Patent Description:
Tandem mass spectrometry, referred to as MS/MS, is a popular and widelyused analytical technique whereby precursor ions derived from a sample are subjected to fragmentation under controlled conditions to produce product ions. The product ion spectra contain information that is useful for structural elucidation and for identification of sample components with high specificity. In a typical MS/MS experiment, a relatively small number of precursor ion species are selected for fragmentation, for example those ion species of greatest abundances or those having mass-to-charge ratios (m/z's) matching values in an inclusion list.

Therapeutic products in BioPharma require detection of < <NUM> % of contaminant components relative to the biological compound of interest to ensure bioactivity, safety and efficacy. A major challenge for detecting such low level contaminants using mass spectrometry is the intra-scan dynamic range. While the difficulty of detecting low level components in the presence of highly abundant components is partly due to ionization efficiency/competition, it has been shown that mass spectral intra-scan dynamic range is a major limitation. In transmission quadrupoles, a mass range is transmitted where dominant ions represent the vast majority of the ion population. In some cases, low level components are not even detectable in the full scan while isolating the mass range and conducting MS/MS of the low level components shows a clear component and a fragmentation spectrum. From the foregoing it will be appreciated that a need exists for detecting low level components.

<CIT> discloses techniques for mass filtering based on a priori biomarker knowledge and elution time intervals for selected ion species from a separation device. A sample may be screened for biomarker patterns based on distinct elution times for selected ions or peptides. A mass spectrum for species of interest can be tailored by filtering out undesired ions by measuring corresponding elution times and determining a priori selected elution time intervals for desired ion species only.

In a first aspect, a method for analyzing a sample is defined in claim <NUM>.

Various embodiments of the first aspect can further include performing data dependent analysis ions of high abundance compounds within a sample.

Various embodiments of the first aspect can further include performing data dependent analysis of features not added to the inclusion list. In particular embodiments, the features not added to the inclusion list are of intermediate intensity and features added to the inclusion list are of low intensity.

In various embodiments of the first aspect, excluding ions within retention time and mass-to-charge ranges of the exclusion list can be accomplished using an ion trap. In particular embodiments, excluding the ions can involve applying an isolation waveform to eject ions within the mass-to-charge ranges of the exclusion list from the trap while trapping ions with mass-to-charge ratios not on the exclusion list.

In various embodiments of the first aspect, excluding ions within retention time and mass-to-charge regions of the exclusion list can be accomplished using a quadrupole mass filter. In particular embodiments, excluding the ions can involve scanning multiple mass sub-ranges separated by exclusion regions. In particular embodiments, excluding the ions can involve closing an ion gate during a time periods corresponding to exclusion regions.

In various embodiments of the first aspect, the second data set can be obtained by performing selected reaction monitoring.

In a second aspect, a system for analyzing components of a sample is defined in claim <NUM>.

In various embodiments of the second aspect, the mass resolving device can be a quadrupole ion trap. In particular embodiments, excluding the ions can involve applying an isolation waveform to ejects ions within the mass-to-charge ranges of the exclusion list from the trap while trapping ions with mass-to-charge ratios not on the exclusion list.

In various embodiments of the second aspect, the mass resolving device can be a quadrupole mass filter. In particular embodiments, excluding the ions can involve scanning multiple mass sub-ranges separated by exclusion regions. In particular embodiments, excluding the ions can involve closing an ion gate during a time periods corresponding to exclusion regions.

In various embodiments of the second aspect, the controller can be further configured to perform a data dependent analysis of high abundance compounds within the sample.

Embodiments of systems and methods for ion isolation are described herein and in the accompanying exhibits.

In this detailed description of the various embodiments, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the embodiments disclosed. One skilled in the art will appreciate, however, that these various embodiments may be practiced with or without these specific details. In other instances, structures and devices are shown in block diagram form. Furthermore, one skilled in the art can readily appreciate that the specific sequences in which methods are presented and performed are illustrative and it is contemplated that the sequences can be varied.

Various embodiments of mass spectrometry platform <NUM> include components as displayed in the block diagram of <FIG>. In various embodiments, elements of <FIG> are incorporated into mass spectrometry platform <NUM>. According to various embodiments, mass spectrometer <NUM> includes an ion source <NUM>, a mass analyzer <NUM>, an ion detector <NUM>, and a controller <NUM>.

In various embodiments, the ion source <NUM> generates a plurality of ions from a sample. The ion source can include, but is not limited to, a matrix assisted laser desorption/ionization (MALDI) source, electrospray ionization (ESI) source, atmospheric pressure chemical ionization (APCI) source, atmospheric pressure photoionization source (APPI), inductively coupled plasma (ICP) source, electron ionization source, chemical ionization source, photoionization source, glow discharge ionization source, thermospray ionization source, and the like.

In various embodiments, the mass analyzer <NUM> can separate ions based on a mass to charge ratio of the ions. For example, the mass analyzer <NUM> can include a quadrupole mass filter analyzer, a quadrupole ion trap analyzer, a time-of-flight (TOF) analyzer, an electrostatic trap (e.g., ORBITRAP) mass analyzer, Fourier transforms ion cyclotron resonance (FT-ICR) mass analyzer, and the like. In various embodiments, the mass analyzer <NUM> can also be configured to fragment the ions using collision induced dissociation (CID) electron transfer dissociation (ETD), electron capture dissociation (ECD), photo induced dissociation (PID), surface induced dissociation (SID), and the like, and further separate the fragmented ions based on the mass-to-charge ratio.

In various embodiments, the ion detector <NUM> can detect ions. For example, the ion detector <NUM> can include an electron multiplier, a Faraday cup, and the like. Ions leaving the mass analyzer can be detected by the ion detector. In various embodiments, the ion detector can be quantitative, such that an accurate count of the ions can be determined.

In various embodiments, the controller <NUM> can communicate with the ion source <NUM>, the mass analyzer <NUM>, and the ion detector <NUM>. For example, the controller <NUM> can configure the ion source or enable/disable the ion source. Additionally, the controller <NUM> can configure the mass analyzer <NUM> to select a particular mass range to detect. Further, the controller <NUM> can adjust the sensitivity of the ion detector <NUM>, such as by adjusting the gain. Additionally, the controller <NUM> can adjust the polarity of the ion detector <NUM> based on the polarity of the ions being detected. For example, the ion detector <NUM> can be configured to detect positive ions or be configured to detected negative ions.

The dynamic range problem can be addressed by integrating feature detection with a multi-notch quadrupole transmission scheme in the instrument data acquisition system. In a first pass, features can be detected and a 'notch exclusion' matrix can be created. M/z regions of high density can be excluded from transmission using a quadrupole. In a second pass, the quadrupole can dynamically adjust the scan range and can transmit ions that are not on the notch regions per unit time. As such, m/z species that are otherwise undetected in the survey scan can now be detected using a feature detector. A third pass can use the newly identified feature list from the second pass as an inclusion list to trigger on low level contaminants that would otherwise be missed due to missing signal in the survey scan. The resulting data collection will have improved dynamic range for detecting low level contaminant components in biopharma and other applications.

<FIG> is a flow diagram illustrating an exemplary method <NUM> of analyzing a sample. At <NUM>, a first portion of the sample is injected, such as into a liquid chromatograph mass spectrometer. In various embodiments, the sample can be injected manually or by auto-sampler. At <NUM>, the sample is chromatographically separated and mass data is obtained. The data can be a survey scan showing the presence of ions at various mass-to-charge ratios at multiple time points in the chromatographic separation.

At <NUM>, feature detection can be performed on the mass data. Various feature detection algorithms are known in the art to be suitable for this purpose. The feature detection can identify intensity peaks within the data showing the elution of various species. However, since the most abundant species at any given time point will dominate the spectra at that time point, low abundance ions may not be detectable to the feature detect algorithm.

At <NUM>, a transmission matrix is generated to exclude high intensity features. The transmission matrix defines regions of retention time and m/z space for which ions should be transmitted which excludes retention time and m/z zones occupied by the high intensity features. Optionally, data dependent analysis can be performed on the high intensity features to identify and/or quantify the high abundance compounds, as indicated at <NUM>.

At <NUM>, a second portion of the sample is injected. At <NUM>, the second portion of the sample is chromatographically separated and mass data is obtained while excluding high density m/z regions. Without the high abundance ions, ion detection is set to a more sensitive setting without overwhelming the detector with high abundance ions.

In various embodiments, ions in the high density m/z regions can be excluded by an ion trap. For example, a notched isolation waveform can be applied that ejects the ions within the excluded regions while trapping ions that are not within the excluded regions. When trapping ions in an ion trap, charge density effects can limit the total number of ions within the trap. The high abundance ions can crowd out low abundance ions, such that the number of low abundance ions is too low to be detected. By excluding the high abundance ions, a larger number of ions from low abundance compounds can populate the trap enabling easier detection and analysis of the low abundance ions.

In various embodiments, ions in the high density m/z regions can be excluded using a quadrupole mass filter. For example, the high density m/z regions can be excluded by gating the ion beam during time periods corresponding to the exclusion regions. In another example, the quadrupole mass filter can perform multiple mass sub-ranges, such as a range below an excluded region and a range above the excluded region, so that the high abundance ions are not passed by the quadrupole mass filter.

At <NUM>, features of the second data set can be detected. With the high abundance ions excluded and more sensitive ion detection, the feature detection algorithm can detect ions at a lower abundance than in the prior data set. At <NUM>, the low abundance features can be added to an inclusion list. Optionally, data dependent analysis can be performed on intermediate abundance features to identify or quantify ions from the sample that were not in excluded regions and are not added to the inclusion list, as indicated at <NUM>.

At <NUM>, a third portion of the sample can be injected. At <NUM>, the third portion of the sample can be chromatographically separated and data independent analysis can be performed for ions on the inclusion list. For example, using selected reaction monitoring (SRM) of ions on the inclusion list, low abundance ions can be fragmentation and the mass-to-charge ratio of the low abundance ions can be determined. At <NUM>, the low abundance compounds can be identified or quantified based mass-to-charge ratio and intensity of the parent and/or fragment ions.

<FIG> is a block diagram that illustrates a computer system <NUM>, upon which embodiments of the present teachings may be implemented as which may incorporate or communicate with a system controller, for example controller <NUM> shown in <FIG>, such that the operation of components of the associated mass spectrometer may be adjusted in accordance with calculations or determinations made by computer system <NUM>. In various embodiments, computer system <NUM> can include a bus <NUM> or other communication mechanism for communicating information, and a processor <NUM> coupled with bus <NUM> for processing information. In various embodiments, computer system <NUM> can also include a memory <NUM>, which can be a random access memory (RAM) or other dynamic storage device, coupled to bus <NUM>, and instructions to be executed by processor <NUM>. Memory <NUM> also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor <NUM>. In various embodiments, computer system <NUM> can further include a read only memory (ROM) <NUM> or other static storage device coupled to bus <NUM> for storing static information and instructions for processor <NUM>. A storage device <NUM>, such as a magnetic disk or optical disk, can be provided and coupled to bus <NUM> for storing information and instructions.

In various embodiments, computer system <NUM> can be coupled via bus <NUM> to a display <NUM>, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device <NUM>, including alphanumeric and other keys, can be coupled to bus <NUM> for communicating information and command selections to processor <NUM>. Another type of user input device is a cursor control <NUM>, such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor <NUM> and for controlling cursor movement on display <NUM>. This input device typically has two degrees of freedom in two axes, a first axis (i.e., x) and a second axis (i.e., y), that allows the device to specify positions in a plane.

A computer system <NUM> can perform the present teachings. Consistent with certain implementations of the present teachings, results can be provided by computer system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in memory <NUM>. Such instructions can be read into memory <NUM> from another computer-readable medium, such as storage device <NUM>. Execution of the sequences of instructions contained in memory <NUM> can cause processor <NUM> to perform the processes described herein. In various embodiments, instructions in the memory can sequence the use of various combinations of logic gates available within the processor to perform the processes describe herein. Alternatively hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. In various embodiments, the hard-wired circuitry can include the necessary logic gates, operated in the necessary sequence to perform the processes described herein. Thus implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

The term "computer-readable medium" as used herein refers to any media that participates in providing instructions to processor <NUM> for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, optical or magnetic disks, such as storage device <NUM>. Examples of volatile media can include, but are not limited to, dynamic memory, such as memory <NUM>. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus <NUM>.

Common forms of non-transitory computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

In various embodiments, the methods of the present teachings may be implemented in a software program and applications written in conventional programming languages such as C, C++, etc..

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

Further, in describing various embodiments, the specification may have presented a method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims.

The embodiments described herein, can be practiced with other computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like. The embodiments can also be practiced in distributing computing environments where tasks are performed by remote processing devices that are linked through a network.

It should also be understood that the embodiments described herein can employ various computer-implemented operations involving data stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Further, the manipulations performed are often referred to in terms, such as producing, identifying, determining, or comparing.

Any of the operations that form part of the embodiments described herein are useful machine operations. The embodiments, described herein, also relate to a device or an apparatus for performing these operations. The systems and methods described herein can be specially constructed for the required purposes or it may be a general purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general purpose machines may be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.

Certain embodiments can also be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of the computer readable medium include hard drives, network attached storage (NAS), read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical data storage devices. The computer readable medium can also be distributed over a network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

<FIG> shows a mass chromatogram where all ions are transmitted arbitrarily. A high abundance peak <NUM> at <NUM> seconds retention time dominates the spectra and low abundance peaks in the region are not detected.

<FIG> shows a mass chromatogram after excluding the region including the high abundance peak <NUM>. Additional peaks of significantly lower intensity (about <NUM> orders of magnitude smaller than peak <NUM>) are detectable. Additionally, fragmentation of the peak at <NUM> yields a number of smaller m/z fragment ions, such as peaks <NUM> and <NUM>.

<FIG> shows a mass chromatogram where all ions are transmitted arbitrarily. A high abundance peak <NUM> at about <NUM> seconds retention time dominates the spectra and low abundance peaks in the region are not detected.

Claim 1:
A method for analyzing a sample, comprising:
performing a survey scan of a first portion of the sample at a first sensitivity of an ion detector (<NUM>) to identify retention time and mass-to-charge ranges corresponding to high abundance compounds within the sample;
generating a transmission matrix defining regions of retention time and m/z space for which ions should be transmitted which excludes the retention time and mass-to-charge ranges corresponding to high abundance compounds in the sample;
separating components of a second portion of the sample using a chromatographic column;
obtaining a first mass data set using a mass analyzer (<NUM>) at a second sensitivity of the ion detector (<NUM>) in accordance with the transmission matrix, wherein the high abundance compounds are excluded from the analysis to prevent overloading the detector (<NUM>), the second sensitivity being higher than the first sensitivity;
generating an inclusion list of features of the first mass data set, the inclusion list including low abundance compounds not detected in the survey scan;
fragmenting ions from a third portion of the sample corresponding to features of the inclusion list;
obtaining a second mass data set from the fragmented ions; and
identifying and/or quantifying the low abundance compounds based on the second mass data set.