Computer-implemented method for controlling a mass spectrometry analyzer system
Technical Field
The invention refers to a computer-implemented method for controlling a mass spectrometry analyzer system for analysis of an analyte of interest, a mass spectrometry analyzer system for analysis of an analyte of interest, a computer program, a computer-readable storage medium and a non-transient computer-readable medium. The proposed methods and devices can be used in the technical field of mass spectrometry, specifically for liquid chromatography-mass spectrometry.
Background art
The development of a liquid-chromatography and/or mass spectrometry method is a technique, which usually consists of several manual steps, which are often accomplished independently. For example, in a first step the mass-spectrometry device is tuned, with different sub-steps as definition of the parent mass of an analyte, identification of suitable mass-fractions of the analyte, and the optimization of different parameters of the mass-spectrometry analyzer, as eg. collision-energies or gas flow parameters. Such a mass-spectrometry tuning process is described, e.g. in “Liquid Chromatography-Mass Spectrometry Methods”, Clinical and Laboratory Standards Institute (CLSI), Approved Guideline C62-A, 2014CLSI EP62. However, most of the mass-spectrometer tuning steps described in said Guideline are conducted on a manual base, with one-at-a-time parameter optimization experiments and manual transactions between the different steps. This approach can lead to non-optimal settings, because interaction effects between tuning parameters are not detected, that the whole parameter space is not fully analyzed and dependencies to already tuned and existing analytes are not taken into account.
Problem to be solved It is therefore desirable to provide methods and devices, which address the above-mentioned shortcomings of known methods and devices. Specifically, an improved mass-spectrometer tuning shall be proposed, which, in particular, allows for a holistic approach.
Summary
This problem is addressed by a computer-implemented method for controlling a mass spectrometry analyzer system for analysis of an analyte of interest, a mass spectrometry analyzer system for analysis of an analyte of interest, a computer program, a computer-readable storage medium and a non-transient computer-readable medium with the features of the independent claims. Advantageous embodiments which might be realized in an isolated fashion or in any arbitrary combinations are listed in the dependent claims as well as throughout the specification.
As used in the following, the terms “have”, “comprise” or “include” or any arbitrary grammatical variations thereof are used in a non-exclusive way. Thus, these terms may both refer to a situation in which, besides the feature introduced by these terms, no further features are present in the entity described in this context and to a situation in which one or more further features are present. As an example, the expressions “A has B”, “A comprises B” and “A includes B” may both refer to a situation in which, besides B, no other element is present in A (i.e. a situation in which A solely and exclusively consists of B) and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D or even further elements.
Further, it shall be noted that the terms “at least one”, “one or more” or similar expressions indicating that a feature or element may be present once or more than once typically will be used only once when introducing the respective feature or element. In the following, in most cases, when referring to the respective feature or element, the expressions “at least one” or “one or more” will not be repeated, non-withstanding the fact that the respective feature or element may be present once or more than once.
Further, as used in the following, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms are used in conjunction with optional features, without restricting alternative possibilities. Thus, features introduced by these terms are optional features and are not intended to restrict the scope of the claims in any way. The invention may, as the skilled person will recognize, be performed by using alternative features. Similarly, features introduced by "in an embodiment of the invention" or similar expressions are intended to be optional features, without any restriction regarding alternative embodiments of the invention, without any restrictions regarding the scope of the invention and without any restriction regarding the possibility of combining the features introduced in such way with other optional or non-optional features of the invention.
In a first aspect, computer-implemented method for controlling a mass spectrometry analyzer system for analysis of an analyte of interest is disclosed. The mass spectrometry analyzer system comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit.
The method comprises the following steps, which, as an example, may be performed in the given order. It shall be noted, however, that a different order is also possible. Further, it is also possible to perform one or more of the method steps once or repeatedly. Further, it is possible to perform two or more of the method steps simultaneously or in a timely overlapping fashion. The method may comprise further method steps, which are not listed.
The method comprises automatically performing the following steps a) providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system, wherein the experimental plan comprises at least one initial parameter set considering initial knowledge of at least one knowledge database, wherein the initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit, wherein the experimental plan comprises scanning at least partially a parameter space of the control parameter; b) transferring the experimental plan into control instructions for said unit; c) executing the control instructions on said unit, thereby performing at least one measurement in accordance with the experimental plan and obtaining at least one measurement result; d) evaluating the measurement result obtained in step c), wherein the evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set; and e) storing and/or updating the optimized parameter set in the knowledge database.
The present invention can allow for providing a software workbench for fully automated tuning of the mass spectrometry analyzer system.
The proposed method can allow for fully automatized controlling, such as tuning and/or optimizing and/or adapting, of mass spectrometry analyzer system parameters. The method can allow for automatic method development for optimal operating the mass spectrometry analyzer system. This can result in significantly reduced development times. In addition, a more robust and higher quality can be achieved for the measurement of analytes, e.g. on an LC/MS analyzer. Dependencies from already tuned analytes can be incorporated.
The method can be used as a software tool, in particular as an automatic method development software tool, e.g. for supporting in vitro diagnostic (IVD) assay development for in vitro sample analysis of one or more analytes of interest to be analyzed such as on a fully automated Mass Spectrometry Analyzer. The proposed method can support fully automated mass spectrometry analyzer system parameter optimization such as incubation times, mixing times, washing steps, pipetting volumes, flow rates, temperatures, valve setting, HPLC or RapidLC column selection, vacuum, and mass spectrometry settings and reagent optimization and composition e.g. sample preparation reagents, provided for example in reagent containers, reagent bottles or reagent packs, comprising internal standards, sample preparation material (e.g. magnetic beads), HPLC and/or RapidLC column material, and system reagents e.g. water, acetonitrile, methanol, ammoniumfluoride, formic acid, ammonia and ammoniumacetate and the like.
The term “computer implemented method” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a method involving at least one computer and/or at least one computer network. The computer and/or computer network may comprise at least one processor which is configured for performing at least one of the method steps of the method according to the present invention. Preferably each of the method steps is performed by the computer and/or computer network. The method may be performed completely automatically, specifically without user interaction. The terms “automatically” and “automated” as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The terms specifically may refer, without limitation, to a process which is performed completely by means of at least one computer and/or computer network and/or machine, in particular without manual action and/or interaction with a user.
The term "system" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary set of interacting or interdependent components parts forming a whole. Specifically, the components may interact with each other in order to fulfill at least one common function. The at least two components may be handled independently or may be coupled or connectable.
The term “unit”, as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a constituent of the system such as a module of a modular system.
The term “mass spectrometry (MS)” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an analytical technique for determining a mass-to-charge ratio of ions. As used herein, the term “mass spectrometry analyzer system” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a system configured for mass spectrometry. The mass spectrometry analyzer system comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit.
As used herein, the term “sample” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary test sample such as a biological sample. The sample may be any composition of matter suspected or known to comprise at least one analyte of interest. For example, the sample may be selected from the group consisting of a physiological fluid, including blood, serum, plasma, saliva, ocular lens fluid, cerebral spinal fluid, sweat, urine, milk, ascites fluid, mucous, synovial fluid, peritoneal fluid, amniotic fluid, tissue, cells or the like. For further details with respect to the sample, reference is made e.g. to EP 3 425 369 Al or WO 2021/094409 Al, the full disclosure is included herewith by reference. Other samples are possible.
The term "analyte of interest", also denoted as “analyte”, as used herein, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to chemical species to be analysed such as any chemical compound or group of compounds which shall be determined in a sample. The terms "analyte", "analyte molecule", or "analyte(s) of interest" are used interchangeably. The analyte may be any kind of molecule present in a sample. In an embodiment, the analyte is a macromolecule, i.e. a compound with a molecular mass of more than 2500 u (i.e. more than 2.5 kDa). In a further embodiment, the analyte is a biological macromolecule, in particular a polypeptide, a polynucleotide, a polysaccharide, or a fragment of any of the aforesaid. In an embodiment, the analyte is a small molecule chemical compound, i.e. a compound with a molecular mass of at most 2500 u (2.5 kDa), in an embodiment at most 1.5 kDa, in a further embodiment at most 1 kDa. The analyte may be any chemical compound of interest; in an embodiment the analyte is a chemical compound metabolized by a body of a subject, is a compound administered to a subject in order to induce a change in the subject's metabolism, is a chemical compound of interest in technical process, e.g. an educt, an intermediate, or a product, is a chemical compound of interest in an environmental sample, or the like. In an embodiment, the analyte is a chemical compound metabolized by a body of a subject, in particular of a human subject, or is a compound administered to a subject in order to induce a change in the subject's metabolism. For example, analytes of interest may be vitamin D, drugs of abuse, therapeutic drugs, hormones, and metabolites in general. Other analytes of interest are possible.
The term "analysis" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of determining one or more of qualitatively, semi-quantitatively or a quantitatively the presence of the analyte of interest in the sample.
The term “sample preparation unit”, as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a unit configured for performing at least one sample preparation workflow. The sample preparation unit may be configured for an automated pre-treatment and/or preparation of samples, e.g. each comprising at least one analyte of interest. The method may comprise at least one sample preparation step comprising prepare solutions and/or samples with the analyte of interest. The term “sample preparation workflow”, as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to at least one process of preparing a sample for subsequent measurement. The workflow may comprise a single step or a plurality of subsequently and/or in parallel performed steps. The sample preparation workflow may comprise generating a mixture which can be used for subsequent measurement. The sample preparation workflow may comprise sample purification and/or sample dilution and/or sample concentration. The sample may be subjected to one or more pretreatment and/or a sample preparation step(s). The sample may be pretreated by physical and/or chemical methods, for example by centrifugation, filtration, mixing, homogenization, chromatography, purification precipitation, dilution, concentration, derivatization by suitable derivatization reagents, contacting with a binding and/or detection reagent, and/or any other method deemed appropriate by the skilled person.
The mass spectrometry analyzer system comprises at least one liquid chromatography (LC) unit. For example, in view of a workflow performed by the mass spectrometry analyzer system the LC unit may be downstream with respect to the sample preparation unit. The sample preparation unit and the LC unit may be coupled by at least one interface. For example, the LC unit may be a separate unit of the mass spectrometry analyzer system. As used herein, the term “liquid chromatography unit” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a unit configured to separate one or more analytes of interest of a sample from other components of the sample. The LC unit may comprise at least one LC column. For example, the LC unit may be a single-column LC unit or a multi-column LC unit having a plurality of LC columns. The LC column may have a stationary phase through which a mobile phase is pumped in order to separate and/or elute and/or transfer the analytes of interest. For example, the mass spectrometry analyzer system may be or may comprise at least one high performance liquid chromatography (HPLC) device or at least one micro liquid chromatography (pLC) device. For example, the LC unit may comprise, e.g. parallel and individual selectable, at least one HPLC column and/or at least one rapid- LC column.
The mass spectrometry analyzer system comprises at least one MS analyzer unit, also denoted as MS unit. The MS analyzer unit may comprise one or more of: at least one ionization source; at least one mass filter; at least one detector; at least one interface for coupling the LC unit and the MS analyzer unit.
As used herein, the term “ionization source”, also denoted as “ion source”, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a device configured for generating ions, e.g. from neutral gas molecules. The ionization source may be or may comprise at least one source selected from the group consisting of at least one gas phase ionization source such as at least one electron impact (El) source or at least one chemical ionization (CI) source; at least one desorption ionization source such as at least one plasma desorption (PDMS) source, at least one fast atom bombardment (FAB) source, at least one secondary ion mass spectrometry (SIMS) source, at least one laser desorption (LDMS) source, and at least one matrix assisted laser desorption (MALDI) source; at least one spray ionization source such as at least one thermospray (TSP) source, at least one atmospheric pressure chemical ionization (APCI) source, at least one electrospray (ESI), and at least one atmospheric pressure ionization (API) source. The interface configured for coupling the LC unit and the mass filter may comprise the ionization source.
As used herein, the term “mass filter” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a device configured for selecting ions injected to the mass filter according to their mass-to-charge ratio m/z. The mass filter may be or may comprise at least one quadrupole. The mass filter may comprise a plurality of quadrupoles. For example, the mass filter may be a triple quadrupole mass filter. The mass filter may comprise at least two pairs of electrodes. The electrodes may be rod-shaped, e.g. cylindrical. In ideal case, the electrodes may be hyperbolic. The electrodes may be designed identical. The electrodes may be arranged in parallel extending along a common axis, e.g. a z axis. The mass filter may comprise at least one power supply circuitry configured for applying at least one direct current (DC) voltage and at least one alternating current (AC) voltage between the two pairs of electrodes of the mass filter. The power supply circuitry may be configured for holding each opposing electrode pair at identical potential. The power supply circuitry may be configured for changing sign of charge of the electrode pairs periodically such that stable trajectories are only possible for ions within a certain mass-to-charge ratio m/z. Trajectories of ions within the mass filter can be described by the Mathieu differential equations. For measuring ions of different m/z values DC and AC voltage may be changed in time such that ions with different m/z values can be transmitted to the detector.
As used herein, the term “detector”, is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an apparatus configured for detecting incoming ions. The detector may be configured for detecting charged particles. The detector may be or may comprise at least one electron multiplier. The mass spectrometry device, e.g. the detector and/or at least one processing unit of the mass spectrometry device, may be configured to determine at least one mass spectrum of the detected ions. As used herein, the term “mass spectrum” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a two dimensional representation of signal intensity vs the charge-to-mass ratio m/z, wherein the signal intensity corresponds to abundance of the respective ion. The mass spectrum may be a pixelated image. For determining resulting intensities of pixels of the mass spectrum, signals detected with the detector within a certain m/z range may be integrated. The analyte in the sample may be identified by using at least one processing unit. The processing unit may be configured for correlating known masses to the identified masses or through a characteristic fragmentation pattern.
For example, the mass spectrometry analyzer system may be a LC/-MS analyzer, comprising a Liquid-Chromatography unit followed by an MS analyzer unit, e.g. a triple-quadrople MS/MS- analzer.
For example, the mass spectrometry analyzer system may be a fully automated mass spectrometry analyzer system, e.g. for in vitro sample analysis. The fully automated mass spectrometry analyzer system may comprise at least one sample preparation unit; a LC unit, wherein the sample preparation unit is connected to the LC unit; a MC unit, wherein the LC unit is connected to the MS unit; at least one processing unit configured for controlling the units of the mass spectrometry analyzer system; at least one database unit; at least one communication interface such as a serial interface, a USB, a wireless network, a cable network, an optical interface; at least one user interface such as a Graphical User Interface (GUI).
For example, the mass spectrometry analyzer system may be a Roche cobas® pro system comprising at least one cobas® i 601 analytical unit or a Roche cobas® i 601 analyzer.
The term “controlling mass spectrometry analyzer system” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process in which at least one parameter to be regulated for operating at least one unit of the mass spectrometry analyzer system is monitored, compared to at least one reference parameter and influenced in the sense of an adjustment to the reference parameter. The controlling may comprise one or more of optimizing, tuning, adapting the at least one parameter to be regulated. One or more of the monitoring, comparing and influencing may be performed directly on the parameter to be regulated and/or on at least one secondary parameter.
The term “control parameter” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a parameter to be regulated. The control parameter may be a parameter influencing the operation, e.g. the measurement for analysis of the analyte of interest, of a unit. The control parameter may be at least one parameter selected from the group consisting of an incubation time, a mixing time, a washing step, a pipetting volume, a flow rate, a temperature, valve setting, HPLC or RapidLC column selection, HPLC and/or RapidLC column material, vacuum setting, mass spectrometry settings, reagent composition e.g. sample preparation reagents, sample preparation material e.g. magnetic beads, system reagents e.g. water, acetonitrile, methanol, ammonium fluoride, formic acid, ammonia and ammonium acetate and the like. For example, the control parameter may be at least one parameter for operating the MS analyzer unit. The control parameter may be selected from the group consisting of a mass scan range; a product ion scan range; at least one collision energy; an ion path parameter such as a ion guide transfer parameter, a quadrupole prefilter parameter, collision energy; at least one ion source parameter such as electrospray high voltage parameter, a parameter defining counter plate voltage, a nebulizer gas parameter [1/min], an auxiliary gas parameter [1/min], a counter gas parameter [l/min]. Steps a) to e) may be performed repeatedly for a plurality of control parameters such as at least partially successively and/or at least partially in parallel. The term “at least one partially successively” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to embodiments in which the steps are performed completely successively, wherein embodiments are possible in which some steps overlap. The term “at least one partially in parallel” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to embodiments in which the steps are performed completely parallel, wherein embodiments are possible in which at least some steps are performed successively. Steps a) to e) may be performed repeatedly for at least one further unit of the mass spectrometry analyzer system such as at least partially successively and/or at least partially in parallel. For example, steps a) to e) may be performed for the MS analyzer unit. Subsequently, steps a) to e) may repeated for the LC unit and subsequently for the sample preparation unit. However, other orders are possible, e.g. starting with the LC unit or with the sample preparation unit and/or omitting one or more units, e.g. in case information about these units is already available. The experimental plan comprises at least one loop from said unit of the mass spectrometry analyzer system to another unit.
Step a) comprises providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system. The term “at least one experimental plan” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a layout for performing at least one measurement on the mass spectrometry analyzer system. The experimental plan may dependent on the at least one analyte of interest to be analyzed in vitro in a provided sample. The experimental plan comprises information about the measurement to be performed such as information about involved unit(s) of the mass spectrometry analyzer system and assay parameters, in particular control parameters.
The experimental plan may comprise information on a single unit of the mass spectrometry analyzer system or a plurality of units. The experimental plan may comprise at least one loop from one of the units of the mass spectrometry analyzer system to another unit. For example, step a) may comprise providing the experimental plan for the MS analyzer unit.
The experimental plan comprises scanning at least partially a parameter space of the control parameter. The experimental plan may comprise scanning the whole parameter space of the control parameter, e.g. the whole parameter spaces for of control parameters. The scanning of the whole parameter space may comprise continuously scanning and/or measuring at supporting points distributed over the parameter space, e.g. and interpolation.
The term “providing at least one experimental plan” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to a process of determining and/or generating and/or retrieving and/or making available the experimental plan. For example, the experimental plan may be generated by using at least on algorithms such as Full-Factorial designs, optimal designs, Space-filling designs. For example, the providing of the experimental plan in step a) comprises retrieving initial knowledge from the knowledge database via at least one communication interface, e.g. by downloading the experimental plan. For example, the providing of the experimental plan in step a) comprises selecting at least one predefined experimental plan from at least one database. For example, the providing of the experimental plan in step a) comprises adapting the selected experimental plan considering the initial knowledge and/or user input such as provided via at least one user interface. The providing of the experimental plan may comprise considering knowledge obtained by executing steps a) to e) before.
The providing of the experimental plan may be performed by the automatic method development software tool.
The providing of the experimental plan in step a) may be performed by using at least one planning unit of the mass spectrometry analyzer system. The planning unit may be configured for generating and/or retrieving the experimental plan. The planning unit may comprise comprises one or more of at least one processor, at least one communication interface, or at least one user interface.
The term "database" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an organized collection of data, generally stored and accessed electronically from a computer or computer system. The database may comprise or may be comprised by a data storage device. The database may comprise at least one database management system, comprising a software running on a computer or computer system, the software allowing for interaction with one or more of a user, an application or the database itself, such as in order to capture and analyze the data contained in the database. The database management system may further encompass facilities to administer the data base. The database, containing the data, may, thus, be comprised by a data base system which, besides the data, comprises one or more associated applications. The database may be at least partially cloud-based. The term “cloud-based” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an outsourcing of the database or of parts of the at least one database to at least partially interconnected external devices, specifically computers or computer networks having larger computing power and/or data storage volume. The external devices may be arbitrarily spatially distributed. The external devices may vary over time, specifically on demand. The external devices may be interconnected by using the internet. The external devices may each comprise at least one communication interface.
The term "knowledge database" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to database for storing knowledge. The knowledge may be stored in the knowledge database in machine readable form and/or the knowledge database may be configured for searching an providing the knowledge, e.g. from a library. The knowledge database may be an external database, e.g. may be at least partially cloud-based. For example, the knowledge database may comprise at least one scientific literature search engine, e.g. Pubmed, and/or at least one scientific search engine to find medical information, e.g. Pubchem, and/or at least one database storage for metabolite mass spectra and metadata such as MONA.
The term “communication interface” as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an item or element forming a boundary configured for transferring information. In particular, the communication interface may be configured for transferring information from a computational device, e.g. a computer, such as to send or output information, e.g. onto another device. Additionally or alternatively, the communication interface may be configured for transferring information onto a computational device, e.g. onto a computer, such as to receive information. The communication interface may specifically provide means for transferring or exchanging information. In particular, the communication interface may provide a data transfer connection, e.g. Bluetooth, NFC, inductive coupling or the like. As an example, the communication interface may be or may comprise at least one port comprising one or more of a network or internet port, a USB-port and a disk drive. The communication interface may comprise at least one web interface.
The term "user interface" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term may refer, without limitation, to an element or unit which is configured for interacting with its environment, such as for the purpose of unidirectionally or bidirectionally exchanging information, such as for exchange of one or more of data or commands. For example, the user interface may be configured to share information with a user and to receive information by the user. The user interface may be a feature to interact visually with a user, such as a display, or a feature to interact acoustically with the user. The user interface, as an example, may comprise one or more of a graphical user interface; a data interface, such as a wireless and/or a wire-bound data interface.
The term “processor”, or “processing unit” as generally used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to an arbitrary logic circuitry configured for performing basic operations of a computer or system, and/or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processing unit may be configured for processing basic instructions that drive the computer or system. As an example, the processor may comprise at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math co-pro- cessor or a numeric coprocessor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an LI and L2 cache memory. In particular, the processor may be a multi-core processor. Specifically, the processing unit may be or may comprise a central processing unit (CPU). Additionally or alternatively, the processor may be or may comprise a microprocessor, thus specifically the processing unit’s elements may be contained in one single integrated circuitry (IC) chip. Additionally or alternatively, the processing unit may be or may comprise one or more applicationspecific integrated circuits (ASICs) and/or one or more field-programmable gate arrays (FPGAs) or the like. The processing unit specifically may be configured, such as by software programming, for performing one or more evaluation operations.
The experimental plan comprises at least one initial parameter set considering initial knowledge of at least one knowledge database.
The term "initial knowledge" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term may refer, without limitation, to knowledge available before performing step a). Steps a) to e) may be performed repeatedly. The initial knowledge, in step a) of a repetition, may comprise knowledge obtained by a previous execution of method steps a) to e). The initial knowledge may comprise knowledge obtained by the knowledge database such as from a search engine or library, e.g. Pubmed, Pubchem, or MONA. For example, the initial knowledge may comprise information about molecular weight of the analyte, known from a literature search. The term "initial parameter set" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term may refer, without limitation, to a plurality of parameters defining the measurement on the mass spectrometry analyzer system. The initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit. For example, the initial parameter set may comprise a plurality of control parameters.
The experimental plan may takes into account interaction effects and/or non-linearity effects between the control parameters. For example, the experimental plan comprises at least one plan for measuring a plurality of combinations of the control parameters, wherein the parameter spaces of at least some of the control parameters are at least partially scanned, wherein the evaluating of the measurement results takes into account interaction effects and/or non-linearity effects between the control parameters by evaluating measurement results obtained for the plurality of combinations of the control parameters. The interaction effects and/or non-linearity effects between the control parameters may be taken into account by using at least one algorithm such as optimization based on linear models, Scheffe-Polynomials or generalized additive models (GAMs).
Step b) comprises transferring the experimental plan into control instructions for said unit. The transferring of the experimental plan into control instructions in step b) may comprise coding the experimental plan into at least one control file. The control instructions may be or may comprise system parameters for controlling the mass spectrometry analyzer system. The system parameters may be used to control one or more of the sample preparation unit, the LC unit, the MS analyzer unit and the one or more interfaces. System parameters may be combined to a system parameter set comprising all instructions performing at least one measurement for at least one analyte of interest to be analyzed. The control file may comprise the system parameters. The control file may be in the following format: XML, JSON, CSV, DIF or binary. Suitable data formats and files for control files are known to the skilled person. The transfer may be performed as follows. The experimental plan may be read as input and processed by a program and/or script which generates the control instruction as output. Suitable programming or script languages are known by the person skilled in the art. For example, suitable programming or script languages are selected from the group consisting of, but not limited to: R, Visual Basic, Java, Python, C, C++, and the like.
Step c) comprises executing the control instructions on said unit, thereby performing at least one measurement. The execution of the control instructions may comprise performing a plurality of measurements such as at least two measurements, at least five measurements or even more measurements. The measurement is performed in accordance with the experimental plan. Step c) further comprises obtaining at least one measurement result generated and/or obtained by the measurement. The executing of the control instructions may be performed by using at least one execution unit of the mass spectrometry analyzer system. The execution may be implemented via software and/or hardware. The execution may comprise executing at least one software which executes the control instructions on the at least one unit of the mass spectrometry analyzer system.
The control file may be generated by the automatic method development software tool. For executing the control file, the automatic method development software tool may transfer the control files to the respective unit, e.g. sample preparation unit, LC unit or MS analyzer unit or their respective control units. The transfer may be performed via at least one communication interface. Additionally or alternatively, the transfer may comprise using at least one software and/or (graphical) user interface configured for controlling the mass spectrometry analyzer system, e.g. by sending and controlling instrument parameters, selection and controlling of reagents, providing in vitro sample information and controlling the order in-formation for the in vitro sample analysis (scheduler).
The method may comprise providing samples, e.g. manually or automatically via using robotics. This step may be performed before step a).
Step c) further comprises storing the measurement result in at least one database. The measurement result may be stored together with meta-information from the execution of the experimental plan.
The measurement result may be transferred via at least one communication interface from the unit on which the measurement was performed to the database. Additionally or alternatively, the transfer may be performed by using at least one software, e.g. software with a (graphical) user interface and a database for storing one or more of raw data acquired on the respective unit, processed data, e.g. peak integration data or calibration data, analyte information and analyzer service and/or analyzer maintenance information. Additionally or alternatively, the automatic method development software tool may directly access the measurement result, e.g. the raw data, obtained by the unit on which the measurement was performed.
Step d) comprises evaluating the measurement result obtained in step c). The term "evaluating" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term may refer, without limitation, to a process for determining information on goodness of at least one control parameter of the initial parameter set from the measurement result, e.g. by performing at least one mathematical operation. The evaluating is performed by the automatic method development software tool. The measurement result may be obtained as raw data. The method further may comprise automated preprocessing of raw data. The evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set. The optimizing may comprise comparing the obtained measurement result to an expected measurement result and/or at least one parameter derived from the obtained measurement result with the corresponding initial parameter. Embodiments for exemplary evaluating steps will be given in the next paragraphs. The evaluating the measurement result may be performed by using at least one processing unit of the mass spectrometry analyzer system. The processing unit may comprise at least one communication interface for providing the optimized parameter set to the knowledge database. The optimizing may comprise replacing and/or adapting the initial parameter.
Step e) comprises storing and/or updating the optimized parameter set in the knowledge database. Step e) may comprise storing and/or updating intermediate analysis results in the knowledge database.
For example, the control parameter comprises a mass scan range. The initial knowledge may comprise information about one or more of molecular weight of the analyte, or single, double charge or custom charge information. The experimental plan may comprise measuring different analyte concentrations in electrospray positive and negative fullscan mode and at different LC compositions. The automated preprocessing of raw data may comprise one or more of summation or average all spectra in a specified chromatographic time range for each LC condition, MS polarity and analyte concentration and find all m/z peaks in the spectra. The evaluating the measurement result further may comprise one or more of annotating m/z peaks, calculating correlation of peak intensities against analyte concentration, annotating the peak with the highest intensity as 100% and normalizing all others, excluding peaks which are below a defined percentage, check in the knowledge database, whether m/z peaks are already saved as results from other analytes. The optimized parameter set may comprise one or more of the best Nl.l precursors selected based on one or more of the following criteria: positive correlation of intensity against concentration, known as precursor in literature, annotated as a known adduct.
For example, the control parameter comprises a product ion scan range and at least one collision energy, wherein the initial knowledge comprises information about one or more of an m/z value and polarity of the first best Nl. l precursor per LC condition and concentration obtained by a previous execution of method steps a) to e) for optimizing a mass scan range. The experimental plan may comprise measuring in product ion scan mode at the selected precursors, identified by a previous execution of method steps a) to e) for optimizing a mass scan range, in a defined scan range at different collision energies. The automated preprocessing of raw data may comprise summation or average all spectra in a specified chromatographic time range for each collision energy measurement. The evaluating the measurement result further may comprise one or more of averaging all individual collision energy spectra to one average spectra, getting the x most abundant peaks in this average spectra, and for each peak, plotting the individual intensity against collision energy. The optimized parameter set may comprise one or more of selected highest fragment peaks at the optimal collision energy. Optionally further knowledge from literature or database information from the knowledge database is used to validate the fragment peaks.
For example, the control parameter comprises at least one ion path parameter. The initial knowledge may comprises information about one or more of optimal transition values with precursor mz value and fragment mz value for defined LC conditions, a first approximation of Collision Energy, or a concentration. The experimental plan may take into account non-linear- ites and/or interaction effects, e.g. by using at least one algorithm. The automated preprocessing of raw data may comprise one or more of summation of total intensity over a defined retention time range. The evaluating the measurement result further may comprise one or more of finding the optimal settings for the ion path parameter, which maximize the total intensity, based on at least one generalized additive model, displaying graphs showing the optimality curves per parameter, and generating for goodness-of-fit evaluations actual-by-predicted plots of the normalizes area and residual plots. The optimized parameter set may comprise optimal settings for the ion path parameter, e.g. for the next repetition of method steps a) to e) such as for ion source parameter optimization.
For example, the control parameter comprises at least one ion source parameter. The initial knowledge may comprise information about one or more of one of the optimal transition values with pecursor mz value and fragment mz value for defined LC conditions, optimal ion path settings, a concentration. The experimental plan may takes into account non-linearites and/or interaction effects. The automated preprocessing of raw data may comprise summation of total ion intensity over the chromatogram and defined retention time range. The evaluating the measurement result further may comprise one or more of finding the optimal settings for the ion source parameter, which maximize the total ion current, based on generalized additive models. The optimized parameter set may comprise optimal settings for the ion path parameter, e.g. for the next repetition of method steps a) to e).
The automatic method development software tool can allows in an automated way one or more of creation of sophisticated experimental plans, which take interaction effects and nonlinearity effects into account and scan the whole parameter space, even with backloops from one part of the analyzer to the other; creation of analyzer-control files, including all measurement execution information for the analyzer; storing of measured data together with all meta-information from the experiment execution; sophisticated automated optimization analysis which takes interaction effects and nonlinearity effects into account as well as dependencies from already tuned analytes.
In a further aspect, a mass spectrometry analyzer system for analysis of an analyte of interest is disclosed.
The mass spectrometry analyzer system comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit. The mass spectrometry analyzer system comprises at least one planning unit configured for providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system, wherein the experimental plan comprises at least one initial parameter set considering initial knowledge, e.g. from a knowledge database, wherein the initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit, wherein the experimental plan comprises scanning at least partially a parameter space of the control parameter, wherein the planning unit is further configured for transferring the experimental plan into control instructions for said unit, at least one execution unit for executing the control instructions on said unit, thereby performing at least one measurement in accordance with the experimental plan and obtaining at least one measurement result; at least one processing unit configured for evaluating the measurement result, wherein the evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set, at least one communication interface configured for storing and/or updating the optimized parameter set in the knowledge database.
One or more of the planning unit, the execution unit or the communication interface may be designed as software.
The mass spectrometry analyzer system may be configured for performing the method according to the present invention, such as according to any one of the embodiments described above and/or according to any one of the embodiments described in further detail below. Thus, for most of the terms, definitions and options of the system, reference may be made to the method as described above or as described in further detail below.
Further disclosed and proposed herein is a computer program including computer-executable instructions for performing the method according to the present invention in one or more of the embodiments enclosed herein when the instructions are executed on a computer or computer network. Specifically, the computer program may be stored on a computer-readable data carrier and/or on a computer-readable storage medium.
As used herein, the terms “computer-readable data carrier” and “computer-readable storage medium” specifically may refer to non-transitory data storage means, such as a hardware storage medium having stored thereon computer-executable instructions. The computer-readable data carrier or storage medium specifically may be or may comprise a storage medium such as a random-access memory (RAM) and/or a read-only memory (ROM).
Thus, specifically, one, more than one or even all of method steps a) to e) as indicated above may be performed by using a computer or a computer network, preferably by using a computer program.
Further disclosed and proposed herein is a computer program product having program code means, in order to perform the method according to the present invention in one or more of the embodiments enclosed herein when the program is executed on a computer or computer network. Specifically, the program code means may be stored on a computer-readable data carrier and/or on a computer-readable storage medium.
Further disclosed and proposed herein is a data carrier having a data structure stored thereon, which, after loading into a computer or computer network, such as into a working memory or main memory of the computer or computer network, may execute the method according to one or more of the embodiments disclosed herein.
Further disclosed and proposed herein is a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to the present invention.
Further disclosed and proposed herein is a computer program product with program code means stored on a machine-readable carrier, in order to perform the method according to one or more of the embodiments disclosed herein, when the program is executed on a computer or computer network. As used herein, a computer program product refers to the program as a tradable product. The product may generally exist in an arbitrary format, such as in a paper format, or on a computer-readable data carrier and/or on a computer-readable storage medium. Specifically, the computer program product may be distributed over a data network.
Finally, disclosed and proposed herein is a modulated data signal which contains instructions readable by a computer system or computer network, for performing the method according to one or more of the embodiments disclosed herein.
Referring to the computer-implemented aspects of the invention, one or more of the method steps or even all of the method steps of the method according to one or more of the embodiments disclosed herein may be performed by using a computer or computer network. Thus, generally, any of the method steps including provision and/or manipulation of data may be performed by using a computer or computer network. Generally, these method steps may include any of the method steps, typically except for method steps requiring manual work, such as providing the samples and/or certain aspects of performing the actual measurements.
Specifically, further disclosed herein are:
- a computer or computer network comprising at least one processor, wherein the processor is adapted to perform the method according to one of the embodiments described in this description,
- a computer loadable data structure that is adapted to perform the method according to one of the embodiments described in this description while the data structure is being executed on a computer,
- a computer program, wherein the computer program is adapted to perform the method according to one of the embodiments described in this description while the program is being executed on a computer,
- a computer program comprising program means for performing the method according to one of the embodiments described in this description while the computer program is being executed on a computer or on a computer network,
- a computer program comprising program means according to the preceding embodiment, wherein the program means are stored on a storage medium readable to a computer,
- a storage medium, wherein a data structure is stored on the storage medium and wherein the data structure is adapted to perform the method according to one of the embodiments described in this description after having been loaded into a main and/or working storage of a computer or of a computer network, and
- a computer program product having program code means, wherein the program code means can be stored or are stored on a storage medium, for performing the method according to one of the embodiments described in this description, if the program code means are executed on a computer or on a computer network. Summarizing and without excluding further possible embodiments, the following embodiments may be envisaged:
Embodiment 1. Computer-implemented method for controlling a mass spectrometry analyzer system for analysis of an analyte of interest, wherein the mass spectrometry analyzer system comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit, wherein the method comprises automatically performing the following steps a) providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system, wherein the experimental plan comprises at least one initial parameter set considering initial knowledge of at least one knowledge database, wherein the initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit, wherein the experimental plan comprises scanning at least partially a parameter space of the control parameter; b) transferring the experimental plan into control instructions for said unit; c) executing the control instructions on said unit, thereby performing at least one measurement in accordance with the experimental plan and obtaining at least one measurement result; d) evaluating the measurement result obtained in step c), wherein the evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set; and e) storing and/or updating the optimized parameter set in the knowledge database.
Embodiment 2. The method according to the preceding embodiment, wherein step a) comprises providing the experimental plan for the MS analyzer unit, wherein the control parameter comprises at least one parameter selected from the group consisting of a mass scan range; a product ion scan range; at least one collision energy; an ion path parameter such as a ion guide transfer parameter, a quadrupole prefilter parameter, collision energy; at least one ion source parameter such as electrospray high voltage parameter, a parameter defining counter plate voltage, a nebulizer gas parameter, an auxiliary gas parameter, a counter gas parameter.
Embodiment 3. The method according to any one of the preceding embodiments, wherein step e) comprises storing and/or updating intermediate analysis results in the knowledge database. Embodiment 4. The method according to any one of the preceding embodiments, wherein steps a) to e) are performed repeatedly for a plurality of control parameters such as at least partially successively and/or at least partially in parallel.
Embodiment 5. The method according to any one of the preceding embodiments, wherein steps a) to e) are performed repeatedly for at least one further unit of the mass spectrometry analyzer system such as at least partially successively and/or at least partially in parallel.
Embodiment 6. The method according to the preceding embodiment, wherein the experimental plan comprises at least one loop from said unit of the mass spectrometry analyzer system to another unit.
Embodiment 7. The method according to any one of the three preceding embodiments, wherein the initial knowledge, in step a) of a repetition, comprises knowledge obtained by a previous execution of method steps a) to e).
Embodiment 8. The method according to any one of the preceding embodiments, wherein the initial parameter set comprises a plurality of control parameters, wherein the experimental plan takes into account interaction effects and/or non-linearity effects between the control parameters.
Embodiment 9. The method according to the preceding embodiment, wherein the experimental plan comprises at least one plan for measuring a plurality of combinations of the control parameters, wherein the parameter spaces of at least some of the control parameters are at least partially scanned, wherein the evaluating of the measurement results takes into account interaction effects and/or non-linearity effects between the control parameters by evaluating measurement results obtained for the plurality of combinations of the control parameters.
Embodiment 10. The method according to any one the preceding embodiments, wherein the experimental plan comprises scanning the whole parameter space of the control parameter.
Embodiment 11. The method according to any one the preceding embodiments, wherein the providing of the experimental plan in step a) comprises retrieving initial knowledge from the knowledge database via at least one communication interface. Embodiment 12. The method according to any one the preceding embodiments, wherein the providing of the experimental plan in step a) comprises selecting at least one predefined experimental plan from at least one database.
Embodiment 13. The method according to the preceding embodiment, wherein the providing of the experimental plan in step a) comprises adapting the selected experimental plan considering the initial knowledge and/or user input such as provided via at least one user interface.
Embodiment 14. The method according to any one the preceding embodiments, wherein the providing of the experimental plan in step a) is performed by using at least one planning unit of the mass spectrometry analyzer system, wherein the planning unit comprises one or more of at least one processor, at least one communication interface, or at least one user interface.
Embodiment 15. The method according to any one the preceding embodiments, wherein the transferring of the experimental plan into control instructions in step b) comprises coding the experimental plan into at least one control file.
Embodiment 16. The method according to any one the preceding embodiments, wherein executing the control instructions is performed by using at least one execution unit of the mass spectrometry analyzer system.
Embodiment 17. The method according to any one the preceding embodiments, wherein step c) comprises storing the measurement result in at least one database.
Embodiment 18. The method according to the preceding embodiment, wherein the measurement result is stored together with meta-information from the execution of the experimental plan.
Embodiment 19. The method according to any one the preceding embodiments, wherein the measurement result is obtained as raw data, wherein the method further comprises automated preprocessing of raw data.
Embodiment 20. The method according to any one the preceding embodiments, wherein the evaluating the measurement result is performed by using at least one processing unit of the mass spectrometry analyzer system, wherein the processing unit comprises at least one communication interface for providing the optimized parameter set to the knowledge database. Embodiment 21. The method according to any one of the preceding embodiments, wherein the control parameter comprises a mass scan range, wherein the initial knowledge comprises information about one or more of molecular weight of the analyte, or single, double charge or custom charge information, wherein the experimental plan comprises measuring different analyte concentrations in electrospray positive and negative fullscan mode and at different LC compositions, wherein the automated preprocessing of raw data comprises one or more of summation or average all spectra in a specified chromatographic time range for each LC condition, MS polarity and analyte concentration and find all m/z peaks in the spectra, wherein the evaluating the measurement result further comprises one or more of annotating m/z peaks, calculating correlation of peak intensities against analyte concentration, annotating the peak with the highest intensity as 100% and normalizing all others, excluding peaks which are below a defined percentage, check in the knowledge database, whether m/z peaks are already saved as results from other analytes, wherein the optimized parameter set comprises one or more of the best Nl.l precursors selected based on one or more of the following criteria: positive correlation of intensity against concentration, known as precursor in literature, annotated as a known adduct.
Embodiment 22. The method according to any one of the preceding embodiments, wherein the control parameter comprises a product ion scan range and at least one collision energy, wherein the initial knowledge comprises information about one or more of an m/z value and polarity of the first best Nl. l precursor per LC condition and concentration obtained by a previous execution of method steps a) to e) for optimizing a mass scan range, wherein the experimental plan comprises measuring in product ion scan mode at the selected precursors, identified by a previous execution of method steps a) to e) for optimizing a mass scan range, in a defined scan range at different collision energies, wherein the automated preprocessing of raw data comprises summation or average all spectra in a specified chromatographic time range for each collision energy measurement, wherein the evaluating the measurement result further comprises one or more of averaging all individual collision energy spectra to one average spectra, getting the x most abundant peaks in this average spectra, and for each peak, plotting the individual intensity against collision energy, wherein the optimized parameter set comprises one or more of selected highest fragment peaks at the optimal collision energy, wherein optionally further knowledge from literature or database information from the knowledge database is used to validate the fragment peaks.
Embodiment 23. The method according to any one of the preceding embodiments, wherein the control parameter comprises at least one ion path parameter, wherein the initial knowledge comprises information about one or more of optimal transition values with precursor mz value and fragment mz value for defined LC conditions, a first approximation of Collision Energy, or a concentration, wherein the experimental plan takes into account non-linearites and/or interaction effects, wherein the automated preprocessing of raw data comprises one or more of summation of total intensity over a defined retention time range, wherein the evaluating the measurement result further comprises one or more of finding the optimal settings for the ion path parameter, which maximize the total intensity, based on at least one generalized additive model, displaying graphs showing the optimality curves per parameter, and generating for goodness-of-fit evaluations actual- by-predicted plots of the normalizes area and residual plots, wherein the optimized parameter set comprises optimal settings for the ion path parameter, e.g. for the next repetition of method steps a) to e) such as for ion source parameter optimization.
Embodiment 24. The method according to any one of the preceding embodiments, wherein the control parameter comprises at least one ion source parameter, wherein the initial knowledge comprises information about one or more of one of the optimal transition values with pecursor mz value and fragment mz value for defined LC conditions, optimal ion path settings, a concentration, wherein the experimental plan takes into account non- linearites and/or interaction effects, wherein the automated preprocessing of raw data comprises summation of total ion intensity over the chromatogram and defined retention time range, wherein the evaluating the measurement result further comprises one or more of finding the optimal settings for the ion source parameter, which maximize the total ion current, based on generalized additive models, wherein the optimized parameter set comprises optimal settings for the ion path parameter, e.g. for the next repetition of method steps a) to e).
Embodiment 25. The method according to any one of the preceding embodiments, wherein the method comprises at least one sample preparation step comprising prepare solutions and/or samples with the analyte of interest.
Embodiment 26. A mass spectrometry analyzer system for analysis of an analyte of interest, wherein the mass spectrometry analyzer system comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit, wherein the mass spectrometry analyzer system comprises
- at least one planning unit configured for providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system, wherein the experimental plan comprises at least one initial parameter set considering initial knowledge, wherein the initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit, wherein the experimental plan comprises scanning at least partially a parameter space of the control parameter, wherein the planning unit is further configured for transferring the experimental plan into control instructions for said unit,
- at least one execution unit for executing the control instructions on said unit, thereby performing at least one measurement in accordance with the experimental plan and obtaining at least one measurement result;
- at least one processing unit configured for evaluating the measurement result, wherein the evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set,
- at least one communication interface configured for storing and/or updating the optimized parameter set in the knowledge database.
Embodiment 27. The mass spectrometry analyzer system according to the preceding embodiment, wherein the mass spectrometry analyzer system is configured for performing the method according to any one of the preceding embodiments referring to a method.
Embodiment 28. The mass spectrometry analyzer system according to any one of the preceding embodiments referring to a mass spectrometry analyzer system, wherein the knowledge database is at least partially cloud based.
Embodiment 29. The mass spectrometry analyzer system according to any one of the preceding embodiments referring to a mass spectrometry analyzer system, wherein the mass spectrometry analyzer system further comprises at least one user interface.
Embodiment 30. A computer program comprising instructions which, when the program is executed by the mass spectrometry analyzer system according to any one of the preceding embodiments referring to a mass spectrometry analyzer system, cause the mass spectrometry analyzer system to perform the method according to any one of the preceding embodiments referring to a method.
Embodiment 31. A computer-readable storage medium comprising instructions which, when the instructions are executed by the mass spectrometry analyzer system according to any one of the preceding embodiments referring to a mass spectrometry analyzer system, cause the mass spectrometry analyzer system to perform the method according to any one of the preceding embodiments referring to a method. Embodiment 32. A non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of the preceding embodiments referring to a method.
Short description of the Figures
Further optional features and embodiments will be disclosed in more detail in the subsequent description of embodiments, preferably in conjunction with the dependent claims. Therein, the respective optional features may be realized in an isolated fashion as well as in any arbitrary feasible combination, as the skilled person will realize. The scope of the invention is not restricted by the preferred embodiments. The embodiments are schematically depicted in the Figures. Therein, identical reference numbers in these Figures refer to identical or functionally comparable elements.
In the Figures:
Figure 1 shows an embodiment of a method and mass spectrometry analyzer system according to the present invention;
Figure 2 shows the average of 5 product ion spectra of the precursor ion 286.2 Da in the m/z range 50-306.2 Da and retention time range 3-15 s, each spectra with increasing Collision Energy. The 20 highest product ion peaks are annotated,
Figure 3 shows individual intensity plots of the 20 highest product ion masses against collision energy,
Figure 4 shows the plot of predicted normalized intensity on ion guide transfer 1 (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted normalized intensity for ion guide transfer 1,
Figure 5 shows the plot of predicted normalized intensity on Q3Pre (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted normalized intensity for Q3Pre,
Figure 6 shows the plot of predicted normalized intensity on collision energy (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted normalized intensity for collision energy,
Figure 7 shows the plot of predicted normalized intensity on QIPre (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted normalized intensity for QIPre,
Figure 8 shows the plot of observed normalized intensity on predicted normalized intensity with line of perfect prediction superimposed,
Figure 9 shows the plot of scaled Pearson residuals on predicted normalized intensity with reference line of 0 (solid line) and tolerable residual limits of +/- 3 superimposed (dashed lines),
Figure 10 shows the plot of predicted logarithmic intensity on ESIHV (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted logarithmic intensity for ESIHV,
Figure 11 shows the plot of predicted logarithmic intensity on counter plate (solid line) with two standard errors around the predicted line superimposed (dashed lines). The vertical line shows the maximum predicted logarithmic intensity for counter plate,
Figure 12 shows the contour plot of predicted logarithmic intensity on AUX gas and nebulizer gas. The intersection of the horizontal solid line with the vertical solid line shows the maximum predicted normalized intensity for AUX gas (horizontal line) and for nebulizer gas (vertical line),
Figure 13 shows the plot of observed logarithmic intensity on predicted logarithmic intensity with line of perfect prediction superimposed, and
Figure 14 shows the plot of scaled Pearson residuals on predicted logarithmic intensity with reference line of 0 (solid line) and tolerable residual limits of +/- 3 superimposed (dashed lines).
Detailed description of the embodiments Figure 1 shows an embodiment of a method and mass spectrometry analyzer system 110 according to the present invention. The mass spectrometry analyzer system 110 comprises at least one sample preparation unit, at least one liquid chromatography (LC) unit and at least one mass spectrometer (MS) analyzer unit.
As shown in Figure 1, the mass spectrometry analyzer system 110 comprises at least three layers, e.g. the automatic method development software tool 112, a software layer 114, an instrument layer 116. In Figure 1 a fourth layer is depicted indicating a layer of external databases 117.
In the embodiment of Figure 1, the automatic method development software tool 112 may comprise the following steps a) providing at least one experimental plan for at least one unit of the mass spectrometry analyzer system, wherein the experimental plan comprises at least one initial parameter set considering initial knowledge of at least one knowledge database, wherein the initial parameter set comprises at least one control parameter used for performing at least one measurement for analysis of the analyte of interest on said unit, wherein the experimental plan comprises scanning at least partially a parameter space of the control parameter; b) transferring the experimental plan into control instructions for said unit; c) executing the control instructions on said unit, thereby performing at least one measurement in accordance with the experimental plan and obtaining at least one measurement result; d) evaluating the measurement result obtained in step c), wherein the evaluating comprises optimizing the initial parameter set in view of the measurement result, thereby determining an optimized parameter set; and e) storing and/or updating the optimized parameter set in the knowledge database.
Moreover, e.g. before executing the named steps, the method may comprise at least one step of preparing solutions/samples with the analyte of interest. These samples can have different concentrations, spanning the desired measurement range of the analyte of interest, e.g at LoQ, at medical decision points, in the upper range of the measurement range. The method may comprise placing aliquots of the solutions on the analyzer, according to the desired order, e.g. which is defined by the automatic method development software tool 112. This can be the only manual step, which can be conducted by lab personnel or may be performed automatically by using robotics.
Steps a) to e) may be performed with a pre-defined order for one or more units of the mass spectrometry analyzer system 110. The method may comprise controlling, e.g. tuning and/or optimizing, the control parameters of a first unit and subsequently controlling the respective parameters of the further units. For example, steps a) to e) may be performed firstly for the MS analyzer unit. Subsequently, steps a) to e) may repeated for the LC unit and subsequently for the sample preparation unit. However, other orders are possible. The automatic method development software tool 112 may proceed in an automated way through the controlling of the different MS parameters. For each unit, similar sub-steps may be performed, defining the interplay between the experimental plan, the execution of the plan on the analyzer with the measurements, the optimization analysis and storing of the results.
In the following, exemplarily, controlling of control parameters of the MS analyzer unit will be described. This procedure may comprise four 4 different subsequent steps:
Step 1.1 : Identification of the precursor ion
In this step the precursor ion may be determined, based on positive and negative fiillscan spectral data;
Step 1.2: Identification of product ions in quadrupole Q3
In this step, a precursor ion (from Step 1.1) is fragmented using collision induced dissociation with a neutral gas to generate product ions.
The product ions are determined, based on positive and negative product ion full scan spectral data with different collision energies;
A product ion is a fragment of a precursor ion that is produced by collision-induced dissociation (CID) in a triple quadrupole mass spectrometer (TQMS). A TQMS consists of two mass filters (QI and Q3) and a collision cell (q2) in series. The precursor ion is selected by QI and then accelerated into q2, where it collides with a neutral gas and breaks into smaller ions. One of these fragments is then selected by Q3 and detected. This process is called multiple reaction monitoring (MRM) and it allows for high sensitivity and specificity in mass analysis, (see Review article: Raffaelli, A., Saba, A. 2021. Ion scanning or ion trapping: Why not both? Mass Spectrometry Reviews 42, 10.1002/mas.21746. 1).
Step 1.3 : Optimization of ion path parameters
The ion path parameters, as e.g. ion guide transfer (IgTl), quadrupole prefilter QI, collision energy, quadrupole prefilter Q3, may be optimized, based on measurements in a space-filling design, wherein a lot of combinations of these parameters may be measured and interaction and non-linearity effects may be taken into account;
Step 1.4: Optimization of Ion Source and Gas parameters
The ion source parameters are optimized, as e.g. electrospray high voltage (ESI HV [V]), counterplate voltage [V], nebulizer gas [1/min], auxiliary gas (Aux Gas [1/min]), counter gas [1/min], are optimized based on measurements in a space-filling design, wherein a lot of combinations of these parameters are measured and interactions and non-linearity effects are taken into account. For example, the identification of the precursor ion in step 1.1 may be embodied as follows. Step a) may comprise generating of the experimental plan, according to the control parameters under consideration and knowledge, e.g. on optimized parameters from steps performed before and/or obtained from a further data source such as from a search engine or library, e.g. Pubmed, Pubchem 118, or MONA 120. The input knowledge for the automatic method development software tool 112 is indicated with dashed arrows in Figure 1.
The following table depicts exemplary knowledge, the sample type, parameters fixed to default values, variable control parameters and the type of the experimental plan:
The following table shows knowledge from steps performed before, the sample type, parameters fixed to default values, variable control parameters and the type of the experimental plan for Morphine:
Step b) comprises transferring the experimental plan into control instructions for the MS analyzer unit. For example, an analyzer-control file with all measurement execution information for the MS analyzer unit may be created. For example, the control file may be an XML file.
The control file may be generated by the automatic method development software tool 112. For executing the control file, the automatic method development software tool 112 may transfer the control files to the MS analyzer unit or its respective control units. The transfer may comprise using at least one software and/or (graphical) user interface 124 configured for controlling the mass spectrometry analyzer system 110, e.g. by sending and controlling instrument parameters, selection and controlling of reagents, providing in vitro sample information and controlling the order in-formation for the in vitro sample analysis (scheduler). The software and/or (graphical) user interface 124 may be an element of the software layer 114. The output from the automatic method development software tool 112 is indicated with dotted arrows in Figure 1. The software and/or (graphical) user interface 124 may provide the control file to the instrument layer 116.
The MS analyzer unit can execute, in step c), the control for performing the one or more measurements 122 defined by the experimental plan. The measurement result may be stored in the database of the MS analyzer unit and/or at least one further database, e.g. of the automatic method development software tool 112.
Next, in steps d) and e) automated preprocessing of raw data, optimization analysis and storing the optimized results in the knowledge database may be performed. The following table depicts an example of automated preprocessing of raw data, optimization analysis and storing the optimized results:
The following table depicts this exemplary for Morphine:
The measurement result may be transferred by using at least one software 126, e.g. software with a (graphical) user interface and a database for storing one or more of raw data acquired on the respective unit, processed data, e.g. peak integration data or calibration data, analyte information and analyzer service and/or analyzer maintenance information. The software 126 may provide the measurement results to a software 127 comprising, for mass spectrometry raw data reduction, a collection of algorithms for peak detection and peak integration of chromatograms or other time- vector data. Additionally or alternatively, the automatic method development software tool 112 may directly access the measurement result, e.g. the raw data, obtained by the unit on which the measurement was performed.
For example, the identification of product ions in Q3 in step 1.2 may be embodied as follows. In step a) the experimental plan may be generated, according to the control parameters under consideration and knowledge, e.g. on optimized parameters from steps performed before and/or obtained from a further data source such as from a search engine or library. The input knowledge for the automatic method development software tool 112 is indicated with dashed arrows in Figure 1. The following table depicts exemplary knowledge (from performing steps a) to e) for identification of the precursor ion in step 1.1), the sample type, parameters fixed to default values, variable control parameters and the type of the experimental plan:
The following table shows this exemplarily for Morphine:
In step b) the experimental plan may be transferred into control instructions for the MS analyzer unit. For example, an analyzer-control file with all measurement execution information for the MS analyzer unit may be created. For example, the control file may be an XML file.
The control file may be generated by the automatic method development software tool 112. For executing the control file, the automatic method development software tool 112 may transfer the control files to the MS analyzer unit or its respective control units. The transfer may comprise using at least one software and/or (graphical) user interface 124 configured for controlling the mass spectrometry analyzer system 110, e.g. by sending and controlling instrument parameters, selection and controlling of reagents, providing in vitro sample information and controlling the order in-formation for the in vitro sample analysis (scheduler). The software and/or (graphical) user interface 124 may be an element of the software layer 114. The output from the automatic method development software tool 112 is indicated with dotted arrows in Figure 1. The software and/or (graphical) user interface 124 may provide the control file to the instrument layer 116.
The MS analyzer unit can execute, in step c), the control for performing the one or more measurements 122 defined by the experimental plan. The measurement result may be stored in the database of the MS analyzer unit and/or at least one further database, e.g. of the automatic method development software tool 112.
Next, in steps d) and e) automated preprocessing of raw data, optimization analysis and storing the optimized results in the knowledge database may be performed. The following table depicts an example of automated preprocessing of raw data, optimization analysis and storing the optimized results:
The following table depicts this exemplary for Morphine: The measurement result may be transferred by using at least one software 126, e.g. software with a (graphical) user interface and a database for storing one or more of raw data acquired on the respective unit, processed data, e.g. peak integration data or calibration data, analyte information and analyzer service and/or analyzer maintenance information. The software 126 may provide the measurement results to a software 127 comprising, for mass spectrometry raw data reduction, a collection of algorithms for peak detection and peak integration of chromatograms or other time- vector data. Additionally or alternatively, the automatic method development software tool 112 may directly access the measurement result, e.g. the raw data, obtained by the unit on which the measurement was performed.
For example, the optimization of ion path parameters in step 1.3 may be embodied as follows. In step a) the experimental plan may be generated, according to the control parameters under consideration and knowledge, e.g. on optimized parameters from steps performed before and/or obtained from a further data source such as from a search engine or library. The input knowledge for the automatic method development software tool 112 is indicated with dashed arrows in Figure 1. The following table depicts exemplary knowledge (from performing steps a) to e) in steps 1.1 and 1.2), the sample type, parameters fixed to default values, variable control parameters and the type of the experimental plan:
The following table shows this exemplarily for Morphine:
In step b) the experimental plan may be transferred into control instructions for the MS analyzer unit. For example, an analyzer-control file with all measurement execution information for the MS analyzer unit may be created. For example, the control file may be an XML file.
The control file may be generated by the automatic method development software tool 112. For executing the control file, the automatic method development software tool 112 may transfer the control files to the MS analyzer unit or its respective control units. The transfer may comprise using at least one software and/or (graphical) user interface 124 configured for controlling the mass spectrometry analyzer system 110, e.g. by sending and controlling instrument parameters, selection and controlling of reagents, providing in vitro sample information and controlling the order in-formation for the in vitro sample analysis (scheduler). The software and/or (graphical) user interface 124 may be an element of the software layer 114. The output from the automatic method development software tool 112 is indicated with dotted arrows in Figure 1. The software and/or (graphical) user interface 124 may provide the control file to the instrument layer 116.
The MS analyzer unit can execute, in step c), the control for performing the one or more measurements 122 defined by the experimental plan. The measurement result may be stored in the database of the MS analyzer unit and/or at least one further database, e.g. of the automatic method development software tool 112.
Next, in steps d) and e) automated preprocessing of raw data, optimization analysis and storing the optimized results in the knowledge database may be performed. The following table depicts an example of automated preprocessing of raw data, optimization analysis and storing the optimized results:
The following table shows this exemplarily for Morphine:
The measurement result may be transferred by using at least one software 126, e.g. software with a (graphical) user interface and a database for storing one or more of raw data acquired on the respective unit, processed data, e.g. peak integration data or calibration data, analyte information and analyzer service and/or analyzer maintenance information. The software 126 may provide the measurement results to a software 127 comprising, for mass spectrometry raw data reduction, a collection of algorithms for peak detection and peak integration of chromatograms or other time- vector data. Additionally or alternatively, the automatic method development software tool 112 may directly access the measurement result, e.g. the raw data, obtained by the unit on which the measurement was performed. For example, the optimization of ion source and gas parameters in step 1.4 may be embodied as follows. In step a) the experimental plan may be generated, according to the control parameters under consideration and knowledge, e.g. on optimized parameters from steps performed before and/or obtained from a further data source such as from a search engine or library. The input knowledge for the automatic method development software tool 112 is indicated with dashed arrows in Figure 1. The following table depicts exemplary knowledge (from performing steps a) to e) in steps 1.1, 1.2 and 1.3), the sample type, parameters fixed to default values, variable control parameters and the type of the experimental plan:
The following table shows this exemplarily for Morphine:
In step b) the experimental plan may be transferred into control instructions for the MS analyzer unit. For example, an analyzer-control file with all measurement execution information for the MS analyzer unit may be created. For example, the control file may be an XML file.
The control file may be generated by the automatic method development software tool 112. For executing the control file, the automatic method development software tool 112 may transfer the control files to the MS analyzer unit or its respective control units. The transfer may comprise using at least one software and/or (graphical) user interface 124 configured for controlling the mass spectrometry analyzer system 110, e.g. by sending and controlling instrument parameters, selection and controlling of reagents, providing in vitro sample information and controlling the order in-formation for the in vitro sample analysis (scheduler). The software and/or (graphical) user interface 124 may be an element of the software layer 114. The output from the automatic method development software tool 112 is indicated with dotted arrows in Figure 1. The software and/or (graphical) user interface 124 may provide the control file to the instrument layer 116.
The MS analyzer unit can execute, in step c), the control for performing the one or more measurements 122 defined by the experimental plan. The measurement result may be stored in the database of the MS analyzer unit and/or at least one further database, e.g. of the automatic method development software tool 112.
Next, in steps d) and e) automated preprocessing of raw data, optimization analysis and storing the optimized results in the knowledge database may be performed. The following table depicts an example of automated preprocessing of raw data, optimization analysis and storing the optimized results:
The following table shows this exemplarily for Morphine:
The measurement result may be transferred by using at least one software 126, e.g. software with a (graphical) user interface and a database for storing one or more of raw data acquired on the respective unit, processed data, e.g. peak integration data or calibration data, analyte information and analyzer service and/or analyzer maintenance information. The software 126 may provide the measurement results to a software 127 comprising, for mass spectrometry raw data reduction, a collection of algorithms for peak detection and peak integration of chromatograms or other time- vector data. Additionally or alternatively, the automatic method development software tool 112 may directly access the measurement result, e.g. the raw data, obtained by the unit on which the measurement was performed.
By performing steps a) to e), all parameters of the MS analyzer unit are optimized and the controlling can proceed to the LC unit. Here similar steps as defined above may be performed. After the controlling of the control parameters of the LC unit is finalized, the automatic method development software tool 112 may proceed to controlling the control parameters of the sample preparation unit. Here similar steps may be performed. As the results of all optimized analytes are saved into a knowledge database, and thus, are easily accessible, machine-readable and can be used for comparisons across analytes. In Figure 1, the controlling of the MS analyzer unit is indicated with reference number 128, the controlling of the LC unit is indicated with reference number 130 and the controlling of the sample preparation unit is indicated with reference number 132. The arrows indicate interactions and/or loops between the controlling of the units.
The controlling may be performed for at least one analyte, as indicated with reference number 134 in Figure 1, resulting in an assay which comprises optimized assay parameters for the at least one analyte, as indicated with reference number 136 in Figure 1. The following table summarizes the optimized result settings of the controlling steps a) to e) for additional analytes.
The automatic method development software tool 112 may further take into account business requirements 138, e.g. the measuring interval of the analyte.
The automatic method development software tool 112 may comprise at least one feasibility procedure 140. For example, the feasibility procedure 140 may comprise at least one study to evaluate analyte performance (e.g. limit of quantitation, linearity, precision, etc.) and interference testing of an assay which comprises of optimized assay parameter 136 for mass spectrometer, liquid chromatography, sample preparation and assay reagents.
The automatic method development software tool 112 may comprise at least one pre-feasi- bility procedure 142. For example, the pre-feasibility procedure 142 may similar to the feasibility procedure 140, but with a smaller sample set to initially check analyte performance, to select the best assay for the feasibility study. The pre-feasibility procedure 142 may comprise checking at least one checklist 144 for the controlling of the mass spectrometry analyzer system 110 such as a guideline to check the assay for instrument compatibility, assay compatibility to other assays, random accessability and LC column robustness and the like.
The automatic method development software tool 112 may comprise issuing at least one report 146 on the controlling. For example, the report 146 may comprise information about one or more of the optimized value of the parameters, the measured data and intermediate analysis results. The report may be provided via at least one user interface and/or at least one communication interface.
List of reference numbers mass spectrometry analyzer system automatic method development software tool software layer instrument layer layer of external databases
Pubmed, Pubchem
MONA measurements software and/or (graphical) user interface software software controlling of the MS analyzer unit controlling of the LC unit controlling of the sample preparation unit at least on analyte optimized assay parameters for the at least one analyte business requirements feasibility procedure pre-feasibility procedure checking at least one checklist report