Charge detection mass spectrometry using plurality of accumulation events

Abstract

To propose a system for charge detection mass spectrometry using a plurality of accumulation events.SOLUTION: A system for charge detection mass spectrometry (CDMS) performs a process including: subdividing an m / z range of interest into a plurality of m / z windows; determining an ion population control parameter for each m / z window; accumulating, in an ion store by one or more accumulation events, a population of ions derived from a sample; transferring the population of ions to a mass analyzer; and mass analyzing the population of ions to acquire a CDMS spectrum of the population of ions. Each accumulation event corresponds to a distinct m / z window of the plurality of m / z windows. The ion population control parameter for each m / z window regulates a quantity of ions accumulated in the ion store during an accumulation event. 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 parameter for the corresponding m / z window.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to charge detection mass spectrometry using multiple accumulation events. [Background technology]

[0002] In the early days of mass spectrometry, all ion sources produced exclusively singly charged ions. Mass spectrometers measure the mass-to-charge ratio (m / z) of ions, and determining mass was straightforward because charge (z) always equals one (1). When mass spectrometry was coupled with ion sources that produce multiply charged ions (e.g., electrospray ionization sources), determining mass became more difficult. If the charge on an ion is ambiguous, the calculated mass is also ambiguous. 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 isotope peaks, since they are known to be one (1) dalton apart. Both of these methods of determining charge are challenged with highly complex spectra in which neither a clear charge state nor an isotope distribution can be easily discerned.

[0003] One solution to resolving charge ambiguity is the use of Charge Detection Mass Spectrometry (CDMS). In some implementations of CDMS, ions are analyzed one at a time using a detector capable of simultaneously sensing both the m / z and charge z of each ion. This is traditionally performed using an Electrostatic Linear Ion Trap (ELIT), in which an imaging current detector is placed between a pair of ion mirrors, allowing ions to pass through the detector multiple times, improving sensitivity and charge assignment accuracy. ELIT works best with single ions only, as m / z resolution is generally poor and ion-ion interactions can perturb trajectories and limit ion lifetimes.

[0004] For ions with enough charge to generate a detectable signal in a single pass, the ELIT can be operated in a trigger mode, in which an optical gate between the ion source and the ELIT is opened to allow ions to enter, and the gate is closed as soon as the first ion passes the detector. However, most ions do not carry enough charge to be detected in a single pass. In these cases, the gate opening time should most commonly be set to the average time it takes for one ion to be trapped. This is somewhat inefficient in that, because ion arrival times are randomly distributed, the probability of observing zero or two ions is just as high as the probability of observing one ion. Therefore, on average, a single ion is detected in less than 50% of spectra.

[0005] Recently, orbital electrostatic trap mass analyzers (manufactured and sold by Thermo Fisher Scientific, Inc., Waltham, MA, in the form of Orbitrap™ mass analyzers) have been used for CDMS. Some of these implementations of CDMS are referred to as individual ion mass spectrometry (I2MS) or Direct Mass Technology™ (DMT, Thermo Fisher Scientific, Inc., Waltham, MA). Due to the nature of orbital electrostatic trap mass analyzers, hundreds of individual ions can be detected simultaneously with both high resolution and high space charge tolerance; this multiplexing significantly increases the speed at which statistically significant data can be acquired, even for complex analyte mixtures. Certain implementations of CDMS involve processing transient signals generated by the axial oscillation of a population of ions to generate a set of Selective Temporal Overview of Resonant Ions (STORI) plots. 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-to-charge calibration function. Using the determined charge z and m / z of each ion species, a true mass spectrum can be generated that plots the detected signal as a function of mass (m).

[0006] The ability to multiplex ion measurements using an orbital electrostatic trap mass analyzer allows for the observation of an excessive number of ions in each spectrum. In an orbital electrostatic trap mass analyzer where the CDMS method is practiced, ions generated by an ion source are transferred through ion optics to an ion trap (alternatively referred to as an ion store) before mass analysis in the orbital electrostatic trap mass analyzer. The duration that ions are stored in the ion trap (often referred to as the ion injection time, storage time, ion storage time, or ion fill time) determines the number of ions measured by the orbital electrostatic trap mass analyzer. In CDMS, each detected signal in a spectrum is resolved along the m / z region from all other signals in that spectrum. When multiple signals overlap in m / z, it becomes difficult to determine whether there are multiple individual ions contributing to the signal or a single ion contributing to the summed charge of the individual ions. For example, when two or more ions of the same m / z are detected simultaneously, the charge state becomes ambiguous because only the net charge of all ions at that m / z is known, and not the charge on each ion, or even the actual number of ions. In less severe cases, ions of resolved but adjacent m / z can reduce the accuracy of charge determination because their signals interfere with each other constructively and destructively, which affects the determination of signal intensity and can make proper assignment of charge difficult.

[0007] To minimize overlap and interference between signals within the m / z region, the ion population measured in each spectrum should be controlled to achieve individual ion resolution so that 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, thereby lengthening the time required to perform CDMS on the sample. Therefore, under ideal conditions, a large number of ions are observed in each spectrum so that acquisition of the entire data set can be completed in the shortest possible time, but the number of ions in any one spectrum is not so large as to cause interference between adjacent or overlapping signals.

[0008] Automatic gain control (AGC) is a technique used to balance spectral dynamic range and space charge effects in trapped ion mass spectrometers. However, AGC is less than ideal for CDMS because it measures the total signal (e.g., total ion current (TIC)) in the acquired spectrum and adjusts subsequent accumulation times to reach a target total signal. In CDMS, the total signal is proportional to the sum of the charge states of all ions. If the charge states of the analyte ions are unknown prior to the experiment, it is impossible to select an appropriate target signal. Therefore, there remains a clear need in mass spectrometry for an automated ion accumulation time selection technique suitable for use with CDMS data acquisition in the individual ion regime. Summary of the Invention

[0009] The following presents a simplified summary of one or more aspects of the methods and systems described herein in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is not intended to identify key or critical elements of all aspects or 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 that is presented below.

[0010] In some exemplary embodiments, a system for charge detection mass spectrometry (CDMS) includes one or more processors, and when executed by the one or more processors, causes the computing device to perform a process including instructing a mass spectrometer to subdivide a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining ion population control parameters for each m / z window, where the ion population control parameters for each m / z window adjust the amount of ions accumulated in the ion store during an accumulation event; accumulating a population of ions from a sample in an ion store by one or more accumulation events each corresponding to a distinct m / z window among the plurality of m / z windows, where during each accumulation event, ions in 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 population of ions to a mass analyzer; and mass analyzing the population of ions to obtain a CDMS spectrum of the population of ions.

[0011] In some exemplary embodiments, 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 including: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining ion population control parameters for each m / z window, where the ion population control parameters for each m / z window adjust an amount of ions accumulated in the ion store during an accumulation event; directing accumulation of a population of ions from the sample in the ion store by one or more accumulation events each corresponding to a distinct m / z window of the plurality of m / z windows, where during each accumulation event, ions in 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 population of ions to a mass analyzer; and directing mass analysis of the population of ions to obtain a CDMS spectrum of the population of ions.

[0012] In some exemplary embodiments, a system for charge detection mass spectrometry (CDMS) includes an ion store that accumulates a population of ions from a sample by one or more accumulation events; a mass analyzer that acquires a mass spectrum of the accumulated population of ions by CDMS after the accumulated population of ions is transferred to a mass analyzer; a mass filter that selectively transmits ions from the sample based on the m / z of the ions; and a computing device that subdivides a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, wherein each accumulation event of the one or more accumulation events corresponds to a distinct m / z window of the plurality of m / z windows. determining ion population control parameters for each m / z window, the ion population control parameters for each m / z window adjusting the amount of ions accumulated in the ion store during a corresponding accumulation event; and directing the accumulation of a population of ions from the sample in the ion store by one or more accumulation events, wherein during each accumulation event, ions in 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. [Brief explanation of the drawings]

[0013] The accompanying drawings illustrate various embodiments and are a part of this specification. The illustrated embodiments are merely examples and are not intended to limit the scope of the present disclosure. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements.

[0014] [Figure 1] 1 illustrates an exemplary embodiment of a mass spectrometer. [Figure 2] 1 illustrates an exemplary automatic ion control system. [Figure 3] 1 illustrates an exemplary method for implementing automatic ion control. [Figure 4] 1 illustrates an exemplary method for implementing automatic ion control according to a peak spacing approach. [Figure 5] 1 illustrates an exemplary method for implementing automatic ion control according to a total intensity approach. [Figure 6] 1 illustrates an exemplary method for performing CDMS with automatic ion control. [Figure 7A] 1 shows an exemplary m / z distribution graph plotting the intensity of ions in a hypothetical incident ion beam as a function of the m / z of the ions. [Figure 7B] 1 shows an exemplary CDMS spectrum obtained by mass spectrometry of a population of ions accumulated in a single accumulation event. [Figure 7C] 7B shows an exemplary CDMS spectrum obtained by mass spectrometry of a population of accumulated ions in a single accumulation event with an accumulation time twice that of the example of FIG. 7B. [Figure 8] FIG. 1 shows a functional diagram of an exemplary mass spectrometer that can be used for CDMS with a multiple accumulation event approach. [Figure 9] 1 shows a functional diagram of an exemplary mass spectrometer including an electrostatic trap mass analyzer. [Figure 10] 1 shows a flowchart of an exemplary method for implementing CDMS using a multiple accumulated events approach. [Figure 11] 7A shows the m / z distribution graph of FIG. 7A subdivided into multiple m / z windows for multiple accumulation events, and an exemplary CDMS spectrum acquired by CDMS using multiple accumulation events. [Figure 12] 1 illustrates an exemplary computing device that may be specifically configured to perform one or more of the operations, methods, and processes described herein. DETAILED DESCRIPTION OF THE INVENTION

[0015] Described herein are methods, systems, and devices for performing CDMS with increased sample throughput and increased data quality compared to conventional CDMS techniques. The CDMS techniques described herein subdivide the m / z range of interest into multiple separate m / z windows and accumulate populations of ions in an ion store in multiple separate accumulation events to achieve a more uniform signal density across the m / z space of the incident ion beam. Each accumulation event corresponds to a separate 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 adjust the population of ions 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) techniques described herein.

[0016] By coordinating the accumulation of ions in multiple separate accumulation events that collectively cover the m / z range of interest, the probability of detecting ions in less dense m / z regions of the incident ion beam is increased, while the probability of multiple ion events and interference in more dense m / z regions is reduced. Thus, ion accumulation in the ion store is not obstructed by the most dense m / z region, allowing more ions to be analyzed per unit time, thus increasing sample throughput without reducing data quality. Additionally, data quality can be higher than conventional CDMS techniques because CDMS techniques using multiple accumulation events, as described herein, better characterize regions of m / z space of the incident ion beam that have less signal density.

[0017] Before describing the multiple accumulated event approach for CDMS, methods, systems, and devices for AIC are described. In some examples, the AIC methods, systems, and devices described herein are used for CDMS analysis. The AIC techniques described herein determine ion population control parameters based on a signal density metric, because the probability of having an interfering signal is related to the signal density in the m / z region. Mass spectra with high signal density are more likely to have overlapping or closely spaced peaks in the m / z region, whereas mass spectra with low signal density are less likely to have these interferences. The ion population control parameters can be adjusted so that the signal density, as measured from the acquired mass spectra, approaches or is sufficiently close to a target signal density. The target signal density is a predetermined value that can be selected to acquire an appropriate number of ions in each spectrum while limiting the occurrence of interfering ions to an acceptably low amount for specific experimental conditions and purposes.

[0018] In some examples, a method for performing AIC includes acquiring a mass spectrum including a plurality of peaks representing intensity as a function of m / z of a population of ions analyzed by a mass analyzer during an acquisition event. A 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 approach. In other examples, the measured signal density is determined according to a sum intensity approach. Ion population control parameters for a subsequent acquisition event are set based on the measured signal density and a target signal density. The ion population control parameters adjust the population of ions analyzed by the mass analyzer during the acquisition event. In some examples, the ion population control parameter is an accumulation time for ions to accumulate in an ion store before the ions are analyzed by the mass analyzer. In other examples, the ion population control parameter is a potential applied to ion optics (e.g., a lens) that adjusts the bundle of ions delivered to the mass analyzer.

[0019] In the peak spacing approach, determining the measured signal density of the mass spectrum includes calculating a peak spacing value for each set of adjacent peaks within a selected m / z range (e.g., an m / z range of interest) of the mass spectrum. A global peak spacing value for the selected m / z range is determined based on the set of calculated peak spacing values. In the sum intensity approach, determining the measured signal density includes determining an occupied m / z space within the selected m / z range of the mass spectrum. The summed intensities of signals within the occupied m / z space are determined. The measured signal density is determined based on the summed intensities within the occupied m / z space and the occupied m / z space. The peak spacing approach and the summed intensity approach are described in more detail below.

[0020] Various embodiments and examples will now be described in more detail with reference to the figures. The methods, systems, and apparatus described herein provide various benefits that will become apparent herein. The methods, systems, and apparatus for performing AIC may be used in conjunction with a mass spectrometer. Accordingly, an exemplary mass spectrometer will now be described. The mass spectrometer described is exemplary and non-limiting.

[0021] 1 illustrates an exemplary functional diagram of a mass spectrometer 100. As shown, mass spectrometer 100 includes an ion source 102, an ion store 104, a mass analyzer 106, a detector 108, and a controller 110. Mass spectrometer 100 may further include any additional or alternative components not shown that may be compatible with a particular implementation (e.g., ion optics, lenses, filters, ion storage devices, ion mobility analyzers, collision cells, ion flux monitors, etc.).

[0022] The ion source 102 is configured to generate ions from a sample and deliver the ions in an ion stream 112 to the ion store 104. The ion source 102 may 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 may include various components for generating ions from a sample and delivering the ions to the ion store 104.

[0023] The ion store 104 is a device configured to accumulate ions contained in the ion stream 112 for an accumulation time. As used herein, "accumulation time" refers to the duration that ions generated by the ion source 102 accumulate in the ion store 104 before being released and traveling to the mass analyzer 106. The accumulation time may also be known as the ion injection time or ion filling time. In some examples, the ion store 104 is an ion storage device configured to buffer downstream processes such as mass analysis, thereby increasing acquisition speed and instrument sensitivity. In some examples, the ion store 104 is a beam-type or trapping device such as a multipole ion guide (e.g., a quadrupole ion guide, a hexapole ion guide, an octapole ion guide, etc.), a linear quadrupole ion trap, a three-dimensional quadrupole ion trap, a cylindrical ion trap, an annular ion trap, an orbital electrostatic trap, or a Kingdon trap. In some examples, the ion store 104 takes the form of a curved trap (also known as a C-trap) of the type used in orbital electrostatic trap mass spectrometers.

[0024] In some examples, the ion store 104 is a collision cell positioned upstream from the mass analyzer 106. As used herein, "collision cell" may refer to any device configured to generate product ions through a controlled dissociation process or an ion-ion reaction process, and is not limited to devices employed for collision-activated dissociation. For example, the collision cell may be configured to fragment ions using collision-induced dissociation (CID), electron transfer dissociation (ETD), electron capture dissociation (ECD), photo-induced dissociation (PID), surface-induced dissociation (SID), etc. The collision cell may be positioned upstream from the mass analyzer 106 and / or a mass filter, and the collision cell separates the fragmented ions based on the m / z of the ions.

[0025] The accumulation of ions in the ion store 104 can be adjusted by the AIC methods, devices, and systems described herein to achieve a target population of ions in the ion store 104, and therefore a target signal density. The accumulation of ions can be adjusted in any suitable manner. In some examples, the accumulation of ions in the ion store 104 is adjusted by a gating device (not shown) that either transmits or blocks the ion stream 112. The gate can be opened for a given time to meter in an appropriate number of ions, after which the gate is closed. The accumulated ions can then be transferred from the ion store 104 to the mass analyzer 106 in the ion stream 114. It will be appreciated that other techniques for adjusting the ion accumulation can be used.

[0026] The mass analyzer 106 is configured to perform mass analysis of the accumulated ions and may be implemented by any suitable mass analyzer or combination of analyzers suitable for charge detection mass analysis and / or Fourier transform mass spectrometry (FTMS), in which an image charge signal of the ions is induced and recorded. Examples of mass analyzer 106 include, but are not limited to, an ion trap mass analyzer (e.g., a linear quadrupole ion trap, a three-dimensional quadrupole ion trap, a cylindrical ion trap, a toroidal ion trap, etc.), a time-of-flight (TOF) mass analyzer, an electric sector mass analyzer (e.g., a dual electric sector ion trap with a central charge tube), a linear quadrupole mass analyzer, an electrostatic ion trap mass analyzer (e.g., an orbital electrostatic trap such as an Orbitrap™ mass analyzer, a Kingdon trap, an electrostatic linear ion trap (ELIT), or an orbital frequency analyzer (OFA)), a magnetic ion trap, and / or a Fourier transform ion cyclotron resonance (FT-ICR) mass analyzer.

[0027] In some examples, the mass spectrometer 100 may be a tandem mass spectrometer (tandem-in-time or tandem-in-space) configured to perform tandem mass spectrometry (e.g., MS / MS), multi-stage mass spectrometry (MS n The mass analyzer 106 is a multi-stage mass analyzer or hybrid mass analyzer configured to perform a mass spectrometry (also referred to as a mass spectrometry). For example, the mass analyzer 106 may include multiple mass analyzers, mass filters, and / or collision cells. In some examples, the mass analyzer 106 includes a combination of multiple mass filters and / or collision cells, such as a triple quadrupole mass analyzer, where a collision cell is positioned in the ion path between independently operable mass filters. In other examples, the mass analyzer 106 includes a ToF mass analyzer and an orbital electrostatic trap mass analyzer positioned in series.

[0028] 1 shows the ion store 104 positioned upstream from the mass analyzer 106, the ion store 104 may be positioned anywhere else along the ion path from the ion source 102 to the detector 108 in a tandem or multi-stage 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, the mass spectrometer 100 may include more than one ion store 104, such as when the mass spectrometer 100 is a tandem or multi-stage mass spectrometer.

[0029] The ion detector 108 is configured to detect ions either in the mass analyzer 106 or in the ion stream 116 at each of a variety of different m / z and, in response, generate an electrical signal representative of the ion intensity. 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 may emit an ejection beam of separated ions to the detector 108, which is configured to detect the ions in the ejection beam and generate or provide data that can be used by the controller 110 to construct a mass spectrum. The ion detector 108 may be implemented by any suitable detection device, including, but not limited to, an electron multiplier, a Faraday cup, or the like.

[0030] As used herein, "mass spectrum" or "spectrum" refers to a plot of the intensity of ions as a function of the m / z of the ions. As used herein, "intensity" or "signal strength" refers to the response of a detector and may represent absolute abundance, relative abundance, ion number, intensity, relative intensity, ion current, or any other suitable measure of ion detection.

[0031] 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 operation of various hardware components included in the ion source 102, the ion store 104, the mass analyzer 106, and / or the detector 108. To illustrate, the controller 110 may be configured to control the accumulation time of the ion store 104 and / or the mass analyzer 106, control the oscillating voltage power supply and / or the DC power supply to supply RF and / or DC voltage to the mass analyzer 106, adjust the values of the RF and DC voltages to select a valid m / z (including mass tolerance window) for analysis, and adjust the sensitivity of the ion detector 108 (e.g., by adjusting the detector gain).

[0032] The controller 110 may also include and / or provide a user interface configured to enable interaction between a user of the mass spectrometer 100 and the controller 110. The user may interact with the controller 110 via a user interface that utilizes tactile, visual, auditory, and / or other sensory communication. For example, the user interface may include a display device (e.g., a liquid crystal display (LCD) screen, a touchscreen, etc.) for displaying information (e.g., mass spectra, notifications, etc.) to the user. The user interface may also include input devices (e.g., a keyboard, a mouse, a touchscreen device, etc.) that allow the user to provide input to the controller 110. In other examples, the display device and / or input device may be separate from the controller 110 but communicatively coupled thereto. For example, the display device and input device may be included within a computer (e.g., a desktop computer, a laptop computer, a mobile device, etc.) communicatively connected to the controller 110 via a wired connection (e.g., via one or more cables) and / or a wireless connection (e.g., Wi-Fi, Bluetooth, near-field communication, etc.).

[0033] Controller 110 may include any suitable hardware (e.g., processor, circuitry, etc.) and / or software as may be useful in a particular implementation. While Figure 1 shows controller 110 included within mass spectrometer 100 (e.g., one or more on-board processors present on mass spectrometer 100), controller 110 may alternatively be implemented completely or partially separately from mass spectrometer 100, such as by a computing device communicatively coupled to mass spectrometer 100 via a wired connection (e.g., 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.).

[0034] 1, various components of the mass spectrometer 100 may be combined into a single component. For example, the ion store 104 and mass analyzer 106 may be combined into a single trapping mass analyzer where ions are accumulated for an accumulation time to reach a target number of ions, after which mass analysis of the accumulated ions is performed.

[0035] 1, the population of ions analyzed by mass analyzer 106 may be adjusted by adjusting the storage time for ions to accumulate in ion store 104. In an alternative example, the population of ions analyzed by mass analyzer 106 may be adjusted by adjusting the potentials applied to ion optics (e.g., lenses) that adjust the flux of ions delivered to mass analyzer 106. For example, voltages applied to lenses (not shown in FIG. 1) may be adjusted to focus or defocus ion stream 112 and / or ion stream 114 to reduce or increase the population of ions delivered and analyzed by mass analyzer 106. In some examples where the population of ions for downstream processes (e.g., mass analysis) is adjusted by ion optics rather than by ion store 104, ion store 104 may be omitted.

[0036] The AIC methods, systems, and devices described herein may operate as part of or in conjunction with the mass spectrometer 100 described herein and / or with any other suitable mass spectrometer or mass spectrometry system, including a combined separation-mass spectrometry system such as a liquid chromatography-mass spectrometry (LC-MS) system, 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 devices described herein may also operate with continuous flow sample sources, such as flow-injection mass spectrometry (Fl-MS), in which analytes are injected into a mobile phase and enter the mass spectrometer without separation in a column.

[0037] In some examples, the mass spectrometer 100 can be used to perform CDMS. CDMS using orbital electrostatic trap mass analyzers has been described extensively in the patent and scientific literature, including 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; STORI Plots Enable Accurate Tracking of Individual Ion Signals by Kafader et al., J. Am. Soc. Mass Spectrom. (2019) 30:2200-2203; and Individual Ion Mass Spectrometry Enhances the Sensitivity and Sequence Coverage of Top Down Mass Spectrometry by Kafader et al., J. Proteome Res. 2020 March 6 19(3) pp.1346-50. Generally, CDMS using an orbital electrostatic trap mass analyzer involves processing transient signals generated by the axial oscillation of a population of ions within the orbital electrostatic trap mass analyzer to generate a set of selective time summary of resonating ions (STORI) plots. The slope of each STORI plot is proportional to the charge of different ion species within the analyzed ion population. The charge of the ions can be determined by a slope-to-charge calibration function. Using the determined charge z and measured m / z of each ion species, 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 plot of the intensity of detected ions as a function of mass.Analytes represented in a mass spectrum can be placed within the mass region by using any suitable CDMS method.

[0038] As noted, in CDMS, each detected signal (also referred to herein as a peak) in a mass spectrum is resolved from every other signal in that spectrum along its m / z range. When multiple signals overlap in m / z, it becomes difficult to determine whether there are multiple individual ions contributing to the signal, or whether there is a single ion with a summed charge of the individual ions. As a result, it is difficult to determine the charge of the ionic species and place the analyte in its true mass range.

[0039] The appropriate number of individual ions that can be acquired in a single acquisition is limited by the number of analyte charge states and isotope channels that can exist without overlap in m / z space. Having too many of these channels (i.e., creating multiple ion events) will result in uncertain charge and therefore uncertain mass assignments for that signal. To minimize the possibility of ambiguous charge assignments, mass analyzers can be operated with small ion populations (e.g., achieved by short accumulation times or attenuated ion beams), which produce sparse spectra with a low probability of coincidence for ions. However, intentionally limiting the ion population increases the number of acquisitions required to generate a statistically relevant mass-domain spectrum (i.e., increasing experimental time).

[0040] The AIC method described herein can be used to optimize the number of ions in each spectrum while simultaneously limiting the occurrence of overlapping interfering ions in m / z to an acceptable number and / or acceptable m / z range. As used herein, "optimize" and variations thereof mean searching for an improved or optimal solution from among a set of possible solutions, although the best solution may not necessarily be obtained when the optimization process terminates before finding the best solution, when multiple solutions exist that meet predefined criteria, when a solution meets minimum criteria, or when the selected optimization technique is unable to converge on the best solution. Similarly, as used herein, an "optimal" parameter (e.g., a "maximum" or "minimum" value of a parameter) refers to a solution obtained as a result of performing an optimization process, and thus may not necessarily be the absolute extreme value of the parameter (e.g., the absolute maximum or minimum value), but the parameter may still be adjusted to provide an improvement.

[0041] One or more operations associated with the AIC may be performed by an automatic ion control system. Figure 2 shows an exemplary automatic ion control system 200 ("system 200"). System 200 may be implemented in whole or in part by mass spectrometer 100 (e.g., by controller 110). Alternatively, system 200 may be implemented separately from mass spectrometer 100 (e.g., by a separate and / or remote computing system or server).

[0042] System 200 may include, but is not limited to, a storage facility 202 and a processing facility 204 selectively and communicatively coupled to one another. Facilities 202 and 204 may each include or be implemented by hardware and / or software components (e.g., a processor, memory, a communication interface, instructions stored in memory for execution by the processor, etc.). In some examples, facilities 202 and 204 may be distributed among multiple devices and / or among multiple locations, as may be useful for particular implementations.

[0043] 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 instance. The storage facility 202 may also maintain any data acquired, received, generated, managed, used, and / or transmitted by the processing facility 204.

[0044] The processing facility 204 may be configured to perform various processing operations described herein (e.g., execute instructions 206 stored in the storage facility 202). It will be recognized that the operations and examples described herein are merely illustrative of many different types of operations that may be performed by the processing facility 204. In the description herein, any reference to an operation performed by the system 200 may 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 may include the system 200 instructing or commanding another computing system, device, or apparatus to perform the operation.

[0045] An exemplary method for performing AIC will now be described. The AIC method will be described herein in connection with CDMS performed using an orbital electrostatic trap mass spectrometer. However, the AIC method should not be construed as being limited thereto and may be consistently applied to other methods of mass spectrometry, as well as other analytical methods, including other methods of CDMS and / or using any other type of mass analyzer (e.g., TOF mass analyzer, FT-ICR mass analyzer, ELIT mass analyzer, OFA, electric sector analyzer, etc.).

[0046] Figure 3 illustrates an example method 300 for performing AIC. While Figure 3 illustrates example operations according to one embodiment, other embodiments may omit, add, reorder, and / or modify any of the operations illustrated in Figure 3. One or more of the operations illustrated in Figure 3 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).

[0047] In operation 302, system 200 acquires a mass spectrum including a plurality of peaks representing intensity as a function of m / z of a population of ions analyzed by a mass analyzer during an acquisition event. In some examples, the population of ions includes ions accumulated in an ion store over an accumulation time. In other examples, the population of ions includes ions in an ion beam delivered to the mass analyzer. System 200 may generate the mass spectrum based on a signal received from a detector (e.g., detector 108), or the mass spectrum may be generated by a mass analyzer (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 includes acquiring a survey spectrum performed prior to experimental analysis of the sample.

[0048] In operation 304, the system 200 determines the measured signal density for a selected m / z range of the mass spectrum based on the mass spectrum. This selected m / z range may 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 may be a narrow m / z range of interest. As explained above, signal density may be defined and determined according to a variety of different approaches. In a simple approach, the AIC adjusts the number of signals observed in the spectrum. In this embodiment, the signal density is the number of peaks divided by the m / z range of the acquisition event. However, this approach may represent an oversimplification. Simply counting the number of signals does not take into account the m / z width of the peaks, which may affect whether adjacent signals interfere or overlap. Consider a mass spectrum acquired using standard conditions and collected at an m / z resolution that is twice the resolution of the first mass spectrum. With higher resolution mass spectra, the probability of having overlapping peaks is smaller because each peak consumes less m / z space.

[0049] In the peak spacing approach, signal density generally represents the spacing between adjacent peaks in a selected m / z range of the mass spectrum in units of peak width (e.g., inversely proportional). In the peak spacing approach, measured signal density is determined from a previously acquired mass spectrum without regard to signal intensity. A method for determining the measured signal density of an acquired mass spectrum according to the peak spacing approach is described in more detail below with reference to FIG. 4. In the total intensity approach, signal density generally represents the amount of signal (signal intensity) per unit of m / z space within a selected m / z range of the mass spectrum. In the total intensity approach, signal density is determined based on a survey spectrum performed prior to the experimental run and a factor of the intensity of the detected signal. A method for determining the measured signal density of an acquired mass spectrum according to the total intensity approach is described in more detail below with reference to FIG. 5. It will be appreciated that other approaches for defining and determining measured signal density are envisioned and are within the scope of method 300.

[0050] In operation 306, the system 200 sets ion population control parameters for a subsequent acquisition event based on the measured signal density and the target signal density. The subsequent acquisition event may be the next acquisition event or any other acquisition event occurring 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 population of ions analyzed by the mass analyzer during the subsequent acquisition event. In some examples, the ion population control parameter is the accumulation time ions are accumulated in the ion store 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 optics that adjusts the flux of ions delivered to the mass analyzer (e.g., delivered to the ion store before moving to the mass analyzer).

[0051] The target signal density is a predetermined value that can be selected to optimize the number of ions in each spectrum while limiting the generation of interfering ions to an acceptably low amount for particular experimental conditions and objectives. The target signal density generally depends on the characteristics and operating parameters of the mass analyzer (e.g., resolving power), and in some examples may also depend on method parameters and analyte characteristics (e.g., concentration, molecular weight, and / or molecular weight distribution of analyte ions). In some examples, the target signal density is determined empirically. In other examples, the target signal density is determined based on one or more method parameters, instrument requirements, ion monitoring devices within the instrument, and / or sample or analyte characteristics for the experimental analysis. In some examples, the system 200 may allow a user to provide input to select or set the target signal density. Alternatively, the system 200 may automatically select or set the target signal density based on one or more instrument, method, and / or analyte parameters and / or conditions for the experiment.

[0052] In some instances, a user may select to operate in a mode in which the target signal density is sufficiently above the signal density that minimizes interference. For example, a user may be interested in collecting data as quickly as possible; in these cases, it may be advantageous to allow interference in high signal density regions to increase the number of signals observed in low signal density regions. Thus, a user may select a narrow m / z range of interest.

[0053] The subsequent acquisition event may be any acquisition event performed after the acquisition event of operation 302. For example, in a peak interval approach, the subsequent acquisition event may be the next acquisition event performed during the experimental run. In a total intensity approach, the subsequent acquisition may be a successive survey acquisition.

[0054] The system 200 may set ion population control parameters for a subsequent acquisition event based on the measured signal density and the target signal density in any suitable manner. An example of setting ion population control parameters will now be described with reference to setting an accumulation time. However, the same or similar principles may be used to set potentials applied to ion optics to adjust the ion flux delivered to a mass analyzer.

[0055] In some examples, the relationship between accumulation time and signal density may be assumed to be linear, with the accumulation time increasing as the ratio of the target signal density to the measured signal density (the "signal density ratio") increases (assuming the signal density is expressed in units that increase with increasing density). For example, the accumulation time for a subsequent acquisition event may be determined according to Equation (1) below:

[0056]

number

[0057] To illustrate the peak spacing approach, suppose the target signal density is 10% (one set of adjacent peaks every 10 peaks wide), the measured signal density is 20% (one set of adjacent peaks every 5 peaks wide), and the acquisition time for an acquisition event is T current was 1 second, the system 200 would then set the accumulation time T subs should be reduced to half (1 / 2) seconds to reduce the signal density. For example, if the target signal density is 10% (one set of adjacent peaks), the measured signal density is 5% (one set of adjacent peaks every 20 peak widths), and the acquisition time T for the acquisition event is current was 1 second, the system 200 would then set the accumulation time T subs would be increased to 2 seconds to determine that the signal density should be increased. If the target signal density is 10% (one set of adjacent peaks), the measured signal density is 9% (one set of adjacent peaks every 9 peaks wide), and the acquisition time T for the acquisition event is current was 1 second, the system 200 would then set the accumulation time T subs is 1 second and should not be changed.

[0058] In alternative examples, the relationship between accumulation time and signal density is nonlinear. Any nonlinear function may be used to correlate accumulation time to the measured signal density and the target signal density. In some examples, a nonlinear function may be used when the measured signal density is high or exceeds a threshold level. In some examples, system 200 may receive user input specifying a linear (e.g., Equation (1)) or nonlinear correlation to be used.

[0059] The system 200 calculates the accumulation time for the subsequent acquisition event based on the calculated accumulation time T subs Thus, subsequent acquisition events are performed with the new accumulation time.

[0060] When operating at low ion abundances in the individual ion regimes of CDMS, signal density and / or signal intensity may be unstable and may vary significantly from one spectrum to the next, whether due to random chance or variability in the ion flux from the ion source. The ion flux from the ion source may vary, for example, due to changing ionization conditions or because the sample is analyzed online via flow injection analysis, liquid chromatography, or capillary electrophoresis. Thus, in some examples, system 200 sets the accumulation time for subsequent acquisition events by determining the amount of change in accumulation time (e.g., an increase or decrease in accumulation time for a subsequent acquisition event relative to the acquisition event of operation 302) and adjusting the amount of change in accumulation time by applying a filter or imposing a change limit on the amount of change in accumulation time. Any suitable filter may be applied, such as an exponential filter with filter coefficients configured to minimize the effects of random chance. Additionally or alternatively, any suitable change limit may be applied, such as a maximum change amount (e.g., a maximum change amount of 10 milliseconds (ms)), or a predetermined percentage of change amount (e.g., 50%, 33%, 20%, etc.) By applying a filter or change limit, the accumulation time may be varied in a discrete or stepwise manner, which serves to avoid or reduce the occurrence of broad spectrum-to-spectrum oscillations in the accumulation time and adds stability to the accumulation time.

[0061] In some examples, system 200 may be configured to receive user input that sets one or more filter parameters (e.g., coefficients) and / or change limit parameters, and may set the filter parameters and / or change limit parameters based on the user input. For example, a user may specify one or more parameters of an exponential filter, a maximum change amount, and / or a predetermined percentage of the change amount, and which filter or change limit should be applied.

[0062] Exemplary AIC methods using the peak spacing approach and the sum intensity approach are now described. The following examples are described with reference to adjusting the accumulation time, but can equally be applied to adjusting the potential applied to the ion optics to adjust the ion flux delivered to the mass analyzer.

[0063] Figure 4 illustrates an example method 400 for performing AIC according to a peak spacing approach. While Figure 4 illustrates example operations according to one embodiment, other embodiments may omit, add, reorder, and / or modify any of the operations illustrated in Figure 4. One or more of the operations illustrated in Figure 4 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).

[0064] In operation 402, system 200 acquires a mass spectrum including a plurality of peaks representing intensity as a function of m / z of ions accumulated in an ion store over an accumulation time and analyzed by a mass analyzer during an acquisition event. System 200 may perform operation 402 in any suitable manner, including any of the methods described above with respect to operation 302.

[0065] In operation 404, system 200 calculates a peak spacing value for each set of adjacent peaks within a selected m / z range of the mass spectrum. As described above, the selected m / z range may include the entire detection window of the mass spectrum, or any portion thereof. In some examples, the selected m / z range may be specified by user input and may be a narrow m / z range of interest. The mass spectrum includes multiple peaks arranged along the m / z range in order of increasing m / z. As used herein, a set of adjacent peaks refers to the two peaks that are closest to each other along the m / z range of the mass spectrum. Thus, each peak, other than the peaks at the lowest and highest detected m / z, will have two adjacent peaks. The peak spacing value for a set of adjacent peaks is the distance between the adjacent peaks along the m / z range. System 200 calculates the spacing between each set of adjacent peaks.

[0066] The probability of ions occurring simultaneously is related to the resolution of the spectrum. Higher resolution reduces the probability that two adjacent signals will interfere with each other. To account for this, signal density is measured in units that reflect the actual peak capacity of the spectrum. Therefore, signal density in the peak spacing approach is measured in terms of peak width instead of simply measuring the number of peaks over the m / z range.

[0067] This peak width can be determined in any suitable manner. In some examples, the peak width is the expected instrument peak width. In other examples, the peak width is the peak width as measured from a mass spectrum. For example, the peak width can be the average peak width of all peaks contained in the mass spectrum or all peaks within a selected m / z range (e.g., a user-selected m / z range of interest). Peak widths measured from a spectrum advantageously account for ions that do not necessarily survive the entire detection period. These ions may produce broader peaks than expected for a given detection time, and having broader peaks increases the probability of overlapping signals. Using the measured peak width also accounts for ions with unstable m / z during the detection period. These unstable ions are most typically large biomolecular complexes that are incompletely desolvated or that fragment covalent bonds during the detection period. When ions collide with background gas molecules, they heat up, effectively boiling off the attached solvent and reducing their mass and, therefore, their measured m / z. Drifting m / z produces broader spectral peaks than would be expected from ions with stable m / z.

[0068] In operation 406, system 200 determines a global peak spacing value for the selected m / z range based on the peak spacing values calculated in operation 404. This global peak spacing value is used as the measured signal density. The global peak spacing value can be an average, a weighted average (e.g., weighting smaller peak spacing values more heavily), a median, or any other statistical representation of the calculated peak spacing values within the selected m / z range. In the example described herein, the global peak spacing value is expressed as a percentage (e.g., one (1) set per M peak widths, where M is the global peak spacing value). However, the global peak spacing value can be expressed in any other way (e.g., by per M peak widths) provided that the formulas described herein are adapted accordingly.

[0069] Mass spectra of real samples generally show that peaks are not randomly distributed across the m / z range, but tend to cluster together due to quantized ion charge (e.g., z can only have integer values, which limits m / z variability) and the many sources of subtle mass variability that can occur within an analyte. Some sources of subtle mass variability include, for example, isotopic variability, post-translational modifications, and cation and solvent addition. 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 when there are local regions of high density with an increased probability of interfering signals.

[0070] To address 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 spacing value based on a subset of the peak spacing values calculated in operation 404. The subset of peak spacing values generally includes the smallest peak spacing values, which represent the densest regions of the spectrum or selected m / z range. In some examples, the subset of calculated peak spacing values includes a percentage (e.g., 50%, 40%, 30%, etc.) of the smallest peak spacing values. For example, system 200 may arrange the calculated peak spacing values in an ascending list and calculate a global peak spacing value based only on the top 50%, 40%, 30%, or other percentage of the calculated peak spacing values. In other examples, the subset of calculated peak spacing values includes all peak spacing values less than a threshold peak spacing value. In some examples, the threshold peak spacing value is set based on a target signal density (e.g., 200% or 300% of the target signal density).

[0071] In operation 408, system 200 sets an accumulation time for the subsequent acquisition event based on the measured signal density (e.g., the global peak spacing value calculated in operation 406) and a predetermined target signal density (e.g., the target peak spacing value). System 200 may set the accumulation time in any suitable manner, including any of the methods described above with respect to operation 306.

[0072] Upon completion of operation 408, processing of method 400 returns to operation 402, where method 400 is performed again for a subsequent acquisition event. With method 400, the peak spacing approach accounts for any changing ion flux by using each spectrum as a guide to determine the accumulation time for subsequent acquisition events.

[0073] In the peak spacing approach, the use of signal density metrics 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 susceptible to contamination. Contaminants are typically low-mass species carrying only one or two charges. To be detectable in CDMS (which generally cannot detect and assign charge to individual ions in low charge states), the signal from the contaminants will arise from a large number of ions at the same m / z. Because these are unique peaks, and a typical mass spectrum acquired by CDMS can contain hundreds of individual ions, these unique peaks do not contribute significantly to signal density and therefore do not affect the optimized accumulation time.

[0074] Figure 5 illustrates an example method 500 for performing AIC according to a sum intensity approach. While Figure 5 illustrates example operations according to one embodiment, other embodiments may omit, add, reorder, and / or modify any of the operations illustrated in Figure 5. One or more of the operations illustrated in Figure 5 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).

[0075] In operation 502, system 200 acquires a first mass spectrum including multiple peaks representing intensity as a function of m / z of ions accumulated in an ion store over an accumulation time and analyzed by a mass analyzer during an acquisition event. System 200 may perform operation 502 in any suitable manner, including any of the methods 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. System 200 will use the first mass spectrum to flag any regions in m / z space (e.g., regions occupied by noise and / or low-mass contaminant signals) that should be excluded when determining the measured signal density.

[0076] In some instances, the first mass spectrum represents a population of ions. In a population measurement, a large ion population (typically, but not exclusively, 10 5 Multiple acquisitions or spectra may be used to sample multiple ions (total charge of ions or more). Multiple spectra may be summed or averaged for a population measurement. In another example, a first mass spectrum is acquired by individual ion measurement. For example, multiple acquisitions or spectra may be used to adequately sample multiple populations of the most abundant analyte populations, such as proteoforms.

[0077] In operation 504, system 200 determines the occupied m / z space within the selected m / z range of the mass spectrum acquired in operation 302. As described above, the selected m / z range may include the entire detection window of the mass spectrum, or any portion thereof. In some examples, the selected m / z range may be specified by user input and may be a narrow m / z range of interest. The occupied m / z space is the space in the m / z region of the mass spectrum that is likely to be occupied by detectable ion signals of interest. System 200 may determine the occupied m / z space in any suitable manner.

[0078] In some examples, system 200 excludes from the occupied m / z space any m / z regions (e.g., m / z bins as described below) that contain only signals below 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 therefore, any m / z regions that contain noise are solely excluded from the occupied m / z space because the inclusion of noise signals is not helpful in determining whether the occupied region has a single-ion level signal.

[0079] In some instances, the occupied m / z space also excludes space occupied primarily or solely by contaminants. Some samples may contain contaminants that interfere with the determination of the occupied m / z space. Contaminants may 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 population measurements, contaminants may dominate the spectrum, pushing target analytes below desired relative abundance. Thus, the occupied m / z space for a contaminated sample will be significantly smaller than the occupied m / z space that would otherwise be expected. A significantly smaller occupied m / z space results in a larger measured signal density, which drives the AIC algorithm to reduce accumulation times and thereby shrink ion populations beyond what is practical for a timely CDMS experiment.

[0080] In some situations, contaminant peaks present as an overly simple distribution, such as a series of dense isotopic distributions. A real-time charge deconvolution algorithm (e.g., Advanced Peak Determination (APD), THRASH, or MaxEnt) used for processing mass spectra can assign a charge to each peak falling within a selected m / z range. Thus, contaminants can be identified based on their assigned charge state. Ions assigned a low charge state are considered contaminants. For example, system 200 can identify as putative contaminants all signals within a selected m / z range that are assigned a charge state below a threshold charge state value (e.g., +2). The threshold charge state value can be selected to match the maximum charge state of the predominant contaminant. When determining the occupied m / z space and measured signal density, system 200 can exclude all regions that contain contaminants.

[0081] Some contaminant peaks may not be identified or assigned a charge state even using a charge deconvolutor. Therefore, in some examples, system 200 also excludes regions from the occupied m / z space that encompass a small number of the most abundant peaks to capture incidental contaminant peaks that do not have an assigned charge state. For example, system 200 may exclude regions that include a threshold number of the most abundant peaks (e.g., 15, 10, 8, 5, etc.). Alternatively, system 200 may exclude regions that include all peaks above a maximum threshold intensity level (e.g., 80%, 90%, 95%, etc. relative abundance). Some non-contaminant peaks may be erroneously removed in this manner, but assuming a certain false discovery rate, these errors can be consistently accounted for in the target signal density.

[0082] Some contaminants, such as polymers or softeners introduced through sample processing or molecular variability within the sample itself, produce peaks (e.g., having intensity values between a minimum and a maximum threshold intensity value) that are not excluded by the methods described above. Thus, in some examples, system 200 excludes from the occupied m / z space any regions of a selected m / z range that have or are likely to have an identified or identifiable contaminant. For example, system 200 may identify contaminants based on a library search of peaks within the selected m / z range and / or a list of m / z of expected contaminants.

[0083] The system 200 divides the selected m / z range into bins with a bin width Δ i The occupied m / z space can be determined by subdividing the m / z spectrum into N non-overlapping bins of m / z = 1 / 2, and calculating the occupied m / z space based on equation (2) below:

[0084]

number

[0085] For example, each bin having an intensity value below a minimum threshold intensity value (e.g., noise) and / or associated with a contaminant signal is 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 another example, bins not associated with noise or contaminant signals are not weighted equally, but instead are weighted based on the signal strength of each bin.

[0086] To illustrate, if the signal level of a bin is below a first threshold intensity value (e.g., a minimum (noise) threshold intensity value), the system 200 assigns the bin a bin weight w of zero (0). i If the signal level of a bin 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 the bin a bin weight w that is greater than 0 but less than 1. i If the signal level of the bin 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 a contaminant cutoff), the system 200 may assign the bin a bin weight w of one (1). i If the signal level of the bin is greater than the third threshold intensity value, the system 200 may assign the bin a bin weight w of zero (0). i For bins between the first and second threshold intensity values, the bin weights assigned to the bins may be i may be the same, may vary linearly as a function of the intensity of each bin between the first and second threshold intensity levels, or may vary non-linearly (e.g., exponentially, stepwise, etc.) based on the signal intensity of each bin.

[0087] 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 as m / z increases) because the intensity of individually resolved ions increases as m / z decreases (charge z increases).

[0088] As described herein, the bin weights w i By scaling the occupied m / z space using , the contribution of bins with low signal intensities to the occupied m / z space can be weighted less than the contribution of bins with higher signal intensities to the occupied m / z space, since bins with higher signal intensities are more likely to contribute to signal interference than bins with lower signal intensities.

[0089] In operation 506, system 200 determines the summed intensity of the signals in the occupied m / z space. System 200 steps through the first mass spectrum, interrogating the signal intensity of each bin, excluding the signal intensity of any bins in the selected m / z range that were excluded from the occupied m / z space. For example, system 200 may determine the summed intensity by summing the signal intensities of the bins in the occupied m / z space according to equation (3) below:

[0090]

number

[0091] In operation 508, system 200 determines a measured signal density based on the summed intensity within the occupied m / z space and the occupied m / z space. For example, system 200 may determine the measured signal density according to equation (4) below:

[0092]

number

[0093] In operation 510, system 200 sets an accumulation time for a subsequent acquisition event based on the measured signal density (intensity per unit m / z) and a predetermined target signal density, as determined in operation 508. System 200 may set the accumulation time in any suitable manner, including any of the methods described above with respect to operation 306.

[0094] In operation 512, system 200 determines whether the target signal density has been achieved. In some examples, 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 system 200 determines that the target signal density has been achieved, processing of method 500 ends and AIC is considered complete. Experimental analysis can then be performed using the accumulation time set in operation 510. If system 200 determines that the target signal density has not been achieved, processing of method 500 proceeds to operation 514.

[0095] In operation 514, system 200 acquires a second mass spectrum. Operation 512 may be performed in any suitable manner, including any of the methods described herein for operation 502. For example, the second mass spectrum may be another survey spectrum. However, the second mass spectrum is acquired using the accumulation time set in operation 510. Upon completion of operation 514, processing of method 500 returns to operation 506 to determine the measured signal density of the second mass spectrum. Processing of method 500 may continue and repeat until system 200 determines that the target signal density has been achieved.

[0096] Method 500 may be performed at any suitable time before and / or during an experimental analysis. For example, method 500 may be performed before starting a CDMS experiment. Additionally or alternatively, method 500 may be performed during an experimental analysis to recalibrate the accumulation time. In some examples, the recalibration is initiated by a user. In an alternative example, system 200 may periodically acquire one or more survey spectra and compare the measured signal density to a target signal density to determine whether recalibration is warranted. For example, system 200 may periodically perform operations 502, 504, 506, 508, and 512 (omitting operation 510 for the time being) to perform method 500 to determine whether to recalibrate the accumulation time.

[0097] As described above, methods 300, 400, or 500 can be performed during a CDMS experiment to characterize a sample and obtain mass-domain spectra of analytes in the sample. The analytes can include, but are not limited to, one or more proteoforms or biomolecular complexes thereof, such as immunoglobulins or their heavy chains, light chains, or Fd domains. As used herein, "proteoform" refers to all of the different molecular forms in which the protein product of a single gene can be found, including variations due to genetic mutations, alternatively spliced RNA transcripts, post-translational modifications, and the like. In some examples, the proteoforms in the sample are immunoglobulins, such as IgG, IgA, IgE, or IgM. CDMS allows for the measurement of complex proteoform mixtures and their complexes without the need to separate the proteoforms prior to identification.

[0098] CDMS techniques using AIC, as described herein, can be successfully integrated into a complete solution for plasma processing and automated CDMS analysis. Automated CDMS analysis opens up research for protein analysis in cohorts of hundreds of patients, regardless of the complexity of the mixture. Mass-domain spectral output does not require any additional deconvolution software, and this platform can enable the analysis and characterization of denatured protein mixtures ranging from 6 to 100 kDa. High-throughput automation of CDMS analysis also enables rapid and robust acquisition of high-resolution mass-domain spectra for intact proteins.

[0099] FIG. 6 illustrates an exemplary method 600 for implementing CDMS using AIC. While FIG. 6 illustrates exemplary operations according to one embodiment, other embodiments may omit, add, reorder, and / or modify any of the operations illustrated in FIG. 6. One or more of the operations illustrated in FIG. 6 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 of the methods described herein.

[0100] In operation 602, ions generated from a sample are accumulated in an ion store (e.g., ion store 104) for an accumulation time. In some examples, the sample is a proteoform.

[0101] In 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 m / z of the ions accumulated during the acquisition event. In some examples, the mass analyzer operates in CDMS mode to acquire a mass spectrum of a population of ions in the individual ion regime.

[0102] In 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 spacing approach. In other examples, the measured signal density is determined according to a sum intensity approach.

[0103] In operation 608, the integration time for the subsequent acquisition event is set based on the measured signal density and the target signal density.

[0104] In operation 610, the charge states of individual ion species represented by peaks within a selected m / z range of the mass spectrum are determined. For example, the charge states may be determined by a charge deconvolution algorithm. Alternatively, the charge states may be determined according to CDMS techniques (e.g., by generating a STORI plot and determining the charge state based on a slope versus charge calibration function).

[0105] In operation 612, a mass domain spectrum representing the intensity of each ion species as a function of mass is generated based on the charge state of each ion species and the m / z of each ion species.

[0106] Processing of method 600 then returns to operation 602, and the process is repeated for the subsequent acquisition event. In operation 602, ions are accumulated in the ion store for the accumulation time set in operation 608 during the previous cycle of method 600.

[0107] In the AIC method described above, ions across a selected m / z range are accumulated in the ion store in a single accumulation event. As a result, the number of ions accumulated in the ion store is limited by the densest m / z region of the incident ion beam (e.g., the ion beam from the ion source). If there is one particular region of m / z space where the incident ion beam is particularly dense, the single accumulation event stops once enough ions from that m / z region have been accumulated. Otherwise, further accumulation of ions from the ion beam risks accumulating ions with the same or very similar m / z values in the densest region in the ion store. When the single accumulation event stops, very few ions in other, less dense m / z regions are accumulated due to the lower probability of ions being included in these m / z regions. Therefore, ions located in the less dense m / z regions of the incident ion beam may not be adequately sampled. To sample ions located in the less dense m / z regions, additional mass analysis must be performed until sufficient signal is acquired from the less dense m / z regions. Therefore, acquiring sufficient signal for the less dense m / z region consumes additional system resources and prolongs the CDMS process.

[0108] These problems are illustrated in Figures 7A-7C. Figure 7A shows an exemplary m / z distribution graph 702, which plots the intensity of ions in a hypothetical incident ion beam as a function of the ion's m / z. The m / z distribution graph 702 represents the m / z distribution of the incident ion beam. If ions across the entire m / z range are accumulated in an ion store in a single accumulation event, the AIC methods described herein may stop accumulating ions once a sufficient or maximum number of ions have been collected from the densest region (e.g., near the most intense peak 704). Mass analysis of the accumulated ions may then be performed.

[0109] 7B shows an exemplary CDMS spectrum 706 obtained by mass spectrometry of ions accumulated in a single accumulation event. CDMS spectrum 706 is assumed to pose no problems for subsequent spectral processing because there are no double ion events (e.g., ions with the same m / z do not accumulate and combine to give a higher peak) and no interfering peaks to distort the estimate. While several peaks are located in the most dense m / z region (the m / z region corresponding to peak 704), very few peaks are located outside of the most dense m / z region.

[0110] Now, assume that the accumulation time is twice that of the analysis in Figure 7B. Figure 7C shows an example CDMS spectrum 708 acquired by mass analysis of accumulated ions in a single accumulation event with an accumulation time twice that of the example in Figure 7B. As shown in CDMS spectrum 708, the less dense m / z regions of the incident ion beam (m / z regions further from peak 704 in m / z distribution graph 702) produce more peaks compared to CDMS spectrum 706 due to the increased accumulation time and, therefore, a higher probability of ions in the less dense m / z region being collected. However, in CDMS spectrum 708, the most dense m / z region contains a higher peak 710, indicating a double ion event that results in inaccurate charge assignment. Additionally, the proximity of adjacent peaks in the most dense m / z region can cause errors in charge estimation due to interference. Therefore, as can be seen from Figures 7A-7C, the most dense m / z region of the incident ion beam precludes the maximum accumulation time that can be used with a single accumulation event approach for CDMS. This single accumulation event approach inherently provides less detectable signal per unit time for the lower density m / z regions. To fully characterize the lower density m / z regions, many analyses would need to be performed, since spectra containing ions in these lower density m / z regions would only occasionally occur.

[0111] To address these issues with the single accumulation event approach, a population of ions is accumulated in an ion store prior to mass analysis in multiple separate accumulation events to achieve a more uniform signal density across m / z space. Each accumulation event corresponds to a distinct m / z region of m / z space, and separate ion population control parameters are determined for each m / z region to accumulate a desired number of ions during each accumulation event. As a result, the probability of detecting ions in the less dense m / z regions of the incident ion beam is increased, while the probability of multiple ion events and interference in the more dense m / z regions is reduced. Thus, ion accumulation in the ion store is not hindered by the most dense m / z region, and more ions in the less dense m / z regions can be sampled in each CDMS acquisition.

[0112] The multiple accumulation event approach utilizes a mass filter positioned upstream of an ion store to selectively transmit ions within a corresponding m / z region for accumulation during each accumulation event. FIG. 8 shows a functional diagram of an exemplary mass spectrometer 800 that can be used for the multiple accumulation event approach. The mass spectrometer 800 is similar to the mass spectrometer 100, except that the mass spectrometer 800 includes a mass filter 802 positioned upstream from the ion store 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 incident ion stream 112-1 from the ion source 102 is filtered by the mass filter 802 to selectively transmit ions within a selected m / z range to the ion store 104, which accumulates the transmitted ions 112-2 during the accumulation event based on ion population control parameters for the accumulation event. As shown in FIG. 8, the detector 108 is positioned downstream of the mass analyzer 106. However, in instances where the mass analyzer 106 is implemented by an orbital electrostatic ion trap mass analyzer, the mass analyzer 106 and the detector 108 are integrated into the same device.

[0113] 9 shows a functional diagram of an exemplary mass spectrometer 900 including an electrostatic trap mass analyzer. Mass spectrometer 900 may implement mass spectrometer 800 and may be used to perform CDMS using a multiple accumulated event approach. 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 may further include additional or alternative components (e.g., ion optics, lenses, mass filters, ion storage devices, ion mobility analyzers, collision cells, ion flux monitors, etc.) as may be suitable for a particular implementation.

[0114] The ion source 902 may implement the ion source 102 (e.g., an electrospray ionization source), and the mass filter 904 may implement the mass filter 802. The ion trap 906 traps ions before introducing them into the mass analyzer 910. The ion trap 906 is downstream of the mass filter 904 and upstream of the mass analyzer 910. The 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).

[0115] The collision cell 908 may be any suitable collision cell, such as a high pressure collision dissociation (HCD) cell. The collision cell 908 is positioned 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 ions received from the ion trap 906. In CDMS applications, the collision cell 908 may function as an ion trap to accumulate ions that have passed through the ion trap 906. The collision cell 908 ejects trapped ions back into the ion trap 906 before the ions are introduced into the mass analyzer 910. The ion trap 906 and / or collision cell 908 may implement the ion store 104 of the mass spectrometer 800.

[0116] Mass analyzer 910 is an electrostatic ion trap mass analyzer such as an orbital electrostatic ion trap mass analyzer (eg, an Orbitrap™ mass analyzer, a Kingdon trap mass analyzer, etc.) or an electrostatic linear ion trap (ELIT).

[0117] An illustrative example of implementing CDMS using a multiple accumulated event approach will now be described with reference to FIG. 10 . FIG. 10 shows a flowchart of an exemplary method 1000 of implementing CDMS using a multiple accumulated event approach. While FIG. 10 shows exemplary operations according to one embodiment, other embodiments may omit, add, reorder, and / or modify any of the operations shown in FIG. 10 . One or more of the operations shown in FIG. 10 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).

[0118] In a first operation 1002, the m / z range of interest is subdivided into multiple m / z windows. The m / z range of interest is the m / z range that is mass analyzed by CDMS and is the m / z range of the resulting CDMS spectrum. Various methods for subdividing the m / z range of interest into multiple m / z windows are described in more detail below.

[0119] In operation 1004, an ion population control parameter is determined for each m / z window. The ion population control parameter for each m / z window adjusts the amount of ions accumulated in an ion store (e.g., ion store 104, ion trap 906, or collision cell 908) during an accumulation event. As mentioned above, in some examples, the ion population control parameter is an accumulation time for ions to accumulate in the ion store during an accumulation event. In other examples, the ion population control parameter is a potential applied to ion optics (e.g., lenses) that adjusts the flux of ions (e.g., ion stream 112-2) delivered to the ion store during an accumulation event. The ion population control parameter for each m / z window may be determined in any suitable manner, including by any of the methods described herein, such as methods 300, 400, or 500. For example, the ion population control parameter may be determined independently for each m / z window using the m / z window as the selected m / z range described above with reference to methods 300, 400, and 500. Various methods for determining ion population control parameters are described in more detail below.

[0120] In operation 1006, a population of ions from the sample is accumulated in the ion store by one or more accumulation events. Each accumulation event corresponds to a distinct m / z window among the multiple m / z windows. During each accumulation event, ions within the m / z window corresponding to the accumulation event (e.g., ions having 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 for the corresponding m / z window. For example, a mass filter (e.g., mass filter 802 or mass filter 904) selectively transmits only ions within the m / z window of the current accumulation event. The accumulation time or the potential applied to the ion optics is set for the current accumulation event based on the ion population control parameters determined in operation 1004 for the corresponding m / z window so that a desired amount of ions is accumulated in the ion store. In some examples, a population of ions is accumulated in the ion store while a previous population of ions is processed in the mass analyzer (e.g., mass analyzed in operation 1010, described below). In this way, the accumulation of ion populations does not limit or inhibit experimental throughput. In some examples, the one or more storage events in which the population of ions is stored in the ion store include storage events for all m / z windows of the plurality of m / z windows (e.g., storage events for all m / z windows spanning the entire m / z range of interest). In other examples, the one or more storage events in which the population of ions is stored in the ion store correspond to one or more subsets of the plurality of m / z windows. In a further example, the population of ions is stored in the ion store by one storage event corresponding to one m / z window.

[0121] In operation 1008, the population of accumulated ions (e.g., a population of ions accumulated by multiple successive 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 obtain a CDMS spectrum.

[0122] In operation 1010, the population of ions is mass analyzed to obtain a CDMS spectrum of the population of ions. In examples where one or more accumulation events correspond to all m / z windows 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.

[0123] In an example where one or more accumulation events correspond to one or more subsets of the plurality of m / z windows, operations 1006-1010 may be repeated until each m / z window of the plurality of m / z windows has been sampled. For example, the entire m / z range of interest may be subdivided into 10 m / z windows. A first population of ions may be accumulated in the ion store by a first subset (e.g., five) of accumulation events corresponding to a first subset (e.g., five) of the 10 m / z windows, transferred to a mass analyzer, and mass analyzed. A second population of ions may be accumulated in the ion store by a second subset (e.g., five) of accumulation events corresponding to a second subset of the m / z windows, transferred to a mass analyzer, and mass analyzed. The CDMS spectra acquired for the first and second populations of ions may be combined to generate a CDMS spectrum spanning the m / z range of interest. In some examples, ions within one or more m / z windows (e.g., one or more sparse m / z regions) may be accumulated in both populations of ions.

[0124] The method operations 1000 will now be described with reference to FIG. 11 . FIG. 11 shows an m / z distribution graph 702 subdivided into multiple m / z windows 1102 for multiple accumulation events, and an exemplary CDMS spectrum 1104 acquired using the multiple accumulation events. As shown in the m / z distribution graph 702, the m / z range of interest is subdivided into separate m / z windows 1102 (e.g., m / z windows 1102-1 through 1102-7) that together span the entire m / z range of interest. As used herein, m / z windows are “separate” if they do not have the same combination of upper and lower m / z limits. As shown in the m / z distribution graph 702, the m / z windows 1102 do not overlap. However, in other examples, any two or more separate m / z windows 1102 may overlap. For example, the first m / z window 1102 may span the entire m / z range of interest, while the smaller m / z windows 1102 may span less than the entire m / z range of interest (e.g., only sparse m / z regions), and any one or more of the smaller m / z windows 1102 may overlap with one or more other smaller m / z windows 1102. Although the m / z distribution graph 702 shows seven m / z windows 1102, the m / z range of interest may be subdivided into any other suitable number of m / z windows 1102. In some cases, the separate m / z windows have gaps (i.e., do not extend continuously across the entire m / z range of interest). This may occur, for example, when ions within a certain range of m / z are not of interest for the particular sample being measured.

[0125] The m / z range of interest may be subdivided into multiple m / z windows 1102 in any suitable manner. In some examples, the m / z range of interest is subdivided into multiple m / z windows 1102 based on a preset constant m / z width (e.g., set regardless of the m / z distribution of the incident ion beam (e.g., ion stream 112-1)). In some examples, the m / z windows 1102 all have substantially the same m / z width (e.g., within 5% of each other). In further examples, the m / z range of interest is subdivided into multiple m / z windows 1102 based on the number of m / z windows into which the m / z range of interest is to be subdivided, which number may be preset or provided by user input. For example, assuming an m / z range of interest of 4,000 to 40,000 m / z is subdivided into 10 m / z windows, each m / z window would have a width of 3,600 m / z.

[0126] In some examples, the m / z range of interest is subdivided into multiple 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 the known m / z distribution of the sample) and / or may 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 may be measured dynamically during the CDMS analysis. For example, a characterization analysis of the sample (e.g., ion stream 112-1) may be performed periodically or randomly during the CDMS analysis or when a calibration condition is met (e.g., when multiple ion events are detected in an acquired CDMS spectrum, when a peak width in the CDMS spectrum exceeds a threshold, etc.). Additionally or alternatively, the measured m / z distribution of the incident ion beam may be determined based on one or more previously acquired CDMS spectra. For example, one or more CDMS spectra acquired by performing method 1000 prior to performing method 1000 to acquire the current CDMS spectrum may be used as the m / z distribution of the currently incident ion beam.

[0127] In some examples, the m / z range of interest is subdivided into multiple m / z windows 1102 based on at least one of signal density or peak intensity in the m / z distribution of the incident ion beam. In some examples, as described above, signal density may be determined for each individual m / z window based on a peak spacing approach or a sum intensity approach. 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 analytes in a particular m / z window (e.g., m / z window 1102-4) than in analytes in other m / z windows 1102. Therefore, the width of the m / z window 1102 encompassing the analyte of interest may be set to have a higher signal density (and therefore a narrower width) than the other m / z windows to avoid multiple ion events and interference in the m / z window of interest.

[0128] In some examples, the m / z range of interest is subdivided into multiple m / z windows 1102 based on the intensities of peaks in the expected or measured m / z distribution of the incident ion beam (e.g., ion stream 112-1). For example, the m / z range of interest may be subdivided into N windows 1102, where N is a positive integer, such that each of the N most intense peaks is located within a unique m / z window 1102. Alternatively, the m / z range of interest may be subdivided into m / z windows 1102 such that each peak having an intensity value greater than a threshold intensity value is located 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 the particular m / z window 1102.

[0129] In a further example, the division of the m / z range of interest into multiple m / z windows 1102 is performed based entirely or in part on user input. For example, the m / z distribution graph 702 may be presented to a user by a display device associated with system 200. The user may provide input to specify the location and width of the m / z windows 1102. In some examples, system 200 provides an initial or default configuration of m / z windows 1102 in any of the ways described herein, which the user may adjust as desired.

[0130] In a further example, the division of the m / z range of interest into multiple m / z windows 1102 is performed based at least in part 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, that time may limit the available injection time if maximum time utilization is desired.

[0131] The ion population control parameters for each m / z window 1102 may be determined in any suitable manner, including any of the methods described herein. For example, an AIC process as described herein (e.g., method 300, 400, or 500) may be performed independently for each m / z window 1102. In the example of FIG. 11 , the accumulation time for m / z window 1102-4, which encompasses the most intense peak 704, is shorter than the accumulation times for the other m / z windows 1102, which have weaker peaks and lower signal densities. Thus, the accumulation of ions in m / z window 1102-4 does not inhibit or limit the accumulation of ions in the less dense m / z windows 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 techniques described herein, but may be determined in any other suitable manner. For example, an ion population control parameter for a particular m / z window 1102 may be inversely proportional to the intensity (e.g., sum, maximum, average, median, etc.) of the measured ion signal for the particular m / z window 1102. In some examples, an ion population control parameter for a particular m / z window 1102 may be set above a minimum value, below a maximum value, or may be set based on user input.

[0132] A separate 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 may be performed for m / z window 1102-1, during which ions outside 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 transmitted by the mass filter and accumulated in an ion store (e.g., ion store 104, ion trap 906, or collision cell 908) for the accumulation time determined for m / z window 1102-1. After the first accumulation event is completed, a second accumulation event may be performed for m / z window 1102-2, during which ions outside m / z window 1102-2 are filtered out by the mass filter and ions within m / z window 1102-2 are transmitted by the mass filter and accumulated in the ion store for the accumulation time determined for m / z window 1102-2. This process may be repeated for the remaining m / z windows 1102 until the final accumulation event is completed for the last m / z window 1102. In this manner, a population of ions having a generally uniform distribution of ions across m / z space is accumulated in the ion store by multiple customized accumulation events. It will be appreciated that any order of accumulation events may be used and need not be performed in order of increasing or decreasing m / z of m / z windows 1102. In some examples, accumulation events for selected m / z windows 1102, such as m / z windows 1102 identified by a user (e.g., that may be of little or no interest to the user), may be omitted or skipped.

[0133] After accumulation of the ion population in the ion store, 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 exhibits more signal in the low-density portion of m / z space of m / z distribution graph 702 compared to CDMS spectrum 708, while also preventing the multiple ion events and interferences of CDMS spectrum 706. Using appropriate division of the m / z window and their respective 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 interferences that would degrade the final quality of the CDMS spectrum. Therefore, more ions can be analyzed per unit time, thus increasing sample throughput without reducing data quality. Additionally, data quality can be higher than conventional CDMS techniques because CDMS techniques using multiple accumulation events, as described herein, better characterize regions of m / z space of the incident ion beam that have less signal density.

[0134] Furthermore, CDMS techniques incorporating multiple accumulation events allow CDMS to be performed with a prior separation process, such as liquid chromatography. Conventional CDMS techniques are typically not performed in conjunction with a prior separation process, such as liquid chromatography, due to time scale mismatches. For example, because LC peak widths are typically shorter than the time required to acquire a CDMS spectrum, conventional CDMS techniques typically require longer time than LC separation. However, the modified CDMS technique described herein, which uses multiple accumulation events, reduces the time required to acquire a CDMS spectrum, thereby allowing CDMS to be performed in conjunction with a prior separation process, such as liquid chromatography, gas chromatography, or capillary electrophoresis. For example, the mass spectrometer 800 or 900 can be coupled to a liquid chromatography system including a column having a stationary phase that separates components contained in the sample after the sample is injected into the mobile phase. The ion source of the mass spectrometer generates ions from the sample as the components contained in the sample elute from the liquid chromatography system. The ions thus generated are introduced into the mass spectrometer for CDMS analysis.

[0135] In some CDMS methods, multiple CDMS spectra are combined and the intensity at each m / z is averaged across the multiple CDMS spectra to provide a more accurate representation of the m / z distribution of ions in the incident ion beam. However, as shown in FIG. 11 , CDMS spectrum 1104 does not represent the m / z distribution of ions in the incident ion beam (represented by m / z distribution graph 702) due to uneven accumulation of ions across m / z window 1102. For example, the accumulation time for m / z window 1102-4 is shorter than that for the other m / z windows 1102 to obtain a more uniform distribution of ions. As a result, CDMS spectrum 1104 shows that each detected ion has the same intensity. Combining multiple spectra acquired in a similar manner does not provide an accurate representation of the actual m / z distribution of ions in the incident ion beam. Furthermore, the position of the m / z window and the ion control parameters for the m / z window may vary from spectrum to spectrum.

[0136] To account for non-uniform accumulation of ions across the m / z windows 1102, the intensity values of the CDMS spectral peaks in each m / z window 1102 may be adjusted (e.g., scaled or normalized) based on the ion population control parameters of the corresponding m / z window 1102 encompassing the peak. For example, the intensity values of the m / z window 1102 may be scaled based on the variance of the ion population control parameters of the m / z window 1102 relative to a reference ion population control parameter value. The reference ion population control parameter value may be preset or may be the ion population control parameter value for the m / z window 1102. For example, the accumulation time of m / z window 1102-3 may be three times the accumulation time of m / z window 1102-4. Thus, the intensity value of the signal in m / z window 1102-3 may be reduced by a factor of three. Similar adjustments may be made to the other m / z windows 1102, using the accumulation time of m / z window 1102-4 as a reference value. In this way, the combined CDMS spectrum more accurately represents the m / z distribution of ions in the ion beam.

[0137] Various modifications may be made to the methods, apparatus, and systems described herein. In some examples, system 200 may be configured to request user input to manage or adjust the settings of the AIC method. For example, system 200 may obtain from the user the method settings, list of target analytes, selected m / z range, and / or any other initial or default values for parameters associated with methods 300, 400, 500, and / or 600. System 200 may also be configured to notify the user of the need for certain changes, such as when accumulation time changes or changes by a threshold amount, or when evaluation indicates that a parameter (e.g., target signal density, accumulation time, etc.) should be adjusted.

[0138] In certain embodiments, one or more of the systems, components, and / or processes described herein may be implemented and / or executed by one or more appropriately configured computing devices. To this end, one or more of the systems and / or components described above may include or be implemented by any computer hardware and / or computer-implemented instructions (e.g., software) embodied on at least one non-transitory computer-readable medium configured to perform one or more of the processes described herein. In particular, system components may be implemented on one physical computing device, or on two or more physical computing devices. Thus, system components may include any number of computing devices and may employ any of several computer operating systems.

[0139] In certain embodiments, one or more of the processes described herein may be implemented, at least in part, as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices. Generally, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., a memory, etc.) and executes those instructions to thereby perform one or more processes, including one or more of the processes described herein. Such instructions may be stored and / or transmitted using any of a variety of known computer-readable media.

[0140] Computer-readable media (also referred to as processor-readable media) include any non-transitory media that participate in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such media may take many forms, including, but not limited to, non-volatile media and / or volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random access memory ("DRAM"), which typically constitutes the main memory. Common forms of computer-readable media include, for example, disks, hard disks, magnetic tape, any other magnetic media, compact disc read-only memory ("CD-ROM"), digital video disc ("DVD"), any other optical media, random access memory ("RAM"), programmable read-only memory ("PROM"), erasable programmable read-only memory ("EPROM"), FLASH-EEPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0141] FIG. 12 illustrates an exemplary computing device 1200 that may be specifically configured to perform one or more of the operations, methods, and processes described herein. As shown in FIG. 12, computing device 1200 may include a communication interface 1202, a processor 1204, a storage device 1206, and an input / output ("I / O") module 1208 that are communicatively coupled to each other via a communication infrastructure 1210. While FIG. 12 illustrates an exemplary computing device 1200, the components illustrated in FIG. 12 are not intended to be limiting. In other embodiments, additional or alternative components may be used. The components of computing device 1200 illustrated in FIG. 12 will now be described in further detail.

[0142] Communications interface 1202 may be configured to communicate with one or more computing devices. Examples of communications 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.

[0143] Processor 1204 generally represents any type or form of processing unit capable of processing data and / or interpreting, executing, and / or directing the execution of one or more of the instructions, processes, and / or operations described herein. Processor 1204 may perform operations by executing computer-executable instructions 1212 (e.g., applications, software, code, and / or other executable data instances) stored on storage device 1206.

[0144] Storage device(s) 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(s) 1206 may include, but are not limited to, any combination of non-volatile and / or volatile media described herein. Electronic data, including data described herein, may be temporarily and / or permanently stored in storage device(s) 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 in storage device(s) 1206. In some examples, data may be located in one or more databases residing in storage device(s) 1206.

[0145] I / O module(s) 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. I / O module(s) 1208 may include any hardware, firmware, software, or combination thereof that supports input and output capabilities. For example, I / O module 1208 may include hardware and / or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (e.g., a touchscreen display), a receiver (e.g., an RF or infrared receiver), a motion sensor, and / or one or more input buttons.

[0146] 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 a particular embodiment, I / O module 1208 is configured to provide graphical data to a display for presentation to a user. The graphical data may represent one or more graphical user interfaces and / or any other graphical content as may be useful in a particular implementation.

[0147] 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.

[0148] In the foregoing specification, various exemplary embodiments and examples have been described with reference to the accompanying drawings. However, it will be apparent that various modifications and changes 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 following claims. For example, certain features of one embodiment or example described herein may be combined with or substituted for features of other embodiments or examples described herein. Accordingly, the specification and drawings should be considered in an illustrative, and not a restrictive, sense.

[0149] The advantages and features of the present disclosure can be further illustrated by the following examples.

[0150] Example 1. A system for charge detection mass spectrometry (CDMS), comprising one or more processors, the system, when executed by the one or more processors, causing a computing device to perform a process including: instructing a mass spectrometer to subdivide a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining ion population control parameters for each m / z window, wherein the ion population control parameters for each m / z window adjust the amount of ions accumulated in the ion store during an accumulation event; accumulating a population of ions from the sample in an ion store by one or more accumulation events each corresponding to a distinct m / z window of the plurality of m / z windows, wherein during each accumulation event, ions in 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; and mass analyzing the ion population to obtain a CDMS spectrum of the ion population.

[0151] Example 2. The system of Example 1, wherein the process further comprises adjusting the intensity of a peak contained in the CDMS spectrum based on an ion population control parameter for an m / z window encompassing the peak.

[0152] Example 3. The system of example 1 or 2, wherein subdividing the m / z range of interest into a plurality of m / z windows is performed based on the m / z distribution of ions from the sample.

[0153] Example 4. The system described in Example 3, wherein the m / z distribution of ions derived from the sample is based on a pre-scan or characterization analysis of the sample.

[0154] Example 5. The system of example 3 or 4, wherein the m / z distribution of ions derived from the sample is based on one or more previously acquired CDMS mass spectra of one or more populations of ions derived from the sample.

[0155] Example 6. The system of any one of Examples 3-5, wherein subdividing the m / z range of interest into a plurality of m / z windows is performed based on at least one of signal density or peak intensity of the m / z distribution of ions.

[0156] Example 7. The system of any one of Examples 3-6, wherein the ion population control parameters include an accumulation time during which ions are accumulated in the ion store during an accumulation event, and the accumulation time for each m / z window is inversely proportional to the summed intensity of the peaks of the m / z distribution of ions in the m / z window.

[0157] Example 8. The system of any one of Examples 3-7, wherein a plurality of m / z windows have substantially the same signal density.

[0158] Example 9. The system of any one of Examples 1-8, wherein the one or more accumulated events include a plurality of accumulated events corresponding to each m / z window of a plurality of m / z windows.

[0159] Example 10. The system of any one of Examples 1-9, wherein storing the population of ions in the ion store is performed during mass analysis of a previously stored population of ions.

[0160] Example 11. A non-transitory computer-readable medium storing instructions that, when executed, cause at least one processor of a computing device for charge-detection mass spectrometry to perform a process including: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows; determining ion population control parameters for each m / z window, wherein the ion population control parameters for each m / z window adjust an amount of ions accumulated in the ion store during an accumulation event; directing accumulation of a population of ions from the sample in an ion store by one or more accumulation events each corresponding to a distinct m / z window of the plurality of m / z windows, wherein during each accumulation event, ions in 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 population of ions to a mass analyzer; and directing mass analysis of the population of ions to obtain a CDMS spectrum of the population of ions.

[0161] Example 12. The computer-readable medium of Example 11, wherein the process further comprises adjusting the intensity of a peak contained in the CDMS spectrum based on an ion population control parameter for an m / z window encompassing the peak.

[0162] Example 13. The computer-readable medium of example 11 or 12, wherein subdividing the m / z range of interest into a plurality of m / z windows is performed based on the m / z distribution of ions from the sample.

[0163] Example 14. The computer-readable medium of Example 13, wherein the ion population control parameters include an accumulation time during which ions accumulate in the ion store during an accumulation event, and the accumulation time for each m / z window is inversely proportional to the summed intensity of the peaks of the m / z distribution of ions in the m / z window.

[0164] Example 15. The computer-readable medium of Example 13 or 14, wherein a plurality of m / z windows have substantially the same signal density.

[0165] Example 16. The computer-readable medium of any one of Examples 11-15, wherein the one or more accumulated events include a plurality of accumulated events corresponding to each m / z window of a plurality of m / z windows.

[0166] Example 17. A system for charge detection mass spectrometry (CDMS), comprising: an ion store that accumulates a population of ions from a sample by one or more accumulation events; a mass analyzer that acquires a mass spectrum of the accumulated population of ions by CDMS after the accumulated population of ions travels to a mass analyzer; a mass filter that selectively transmits ions from the sample based on the m / z of the ions; and a computing device that subdivides a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, each accumulation event of the one or more accumulation events corresponding to a distinct m / z window of the plurality of m / z windows. determining ion population control parameters for each m / z window, the ion population control parameters for each m / z window adjusting an amount of ions accumulated in the ion store during a corresponding accumulation event; and directing the accumulation of a population of ions from the sample in the ion store by one or more accumulation events, wherein during each accumulation event, ions in 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.

[0167] Example 18. The system of Example 17, wherein directing the accumulation of the population of ions includes instructing a mass filter to selectively transmit, during each accumulation event of the one or more accumulation events, ions within an m / z window corresponding to the accumulation event.

[0168] Example 19. The system of example 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 store comprises a collision cell or a C-trap.

[0169] Example 20. The system of any one of Examples 17 to 19, further comprising: a liquid chromatography system including a column having a stationary phase that separates components contained in the sample after the sample is injected into a mobile phase; and an ion source that generates ions from the sample as the components contained in the sample elute from the liquid chromatography system.

Claims

1. 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 the computing device to instruct the mass spectrometer to: 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, the ion population control parameter for each m / z window adjusting the amount of ions accumulated in the ion store during an accumulation event; storing a population of ions from the sample in the ion store by one or more storage events each corresponding to a distinct m / z window of the plurality of m / z windows, wherein during each storage event, ions in the m / z window corresponding to the storage event are stored in the ion store based on the ion population control parameters for the corresponding m / z window; transferring the accumulated population of ions to a mass analyzer; and mass analyzing the population of ions to obtain a CDMS spectrum of the population of ions.

2. 2. The system of claim 1, wherein the process further comprises adjusting the intensity of a peak contained in the CDMS spectrum based on the ion population control parameters for the m / z window that encompasses the peak.

3. The system of claim 1 , wherein the subdividing of the m / z range of interest into the plurality of m / z windows is performed based on an m / z distribution of ions from the sample.

4. The system of claim 3 , wherein the m / z distribution of ions from the sample is based on a pre-scan or characterization analysis of the sample.

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

6. 4. The system of claim 3, wherein the subdivision of 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 m / z distribution of the ions.

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

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 accumulated events include a plurality of accumulated events corresponding to each m / z window of the plurality of m / z windows.

10. The system of claim 1 , wherein said storing said population of ions in said ion store is performed during mass analysis of a previously stored population of ions.

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: 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, the ion population control parameter for each m / z window adjusting the amount of ions accumulated in the ion store during an accumulation event; directing the accumulation of a population of ions from the sample into the ion store by one or more accumulation events, each corresponding to a distinct 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 the transfer of the accumulated population of ions to a mass analyzer; and directing mass analysis of the population of ions to obtain a CDMS spectrum of the population of ions.

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

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

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

15. 14. 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 accumulated events include a plurality of accumulated events corresponding to each m / z window of the plurality of m / z windows.

17. 1. A system for charge detection mass spectrometry (CDMS), comprising: an ion store that accumulates a population of ions from the sample through one or more accumulation events; a mass analyzer for acquiring a mass spectrum of the accumulated ion population by CDMS after the accumulated ion population has traveled to the mass analyzer; a mass filter that selectively transmits ions from the sample based on the m / z of the ions; 1. A computing device comprising: subdividing a mass-to-charge ratio (m / z) range of interest into a plurality of m / z windows, wherein each accumulation event of the one or more accumulation events corresponds to a distinct m / z window of the plurality of m / z windows; determining an ion population control parameter for each m / z window, the ion population control parameter for each m / z window adjusting an amount of ions accumulated in the ion store during a corresponding accumulation event; and a computing device configured to perform a process including directing the accumulation of a population of ions derived from the sample in the ion store by the one or more accumulation events, wherein during each accumulation event, ions in 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. 18. The system of claim 17, wherein directing the accumulation of the population of ions comprises instructing the mass filter to selectively transmit, during each accumulation event of the one or more accumulation events, ions within the m / z window corresponding to the accumulation event.

19. the mass analyzer comprises an orbital electrostatic ion trap mass analyzer or an electrostatic linear ion trap mass analyzer; the ion store comprises a collision cell or a C-trap; 20. The system of claim 17.

20. a liquid chromatography system including a column having a stationary phase for separating components contained in the sample after the sample is injected into a mobile phase; an ion source that generates the ions from the sample as the components contained in the sample elute from the liquid chromatography system; The system of claim 17 further comprising:

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