the m / z space of the incident ion beam with less signal density.

[0029] Before describing the multiple cumulative event method for CDMS, the method, system and apparatus for AIC will be described. In some examples, the AIC method, system and apparatus described herein are used for CDMS analysis. The AIC technology described herein determines ion population control parameters based on signal density metrics because the probability of having an interfering signal is related to the signal density in the m / z domain. A mass spectrum with higher signal density is more likely to have overlapping or closely spaced peaks in the m / z domain, while a mass spectrum with lower signal density is less likely to have these interferences. The ion population control parameters can be adjusted so that the signal density measured from the collected mass spectrum is close to or sufficiently close to the target signal density. The target signal density is a predetermined value that can be selected to obtain an appropriate number of ions in each spectrum for specific experimental conditions and targets while limiting the occurrence of interfering ions to an acceptable low amount.

[0030] In some examples, the method for performing AIC includes acquiring a mass spectrum comprising a plurality of peaks representing the intensity of a function of m / z of the ion population analyzed by the mass analyzer during the acquisition event. The measured signal density of the mass spectrum is determined based on the mass spectrum. In some examples, the measured signal density is determined according to a peak spacing method. In other examples, the measured signal density is determined according to a sum intensity method. Ion population control parameters for subsequent acquisition events are set based on the measured signal density and the target signal density. The ion population control parameters regulate the ion population analyzed by the mass analyzer during the acquisition event. In some examples, the ion population control parameter is the accumulation time that ions accumulate in the ion storage before being analyzed by the mass analyzer. In other examples, the ion population control parameter is a potential applied to an ion optical device (e.g., a lens) that regulates the flux of ions delivered to the mass analyzer.

[0031] In the peak spacing method, determining the measurement signal density of a mass spectrum includes calculating the peak spacing value for each set of adjacent peaks included in a selected m / z range of the mass spectrum (e.g., an m / z range of interest). Based on the set of calculated peak spacing values, a global peak spacing value for the selected m / z range is determined. In the sum intensity method, determining the measurement signal density includes determining the occupied m / z space within the selected m / z range of the mass spectrum. The sum intensity of the signal within the occupied m / z space is determined. The measurement signal density is determined based on the occupied m / z space and the sum intensity within the occupied m / z space. The peak spacing method and the sum intensity method will be described in more detail below.

[0032] Various embodiments and examples will now be described in more detail with reference to the accompanying drawings. The methods, systems, and apparatus described herein provide various benefits that will become apparent herein. The methods, systems, and apparatus for performing AIC can be used in conjunction with a mass spectrometer. Thus, an exemplary mass spectrometer will now be described. The mass spectrometer described is illustrative and non-restrictive.

[0033] Figure 1 A functional diagram of an exemplary mass spectrometer 100 is shown. As shown, the mass spectrometer 100 includes an ion source 102, an ion storage 104, a mass analyzer 106, a detector 108, and a controller 110. The mass spectrometer 100 may further include any additional or alternative components not shown that may be suitable for a particular embodiment (e.g., ion optics, lenses, filters, ion storage devices, ion mobility analyzers, collision cells, ion flux monitors, etc.).

[0034] The ion source 102 is configured to generate ions from a sample and deliver the ions in an ion stream 112 to an ion reservoir 104. The ion source 102 can use any suitable ionization technique, including but not limited to electron ionization, chemical ionization, matrix-assisted laser desorption / ionization, electrospray ionization, atmospheric pressure chemical ionization, atmospheric pressure photoionization, inductively coupled plasma, etc. The ion source 102 can include various components for generating ions from a sample and delivering the ions to the ion reservoir 104.

[0035] The ion storage 104 is a device configured to accumulate ions included in the ion flow 112 within an accumulation time. As used herein, "accumulation time" refers to the duration that ions generated by the ion source 102 accumulate in the ion storage 104 before being released and transferred to the mass analyzer 106. The accumulation time may also be referred to as the ion injection time or ion fill time. In some examples, the ion storage 104 is an ion storage device configured to buffer downstream processes (such as mass analysis), thereby improving acquisition speed and instrument sensitivity. In some examples, the ion storage 104 is a beam-type device or a capture device, such as a multipole ion guide (e.g., a quadrupole ion guide, a hexapole ion guide, an octopole ion guide, etc.), a linear quadrupole ion trap, a three-dimensional quadrupole ion trap, a cylindrical ion trap, a toroidal ion trap, an orbital electrostatic trap, a Kingdon trap, and similar ion traps. In some examples, the ion storage 104 takes the form of a curved trap (also known as a C trap) of the type used in an orbital electrostatic trap mass spectrometer.

[0036] In some examples, the ion storage 104 is a collision cell located upstream of the mass analyzer 106. As used herein, the term "collision cell" can refer to any device arranged to produce product ions via a controlled dissociation process or an ion-ion reaction process, and is not limited to devices for collision-activated dissociation. For example, the collision cell can be configured to fragment ions using collision-induced dissociation (CID), electron transfer dissociation (ETD), electron capture dissociation (ECD), photoinduced dissociation (PID), surface-induced dissociation (SID), etc. The collision cell can be positioned upstream of the mass analyzer 106 and / or a mass filter that separates the fragmented ions based on the m / z of the ions.

[0037] The accumulation of ions in the ion storage 104 can be adjusted by the AIC method, device and system described herein to achieve the target ion population in the ion storage 104, and therefore achieve the target signal density. The accumulation of ions can be adjusted in any suitable manner. In some examples, the accumulation of ions in the ion storage 104 can be adjusted by a gate device (not shown) that transmits or blocks the ion flow 112. The door can be opened for a given amount of time to meter the appropriate number of ions, and then the door is closed. The accumulated ions can then be transferred from the ion storage 104 to the mass analyzer 106 in the ion flow 114. It will be appreciated that other techniques for adjusting ion accumulation can be used.

[0038] The mass analyzer 106 is configured to perform mass analysis of the accumulated ions. The mass analyzer 106 can be implemented by any suitable mass analyzer or combination of analyzers suitable for charge detection mass spectrometry and / or Fourier transform mass spectrometry (FTMS), wherein the image charge signals of the ions are sensed and recorded. Examples of the mass analyzer 106 include, but are not limited to, ion trap mass analyzers (e.g., linear quadrupole ion trap, three-dimensional quadrupole ion trap, cylindrical ion trap, annular ion trap, etc.), time of flight (TOF) mass analyzers, electron sector mass analyzers (e.g., a double electron sector ion trap with a central charge tube), linear quadrupole mass analyzers, electrostatic ion trap mass analyzers (e.g., an orbital electrostatic trap, such as an Orbitrap TM Trap mass analyzers, Kingdon trap, electrostatic linear ion trap (ELIT), orbital frequency analyzer (OFA), etc.), magnetic ion trap and / or Fourier transform ion cyclotron resonance (FT-ICR) mass analyzers.

[0039] In some examples, the mass spectrometer 100 is a tandem mass spectrometer (temporal tandem or spatial tandem) configured to perform tandem mass spectrometry (e.g., MS / MS), a multistage mass spectrometer (also denoted as MS), or a multistage mass spectrometer (also denoted as MS). n ) of a multi-stage mass spectrometer or a hybrid mass spectrometer. For example, mass analyzer 106 may include multiple mass analyzers, mass filters, and / or collision cells. In some examples, mass analyzer 106 includes a combination of multiple mass filters and / or collision cells, such as a triple quadrupole mass analyzer, wherein a collision cell is inserted into the ion path between independently operable mass filters. In other examples, mass analyzer 106 includes a ToF mass analyzer and an orbital electrostatic trap mass analyzer positioned in series.

[0040] Although Figure 1 Ion storage 104 is shown as being located upstream of mass analyzer 106, but ion storage 104 can be located at any other location along the ion path from ion source 102 to detector 108 within a tandem mass spectrometer or a multistage mass spectrometer (e.g., between the first mass filter (Q1) and the collision cell (Q2) and / or between the collision cell (Q2) and the second mass filter (Q3)). Additionally, mass spectrometer 100 can include more than one ion storage 104, such as when mass spectrometer 100 is a tandem mass spectrometer or a multistage mass spectrometer.

[0041] The ion detector 108 is configured to detect ions at each of the various m / z values within the mass analyzer 106 or in the ion stream 116 and, in response, generate an electrical signal representing the intensity of the ions. The electrical signal is transmitted to the controller 110 for processing, such as constructing a mass spectrum of the detected ions. For example, the mass analyzer 106 can transmit an emission beam of separated ions to the detector 108, which is configured to detect the ions in the emission beam and generate or provide data that can be used by the controller 110 to construct a mass spectrum. The ion detector 108 can be implemented by any suitable detection device, including but not limited to an electron multiplier, a Faraday cup, etc.

[0042] As used herein, "mass spectrum" or "spectrum" refers to a plot of ion intensity as a function of the m / z of the ions. As used herein, "intensity" or "signal intensity" refers to the response of a detector and can represent absolute abundance, relative abundance, ion counts, intensity, relative intensity, ion current, or any other suitable measure of ion detection.

[0043] The controller 110 is configured to control various operations of the mass spectrometer 100. For example, the controller 110 may be configured to control the operations of various hardware components included in the ion source 102, the ion storage 104, the mass analyzer 106, and / or the detector 108. For illustration, the controller 110 may be configured to control the accumulation time of the ion storage 104 and / or the mass analyzer 106, control the oscillating voltage power supply and / or the DC power supply to supply the RF voltage and / or the DC voltage to the mass analyzer 106, adjust the values of the RF voltage and the DC voltage to select a valid m / z (including a mass tolerance window) for analysis, and adjust the sensitivity of the ion detector 108 (e.g., by adjusting the detector gain).

[0044] The controller 110 may also include and / or provide a user interface that is configured to enable interaction between a user of the mass spectrometer 100 and the controller 110. The user can interact with the controller 110 via the user interface through tactile, visual, auditory, and / or other sensory types of communication. For example, the user interface may include a display device (e.g., a liquid crystal display (LCD) display, a touch screen, etc.) for displaying information (e.g., a mass spectrum, notifications, etc.) to the user. The user interface may also include an input device (e.g., a keyboard, a mouse, a touch screen device, etc.) that allows the user to provide input to the controller 110. In other examples, the display device and / or the input device may be separated from the controller 110 but communicatively coupled to the controller. For example, the display device and the input device may be included in a computer (e.g., a desktop computer, a laptop computer, a mobile device, etc.) that is communicatively connected to the controller 110 through a wired connection (e.g., through one or more cables) and / or a wireless connection (e.g., Wi-Fi, Bluetooth, near field communication, etc.).

[0045] The controller 110 may include any suitable hardware (eg, processor, circuitry, etc.) and / or software as may serve a particular implementation. Figure 1 The controller 110 is shown as being included in the mass spectrometer 100 (e.g., one or more onboard processors located on the mass spectrometer 100), but the controller 110 may alternatively be implemented in a manner that is completely or partially separate from the mass spectrometer 100, such as by a computing device that is communicatively coupled to the mass spectrometer 100 via a wired connection (e.g., a cable) and / or a network (e.g., a local area network, a wireless network (e.g., Wi-Fi), a wide area network, the Internet, a cellular data network, etc.).

[0046] exist Figure 1 In an example, various components of the mass spectrometer 100 may be combined into a single component. For example, the ion storage 104 and the mass analyzer 106 may be combined into a single trapping mass analyzer, in which ions are accumulated for an accumulation time to achieve a target ion count, and the accumulated ions are then mass analyzed.

[0047] exist Figure 1 In the example of FIG, the ion population analyzed by the mass analyzer 106 can be adjusted by adjusting the accumulation time that ions are accumulated in the ion storage 104. In an alternative example, the ion population analyzed by the mass analyzer 106 can be adjusted by adjusting the potential applied to the ion optical device (e.g., lens) that adjusts the ion flux delivered to the mass analyzer 106. For example, the potential applied to the lens ( Figure 1 The voltage of the ion optics (not shown) can be adjusted to focus or defocus the ion flow 112 and / or the ion flow 114, thereby reducing or increasing the ion population delivered to and analyzed by the mass analyzer 106. In some examples where the ion population for a downstream process (e.g., mass analysis) is adjusted by the ion optics rather than by the ion store 104, the ion store 104 can be omitted.

[0048] The methods, systems, and apparatus of the AIC described herein can be used as the mass spectrometer 100 described herein and / or as part of or run with any other suitable mass spectrometer or mass spectrometry system, including a combined separation-mass spectrometry system, such as a liquid chromatography-mass spectrometry system (LC-MS), a high performance liquid chromatography-mass spectrometry (HPLC-MS) system, a gas chromatography-mass spectrometry (GC-MS) system, or a capillary electrophoresis-mass spectrometry (CE-MS) system. The methods, systems, and apparatus described herein can also be operated with a continuous flow sample source, such as with flow injection mass spectrometry (FI-MS), where the analyte is injected into the mobile phase without separation in a column and enters the mass spectrometer.

[0049] In some examples, the mass spectrometer 100 can be used to perform CDMS. CDMS using an orbital electrostatic trap mass analyzer has been extensively described in the patent and scientific literature, including in the following publications, all of which are incorporated herein by reference: PCT Publication WO 2020 / 219605 by Senko et al.; Multiplexed Mass Spectrometry of Individual Ions Improves Measurements of Proteoforms and Their Complexes by Kafader et al., Nat Methods 2020 April 17(4), pp. 391-94; and STORIPlots Enable Accurate Tracking of Individual Ion Signals by Kafader et al., J. Am. Soc. Mass Spectrom. Mass Spectrom. (2019) 30: 2200-2203; and Kafader et al. Individual Ion Mass Spectrometry Enhances the Sensitivity and Sequence Coverage of Top Down Mass Spectrometry, J. Proteome Res. 2020 March 619 (3) pp. 1346-50. Typically, CDMS using an orbital electrostatic trap mass analyzer involves processing transient signals generated by the axial oscillations of the ion population within the orbital electrostatic trap mass analyzer to produce a selective time overview (STORI) diagram of a set of resonant ions. The slope of each STORI diagram is proportional to the charge of the different ion species in the analyzed ion population. The charge of the ion can be determined by a slope-charge calibration function. Using the determined charge z and the m / z of each ion species measured, a mass domain spectrum can be generated that plots the detected signal as a function of mass m. As used herein, "mass domain spectrum" or "true mass spectrum" refers to a graph of the intensity of the detected ions as a function of mass. The analytes represented in the mass spectrum can be placed into the mass domain by using any suitable CDMS method.

[0050] As mentioned, in CDMS, each detected signal (also referred to herein as a peak) within a mass spectrum is resolved from every other signal in the spectrum along the m / z domain. If the signals overlap in m / z, it is difficult to determine whether there are multiple individual ions contributing to the signal, or whether there is one ion that is the sum of the charges of the individual ions. Therefore, it is difficult to determine the charge of the ion species and place the analyte in the true mass domain.

[0051] The appropriate number of individual ions that can be collected in one acquisition is limited by the number of analyte charge states and the number of isotope channels that can be populated without overlapping in m / z space. Overpopulating these channels (i.e., creating multiple ion events) will result in charge uncertainty and, therefore, in uncertainty in the mass assignment of the signal. In order to minimize the possibility of ambiguous charge assignments, the mass analyzer can be operated with a smaller ion population (e.g., achieved by a short accumulation time or an attenuated ion beam), which produces a sparse spectrum in which the probability of overlapping ions is low. However, intentionally limiting the ion population will increase the number of acquisitions required to produce a statistically relevant mass domain spectrum (i.e., increase the experimental time).

[0052] AIC method as described herein can be used to optimize the ion quantity in each frequency spectrum, and the appearance of the interfering ions that overlap on m / z is limited to acceptable quantity and / or acceptable m / z zone simultaneously.As used herein, " optimization " and variants thereof mean in the set of possible solutions, seek improved solution or the best solution, but may not necessarily obtain best solution, as when optimization process terminates before finding best solution, when there are multiple solutions that meet predefined criteria, when solution meets minimum standard or when selected optimization technique cannot converge on best solution.Similarly, as used herein, " best " parameter (for example, " maximum " or " minimum " value of parameter) means the solution obtained owing to execution optimization process, and therefore not necessarily the absolute extreme value (for example, absolute maximum or minimum value) of parameter, but still can adjust parameter and cause improvement.

[0053] One or more operations associated with the AIC may be performed by an automated ion control system. Figure 2 An exemplary automated ion control system 200 ("system 200") is shown. System 200 can be implemented in whole or in part by mass spectrometer 100 (e.g., by controller 110). Alternatively, system 200 can be implemented separately from mass spectrometer 100 (e.g., by a separate and / or remote computing system or server).

[0054] System 200 may include, but is not limited to, a storage facility 202 and a processing facility 204 selectively and communicatively coupled to each other. Facilities 202 and 204 may each include or be implemented by hardware components and / or software components (e.g., a processor, memory, a communication interface, instructions stored in the memory for execution by the processor, etc.). In some examples, facilities 202 and 204 may be distributed across multiple devices and / or multiple locations, as may be required for a particular implementation.

[0055] The storage facility 202 may maintain (e.g., store) executable data used by the processing facility 204 to perform any of the operations described herein. For example, the storage facility 202 may store instructions 206 that may be executed by the processing facility 204 to perform any of the operations described herein. The instructions 206 may be implemented by any suitable application, software, code, and / or other executable data instances. The storage facility 202 may also maintain any data collected, received, generated, managed, used, and / or transmitted by the processing facility 204.

[0056] The processing facility 204 can be configured to perform (e.g., execute instructions 206 stored in the storage facility 202 to perform) the various processing operations described herein. It will be appreciated that the operations and examples described herein are merely illustrative of the many different types of operations that can be performed by the processing facility 204. In the following description, any reference to an operation performed by the system 200 can be understood to be performed by the processing facility 204 of the system 200. Furthermore, in the description herein, any operation performed by the system 200 includes the system 200 directing or instructing another computing system, device, or apparatus to perform an operation.

[0057] An exemplary method for performing AIC will now be described. The AIC method is described herein in conjunction with CDMS performed using an orbital electrostatic trap mass spectrometer. However, the AIC method should not be construed as being limited thereto and can be successfully applied to other analytical methods, including other mass spectrometry methods and other CDMS methods, and / or using any other type of mass analyzer (e.g., TOF mass analyzer, FT-ICR mass analyzer, ELIT mass analyzer, OFA, electronic sector analyzer, etc.).

[0058] Figure 3 An exemplary method 300 for performing AIC is shown. Figure 3 Exemplary operations according to one embodiment are shown, but other embodiments may omit, add to, reorder, and / or modify Figure 3 Any of the actions shown. Figure 3 One or more of the operations shown may be performed by system 200, any components included therein, and / or any implementation thereof (e.g., mass spectrometer 100, one or more components of mass spectrometer 100, and / or a remote computing system separate from mass spectrometer 100).

[0059] At operation 302, system 200 acquires a mass spectrum comprising a plurality of peaks representing intensities as a function of m / z of ion populations analyzed by a mass analyzer during an acquisition event. In some examples, the ion population comprises ions accumulated in an ion store over an accumulation time. In other examples, the ion population comprises ions within an ion beam delivered to the mass analyzer. System 200 can generate a mass spectrum based on a signal received from a detector (e.g., detector 108), or the mass spectrum can be generated by a mass spectrometer (e.g., controller 110) and provided to system 200. In some examples, the acquisition event is part of an experimental analysis of a sample. In other examples, the acquisition event comprises acquiring a survey spectrum performed prior to the experimental analysis of the sample.

[0060] At operation 304, system 200 determines the measured signal density of the selected m / z range of the mass spectrum based on the mass spectrum. The selected m / z range can include the entire detection window of the mass spectrum or any part thereof. In some examples, the selected m / z range is specified by user input and can be a narrow m / z range of interest. As described above, signal density can be defined and determined according to various different methods. In a simple method, AIC adjusts the number of signals observed in the spectrum. In this embodiment, signal density is the number of peaks divided by the m / z range of the acquisition event. However, this method may represent an oversimplification. Simply counting the number of signals does not take into account the m / z width of the peak, which may affect whether adjacent signals interfere or overlap. Consider a mass spectrum collected under standard conditions, and a mass spectrum collected with an m / z resolution that is twice the m / z resolution of the first mass spectrum. For a higher resolution mass spectrum, the probability of having overlapping peaks is smaller because each peak consumes less m / z space.

[0061] In the peak spacing method, signal density generally represents (e.g., inversely indicates) the spacing in units of peak width between adjacent peaks within a selected m / z range of a mass spectrum. In the peak spacing method, the measured signal density is determined from the mass spectrum acquired earlier without regard to signal intensity. Figure 4 The method for determining the measured signal density of an acquired mass spectrum according to the peak spacing method is described in more detail. In the sum intensity method, the signal density generally represents the amount of signal per unit m / z space (signal intensity) within a selected m / z range of the mass spectrum. In the sum intensity method, the signal density is determined based on factors in the survey spectrum performed before the experimental run and the intensity of the detected signal. Figure 5 A method of determining the measurement signal density of an acquired mass spectrum according to the sum intensity method is described in more detail. It will be appreciated that other methods for defining and determining the measurement signal density are contemplated and within the scope of method 300.

[0062] At operation 306, system 200 sets ion population control parameters for subsequent acquisition events based on the measured signal density and the target signal density. The subsequent acquisition event can be the next acquisition event or any other acquisition event that occurs after the acquisition event of operation 302 (e.g., the Nth acquisition event, where N is an integer greater than 1). The ion population control parameters adjust the ion population analyzed by the mass analyzer during the subsequent acquisition event. In some examples, the ion population control parameter is the cumulative time that ions accumulate in the ion storage before being analyzed by the mass analyzer during the subsequent acquisition event. In other examples, the ion population control parameter is a potential applied to the ion optical device that adjusts the flux of ions delivered to the mass analyzer (delivered to the ion storage before being transferred to the mass analyzer).

[0063] Target signal density is a predetermined value, and for specific experimental conditions and target, this predetermined value can be selected to optimize the quantity of ion in each frequency spectrum, while limiting the occurrence of interfering ions to an acceptable low amount. Target signal density depends generally on the characteristic and operating parameters (for example, resolving power) of mass analyzer, and can also depend on method parameters and analyte characteristics (for example, concentration, molecular weight and / or molecular weight distribution of analyte ions) in some examples. In some examples, target signal density is determined empirically. In other examples, target signal density is determined based on the ion monitoring device in one or more method parameters, instrument conditions, instrument and / or the sample or analyte characteristics for experimental analysis. In some examples, system 200 can allow the user to provide the input for selecting or setting target signal density. Alternatively, system 200 can automatically select or set target signal density based on one or more instruments, methods and / or analyte parameters and / or experimental conditions.

[0064] In some examples, the user may choose to operate in a mode where the target signal density is much higher than the signal density that minimizes interference. For example, the user may be interested in collecting data as quickly as possible, and in these cases, it may be advantageous to allow for interference in areas of high signal density in order to increase the amount of signal observed in areas of low signal density. Therefore, the user may select a narrow m / z range of interest.

[0065] A subsequent acquisition event may be any acquisition event performed after the acquisition event of operation 302. For example, in the peak interval method, the subsequent acquisition event may be the next acquisition event performed during the experimental run. In the sum intensity method, the subsequent acquisition may be a subsequent survey acquisition.

[0066] The system 200 can set the ion population control parameters for subsequent acquisition events based on the measured signal density and the target signal density in any suitable manner. An example of setting the ion population control parameters will now be directed to setting the accumulation time. However, the same or similar principles can be applied to setting the potential applied to the ion optical device that regulates the ion flux delivered to the mass analyzer.

[0067] In some examples, the relationship between accumulation time and signal density can be assumed to be linear: as the ratio of target signal density to measured signal density increases ("signal density ratio"), the accumulation time increases (assuming signal density is expressed in units that increase as density increases). For example, the accumulation time of subsequent acquisition events can be determined according to the following equation (1):

[0068]

[0069] Where T subs is the cumulative time of subsequent acquisitions, T current is the accumulation time used in the current (most recent) acquisition event of operation 302, the target signal density is the predetermined target signal density, and the measured signal density is as determined in operation 304. When the signal density ratio is greater than 1.0, it is assumed that the measured signal density is too sparse, and thus the accumulation time for subsequent acquisition events should be increased accordingly. When the signal density ratio is less than 1.0, it is assumed that the measured signal density is too dense, and thus the accumulation time for subsequent acquisition events should be decreased accordingly. When the signal density ratio is equal to 1.0 (or within a threshold amount of 1.0, e.g., ±10%), it is assumed that the measured signal density is sufficiently close to the target signal density, and thus the accumulation time for subsequent acquisition events can remain the same.

[0070] To illustrate the peak spacing method, if the target signal density is 10% (1 adjacent peak per 10 peak widths), the measured signal density is 20% (1 adjacent peak per 5 peak widths), and the acquisition time T of the acquisition event is current is 1 second, the system 200 will determine the cumulative time T of subsequent acquisition events subs On the other hand, if the target signal density is 10% (a collection of 1 adjacent peak), the measured signal density is 5% (a collection of 1 adjacent peak per 20 peak widths), and the acquisition time T of the acquisition event is current is 1 second, the system 200 will determine the cumulative time T of subsequent acquisition events subs will be increased to 2 seconds to increase the signal density. If the target signal density is 10% (a collection of 1 adjacent peak), the measured signal density is 9% (a collection of 1 adjacent peak for every 9 peak widths), and the acquisition time T of the acquisition event iscurrent is 1 second, the system 200 will determine the cumulative time T of subsequent acquisition events subs It is 1 second and is not changed.

[0071] In an alternative example, the relationship between the cumulative time and the signal density is nonlinear. Any nonlinear function can be used to relate the cumulative time to the measured signal density and the target signal density. In some examples, a nonlinear function can be used when the measured signal density is high or exceeds a threshold level. In some examples, the system 200 can receive user input specifying a linear (e.g., equation (1)) or nonlinear correlation to be used.

[0072] The system 200 can set the cumulative time of subsequent acquisition events to the calculated cumulative time T sub Therefore, subsequent acquisition events are performed using the new accumulated time.

[0073] When operating with low ion loads in a single ion system of a CDMS, signal density and / or signal intensity may be unstable and vary significantly from one spectrum to the next, whether due to random chance or due to changes in the ion flux from the ion source. The ion flux from the ion source can vary, for example, due to changing ionization conditions or due to online analysis of a sample via flow injection analysis, liquid chromatography, or capillary electrophoresis. Therefore, in some examples, the system 200 sets the accumulation time of subsequent acquisition events by determining the amount of change in the accumulation time (e.g., an increase or decrease in the accumulation time of the subsequent acquisition event relative to the acquisition event of operation 302) and adjusting the amount of change in the accumulation time by applying a filter or applying a change limit to the amount of change in the accumulation time. Any suitable filter can be applied, such as an exponential filter having a filter coefficient configured to minimize the effects of random chance. Additionally or alternatively, any suitable change limit can be applied, such as a maximum change (e.g., a maximum change of 10 milliseconds (ms)) or a predetermined percentage of the change (e.g., 50%, 33%, 20%, etc.). By applying filters or variation limits, the integration time may be varied in a fractional or stepwise manner, which serves to avoid or reduce the occurrence of broad inter-spectral oscillations in the integration time and to increase the stability of the integration time.

[0074] In some examples, system 200 can be configured to receive user input for setting one or more filter parameters (e.g., coefficients) and / or variation limit parameters, and can set the filter parameters and / or variation limit parameters based on the user input. For example, a user can specify one or more parameters of an exponential filter, a maximum variation and / or a predetermined percentage of variation, and which filter or variation limit to apply.

[0075] Exemplary AIC methods according to the peak separation method and the summed intensity method will now be described. The following examples are described with reference to adjusting the integration time, but can be similarly applied to adjusting the potential applied to the ion optics to adjust the ion flux delivered to the mass analyzer.

[0076] Figure 4 An exemplary method 400 for performing AIC according to the peak separation method is shown. Figure 4 Exemplary operations according to one embodiment are shown, but other embodiments may omit, add to, reorder, and / or modify Figure 4 Any of the actions shown. Figure 4 One or more of the operations shown may be performed by system 200, any components included therein, and / or any implementation thereof (e.g., mass spectrometer 100, one or more components of mass spectrometer 100, and / or a remote computing system separate from mass spectrometer 100).

[0077] At operation 402, system 200 acquires a mass spectrum comprising a plurality of peaks representing intensities as a function of m / z of ions accumulated in the ion storage during the accumulation time and analyzed by the mass analyzer during the acquisition event. System 200 may perform operation 402 in any suitable manner, including any of the manners described above with respect to operation 302.

[0078] At operation 404, system 200 calculates the peak separation value for each set of adjacent peaks included in the selected m / z range of the mass spectrum. As described above, the selected m / z range can include the entire detection window of the mass spectrum or any portion thereof. In some examples, the selected m / z range is specified by user input and can be a narrow m / z range of interest. The mass spectrum includes a plurality of peaks arranged in order of increasing m / z along the m / z domain. As used herein, a set of adjacent peaks refers to the two peaks that are closest to each other along the m / z domain of the mass spectrum. Therefore, except for the peak at the lowest detected m / z and the peak at the highest detected m / z, each peak will have two adjacent peaks. The peak separation value for a set of adjacent peaks is the distance between adjacent peaks along the m / z domain. System 200 calculates the interval between each set of adjacent peaks.

[0079] The probability of coincident ions is related to the spectral resolution. Higher resolution reduces the probability of two adjacent signals interfering with each other. To address this issue, signal density is measured in units that reflect the actual peak capacity of the spectrum. Thus, signal density in the peak separation method is measured based on peak width, rather than simply measuring the number of peaks spanning the m / z range.

[0080] Peak width can be determined in any suitable manner. In some examples, peak width is the expected instrument peak width. In other examples, peak width is the peak width measured from a mass spectrometer. For example, peak width can be the average peak width of all peaks included in the mass spectrum or all peaks within a selected m / z range (e.g., the m / z range of interest selected by the user). The peak width measured from the spectrum advantageously takes into account ions that cannot survive the entire detection cycle. These ions can produce wider peaks than expected for a given detection time, and having wider peaks will increase the probability of overlapping signals. Using the measured peak width also takes into account ions with unstable m / z during the detection cycle. These unstable ions are most typically larger biomolecule complexes that are usually not completely desolvated during detection, or these unstable ions that fragment covalent bonds during detection. When ions collide with background gas molecules, the ions heat and effectively evaporate the attached solvent, thereby reducing mass and therefore reducing the measured m / z. Drift m / z produces wider peaks than the peaks expected from ions with stable m / z.

[0081] At operation 406, the system 200 determines a global peak separation value for the selected m / z range based on the peak separation values calculated at operation 404. The global peak separation value is used as a measure of signal density. The global peak separation value can be an average, a weighted average (e.g., weighting smaller peak separation values more heavily), a median, or any other statistical representation of the calculated peak separation values within the selected m / z range. In the examples described herein, the global peak separation value is expressed as a percentage (e.g., a set of one (1) adjacent peaks per M peak widths, where M is the global peak separation value). However, the global peak separation value can be expressed in any other manner (e.g., by M peak widths) as long as the equations described herein are modified accordingly.

[0082] Mass spectra of actual samples show that peaks are generally not randomly distributed over the m / z domain, but rather tend to cluster together due to the quantified ion charge (e.g., z has only integer values, which constrains m / z variability) and due to the many sources of possible subtle mass variability within the analyte. Some sources of subtle mass variability include, for example, isotope variability, post-translational modifications, and addition of cations and solvents. This clustering can leave large regions of the spectrum completely empty, especially if the ion signal does not extend to the upper and lower m / z limits of the acquisition range. Empty m / z regions can artificially reduce the measured signal density even if there are high-density local regions with an increased probability of interfering signals.

[0083] To account for uneven distribution and clustering of peaks, method 400 may consider only the densest regions of the spectrum or a selected m / z range. This may be achieved by calculating a global peak separation value based on a subset of the peak separation values calculated in operation 404. The subset of peak separation values typically includes a minimum peak separation value, which represents the densest region of the spectrum or the selected m / z range. In some examples, the subset of calculated peak separation values includes a percentage of the minimum peak separation value (e.g., 50%, 40%, 30%, etc.). For example, system 200 may arrange the calculated peak separation values in an ascending order in a list and calculate a global peak separation value based only on the top 50%, 40%, 30%, or other percentage of the calculated peak separation values. In other examples, the subset of calculated peak separation values includes all peak separation values that are less than a threshold peak separation value. In some examples, the threshold peak separation value is set based on a target signal density (e.g., 200% or 300% of the target signal density).

[0084] At operation 408, the system 200 sets an accumulation time for subsequent acquisition events based on the measured signal density (e.g., the global peak separation value calculated at operation 406) and a predetermined target signal density (e.g., the target peak separation value). The system 200 can set the accumulation time in any suitable manner, including any of the manners described above with respect to operation 306.

[0085] After completing operation 408, processing of method 400 returns to operation 402 and method 400 is executed again for subsequent acquisition events. Using method 400, the peak separation method accounts for any varying ion flux by using each spectrum as a guide to determine the integration time for subsequent acquisition events.

[0086] In the peak spacing method, the use of a signal density metric is beneficial in eliminating the effects of low-charge contaminants that may appear in the spectrum. The presence of a small number of ions allows CDMS to be performed with low-concentration samples that are more sensitive to contamination. Contaminants are typically low-mass species that carry only one or two charges. In order to be detectable in CDMS (which is generally unable to detect charge and assigns charge to individual ions in a low-charge state), the signal from the contaminant will be generated by a large number of ions of the same m / z. Since these are singular peaks, and a typical mass spectrum collected by CDMS can contain hundreds of individual ions, these singular peaks do not contribute significantly to the signal density and therefore do not affect the optimized accumulation time.

[0087] Figure 5 An exemplary method 500 for performing AIC according to the sum strength method is shown. Figure 5 Exemplary operations according to one embodiment are shown, but other embodiments may omit, add to, reorder, and / or modify Figure 5 Any of the actions shown. Figure 5 One or more of the operations shown may be performed by system 200, any components included therein, and / or any implementation thereof (e.g., mass spectrometer 100, one or more components of mass spectrometer 100, and / or a remote computing system separate from mass spectrometer 100).

[0088] At operation 502, the system 200 acquires a first mass spectrum comprising a plurality of peaks representing intensities as a function of m / z of ions accumulated in the ion storage during an accumulation time and analyzed by the mass analyzer during an acquisition event. The system 200 may perform operation 502 in any suitable manner, including any of the manners described above with respect to operation 302. In some examples, the first mass spectrum is acquired by setting a high accumulation time and acquiring a survey spectrum. The system 200 will use the first mass spectrum to mark any regions in the m / z space that should be excluded when determining the measurement signal density (e.g., regions occupied by noise and / or low-mass contaminant signals).

[0089] In some examples, the first mass spectrum represents the ensemble of ion populations. In ensemble measurements, multiple acquisitions or spectra may be used to sample the large ion population (typically, but not exclusively, 10 5 or more total charge). Multiple spectra can be summed or averaged for an overall measurement. In other examples, the first mass spectrum is acquired by individual ion measurements. For example, multiple acquisitions or spectra can be used to fully sample the most abundant analyte population (such as a protein form).

[0090] At operation 504, the system 200 determines the occupied m / z space within the selected m / z range of the mass spectrum acquired at operation 302. As described above, the selected m / z range can include the entire detection window of the mass spectrum or any portion thereof. In some examples, the selected m / z range is specified by user input and can be a narrow m / z range of interest. The occupied m / z space is the space in the m / z domain of the mass spectrum that may be occupied by a detectable ion signal of interest. The system 200 can determine the occupied m / z space in any suitable manner.

[0091] In some examples, the system 200 excludes from the occupied m / z space any m / z regions (e.g., m / z bins, as explained below) that contain only signals less than a minimum threshold intensity value (e.g., 5% relative abundance, 3% relative abundance, etc.). Signals below the minimum threshold intensity value are considered noise, and thus any m / z regions containing only noise are excluded from the occupied m / z space because including noise signals does not help determine whether the occupied region has signal at the single ion level.

[0092] In some examples, the occupied m / z space also excludes space occupied primarily or solely by contaminants. Some samples may contain contaminants that prevent the determination of the occupied m / z space. Contaminants can include abundant low-mass species from the sample that are not removed during sample preparation, or other substances that may be added during the preparation process. During the overall measurement, contaminants can dominate the spectrum, pushing the target analyte to a lower relative abundance than expected. Therefore, the m / z space occupied by the contaminated sample will be much smaller than the m / z space originally expected. The much smaller occupied m / z space results in a larger measured signal density, which drives the AIC algorithm to reduce the accumulation time and thereby narrow the ion population beyond the practical range of a timely CDMS experiment.

[0093] In some cases, the pollutant peaks exist in an overly simple distribution, such as a series of tight isotope distributions. The real-time charge deconvolution algorithm for processing mass spectra (e.g., advanced peak determination (APD), THRASH, or MaxEnt) can be assigned to each peak included in the selected m / z range. Therefore, pollutants can be identified based on the charge state they are assigned. Ions assigned a low charge state are considered to be pollutants. For example, system 200 can identify all signals within a selected m / z range that are assigned a charge state less than or equal to a threshold charge state value (e.g., +2) as inferred pollutants. The threshold charge state value can be selected to be consistent with the maximum charge state of a common pollutant. When determining the occupied m / z space and measuring signal density, system 200 can exclude all regions containing pollutants.

[0094] Using the charge deconvolution, some contaminant peaks may not be identified or assigned a charge state. Therefore, in some examples, the system 200 also excludes regions containing a small number of most abundant peaks from the occupied m / z space to capture occasional contaminant peaks that are not assigned a charge state. For example, the system 200 can exclude regions containing a threshold number of most abundant peaks (e.g., 15, 10, 8, 5, etc.). Alternatively, the system 200 can exclude regions containing all peaks exceeding a maximum threshold intensity level (e.g., a relative abundance of 80%, 90%, 95%, etc.). Some non-contaminant peaks may be erroneously removed in this manner, but, assuming a constant false discovery rate, these errors may be consistently accounted for in the target signal density value.

[0095] Some contaminants, such as polymers or plasticizers introduced through sample processing or molecular changes within the sample itself, produce peaks that are not excluded by the above methods (e.g., having intensity values between a minimum threshold intensity value and a maximum threshold intensity value). Thus, in some examples, the system 200 excludes from the occupied m / z space any regions of the selected m / z range that have or may have an identified or identifiable contaminant. For example, the system 200 can identify contaminants based on a library search of peaks within the selected m / z range and / or an m / z list of expected contaminants.

[0096] The system 200 can be implemented by subdividing the selected m / z range into bins of width Δ i The occupied m / z space is determined by taking N non-overlapping intervals of and calculating the occupied m / z space based on the following equation (2):

[0097]

[0098] Where N is the number of intervals, index i indicates an interval from 1 to N, and the interval width Δ i is the width of each interval (in m / z units), and w i is the interval weight assigned to each interval. Interval width Δ i The bin weight w may be constant for each bin (e.g., 5 m / z, 10 m / z, etc.), vary as a function of m / z, vary as a function of the density of detectable signal at a particular m / z, be user-defined, or otherwise assigned. In some examples, a bin weight w is assigned to the bin that is equal to or greater than zero (0) and less than or equal to one (1). i The interval weight w can be determined based on the signal strength of the interval i The signal intensity of an interval can be, for example, the maximum value, average value, median value, or total (sum) of all peak intensities within the interval.

[0099] For example, each bin having an intensity value less than a minimum threshold intensity value (eg, noise) and / or associated with a pollutant signal may be assigned a bin weight w of zero (0). i In this way, the system 200 excludes regions containing noise and / or contaminants from the occupied m / z space. In some examples, each bin not associated with a noise or contaminant signal is assigned a bin weight w of one (1). i In other examples, bins not associated with noise or contaminant signals are not weighted equally, but are weighted based on the signal strength of each bin.

[0100] To illustrate, if the signal level of an interval is less than a first threshold strength value (eg, a minimum (noise) threshold strength value), the system 200 may assign an interval weight w of zero (0) to the interval. iIf the signal level of an interval is greater than or equal to a first threshold intensity value but less than a second threshold intensity value (e.g., a relative abundance of 15%, 20%, 25%, etc.), the system 200 may assign an interval weight w greater than 0 but less than 1 to the interval. i If the signal level of the interval is greater than or equal to the second threshold intensity value but less than a third threshold intensity value (e.g., a maximum intensity value for cutting off pollutants), the system 200 may assign an interval weight w of one (1) to the interval. i If the signal level of the interval is greater than the third threshold strength value, the system 200 may assign an interval weight w of zero (0) to the interval. i For the interval between the first threshold intensity value and the second threshold intensity value, the interval weight w assigned to the interval is i It may be the same, may vary linearly as a function of the intensity of each interval between the first threshold intensity level and the second threshold intensity level, or may vary non-linearly (e.g., exponentially, stepwise, etc.) based on the signal strength of each interval.

[0101] In some examples, any one or more of the first threshold intensity level, the second threshold intensity level, and the third threshold intensity level vary linearly with m / z (e.g., decrease linearly with increasing m / z) because the intensity of individually resolved ions increases with decreasing m / z (increasing charge z).

[0102] By using the interval weight w i By scaling the occupied m / z space as described herein, the contribution to the occupied m / z space of intervals with lower signal strength can be weighted less than the contribution to the occupied m / z space of intervals with higher signal strength. This is because intervals with higher signal strength are more likely to cause signal interference than intervals with lower signal strength.

[0103] At operation 506, the system 200 determines the summed intensity of the signals within the occupied m / z space. The system 200 steps through the first mass spectrum and queries the signal density of each bin, excluding the signal density of any bins within the selected m / z range that are excluded from the occupied m / z space. For example, the system 200 can determine the summed intensity by summing the signal intensities of the bins within the occupied m / z space according to the following equation (3):

[0104]

[0105] where N is the number of intervals (as in equation (2)), index i indicates an interval from 1 to N, and W i is the signal weight assigned to each interval, and r i is the signal strength of each interval. System 200 can be used for the interval weight wi The signal weight W of each interval is determined by any of the methods described i For example, each bin having a signal strength less than a minimum threshold strength value and / or a signal strength associated with a pollutant signal may be assigned a signal weight W of zero (0). i , and each interval not associated with noise or contaminants can be assigned a signal weight W of one (1). i In other examples, bins not associated with noise or contaminants are not weighted equally, but rather based on the signal strength r of each bin. i To weight, as above for the interval weight w i In some examples, the signal weight W of each interval is i and interval weight w i are the same (for example, W i =w i ). In other examples, the signal weight W of the interval i With interval weight w i By scaling the signal strength of the intervals as described herein, the contribution of intervals with lower signal strength to the total strength can be weighted less than the contribution of intervals with higher signal strength. Again, this is because intervals with higher signal strength are more likely to cause signal interference than intervals with lower signal strength.

[0106] At operation 508, the system 200 determines the measured signal density based on the summed intensity within the occupied m / z space and the occupied m / z space. For example, the system 200 may determine the measured signal density according to the following equation (4):

[0107]

[0108] wherein the occupied m / z space is as determined in operation 504 , and the sum intensity is as determined in operation 506 .

[0109] At operation 510, the system 200 sets an integration time for subsequent acquisition events based on the measured signal density (intensity per unit m / z) and the predetermined target signal density as determined in operation 508. The system 200 may set the integration time in any suitable manner, including any of the manners described above with respect to operation 306.

[0110] At operation 512, the system 200 determines whether the target signal density has been achieved. In some examples, the system 200 determines that the target signal density has been achieved when the measured signal density is within a threshold amount (e.g., 5%, 10%, 15%, etc.) of the measured signal density. If the system 200 determines that the target signal density has been achieved, then processing of the method 500 ends and the AIC is considered complete. The experimental analysis can then be performed using the accumulation time set in operation 510. If the system 200 determines that the target signal density has not been achieved, then processing of the method 500 proceeds to operation 514.

[0111] At operation 514, the system 200 acquires a second mass spectrum. Operation 512 can be performed in any suitable manner, including any of the manners described herein in operation 502. For example, the second mass spectrum can be another survey spectrum. However, the second mass spectrum is acquired using the accumulation time set in operation 510. After completing operation 514, the processing of method 500 returns to operation 506 to determine the measured signal density of the second mass spectrum. The processing of method 500 can continue and repeat until the system 200 determines that the target signal density has been reached.

[0112] Method 500 can be performed at any suitable time before and / or during the experimental analysis. For example, method 500 can be performed before starting the CDMS experiment. Additionally or alternatively, method 500 can be performed during the experimental analysis to recalibrate the accumulation time. In some examples, the recalibration is initiated by the user. In an alternative example, system 200 periodically acquires one or more survey spectra to compare the measured signal density with the target signal density and determine whether recalibration is warranted. For example, system 200 can periodically perform operations 502, 504, 506, 508 and 512 (operation 510 is temporarily omitted) to evaluate whether method 500 should be performed to recalibrate the accumulation time.

[0113] As described above, methods 300, 400, or 500 can be performed during a CDMS experiment to characterize a sample and obtain a mass spectra of an analyte in the sample. The analyte may include, but is not limited to, one or more protein forms or biomolecular complexes thereof, such as immunoglobulins or their heavy chains, light chains, or Fd domains. As used herein, "protein forms" refers to all the different molecular forms of a protein product of a single gene, including changes due to genetic variation, alternative splicing of RNA transcripts, and post-translational modifications, as well as similar causes. In some examples, the protein form in the sample is an immunoglobulin, such as IgG. IgA, IgE, or IgM. CDMS allows for the measurement of complex protein form mixtures and their complexes without the need to separate the protein forms prior to identification.

[0114] As described herein, CDMS technology using AIC can be successfully integrated into a complete solution for processing plasma and automated CDMS analysis. Automated CDMS analysis has enabled research into protein analysis in hundreds of patient cohorts, independent of the complexity of the mixture. The spectral output in the mass domain does not require any further deconvolution software, and the platform can enable analysis and characterization of denatured protein mixtures ranging from 6 kDa to 100 kDa. High-throughput automation of CDMS analysis also enables rapid and robust acquisition of high-resolution mass domain spectra of intact proteins.

[0115] Figure 6 An exemplary method 600 for performing CDMS using AIC is shown. Figure 6 Exemplary operations according to one embodiment are shown, but other embodiments may omit, add to, reorder, and / or modify Figure 6 Any of the actions shown. Figure 6 One or more of the operations shown may be performed by system 200 and / or mass spectrometer 100, any components included therein, and / or any implementation thereof (e.g., mass spectrometer 100, one or more components of mass spectrometer 100, and / or a remote computing system separate from mass spectrometer 100). The operations of method 600 may be performed in any suitable manner, including any manner described herein.

[0116] At operation 602, ions generated from a sample are accumulated in an ion storage (eg, ion storage 104) for an accumulation time. In some examples, the sample is in the form of a protein.

[0117] At operation 604, a high-resolution mass analyzer (e.g., an orbital electrostatic trap mass analyzer) acquires a mass spectrum representing the intensity as a function of the m / z of the accumulated ions during the acquisition event. In some examples, the mass analyzer operates in CDMS mode to acquire a mass spectrum of ion populations in a single ion system.

[0118] At operation 606, a measured signal density for a selected m / z range of the mass spectrum is determined based on the mass spectrum. In some examples, the measured signal density is determined according to a peak separation method. In other examples, the measured signal density is determined according to a sum intensity method.

[0119] At operation 608 , an accumulation time for subsequent acquisition events is set based on the measured signal density and the target signal density.

[0120] At operation 610, the charge state of the individual ion species represented by the peaks within the selected m / z range of the mass spectrum is determined. For example, the charge state can be determined by a charge deconvolution algorithm. Alternatively, the charge state can be determined according to CDMS techniques (e.g., by generating a STORI plot and determining the charge state based on a slope-charge calibration function).

[0121] At operation 612 , a mass domain spectrum representing the intensity of the single ion species as a function of mass is generated based on the charge state of the single ion species and the m / z of the single ion species.

[0122] Processing of method 600 then returns to operation 602 and the process is repeated for subsequent acquisition events.At operation 602, ions are accumulated in the ion store for the accumulation time set in operation 608 during the previous cycle of method 600.

[0123] In the above-mentioned AIC method, ions across the entire selected m / z range are accumulated in the ion storage in a single accumulation event. Therefore, the number of ions accumulated in the ion storage will be constrained by the most dense m / z region of the incident ion beam (for example, the ion beam from the ion source). If there is a specific region of m / z space where the incident ion beam is particularly dense, once enough ions from this m / z region have been accumulated, the single accumulation event will stop. Otherwise, further accumulation of ions from the ion beam will have the risk of ions with the same or very similar m / z values being accumulated in the most dense region in the ion storage. When the single accumulation event stops, since the probability of ions being contained in these m / z regions is low, very few ions will be accumulated in other m / z regions of lower density. Therefore, ions located in the lower density m / z regions of the incident ion beam may not be fully sampled. In order to sample ions located in the lower density m / z regions, it will be necessary to perform additional mass analysis until enough signals from the lower density m / z regions have been collected. Therefore, acquiring sufficient signal for the lower density m / z region consumes additional system resources and prolongs the CDMS process.

[0124] These issues are 7A to 7C Example. Figure 7A An exemplary m / z distribution graph 702 is shown, which plots the intensity of ions in a hypothetical incident ion beam as a function of the m / z of the ions. The m / z distribution graph 702 represents the m / z distribution of the incident ion beam. If ions spanning the entire m / z range are accumulated in the ion store in a single accumulation event, the AIC method described herein can stop ion accumulation once a sufficient or maximum number of ions from the densest region (e.g., near the most intense peak 704) has been collected. Mass analysis of the accumulated ions can then be performed.

[0125] Figure 7B An exemplary CDMS spectrum 706 acquired by mass analysis of ions accumulated in a single accumulation event is shown. It is assumed that CDMS spectrum 706 does not cause problems for subsequent spectral processing because there are no double ion events (e.g., no ions with the same m / z are accumulated and combined to give a higher peak) and there are no interfering peaks that distort the estimate. Although several peaks are located in the most dense m / z region (corresponding to the m / z region of peak 704), very few peaks are located outside the most dense m / z region.

[0126] Now, suppose the cumulative time is Figure 7B Twice the cumulative time of the analysis. Figure 7C shows that by having twice the Figure 7B 706. An exemplary CDMS spectrum 708 is collected for mass analysis of ions accumulated in a single accumulation event with an exemplary accumulation time of . As shown in CDMS spectrum 708, the lower density m / z regions of the incident ion beam (m / z regions away from peak 704 in m / z profile 702) produce more peaks as compared to CDMS spectrum 706 due to the increased accumulation time and, therefore, the greater probability of collecting ions in the lower density m / z regions. However, in CDMS spectrum 708, the densest m / z region includes a higher peak 710, indicating a dual ion event that would result in an incorrect charge assignment. Additionally, the proximity of adjacent peaks in the densest m / z region may result in errors in charge estimation due to interference. Thus, as shown by 7A to 7C As can be seen, the densest m / z region of the incident ion beam constrains the maximum accumulation time that can be used with the single accumulation event method for CDMS. For less dense m / z regions, the single accumulation event method will inherently provide less detectable signal per unit time. To fully characterize the less dense m / z regions, many analyses would have to be performed, as only occasional spectra would happen to contain ions in those less dense m / z regions.

[0127] In order to solve these problems of the single accumulation event method, ion population is accumulated in the ion storage before mass analysis in multiple different accumulation events to achieve a more uniform signal density across the m / z space. Each accumulation event corresponds to a different m / z region of the m / z space, and a separate ion population control parameter is determined for each m / z region to accumulate the desired number of ions during each accumulation event. Therefore, the probability of detecting ions in the lower density m / z region of the incident ion beam is increased, while the probability of multiple ion events and interference in the higher density m / z region is reduced. Therefore, the accumulation of ions in the ion storage is not bound by the most dense m / z region, and more ions in the lower density m / z region can be sampled in each CDMS acquisition.

[0128] The multiple accumulation event method utilizes a mass filter located upstream of the ion store to selectively transmit ions within the corresponding m / z region for accumulation during each accumulation event. Figure 8 A functional diagram of an exemplary mass spectrometer 800 that can be used for a multiple accumulation event method is shown. The mass spectrometer 800 is similar to the mass spectrometer 100, except that the mass spectrometer 800 includes a mass filter 802 located upstream of the ion storage 104. The mass filter 802 can be implemented by any suitable mass filter, such as a linear multipole mass filter (e.g., a quadrupole mass filter). The incoming ion stream 112-1 from the ion source 102 is filtered by the mass filter 802 to selectively transfer ions within a selected m / z range to the ion storage 104, which accumulates the transferred ions 112-2 during the accumulation event based on the ion population control parameters used for the accumulation event. Figure 8 As shown, the detector 108 is located downstream of the mass analyser 106. However, in examples where the mass analyser 106 is implemented as an orbital electrostatic ion trap mass analyser, the mass analyser 106 and the detector 108 are integrated into the same device.

[0129] Figure 9 A functional diagram of an exemplary mass spectrometer 900 including an electrostatic trap mass analyzer is shown. Mass spectrometer 900 can implement mass spectrometer 800 and can be used to perform CDMS using a multi-accumulation event method. As shown, mass spectrometer 900 includes an ion source 902, a mass filter 904, an ion trap 906, a collision cell 908, and an electrostatic trap mass analyzer 910. Mass spectrometer 900 can further include additional or alternative components that may be suitable for a particular embodiment (e.g., ion optics, lenses, mass filters, ion storage devices, ion mobility analyzers, collision cells, ion flux monitors, etc.).

[0130] Ion source 902 may implement ion source 102 (e.g., an electrospray ionization source) and mass filter 904 may implement mass filter 802. Ion trap 906 captures ions before introducing them into mass analyzer 910. Ion trap 906 is located downstream of mass filter 904 and upstream of mass analyzer 910. Ion trap 906 may be, for example, a multipole ion trap, such as a multipole linear ion trap, including a curved linear ion trap (C-trap).

[0131] The collision cell 908 can be any suitable collision cell, such as a high pressure collision dissociation (HCD) cell. The collision cell 908 is located downstream of the mass filter 904 and the ion trap 906 and upstream of the mass analyzer 910. In non-CDMS applications, such as MS / MS, the collision cell 908 is configured to fragment the ions received from the ion trap 906. In CDMS applications, the collision cell 908 can be used as an ion trap to accumulate ions that have passed through the ion trap 906. Before the ions are introduced into the mass analyzer 910, the collision cell 908 ejects the captured ions back into the ion trap 906. The ion trap 906 and / or the collision cell 908 can implement the ion storage 104 of the mass spectrometer 800.

[0132] The mass analyzer 910 is an electrostatic ion trap mass analyzer, such as an orbital electrostatic ion trap mass analyzer (e.g., Orbitrap TM mass analyzer, Kingdon trap mass analyzer, etc.) or electrostatic linear ion trap (ELIT).

[0133] Now refer to Figure 10 An illustrative example of implementing a CDMS using a multiple accumulation event method is described. Figure 10 A flow chart of an exemplary method 1000 for implementing a CDMS using a multiple accumulation event approach is shown. Figure 10 Exemplary operations according to one embodiment are shown, but other embodiments may omit, add to, reorder, and / or modify Figure 10 Any of the actions shown. Figure 10 One or more of the operations shown may be performed by system 200, any components included therein, and / or any implementation thereof (e.g., mass spectrometer 800 or 900, one or more components of mass spectrometer 800 or 900, and / or a remote computing system separate from mass spectrometer 800 or 900).

[0134] At a first operation 1002, an m / z range of interest is subdivided into a plurality of m / z windows. The m / z range of interest is the m / z range to be mass analyzed by CDMS and will be the m / z range of the resulting CDMS spectrum. Various methods for subdividing the m / z range of interest into a plurality of m / z windows are described in more detail below.

[0135] At operation 1004, ion population control parameters are determined for each m / z window. The ion population control parameters of each m / z window regulate the amount of ions accumulated in the ion storage (e.g., ion storage 104, ion trap 906, or collision chamber 908) during the accumulation event. As described above, in some examples, the ion population control parameter is the cumulative time that ions accumulate in the ion storage during the accumulation event. In other examples, the ion population control parameter is the potential applied to the ion optical device (e.g., lens), which regulates the ion flux (e.g., ion flow 112-2) delivered to the ion storage during the accumulation event. The ion population control parameters of each m / z window can be determined in any suitable manner, including by any method described herein, such as method 300, 400, or 500. For example, the m / z window can be used as the selected m / z range described above with reference to method 300, 400, and 500 to independently determine the ion population control parameters for each m / z window. Various methods for determining the ion population control parameters will be described in more detail below.

[0136] At operation 1006, the ion populations derived from the sample are accumulated in the ion store according to one or more cumulative events. Each cumulative event corresponds to a different m / z window in the multiple m / z windows. During each cumulative event, the ions in the m / z window corresponding to the cumulative event (e.g., ions with m / z within the m / z range of the m / z window) are accumulated in the ion store based on the ion population control parameters of the corresponding m / z window. For example, a mass filter (e.g., mass filter 802 or mass filter 904) only selectively transmits ions in the m / z window of the current cumulative event. Based on the ion population control parameters determined for the corresponding m / z window at operation 1004, the accumulation time or potential applied to the ion optical device is set for the current cumulative event so that the desired amount of ions is accumulated in the ion store. In some examples, the ion population is accumulated in the ion store while the previous ion population is processed in the mass analyzer (e.g., mass analysis is performed at operation 1010, as described below). In this way, the accumulation of ion population does not limit or constrain the throughput of the experiment. In some examples, the one or more cumulative events by which the ion population is accumulated in the ion storage include cumulative events for all m / z windows in a plurality of m / z windows (e.g., cumulative events for all m / z windows spanning the entire m / z range of interest). In other examples, the one or more cumulative events by which the ion population is accumulated in the ion storage correspond to a subset of one or more of the plurality of m / z windows. In further examples, the ion population is accumulated in the ion storage by one cumulative event corresponding to one m / z window.

[0137] At operation 1008, the accumulated ion population (e.g., ion population accumulated from multiple consecutive accumulation events or during one or more of one or more accumulation events) is transferred to a mass analyzer (e.g., mass analyzer 106 or mass analyzer 910) for mass analysis to acquire a CDMS spectrum.

[0138] At operation 1010, the ion population is mass analyzed to acquire a CDMS spectrum of the ion population. In examples where one or more accumulation events correspond to all of a plurality of m / z windows, the m / z range of the CDMS spectrum is the m / z range of interest spanned by the plurality of m / z windows.

[0139] In the example in which one or more cumulative events correspond to a subset of one or more of a plurality of m / z windows, operations 1006 to 1010 can be repeated until each m / z window in a plurality of m / z windows has been sampled. For example, the entire m / z range of interest can be subdivided into ten m / z windows. The first ion population can be accumulated in the ion store by a first subset (e.g., five) cumulative events corresponding to a first subset (e.g., five) in the ten m / z windows, transferred to a mass analyzer, and mass analysis is performed. The second ion population can be accumulated in the ion store by a second subset (e.g., five) cumulative events corresponding to the m / z window of the second subset, transferred to a mass analyzer, and mass analysis is performed. The CDMS spectrum of the first ion population and the second ion population collected can be combined to generate a CDMS spectrum spanning the m / z range of interest. In some examples, ions in one or more m / z windows (e.g., one or more sparse m / z regions) can be accumulated in two ion populations.

[0140] Now refer to Figure 11 The operations of method 1000 are described. Figure 11Shown is an m / z distribution diagram 702 that is subdivided into multiple m / z windows 1102 for multiple cumulative events and an exemplary CDMS spectrum 1104 collected using multiple cumulative events. As shown in the m / z distribution diagram 702, the m / z range of interest is subdivided into different m / z windows 1102 (e.g., m / z windows 1102-1 to 1102-7), which together span the entire m / z range of interest. As used herein, an m / z window is "different" if it does not have the same m / z upper and lower limit combinations. As shown in the m / z distribution diagram 702, the m / z windows 1102 are non-overlapping. However, in other examples, any two or more different m / z windows 1102 can overlap. For example, a first m / z window 1102 can span the entire m / z range of interest, while smaller m / z windows 1102 span less than the entire m / z range of interest (e.g., only a sparse m / z region), and any one or more of the smaller m / z windows 1102 can overlap with one or more other smaller m / z windows 1102. Although m / z distribution plot 702 shows seven m / z windows 1102, the m / z range of interest can be subdivided into any other suitable number of m / z windows 1102. In some cases, different m / z windows have gaps (i.e., do not continuously span the entire m / z range of interest). This can occur, for example, if ions within certain m / z ranges are not of interest for the particular sample being measured.

[0141] The m / z range of interest can be subdivided into a plurality of m / z windows 1102 in any suitable manner. In some examples, the m / z range of interest is subdivided into a plurality of m / z windows 1102 based on a preset fixed m / z width (e.g., set regardless of the m / z distribution of the incident ion beam (e.g., ion current 112-1)). In some examples, the m / z windows 1102 all have substantially the same m / z width (e.g., widths within 5% of each other). In further examples, the m / z range of interest is subdivided into a plurality of m / z windows 1102 based on a number of m / z windows into which the m / z range of interest is to be subdivided (which can be preset or provided by user input). For example, assuming that an m / z range of interest of 4,000–40,000 m / z is to be subdivided into ten m / z windows, each m / z window will have a width of 3,600 m / z.

[0142] In some examples, the m / z range of interest is subdivided into a plurality of m / z windows 1102 based on the m / z distribution of ions in the incident ion beam. In some examples, the m / z distribution of ions in the incident ion beam is known (e.g., based on a known m / z distribution of the sample) and / or can be determined based on a characterization analysis or pre-scan of the sample performed prior to the CDMS analysis. In other examples, the m / z distribution of the incident ion beam can be dynamically measured during the CDMS analysis. For example, a characterization analysis of the sample (e.g., ion stream 112-1) can be performed periodically or randomly during the CDMS analysis or when a calibration condition is met (e.g., multiple ion events are detected in the acquired CDMS spectrum, a peak width in the CDMS spectrum exceeds a threshold, etc.). Additionally or alternatively, the measured m / z distribution of the incident ion beam can be determined based on one or more CDMS spectra previously acquired. For example, one or more CDMS spectra acquired by executing method 1000 prior to executing method 1000 for the current CDMS spectrum acquisition can be used as the m / z distribution of the current incident ion beam.

[0143] In some examples, the m / z range of interest is subdivided into multiple m / z windows 1102 based on at least one of the signal density or peak intensity in the m / z distribution of the incident ion beam. In some examples, as explained above, the signal density can be determined based on a peak separation method or a summed intensity method for each individual m / z window. In some examples, the m / z range of interest is subdivided so that each m / z window 1102 has substantially the same signal density (e.g., within 5% of each other). However, in other examples, the multiple m / z windows do not have the same signal density. For example, a user may be more interested in an analyte in a particular m / z window (e.g., m / z window 1102-4) than in an analyte in another m / z window 1102. Therefore, the width of the m / z window 1102 surrounding the analyte of interest can be set so as to have a higher signal density (and therefore a narrower width) than other m / z windows to avoid multiple ion events and interference in the m / z window of interest.

[0144] In some examples, based on the peak intensities in the expected or measured m / z distribution of the incident ion beam (e.g., ion current 112-1), the m / z range of interest is subdivided into a plurality of m / z windows 1102. For example, the m / z range of interest can be subdivided into N windows 1102, where N is a positive integer, such that each of the N most intense peaks lies within a unique m / z window 1102. Alternatively, the m / z range of interest can be subdivided into m / z windows 1102 such that each peak having an intensity value greater than a threshold intensity value lies within a unique m / z window 1102. In some examples, the width of each m / z window 1102 is weighted based on factors such as the total peak intensity, maximum peak intensity, average peak intensity, or median peak intensity of a particular m / z window 1102.

[0145] In yet other examples, dividing the m / z range of interest into a plurality of m / z windows 1102 is performed in whole or in part based on user input. For example, the m / z distribution graph 702 can be presented to the user via a display device associated with the system 200. The user can provide input to specify the position and width of the m / z windows 1102. In some examples, the system 200 provides an initial or default configuration of the m / z windows 1102 in any manner described herein, and the user can adjust the configuration as needed.

[0146] In yet other examples, dividing the m / z range of interest into a plurality of m / z windows 1102 is performed at least in part based on available time. For example, if a mass analyzer (e.g., mass analyzer 106 or mass analyzer 910) requires a certain amount of time for mass analysis, this time can limit the available injection time (if maximum time utilization is desired).

[0147] The ion population control parameters for each m / z window 1102 can be determined in any suitable manner, including any of the manners described herein. For example, an AIC process as described herein (e.g., method 300, 400, or 500) can be performed independently for each m / z window 1102. Figure 11In the example of , the accumulation time of the m / z window 1102-4 including the strongest peak 704 will be shorter than the accumulation time of other m / z windows 1102 with weaker peaks and weaker signal density. Therefore, the accumulation of ions in the m / z window 1102-4 will not constrain or limit the accumulation of ions in the lower density m / z window 1102 of the incident ion beam. It will be appreciated that the determination of the ion population control parameters for each m / z window 1102 is not limited to the AIC technology described herein, but can be determined in any other suitable manner. For example, the ion population control parameters for a specific m / z window 1102 can be inversely proportional to the intensity (e.g., sum, maximum, average, mean, etc.) of the measured ion signal of the specific m / z window 1102. In some examples, the ion population control parameters for a specific m / z window 1102 can be set to be higher than a minimum value, lower than a maximum value, or can be set based on user input.

[0148] A different accumulation event is performed for each m / z window 1102 using the ion population control parameters for the corresponding m / z window 1102. For example, a first accumulation event can be performed for m / z window 1102-1, during which ions outside of m / z window 1102-1 are filtered out by a mass filter (e.g., mass filter 802 or mass filter 904), and ions within m / z window 1102-1 are transferred by the mass filter and accumulated in an ion storage (e.g., ion storage 104, ion trap 906, or collision cell 908) for an accumulation time determined for m / z window 1102-1. After the first accumulation event is completed, a second accumulation event can be performed for m / z window 1102-2, during which ions outside of m / z window 1102-2 are filtered out by the mass filter, and ions within m / z window 1102-2 are transferred by the mass filter and accumulated in the ion storage for an accumulation time determined for m / z window 1102-2. This process can be repeated for the remaining m / z windows 1102 until the last accumulation event is completed for the last m / z window 1102. In this way, an ion population having a substantially uniform ion distribution across the m / z space is accumulated in the ion store due to the multiple customized accumulation events. It will be appreciated that any order of accumulation events can be used and need not be performed in an order of increasing or decreasing m / z of the m / z windows 1102. In some examples, accumulation events can be omitted or skipped for selected m / z windows 1102, such as m / z windows 1102 identified by a user (e.g., which may be of little or no interest to the user).

[0149] After the ion population is accumulated in the ion storage, the ion population is transferred to a mass analyzer (e.g., mass analyzer 106 or mass analyzer 910) and mass analyzed. The resulting CDMS spectrum 1104 shows a larger number of signals in the lower density portion of the m / z space of the m / z distribution diagram 702 compared to the CDMS spectrum 708, while also preventing interference from multiple ion events and the CDMS spectrum 706. Utilizing the appropriate division of the m / z window and their corresponding ion population control parameters, a much larger number of ions can be injected into the mass analyzer while minimizing the risk of multiple ion events and interference that would reduce the final mass of the CDMS spectrum. Therefore, more ions can be analyzed per unit time, thereby improving sample throughput without reducing data quality. In addition, data quality can be higher than conventional CDMS techniques because the CDMS technique using multiple accumulated events as described herein better characterizes the region of the m / z space of the incident ion beam with a smaller signal density.

[0150] In addition, the CDMS technology that incorporates multiple cumulative events enables CDMS to be performed together with a pre-separation process (such as liquid chromatography). Due to the mismatch of time scale, conventional CDMS technology is usually not combined with a pre-separation process such as liquid chromatography to perform. For example, conventional CDMS technology usually spends longer time than LC separation because the LC peak width is usually shorter than the time required for collecting CDMS spectrum. However, the improved CDMS technology using multiple cumulative events described herein shortens the time required for collecting CDMS spectrum, thereby enabling CDMS to be combined with a pre-separation process (such as liquid chromatography, gas chromatography or capillary electrophoresis) to perform. For example, mass spectrometer 800 or 900 can be coupled with a liquid chromatography system, and the liquid chromatography system includes a column with a stationary phase, and after the sample is injected into the mobile phase, the column separates the components included in the sample. When the components included in the sample are eluted from the liquid chromatography system, the ion source of the mass spectrometer produces ions from the sample. The ions thus generated are introduced into the mass spectrometer for CDMS analysis.

[0151] In some CDMS methods, multiple CDMS spectra are combined, where the intensity at each m / z across the multiple CDMS spectra is averaged to provide a more accurate representation of the m / z distribution of ions in the incident ion beam. Figure 11As shown, CDMS spectrum 1104 does not represent the m / z distribution of ions in the incident ion beam (represented by m / z profile 702) due to the uneven accumulation of ions across m / z window 1102. For example, the accumulation time for m / z window 1102-4 will be shorter than the accumulation time for the other m / z windows 1102 in order to obtain a more uniform ion distribution. Therefore, CDMS spectrum 1104 shows that each detected ion has the same intensity. Combining multiple spectra collected in a similar manner will not result in an accurate representation of the actual m / z distribution of ions in the incident ion beam. In addition, the position of the m / z windows and the ion control parameters of the m / z windows can vary from spectrum to spectrum.

[0152] In order to illustrate the uneven accumulation of ions across m / z windows 1102, the intensity value of the CDMS spectrum peak in each m / z window 1102 can be adjusted (e.g., scaled or standardized) based on the ion population control parameters of the corresponding m / z window 1102 that contains the peak. For example, the intensity value of an m / z window 1102 can be scaled based on the variance of the ion population control parameters of the m / z window 1102 relative to the reference ion population control parameter value. The reference ion population control parameter value can be preset or can be the ion population control parameter value of the m / z window 1102. For example, the accumulation time of m / z window 1102-3 can be three times the accumulation time of m / z window 1102-4. Therefore, the intensity value of the signal in m / z window 1102-3 can be reduced by three times. The accumulation time of m / z window 1102-4 can be used as a reference value to perform similar adjustments on other m / z windows 1102. In this way, the combined CDMS spectrum more accurately represents the m / z distribution of ions in the ion beam.

[0153] Various modifications can be made to the methods, devices, and systems described herein. In some examples, system 200 can be configured to request user input to manage or adjust the settings of the AIC method. For example, system 200 can obtain from the user a list of method settings, target analytes, selected m / z ranges, and / or any other initial or default values of parameters associated with method 300, 400, 500, and / or 600. System 200 can also be configured to notify the user of certain changes, such as when the cumulative time changes or the threshold value changes, or when changes are needed, such as when an assessment indicates that a parameter (e.g., target signal density, cumulative time, etc.) should be adjusted.

[0154] In certain embodiments, one or more of the systems, components and / or processes described herein can be implemented and / or executed by one or more appropriately configured computing devices. To this end, one or more of the above-mentioned systems and / or components may include or be implemented by any computer hardware and / or computer implementation instructions (e.g., software) embodied on at least one non-transient computer-readable medium configured to perform one or more processes in the processes described above. Specifically, the system components can be implemented on a physical computing device, or can be implemented on more than one physical computing device. Therefore, the system components can include any number of computing devices and can adopt any number of computer operating systems.

[0155] In certain embodiments, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transient computer-readable medium and executable by one or more computing devices. Typically, a processor (e.g., a microprocessor) receives instructions from a non-transient computer-readable medium (e.g., a memory, etc.) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. Any of a variety of known computer-readable media may be used to store and / or transmit such instructions.

[0156] Computer-readable media (also referred to as processor-readable media) include any non-transitory media that participates in providing data (e.g., instructions) that can be read by a computer (e.g., by a computer's processor). Such media can take many forms, including but not limited to non-volatile media and / or volatile media. Non-volatile media can include, for example, optical or magnetic disks and other permanent memories. Volatile media can include, for example, dynamic random access memory ("DRAM") that typically constitutes main memory. Common forms of computer-readable media include, for example, magnetic disks, hard disks, tapes, any other magnetic media, compact disc read-only memory ("CD-ROM"), digital video discs ("DVD"), any other optical media, random access memory ("RAM"), programmable read-only memory ("PROM"), electrically erasable programmable read-only memory ("EPROM"), FLASH-EEPROM, any other memory chip or cartridge, or any other tangible medium that a computer can read.

[0157] Figure 12 An exemplary computing device 1200 is shown, which may be specifically configured to perform one or more of the processes described herein. Figure 12 As shown, computing device 1200 may include a communication interface 1202, a processor 1204, a storage device 1206, and an input / output ("I / O") module 1208 communicatively connected to one another via a communication infrastructure 1210. Figure 12 An exemplary computing device 1200 is shown, but Figure 12 The components shown are not intended to be limiting. In other embodiments, additional or alternative components may be used. Figure 12 Components of computing device 1200 are shown.

[0158] The communication interface 1202 can be configured to communicate with one or more computing devices. Examples of the communication interface 1202 include, but are not limited to, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio / video connection, and any other suitable interface.

[0159] The processor 1204 generally represents any type or form of processing unit capable of processing data and / or interpreting, executing, and / or directing one or more of the instructions, processes, and / or operations described herein. The processor 1204 may perform operations by executing computer-executable instructions 1212 (e.g., applications, software, code, and / or other executable data instances) stored in the storage device 1206.

[0160] Storage device 1206 may include one or more data storage media, devices, or configurations, and may employ any type, form, and combination of data storage media and / or devices. For example, storage device 1206 may include, but is not limited to, any combination of non-volatile media and / or volatile media as described herein. Electronic data, including the data described herein, may be temporarily and / or permanently stored in storage device 1206. For example, data representing computer-executable instructions 1212 configured to direct processor 1204 to perform any of the operations described herein may be stored within storage device 1206. In some examples, the data may be arranged in one or more databases residing within storage device 1206.

[0161] The I / O module 1208 may include one or more I / O modules configured to receive user input and provide user output. One or more I / O modules may be used to receive input for a single virtual experience. The I / O module 1208 may include any hardware, firmware, software, or combination thereof that supports input capabilities and output capabilities. For example, the I / O module 1208 may include hardware and / or software for capturing user input, including but not limited to a keyboard or keypad, a touch screen component (e.g., a touch screen display), a receiver (e.g., an RF or infrared receiver), a motion sensor, and / or one or more input buttons.

[0162] I / O module 1208 may include one or more devices for presenting output to a user, including but not limited to a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In certain embodiments, I / O module 1208 is configured to provide graphic data to the display for presentation to the user. Graphics data may represent one or more graphical user interfaces and / or any other graphical content as may be used to serve a particular embodiment.

[0163] In some examples, any of the systems, computing devices, and / or other components described herein may be implemented by computing device 1200. For example, storage facility 202 may be implemented by storage device 1206, and processing facility 204 may be implemented by processor 1204.

[0164] In the preceding description, various exemplary embodiments and examples have been described with reference to the accompanying drawings. However, it will be apparent that various modifications and alterations may be made thereto, and additional embodiments and examples may be implemented, without departing from the scope of the subject matter as set forth in the appended claims. For example, certain features of one embodiment or example described herein may be combined with or substituted for features of another embodiment or example described herein. Accordingly, the description and drawings should be regarded as illustrative rather than restrictive.

[0165] The advantages and features of the present disclosure are further described by the following examples:

[0166] Embodiment 1. A system for charge detection mass spectrometry (CDMS), the system comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause a computing device to direct a mass spectrometer to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in an ion storage during an accumulation event; accumulating ion populations from a sample in the ion storage according to one or more accumulation events corresponding to different m / z windows in the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window; transferring the accumulated ion population to a mass analyzer; and performing mass analysis on the ion population to acquire a CDMS spectrum of the ion population.

[0167] Embodiment 2. The system of embodiment 1, wherein the process further comprises adjusting the intensity of peaks included in the CDMS spectrum based on the ion population control parameters for the m / z window containing the peaks.

[0168] Embodiment 3. The system of embodiment 1 or 2, wherein subdividing the m / z range of interest into the plurality of m / z windows is performed based on the m / z distribution of ions originating from the sample.

[0169] Embodiment 4. The system of embodiment 3, wherein the m / z distribution of ions derived from the sample is based on a pre-scan or characterization analysis of the sample.

[0170] Embodiment 5. The system of embodiment 3 or 4, wherein the m / z distribution of ions originating from the sample is based on one or more previously acquired CDMS mass spectra of one or more ion populations originating from the sample.

[0171] Embodiment 6. A system according to any one of embodiments 3 to 5, wherein subdividing the m / z range of interest into the multiple m / z windows is performed based on at least one of the signal density or peak intensity of the m / z distribution of ions.

[0172] Example 7. A system according to any one of Examples 3 to 6, wherein: the ion population control parameter includes the accumulation time of ions accumulated in the ion storage during the accumulation event; and the accumulation time of each m / z window is inversely proportional to the sum intensity of the peaks of the ion m / z distribution within the m / z window.

[0173] Embodiment 8. The system of any one of embodiments 3 to 7, wherein the plurality of m / z windows have substantially the same signal density.

[0174] Embodiment 9. The system of any preceding embodiment, wherein the one or more cumulative events include a plurality of cumulative events corresponding to each m / z window in the plurality of m / z windows.

[0175] Embodiment 10. The system of any preceding embodiment, wherein the accumulating the ion population in the ion storage is performed during mass analysis of previously accumulated ion populations.

[0176] Embodiment 11. A non-transitory computer-readable medium storing instructions that, when executed, direct at least one processor of a computing device for charge detection mass spectrometry to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in an ion storage during an accumulation event; directing accumulation of ion populations originating from a sample in the ion storage according to one or more accumulation events corresponding to different m / z windows among the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window; directing transfer of the accumulated ion population to a mass analyzer; and directing mass analysis of the ion population to acquire a CDMS spectrum of the ion population.

[0177] Embodiment 12. The computer-readable medium of embodiment 11, wherein the process further comprises adjusting the intensity of the peak based on the ion population control parameter for the m / z window containing the peak included in the CDMS spectrum.

[0178] Embodiment 13. The computer-readable medium of embodiment 11 or 12, wherein subdividing the m / z range of interest into the plurality of m / z windows is performed based on the m / z distribution of ions originating from the sample.

[0179] Example 14. A computer-readable medium according to Example 13, wherein: the ion population control parameter includes the accumulation time of ions accumulated in the ion storage during the accumulation event; and the accumulation time of each m / z window is inversely proportional to the sum intensity of the peaks of the ion m / z distribution within the m / z window.

[0180] Embodiment 15. The computer-readable medium of embodiment 13 or 14, wherein the plurality of m / z windows have substantially the same signal density.

[0181] Embodiment 16. The computer-readable medium of any one of embodiments 11 to 15, wherein the one or more cumulative events include a plurality of cumulative events corresponding to each m / z window of the plurality of m / z windows.

[0182] Embodiment 17. A system for charge detection mass spectrometry (CDMS), the system comprising: an ion storage that accumulates ion populations from a sample according to one or more accumulation events; a mass analyzer that acquires a mass spectrum of the accumulated ion populations via CDMS after the accumulated ion populations are transferred to the mass analyzer; a mass filter that selectively transmits ions from the sample based on their m / z; and a computing device configured to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, wherein Each of the one or more accumulation events corresponds to a different m / z window among a plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts the amount of ions accumulated in the ion storage during the corresponding accumulation event; and directing accumulation of the ion populations originating from the sample in the ion storage in accordance with the one or more accumulation events, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion storage based on the ion population control parameter for the corresponding m / z window.

[0183] Embodiment 18. The system of embodiment 17, wherein directing the accumulation of the ion population comprises directing the mass filter to selectively transmit ions within the m / z window corresponding to the accumulation event during each of the one or more accumulation events.

[0184] Embodiment 19. A system according to embodiment 17 or 18, wherein: the mass analyzer comprises an orbital electrostatic ion trap mass analyzer or an electrostatic linear ion trap mass analyzer; and the ion storage comprises a collision cell or a C-trap.

[0185] Example 20. A system according to any one of Examples 17 to 19, wherein the system further comprises: a liquid chromatography system comprising a column having a stationary phase, wherein the column separates the components included in the sample after the sample is injected into a mobile phase; and an ion source, wherein the ions from the sample are generated when the components included in the sample are eluted from the liquid chromatography system.

Claims

1. A system for charge detection mass spectrometry (CDMS), comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause a computing device to direct a mass spectrometer to perform a process comprising: Subdivide the mass-to-charge ratio (m / z) range of interest into multiple m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts an amount of ions accumulated in the ion store during an accumulation event; accumulating ion populations originating from the sample in the ion store according to one or more accumulation events, each corresponding to a different m / z window in the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion store based on the ion population control parameters for the corresponding m / z window; transferring the accumulated ion population to a mass analyzer; as well as The ion population is mass analyzed to acquire a CDMS spectrum of the ion population.

2. The system of claim 1, wherein the process further comprises adjusting the intensity of the peaks included in the CDMS spectrum based on the ion population control parameters for the m / z window containing the peaks. 3 . The system of claim 1 , wherein subdividing the m / z range of interest into the plurality of m / z windows is performed based on the m / z distribution of ions originating from the sample. 4 . The system of claim 3 , wherein the ion m / z distribution derived from the sample is based on a pre-scan or characterization analysis of the sample.

5. The system of claim 3, wherein the m / z distribution of ions originating from the sample is based on one or more previously acquired CDMS mass spectra of one or more ion populations originating from the sample.

6. The system of claim 3, wherein subdividing the m / z range of interest into the plurality of m / z windows is performed based on at least one of signal density or peak intensity of the ion m / z distribution.

7. The system of claim 3, wherein: The ion population control parameter includes an accumulation time for which ions accumulate in the ion store during an accumulation event; and The integration time for each m / z window is inversely proportional to the summed intensity of the peaks of the ion m / z distribution within the m / z window.

8. The system of claim 3, wherein the plurality of m / z windows have substantially the same signal density.

9. The system of claim 1, wherein the one or more cumulative events comprise a plurality of cumulative events corresponding to each m / z window of the plurality of m / z windows.

10. The system of claim 1, wherein said accumulating said ion populations in said ion storage is performed during mass analysis of previously accumulated ion populations.

11. A non-transitory computer-readable medium storing instructions that, when executed, direct at least one processor of a computing device for performing charge detection mass spectrometry to perform a process comprising: Subdivide the mass-to-charge ratio (m / z) range of interest into multiple m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts an amount of ions accumulated in the ion store during an accumulation event; directing accumulation of ion populations originating from a sample in the ion store according to one or more accumulation events, each corresponding to a different m / z window of the plurality of m / z windows, wherein during each accumulation event, ions within the m / z window corresponding to the accumulation event are accumulated in the ion store based on the ion population control parameters for the corresponding m / z window; directing transfer of the accumulated ion population to a mass analyzer; and The ion population is directed to be mass analyzed to acquire a CDMS spectrum of the ion population.

12. The computer-readable medium of claim 11, wherein the process further comprises adjusting the intensity of the peaks included in the CDMS spectrum based on the ion population control parameters for the m / z window containing the peaks.

13. The computer-readable medium of claim 11, wherein subdividing the m / z range of interest into the plurality of m / z windows is performed based on an m / z distribution of ions originating from the sample.

14. The computer-readable medium of claim 13, wherein: The ion population control parameter includes an accumulation time for which ions accumulate in the ion store during an accumulation event; and The integration time for each m / z window is inversely proportional to the summed intensity of the peaks of the ion m / z distribution within the m / z window.

15. The computer-readable medium of claim 13, wherein the plurality of m / z windows have substantially the same signal density.

16. The computer-readable medium of claim 11, wherein the one or more cumulative events comprise a plurality of cumulative events corresponding to each m / z window of the plurality of m / z windows.

17. A system for charge detection mass spectrometry (CDMS), the system comprising: an ion store that accumulates ion populations originating from the sample according to one or more accumulation events; a mass analyzer that acquires a mass spectrum of the accumulated ion population by CDMS after the accumulated ion population is transferred to the mass analyzer; a mass filter that selectively transmits ions originating from the sample based on their m / z; and A computing device configured to perform a process comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, wherein each of the one or more cumulative events corresponds to a different m / z window in the plurality of m / z windows; determining an ion population control parameter for each m / z window, wherein the ion population control parameter for each m / z window adjusts an amount of ions accumulated in the ion store during a corresponding accumulation event; as well as Accumulation of the ion population originating from the sample in the ion store is directed by the one or more accumulation events, wherein during each accumulation event, ions within an m / z window corresponding to the accumulation event are accumulated in the ion store based on the ion population control parameters for the corresponding m / z window.

18. The system of claim 17, wherein directing the accumulation of the ion population comprises directing the mass filter to selectively transmit ions within the m / z window corresponding to the accumulation event during each of the one or more accumulation events.

19. The system of claim 17, wherein: The mass analyzer comprises an orbital electrostatic ion trap mass analyzer or an electrostatic linear ion trap mass analyzer; and The ion storage comprises a collision cell or a C-trap.

20. The system of claim 17, further comprising: a liquid chromatography system including a column having a stationary phase, the column separating components included in the sample after the sample is injected into a mobile phase; and An ion source generates the ions from the sample when the components included in the sample are eluted from the liquid chromatography system.

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