SYSTEM FOR INSTRUMENT OPTIMIZATION USING ANALYTE-BASED MASS SPECTROMETER AND ALGORITHM PARAMETERS - Patent application

Abstract

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, an instrument includes a sample introduction device and a mass spectrometer. The mass spectrometer includes first logic for receiving a sample containing an analyte from a queue, second logic for determining instrument methods and data analysis parameters based on a property of the analyte, and third logic for processing the sample by applying the instrument methods and data analysis parameters.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 480,081, filed January 16, 2023, entitled "SYSTEM FOR INSTRUMENT OPTIMIZATION USING ANALYTE-BASED MASS SPECTROMETER AND ALGORITHM," the entire contents of which are incorporated herein by reference. [Background technology]

[0002] Chromatography is a technique for separating components of a sample mixture that exploits the different properties of the constituents when interacting with other materials. Mass spectrometry is a technique for detecting, identifying, and quantifying molecules in a sample based on the mass-to-charge ratio of the molecules after ionization. [Brief explanation of the drawings]

[0003]

[0013] The embodiments will be readily understood by the following detailed description taken in conjunction with the accompanying drawings, in which:

[0014] To facilitate this description, like reference numerals refer to like structural elements;

[0015] The embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which: [Figure 1] FIG. 1 is a block diagram of an exemplary mass spectrometry instrument support module, according to various embodiments. [Figure 2] FIG. 2 is a flowchart of an exemplary method for performing support operations, according to various embodiments. [Figure 3] FIG. 3 is an example of a graphical user interface that may be used in performing some or all of the support methods disclosed herein, according to various embodiments. [Figure 4] FIG. 4 is a block diagram of an exemplary computing device capable of performing some or all of the mass spectrometry instrument support methods disclosed herein, according to various embodiments. [Figure 5] FIG. 5 is a block diagram of an exemplary mass spectrometry instrument support system in which some or all of the mass spectrometry instrument support methods disclosed herein may be performed, according to various embodiments. [Figure 6] FIG. 6 is an example of a visual indicator marking sample quality determinations generated using the instrument control and data analysis techniques disclosed herein for an array of samples placed in the wells of a tray, according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0004] Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a mass spectrometry instrument support apparatus includes first logic for receiving a sample containing an analyte from a queue, second logic for determining instrument method and data analysis parameters based on a property of the analyte, and third logic for processing the sample by applying the instrument method and data analysis parameters to an elution time, where mass as a function of elution time is predicted.

[0005] Proteins and protein complexes have been studied using mass spectrometry for decades. Therefore, there is a need to democratize mass spectrometry to make it more user-friendly and automatically calibrate for complexity without user intervention. Furthermore, applying precise parameters for a given mass range dramatically improves the sensitivity and specificity of analysis for protein complexes and other large masses (e.g., 50 kilodaltons (KDa)-1000 KDa and above).

[0006] As described in more detail below, size exclusion chromatography, or the lower resolution online buffer exchange, separates molecules based on size, with larger molecules passing through first and smaller molecules passing through later, slowed down by pore interactions. Thus, in some embodiments, the described systems use this separation property to determine when molecules of different sizes elute from the column and configure the method and parameters accordingly.

[0007] Mass spectrometers typically have various settings that allow the instrument to be optimized, for example, to improve analysis of a given mass range. These settings include, for example, hardware settings (e.g., temperature, pressure), ion optics (e.g., voltage, trap time), and detector parameters (e.g., transient length, mass calibration). In some embodiments, these settings can be pre-optimized for a mass range and then applied in sync with the different masses emerging from the column.

[0008] Additionally, data analysis algorithms are generally not applied well and uniformly across all masses, as other factors besides varying parameters are often desired. For example, different algorithms are often required for noise filtering, feature detection, mass deconvolution, mass clustering, etc. In such instances, the parameters and algorithms assigned as a function of mass are also be selected.

[0009] Accordingly, embodiments of mass spectrometry instrument support disclosed herein can include applying predetermined optimal parameters when a known mass is received. In some embodiments, these parameters are applied for elution time, where mass is predicted as a function of elution time (e.g., retention time). Accordingly, embodiments of scientific instruments disclosed herein can achieve improved performance compared to conventional approaches. Specifically, embodiments can be used to screen samples for cryo-electron microscopy (cryo-EM), improving the yield of good structures per sample. For example, defective samples can be screened or triaged so that only the highest quality samples are sent for vitrification and cryo-EM.

[0010] In some embodiments, the described systems have been used to analyze a wide range of compounds by combining size-based separation (including online desalting), mass spectrometry, and data analysis. In some embodiments, the systems utilize column chemistry separation modes to automatically select optimized instrument method and data analysis parameters based on analyte characteristics. For example, instrument method and data analysis parameters can be selected to match analyte size to improve sample fidelity analysis at the system level. Thus, the described systems avoid typical pitfalls of manual selection and therefore collect and analyze data under suboptimal conditions. In some embodiments, the system is deployed to a mass spectrometer to collect samples from a queue, which are then analyzed without user intervention.

[0011] The automated software system described herein stores all settings (e.g., instruments, methods, parameters, algorithms) into a collection of “automatable units,” where they are cached until needed. When a sample is acquired by the instrument, the automatable units are automatically triggered upon completion of the run. In some embodiments, parameters are applied, and data analysis is performed to calculate sample quality and sample purity. In some embodiments, a stoplight color system (e.g., red, yellow, green) is used to mark sample quality in an easy-to-consume visual manner, in addition to numerical quality metrics. In some embodiments, the color system and metrics can be used on quality control samples to indicate instrument performance relative to known standard samples. The system enables large-scale automated applications for screening and quality control, for example, by automatically analyzing data and displaying sample quality.

[0012] As further described herein, some embodiments of the systems and methods disclosed herein can generate instrument control and data analysis parameters based on the mass of an analyte of interest. Using mass as a value from which to set parameters can be particularly advantageous when mass spectrometry is combined with size exclusion chromatography or online buffer exchange, as these methods separate constituents according to their mass and, therefore, provide well-characterized results for input to a mass-based mass spectrometry parameter determination system.

[0013] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, where like reference numerals refer to like parts throughout and which show, by way of example, embodiments that may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be taken in a limiting sense.

[0014] Various operations may be described, in sequence, as multiple discrete actions or operations in a manner that is most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed to imply that these operations are necessarily order dependent. In particular, these operations may not be performed in the order presented. The operations described may be performed in a different order than in the described embodiment. Various additional operations may be performed and / or described operations may be omitted in additional embodiments.

[0015] For purposes of this disclosure, the phrases "A and / or B" and "A or B" mean (A), (B), or (A and B). For purposes of this disclosure, the phrases "A, B, and / or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). While some elements may be referred to in the singular (e.g., "processor"), any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processor may be implemented with different ones of the operations performed by different processors.

[0016] This description uses the phrases "an embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. Furthermore, when used with respect to embodiments of the present disclosure, the terms "comprising," "including," and "having" are synonymous. When used to describe a dimensional range, the phrase "between X and Y" represents a range that includes X and Y. As used herein, an "apparatus" may refer to any individual device, a collection of devices, a portion of a device, or a collection of portions of devices. The drawings are not necessarily to scale.

[0017] FIG. 1 is a block diagram of a mass spectrometry instrument support module 1000 for determining when molecules of different sizes elute from a column and setting methods and parameters accordingly. The mass spectrometry instrument support module 1000 may be implemented by a circuit (e.g., including electrical and / or optical components) such as a programmed computing device (e.g., a device for measuring mass). The logic of the mass spectrometry instrument support module 1000 may be contained on a single computing device or may be distributed across multiple computing devices that communicate with each other, as appropriate. An example of a computing device that may implement the mass spectrometry instrument support module 1000, alone or in combination, is described herein with reference to the computing device 4000 of FIG. 4. And, an example of a system of interconnected computing devices in which the mass spectrometry instrument support module 1000 may be implemented on one or more of the computing devices is described herein with reference to the mass spectrometry instrument support system 5000 of FIG. 5. Mass spectrometry instrument support module 1000 may include sample receiving logic 1002 , method and parameter determination logic 1004 , processing logic 1006 , and display logic 1008 .

[0018] As used herein, the term “logic” may include a device that performs a set of operations associated with the logic. For example, any of the logical elements included in support module 1000 may be implemented by one or more computing devices programmed with instructions that cause one or more processing units of the computing devices to perform a set of associated operations. In particular embodiments, a logical element may include one or more non-transitory computer-readable media having instructions that, when executed by one or more processing units of one or more computing devices, cause the one or more computing devices to perform a set of associated operations. As used herein, the term “module” may refer to a collection of one or more logical elements that together perform one or more functions associated with the module. Different logical elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by a programmed general-purpose processing unit, while other logic within the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different logical elements within a module may be associated with different sets of instructions executed by one or more processing units. A module may not include all of the logical elements shown in the associated figures. For example, a module may include a subset of the logical elements shown in the associated figure, as that module performs a subset of the operations described herein with reference to that module.

[0019] The sample receiving logic 1002 may be configured to receive samples from the queue. In some examples, the samples include an analyte. The sample receiving logic 1002 may receive samples on a regular chronological schedule (e.g., a set number of seconds or minutes), after a certain number of samples (e.g., 20) have accumulated in the queue, according to any other suitable schedule, or at the command of a user (e.g., received via a GUI such as GUI 3000 of FIG. 3).

[0020] The method and parameter determination logic 1004 may be configured to determine instrument method and data analysis parameters based on the properties of the analyte. In some examples, the properties of the analyte include size, mass, shape, structure, or chemical composition. In particular, in certain embodiments, a user may specify the mass (i.e., molecular weight) of an analyte of interest or specify the chemical structure of the analyte of interest (e.g., in the form of a FASTA file or other format for describing nucleotide or protein sequences). Once the user specifies the chemical structure of the analyte of interest (e.g., by selecting or otherwise pointing to a FASTA or other appropriate file), the logic 1004 may calculate the mass of the analyte of interest based on the chemical structure using known techniques and then determine instrument method and data analysis parameters based on the calculated mass.

[0021] As described above, logic 1004 may determine instrument methods (e.g., instrument parameters) based on the properties of the analyte (e.g., the mass of the analyte, as specified by a user). In some examples, the parameters include hardware settings, ion optics, or detector parameters. In some particular embodiments, the instrument parameters may include a scan range (specified as an m / z range), a desolvation voltage, a trap gas setting, or a resolution. In one example of such an embodiment, logic 1004 may determine the following: a scan range of 1500-6000 for analytes with molecular weights between 0 kDa and 50 kDa; For analytes with molecular weights between 50 and 300 kDa, the scan range may be 2500-10000 m / z, the desolvation voltage may be 100 V, the trapping gas setting may be 5, and the resolution may be 12500; for analytes with molecular weights between 300 and 700 kDa, the scan range may be 5000-20000 m / z, the desolvation voltage may be 100 V, the trapping gas setting may be 6, and the resolution may be 6250; and for analytes with molecular weights between 700 and 1000 kDa, the scan range may be 6000-24000 m / z, the desolvation voltage may be 100 V, the trapping gas setting may be 7, and the resolution may be 3125.

[0022] As described above, logic 1004 can determine data analysis parameters (e.g., which algorithm to run and / or which parameters to use with a selected algorithm to analyze data from the instrument) based on the properties of the analyte (e.g., the mass of the analyte, as specified by a user). For example, in some embodiments, logic 1004 can determine which of different algorithm options to select for different data analysis processes, such as noise filtering, feature detection, mass deconvolution, and mass clustering, among others. Logic 1004 can select from any suitable known algorithms for different ones of these processes.

[0023] In some embodiments, logic 1004 may utilize mass information to determine which algorithm to select. For example, logic 1004 may select from different available mass deconvolution algorithms (e.g., the “Zscape” algorithm described in U.S. Pat. No. 10,217,619, “Methods for data-dependent mass spectrometry of mixed intact protein analytes,” or the “BCDecon” algorithm described in U.S. patent application Ser. No. 18 / 337,183, “Bayesian decremental scheme for charge state deconvolution”). In a particular example, for analytes having molecular weights between 0 kDa and 50 kDa (those with “high-resolution” data containing features in which compound isotopologues are fully or partially resolved), logic 1004 may select a predetermined algorithm (e.g., the Zscape algorithm for mass deconvolution). And for analytes with molecular weights between 50 kDa and 1000 kDa (those with "low-resolution data" where features include unresolved compound isotopomers but charge states are still resolvable), logic 1004 may select other algorithms (e.g., the BCDecon algorithm for mass deconvolution).

[0024] In some embodiments, logic 1004 may utilize the mass information to set parameters for one or more selected algorithms. An association between the mass of an analyte of interest and appropriate parameters may be stored in memory available to logic 1004 so that logic 1004 may generate or identify appropriate parameters in response to a particular mass. Values ​​for appropriate parameters may be generated by routine experimentation for different types of analytes and sets of conditions and stored for access by logic 1004. For example, for mass deconvolution, examples of parameters whose values ​​may be selected based on the mass of the analyte of interest may include parameters for the Zscape algorithm, the BCDecon algorithm, or any other suitable mass deconvolution algorithm.

[0025] The processing logic 1006 can be configured to process the sample by applying instrument method and data analysis parameters to elution time, where mass is predicted as a function of elution time. In some cases, the sample is processed by coupling a size-based separation. In some examples, parameters are pre-optimized for a mass range and applied in sync with the different masses emerging from the column.

[0026] The display logic 1008 may provide the user, via a GUI (such as GUI 3000 of FIG. 3), with the option to view the results of the processing logic 1006 or to select a sample from a queue for the sample receiving logic 1002.

[0027] 2 is a flowchart of a method 2000 of performing support operations, according to various embodiments. The operations of method 2000 may be illustrated with reference to particular embodiments disclosed herein (e.g., scientific instrument support module 1000 described herein with reference to FIG. 1 , GUI 3000 described herein with reference to FIG. 3 , computing device 4000 described herein with reference to FIG. 4 , and / or scientific instrument support system 5000 described herein with reference to FIG. 5 ), but method 2000 may be used in any suitable configuration to perform any suitable support operations. Although the operations are illustrated in FIG. 2 once each and in a particular order, the operations may be reordered and / or repeated as desired and appropriate (e.g., different operations performed may be performed in parallel, as appropriate).

[0028] A first operation may be performed in 2002. For example, the sample receiving logic 1002 of the support module 1000 may perform the operation of 2002. The first operation may include receiving a sample containing an analyte from a queue.

[0029] A second operation may be performed in 2004. For example, the method and parameter determination logic 1004 of the support module 1000 may perform the operation of 2004. The second operation may include determining instrument method and data analysis parameters based on the properties of the analyte.

[0030] A third operation may be performed in 2006. For example, processing logic 1006 of support module 1000 may perform the operation of 2006. The third operation may include processing the sample by applying instrument methods and data analysis parameters for elution time, where mass as a function of elution time is predicted.

[0031] As described above, in some embodiments, data analysis performed on instrument data may generate an assessment of sample quality and / or sample purity. In some embodiments, a stop light color system (e.g., red, yellow, green, or other color or visual indicator) may be employed in addition to, or instead of, numerical quality metrics to provide a consumable visual indication of sample quality. FIG. 6 is an example of a visual indicator marking a sample quality determination generated using the instrument control and data analysis techniques disclosed herein for an array of samples placed in the wells of a tray. While FIG. 6 is shown in various levels of gray for ease of image reproduction, a stop light color system (red, yellow, green) may similarly be used.

[0032] The scientific instrument support methods disclosed herein may include interactions with a human user (e.g., via a user local computing device 5020 described herein with reference to FIG. 5). These interactions may include providing information to the user (e.g., information about the operation of a scientific instrument such as the scientific instrument 5010 of FIG. 5, information about a sample being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information), or providing options for the user to enter commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 5010 of FIG. 5 or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a visual display on a display device (e.g., display device 4010 described herein with reference to FIG. 4) that provides output to a user and / or prompts the user to provide input (e.g., via one or more input devices such as a keyboard, mouse, trackpad, or touchscreen included in other I / O device 4012 described herein with reference to FIG. 4). The scientific instrument support system disclosed herein may include any suitable GUI for interaction with a user.

[0033] 3 illustrates an exemplary GUI 3000 that may be used in performing some or all of the support methods disclosed herein, according to various embodiments. As described above, the GUI 3000 may be provided on a display device (e.g., the display device 4010 described herein with reference to FIG. 4 ) of a computing device (e.g., the computing device 4000 described herein with reference to FIG. 4 ) of a scientific instrument support system (e.g., the scientific instrument support system 5000 described herein with reference to FIG. 5 ). A user may then interact with the GUI 3000 using any suitable input device (e.g., any input device included in the other I / O devices 4012 described herein with reference to FIG. 4 ) and input technique (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation, etc.).

[0034] GUI 3000 may include a data display area 3002, a data analysis area 3004, a scientific instrument control area 3006, and a settings area 3008. The particular number and arrangement of areas shown in Figure 3 are merely exemplary, and any number and arrangement of areas, including any desired features, may be included in GUI 3000.

[0035] The data display area 3002 may display data generated by a scientific instrument (e.g., the scientific instrument 5010 described herein with reference to FIG. 5). The data analysis area 3004 may display the results of a data analysis (e.g., the results of analyzing the data shown in the data display area 3002 and / or other data). For example, the results of the processing logic 1006 of the support module 1000 may be provided to the user. In some embodiments, the data display area 3002 and the data analysis area 3004 may be combined within the GUI 3000 (e.g., to include data output from the scientific instrument and some analysis of the data within a common graph or area).

[0036] The scientific instrument control area 3006 may include options that allow a user to control a scientific instrument (e.g., the scientific instrument 5010 described herein with reference to FIG. 5). For example, the data display area 3002 may provide the user with the option to select samples from a queue for the receiving logic 1002 of the support module 1000.

[0037] Settings area 3008 may include options that enable a user to control features and functionality of GUI 3000 (and / or other GUIs) and / or perform common computing operations with respect to data display area 3002 and data analysis area 3004 (e.g., saving data to a storage device such as storage device 4004 described herein with reference to FIG. 4 , sending data to another user, labeling data, etc.).

[0038] As mentioned above, the scientific instrument support module 1000 may be implemented by one or more computing devices. Figure 5 is a block diagram of a computing device 4000 that may perform some or all of the scientific instrument support methods disclosed herein, according to various embodiments. In some embodiments, the scientific instrument support module 1000 may be implemented by a single computing device 4000 or by multiple computing devices 4000. Furthermore, as described below, the computing device 4000 (or multiple computing devices 4000) implementing the scientific instrument support module 1000 may be part of one or more of the scientific instrument 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 of Figure 5.

[0039] While the computing device 4000 of FIG. 4 is shown as having several components, any one or more of these components may be omitted or duplicated as suitable for the application and configuration. In some embodiments, some or all of the components included in the computing device 4000 may be mounted on one or more motherboards and enclosed within a housing (e.g., comprising plastic, metal, and / or other materials). In some embodiments, some of these components may be fabricated on a single system-on-chip (SoC) (e.g., an SoC may include one or more processing units 4002 and one or more storage units 4004). Additionally, in various embodiments, the computing device 4000 may not include one or more of the components shown in FIG. 4 but may include interface circuitry (not shown) for coupling to one or more components using any suitable interface (e.g., a universal serial bus (USB) interface, a high-definition multimedia interface (HDMI) interface, a controller area network (CAN) interface, a serial peripheral interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, the computing device 4000 may not include a display device 4010, but may include display device interface circuitry (eg, connectors and driver circuitry) to which the display device 4010 may be coupled.

[0040] The computing device 4000 may include a processing unit 4002 (e.g., one or more processing units). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory and converts the electronic data into other electronic data that may be stored in registers and / or memory. The processing unit 4002 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptoprocessors (dedicated processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing units.

[0041] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include one or more memory devices, such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive bridge RAM (CBRAM) devices), hard drive-based memory devices, solid state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 4004 may include memory that shares a die with the processing unit 4002. In such embodiments, the memory is used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin transfer torque magnetic random access memory (STT-MRAM). In some embodiments, storage device 4004 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing units (e.g., processing unit 4002), cause computing device 4000 to perform any suitable one or portions of the methods disclosed herein.

[0042] The computing device 4000 may include an interface device 4006 (e.g., one or more interface devices 4006). The interface device 4006 may include one or more communication chips, connectors, and / or connectors for managing communications between the computing device 4000 and other computing devices. Other hardware and software may be included. For example, the interface unit 4006 may include circuitry for managing wireless communications for transferring data to and from the computing device 4000. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can communicate data through the use of modulated electromagnetic radiation over a nonsolid medium. The term does not imply that the associated devices do not include wires, although in some embodiments this may not be the case. The circuitry included in the interface device 4006 for managing wireless communications may implement any of several wireless standards or protocols, including, but not limited to, Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 family), the IEEE 802.16 standard (e.g., IEEE 802.16-2005 amendment), the Long Term Evolution (LTE) project with any amendments, updates, and / or revisions (e.g., the Advanced LTE project, the Ultra Mobile Broadband (UMB) project (also known as "3GPP2"), etc.). In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN).In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution Data Optimized (EV-DO), and derivatives thereof, as well as any other wireless protocols designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0043] In some embodiments, the interface device 4006 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, the interface device 4006 may include circuitry supporting communications according to Ethernet technology. In some embodiments, the interface device 4006 may support both wireless and wired communications and / or multiple wired and / or wireless communications protocols. For example, a first set of circuits in the interface device 4006 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, and a second set of circuits in the interface device 4006 may be dedicated to long-range wireless communications, such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, or EV-DO. In some embodiments, the first set of circuits in the interface device 4006 may be dedicated to wireless communications and the second set of circuits in the interface device 4006 may be dedicated to wired communications.

[0044] Computing device 4000 may include battery / power circuitry 4008. Battery / power circuitry 4008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 4000 to an energy source separate from computing device 4000 (e.g., AC line power).

[0045] The computing device 4000 may include a display device 4010 (e.g., multiple display devices). The display device 4010 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0046] The computing device 4000 may include other input / output (I / O) devices 4012. The other I / O devices 4012 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a location device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 4000, as known in the art), an audio codec, a video codec, a printer, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), an image capture device such as a camera, a cursor control device such as a keyboard, a mouse, a stylus, a trackball, or a touchpad, a barcode reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0047] Computing device 4000 may have any suitable form factor for its application and configuration, such as a handheld or mobile computing device (e.g., a mobile phone, a smartphone, a mobile Internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0048] One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. Figure 5 is a block diagram of an exemplary mass spectrometry instrument support system 5000 in which some or all of the mass spectrometry instrument support methods disclosed herein may be performed, according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., scientific instrument support module 1000 of Figure 1 and method 2000 of Figure 2) may be implemented by one or more of the scientific instrument 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 of the scientific instrument support system 5000.

[0049] Any of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may include any of the embodiments of computing device 4000 described herein with reference to Figure 4. And any of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the form of any suitable of the embodiments of computing device 4000 described herein with reference to Figure 4.

[0050] Each of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may include a processing unit 5002, a storage unit 5004, and an interface unit 5006. The processing unit 5002 may take any suitable form, including any of the forms of the processing unit 4002 described herein with reference to FIG. 4. The processing units 5002 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same or different forms. The storage unit 5004 may take any suitable form, including any of the forms of the storage unit 4004 described herein with reference to FIG. 4. The storage units 5004 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same or different forms. The interface device 5006 may take any suitable form, including any of the forms of the interface device 4006 described herein with reference to Figure 4. And the interface devices 5006 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same or different forms.

[0051] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040 may communicate with other elements of the scientific instrument support system 5000 via communication pathways 5008. The communication pathways 5008 may communicatively couple the interface devices 5006 of different ones of the elements of the scientific instrument support system 5000, as shown, and may be wired or wireless communication pathways (e.g., according to any of the communication techniques described herein with reference to the interface device 4006 of the computing device 4000 of FIG. 4). While the particular scientific instrument support system 5000 shown in FIG. 5 includes communication pathways between each pair of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040, this “fully connected” implementation is merely exemplary, and in various embodiments, various ones of the communication pathways 5008 may not be present. For example, in some embodiments, the service local computing device 5030 may not have a direct communication path 5008 between its interface device 5006 and the interface device 5006 of the scientific instrument 5010, but instead may communicate with the scientific instrument 5010 via a communication path 5008 between the service local computing device 5030 and the user local computing device 5020, and a communication path 5008 between the user local computing device 5020 and the scientific instrument 5010.

[0052] The user local computing device 5020 may be a computing device that is local to a user of the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 described herein). In some embodiments, the user local computing device 5020 may be local to the scientific instrument 5010, but this need not be the case. That is, for example, a user local computing device 5020 in a user's home or office may be remote from, but communicate with, the scientific instrument 5010. As a result, the user may use the user local computing device 5020 to control and / or access data from the scientific instrument 5010. In some embodiments, the user local computing device 5020 may be a laptop, smartphone, or tablet device. In some embodiments, the user local computing device 5020 may be a portable computing device.

[0053] The service local computing device 5030 may be a computing device that is local to an entity that provides services to the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 described herein). For example, the service local computing device 5030 may be local to the manufacturer of the scientific instrument 5010 or a third-party service company. In some embodiments, the service local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., via a direct communication path 5008 or via multiple "indirect" communication paths 5008, as described above) to receive data regarding the operation of the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., results of self-tests of the scientific instrument 5010, calibration coefficients used by the scientific instrument 5010, measurements of sensors associated with the scientific instrument 5010, etc.). In some embodiments, the service local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., via a direct communication path 5008 or via multiple "indirect" communication paths 5008, as described above) and transmit data to the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., to update programmed instructions, such as firmware, in the scientific instrument 5010, to initiate the execution of a test or calibration sequence in the scientific instrument 5010, to update programmed instructions, such as software, in the user local computing device 5020 or the remote computing device 5040, etc.).A user of the scientific instrument 5010 may utilize the scientific instrument 5010 or the user local computing device 5020 to communicate with the service local computing device 5030 to report problems with the scientific instrument 5010 or the user local computing device 5020, request a visit from a technician to improve the operation of the scientific instrument 5010, order consumables or replacement parts related to the scientific instrument 5010, or for other purposes.

[0054] The remote computing device 5040 may be a computing device that is remote from the scientific instrument 5010 and / or the user local computing device 5020 (e.g., according to any of the embodiments of the computing device 4000 described herein). In some embodiments, the remote computing device 5040 may be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 may include network-attached storage (e.g., as part of the storage appliance 5004). The remote computing device 5040 may store data generated by the scientific instrument 5010, perform analysis of the data generated by the scientific instrument 5010 (e.g., according to programmed instructions), facilitate communications between the user local computing device 5020 and the scientific instrument 5010, and / or facilitate communications between the service local computing device 5030 and the scientific instrument 5010.

[0055] In some embodiments, one or more of the elements of the scientific instrument support system 5000 shown in FIG. 5 may not be present. Furthermore, in some embodiments, more than one of various elements of the elements of the scientific instrument support system 5000 of FIG. 5 may be present. For example, the scientific instrument support system 5000 may include multiple user local computing devices 5020 (e.g., different user local computing devices 5020 associated with different users or in different locations). In another example, the scientific instrument support system 5000 may include multiple scientific instruments 5010 that all communicate with a service local computing device 5030 and / or a remote computing device 5040. That is, in such an embodiment, the service local computing device 5030 monitors these multiple scientific instruments 5010, and the service local computing device 5030 may trigger updates or other information to the multiple scientific instruments 5010 simultaneously. Different scientific instruments 5010 in the scientific instrument support system 5000 may be located near each other (e.g., in the same room) or far from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instruments 5010 may be connected to an Internet of Things (IoT) stack that enables command and control of the scientific instruments 5010 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user operating a user local computing device 5020 that communicates with the scientific instruments 5010 through an intervening remote computing device 5040. In some embodiments, the scientific instruments 5010 may be sold by a manufacturer as part of a local scientific instrument computing unit 5012, along with one or more associated user local computing devices 5020.

[0056] The following paragraphs provide various examples of the embodiments disclosed herein.

[0057] Example A1 is a mass spectrometry support device including first logic to receive a sample from a queue, the sample including an analyte; second logic to determine instrument methods and data analysis parameters based on a property of the analyte; and third logic to process the sample by applying the instrument methods and data analysis parameters to a mass spectrometry elution time, wherein mass is predicted as a function of elution time.

[0058] Example A2 includes the subject matter of Example A1 and further specifies that the analyte properties include size, mass, shape, structure, or chemical composition.

[0059] Example A3 includes the technical details of either Example A1 or A2, and further specifies that the sample is processed by combining size-based separation.

[0060] Example A4 includes the technical details of any of Examples A1-3 and further specifies that the parameters include hardware settings, ion optics, or detector parameters.

[0061] Example A5 includes the technical details of any of Examples A1-4 and further specifies that the parameters are pre-optimized for a mass range and are applied in a manner that is synchronized with the different masses coming off the column.

[0062] Example A6 includes the technical subject matter of any of Examples A1-5, and further specifies that the apparatus is a device for measuring mass.

[0063] Example B1 is a scientific instrument support method including receiving, by a computing device, a mass indicator of an analyte of interest, the analyte of interest may be present in a sample; generating, by the computing device, one or more instrument control parameters and one or more data processing parameters based at least in part on the mass indicator; providing, by the computing device, the one or more instrument control parameters for use by a mass spectrometer during analysis of the sample, the mass spectrometer generating sample data based on the analysis of the sample; and providing, by the computing device, the one or more data processing parameters for use in processing the sample data.

[0064] Example B2 includes the technical subject matter of Example B1 and further includes processing, by the computing device, the sample data using one or more data processing parameters.

[0065] Example B3 includes the technical subject matter of Example B2 and further includes providing, by the computing device, a display of a visual indicator of sample quality or sample purity based on the processed sample data.

[0066] Example B4 includes the technical subject matter of Example B3 and further specifies that the visual indicator includes a stoplight color system that indicates sample quality or sample purity.

[0067] Example B5 includes the technical subject matter of any of Examples B1-4 and further specifies that the data processing parameters include instructions for at least one of a noise filtering algorithm, a feature detection algorithm, a mass deconvolution algorithm, or a mass clustering algorithm.

[0068] Example B6 includes the technical subject matter of any of Examples B1-5, and further specifies that the data processing parameters include parameter values ​​for a specific data processing algorithm.

[0069] Example B7 includes the technical matter of any of Examples B1-6 and further specifies that the instrument control parameters include at least one of hardware settings, ion optics, or detector parameters.

[0070] Example B8 includes the technical subject matter of any of Examples B1-7 and further specifies that receiving a mass indicator of an analyte of interest includes receiving a user designation of the chemical structure or composition of the analyte.

[0071] Example B9 is a method for supporting cryogenic electron microscopy (cryo-EM), including receiving, by a computing device, a mass indicator of an analyte of interest, where the analyte of interest may be present in a set of multiple samples; generating, by the computing device, one or more instrument control parameters and one or more data processing parameters based at least in part on the mass indicator; resulting, by the computing device, in analysis of the sample according to the instrument control parameters and the data analysis parameters; and identifying, by the computing device, one or more of the samples for further analysis by cryo-EM based at least in part on results of the sample analysis.

[0072] Example B10 includes the technical matter of Example B9 and further specifies that identifying one or more of the samples for further analysis by cryo-EM includes providing a visual indicator of one or more characteristics of the set of multiple samples.

[0073] Example B11 includes the technical subject matter of any of Examples B9-10, and further specifies that the analysis of the sample includes a step of coupling a size-based separation.

[0074] Example B12 includes the technical subject matter of any of Examples B9-10, and further includes a step of causing a computing device to perform a vitrification process on the identified sample.

[0075] Example B13 is a method of performing a mass spectrometry process, including receiving, by a computing device, a property indicator of an analyte of interest, the analyte of interest being present in a sample, the property of the analyte including size, mass, shape, structure, or chemical composition; generating, by the computing device, one or more instrument control parameters and one or more data processing parameters based at least in part on the property indicator; and causing, by the computing device, an analysis of the sample in accordance with the instrument control parameters and the data analysis parameters.

[0076] Example B14 includes the technical details of Example B13 and further specifies that the analysis of the sample includes size-based separation.

[0077] Example B15 includes the technical details of any of Examples B13-14, and further specifies that the parameters are pre-optimized for a mass range and applied synchronously with the different masses coming off the column.

[0078] Example B16 includes the subject matter of any of Examples B13-14, and further includes the step of causing, by the computing device, display of a visual indicator of the property of the sample based on the analysis.

[0079] Example B17 includes the technical matter of Example B16, and further specifies that the visual indicator includes a stop light color system that indicates the characteristic.

[0080] Example B18 includes the technical subject matter of any of Examples B13-17 and further specifies that the computing device is configured to generate one or more instrument control parameters and one or more data processing parameters for an analyte of interest having a size between 0 kilodaltons and 1000 kilodaltons.

[0081] Example B19 includes the technical details of any of Examples B13-18 and further specifies that analysis of the sample includes size exclusion chromatography or online buffer exchange.

[0082] Example B20 includes the technical subject matter of any of Examples B13-19 and further specifies that the analyte of interest is a protein.

Claims

1. 1. A scientific instrument support method, comprising: receiving, by a computing device, a mass indicator of an analyte of interest; the analyte of interest may be present in a sample; generating, by the computing device, one or more instrument control parameters and one or more data processing parameters based at least in part on the mass indicator; providing, by the computing device, the one or more instrument control parameters for use by a mass spectrometer during analysis of the sample; the mass spectrometer generating sample data based on the analysis of the sample; providing, by the computing device, the one or more data processing parameters for use in processing the sample data; A method comprising:

2. The method further comprises: processing, by the computing device, the sample data using the one or more data processing parameters; The method of claim 1 , comprising:

3. The method further comprises: displaying, by the computing device, a visual indicator of sample quality or sample purity based on the processed sample data; The method of claim 2 , comprising:

4. The visual indicator comprises a stoplight color system that indicates sample quality or sample purity. The method of claim 3.

5. the data processing parameters include an indicator of at least one of a noise filtering algorithm, a feature detection algorithm, a mass deconvolution algorithm, or a mass clustering algorithm; The method of claim 1.

6. the data processing parameters include parameter values ​​for a particular data processing algorithm; The method of claim 1.

7. the instrument control parameters include at least one of hardware settings, ion optics, or detector parameters; The method of claim 1.

8. receiving a mass indicator of the analyte of interest; receiving a user specification of the chemical structure or composition of the analyte; The method of claim 1.

9. 1. A method for supporting cryo-electron microscopy (cryo-EM), comprising: receiving, by a computing device, a mass indicator of an analyte of interest; the analyte of interest may be present in a set of multiple samples; generating, by the computing device, one or more instrument control parameters and one or more data analysis parameters based at least in part on the mass indicator; causing, by the computing device, an analysis of the sample according to the instrument control parameters and the data analysis parameters; identifying, by the computing device, one or more of the samples for further analysis by cryo-EM based at least in part on results of the analysis of the samples; A method comprising:

10. identifying one or more of the samples for further analysis by cryo-EM, providing a display of a visual indicator of one or more properties of the set of multiple samples.

10. The method of claim 9.

11. analysis of the sample by coupling a size-based separation; 10. The method of claim 9, comprising:

12. The method further comprises: causing the computing device to perform a vitrification process on the identified sample; 10. The method of claim 9, comprising:

13. 1. A method of performing a mass spectrometry process, comprising: receiving, by a computing device, a property indicator of the analyte of interest; the analyte of interest is present in a sample; The properties of the analyte include size, mass, shape, structure, or chemical composition. Steps and generating, by the computing device, one or more instrument control parameters and one or more data analysis parameters based at least in part on the quality indicators; causing, by the computing device, an analysis of the sample according to the instrument control parameters and the data analysis parameters; A method comprising:

14. analysis of the sample includes size-based separation; The method of claim 13.

15. The parameters are pre-optimized for a mass range and are applied synchronously as different masses emerge from the column. The method of claim 13.

16. The method further comprises: causing, by the computing device, the display of a visual indicator of a property of the sample based on the analysis; 14. The method of claim 13, comprising:

17. the visual indicator includes a stoplight color system that indicates the characteristic.

17. The method of claim 16.

18. the computing device, generating one or more instrument control parameters and one or more data processing parameters for an analyte of interest having a size between 0 kilodaltons and 1000 kilodaltons; The method of claim 13.

19. Analysis of the sample includes size exclusion chromatography or online buffer exchange. The method of claim 13.

20. wherein the analyte of interest is a protein; The method of claim 13.

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