Instrument optimization using analyte-based mass spectrometer and algorithm parameters

Abstract

Scientific instrument support systems and related methods, computing devices, and computer readable media are disclosed herein. For example, in some embodiments, an apparatus includes a sample introduction device and a mass spectrometer. The mass spectrometer includes a first logic to receive a sample from a queue, the sample including an analyte; a second logic to determine instrumentation method and data analysis parameters based on a property of the analyte; and a third logic to process the sample by applying the instrumentation method and data analysis parameters.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional No. 63 / 480,081, filed January 16, 2023, entitled “System for Instrument Optimization Using Analyte-Based Mass Spectrometer and Algorithm Parameters,” the entire contents of which are incorporated herein by reference. Background Art

[0003] Chromatography is a technique used to separate the components of a sample mixture by exploiting their different properties when interacting with other materials. Mass spectrometry is a technique used to detect, identify, and quantify molecules within a sample based on their mass-to-charge ratio following ionization. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, identical reference numerals are used to indicate identical structural elements. The embodiments are illustrated in the figures of the accompanying drawings by way of example and not by way of limitation.

[0005] Figure 1 is a block diagram of an example mass spectrometry instrument support module according to various embodiments.

[0006] Figure 2 is a flowchart of an example method of performing support operations according to various embodiments.

[0007] Figure 3 are examples of graphical user interfaces that may be used in the performance of some or all of the supporting methods disclosed herein, according to various embodiments.

[0008] Figure 4 is a block diagram of an example computing device that may perform some or all of the mass spectrometry instrument support methods disclosed herein, according to various embodiments.

[0009] Figure 5 is a block diagram of an example 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.

[0010] Figure 6 are examples of visual indicators for marking sample quality determinations generated for an array of samples disposed in wells of a tray using the instrument control and data analysis techniques disclosed herein, according to various embodiments. DETAILED DESCRIPTION

[0011] Disclosed herein are scientific instrument support systems and related methods, computing devices, and computer-readable media. For example, in some embodiments, a mass spectrometry instrument support device may include: first logic for receiving a sample from a queue, the sample comprising an analyte; second logic for determining an instrument method and data analysis parameters based on properties of the analyte; and third logic for processing the sample by applying the instrument method and data analysis parameters for an elution time, wherein mass is predicted as a function of the elution time.

[0012] Mass spectrometry has been used to study proteins and protein complexes for decades. Therefore, there is a need to make mass spectrometry more accessible so that it is easier to use and can automatically calibrate complexity without user intervention. Furthermore, applying calibration parameters for a given mass range significantly improves the sensitivity and specificity of analyses of protein complexes and other large molecular weight substances (e.g., 50 kilodaltons (KDa) to 1000 KDa and above).

[0013] As discussed in further detail below, size exclusion chromatography or lower resolution online buffer exchange separates molecules based on size, with larger molecules passing first and smaller molecules, which are slowed by pore interactions, passing later. Thus, in some embodiments, the described systems exploit this separation characteristic to determine when molecules of different sizes will elute from the column and set the methods and parameters accordingly.

[0014] Typically, a mass spectrometer has various settings that allow the instrument to be optimized to improve analysis, for example, over a certain mass range. These settings include, for example, hardware settings (e.g., temperature, pressure), ion optics (e.g., voltage, capture time), and detector parameters (e.g., transient length, mass calibration). In some embodiments, these settings can be pre-optimized for a mass range and applied simultaneously to the different masses eluting from the chromatographic column.

[0015] Furthermore, data analysis algorithms often don't work well across the entire quality range, as they often need to consider factors beyond just parameter variation. For example, different algorithms are often required for noise filtering, feature detection, mass deconvolution, and mass clustering. In these examples, the parameters and algorithms assigned as a function of quality are also selected.

[0016] Therefore, the mass spectrometer support embodiments disclosed herein can include applying predetermined optimal parameters for when to receive a known mass substance. In some embodiments, these parameters are applied up to elution time, where mass prediction is a function of elution time (e.g., retention time). Therefore, relative to conventional methods, the scientific instrument embodiments disclosed herein can achieve improved performance. Specifically, embodiments can be used to screen samples for cryogenic electron microscopy (cryo-EM), thereby increasing the yield of good structures for each sample. For example, poor samples can be screened or shunted, and only the highest quality samples are sent to vitrification and cryo-EM links.

[0017] In some embodiments, the described system is used to analyze a wide range of compounds by coupling size-based separation (including online desalting), mass spectrometry, and data analysis. In some embodiments, the system utilizes the separation mode of column chemistry to automatically select optimized instrument methods and data analysis parameters based on the characteristics of the analyte. For example, the instrument method and data analysis parameters can be selected to match the size of the analyte, thereby improving the fidelity analysis of the sample at the system level. Therefore, the described system avoids the typical pitfalls of manual selection, thereby avoiding collecting and analyzing data under suboptimal conditions. In some embodiments, the system is deployed on a mass spectrometer to collect samples from a queue and then analyze the samples without user intervention.

[0018] The automation software system described here stores all settings (e.g., instruments, methods, parameters, algorithms) into a collection of "automatable units" that are cached until needed. When a sample is obtained by an instrument, the automated unit automatically triggers when the run is complete. In some embodiments, parameters are applied and data analysis is performed to calculate sample quality and sample purity. In some embodiments, in addition to digital quality metrics, a signal light color system (e.g., red, yellow, green) is used to mark sample quality in a visual manner that is easy to attract attention. In some embodiments, color systems and metrics can be used for quality control samples to indicate the performance of the instrument in handling known standard samples. This system allows for large-scale, automated applications of screening and quality control by, for example, automatically analyzing data and displaying sample quality.

[0019] As discussed further herein, some embodiments of the systems and methods disclosed herein can generate instrument control and data analysis parameters based on the mass of the target analyte. Using mass values ​​as the basis for parameter setting can have significant advantages when mass spectrometry is combined with size exclusion chromatography or online buffer exchange, as these separation methods separate components based on molecular weight, thus providing well-characterized input to mass spectrometry-based parameter determination systems.

[0020] In the following detailed description, reference is made to the accompanying drawings forming a part thereof, in which like numerals represent like parts throughout, and in which are shown by way of illustration embodiments that may be practiced. It should 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 should not be construed as limiting.

[0021] Various operations can be described as multiple discrete actions or operations performed in sequence in a manner that is most helpful for understanding the subject matter disclosed herein. However, the described order should not be interpreted as implying that these operations must rely on the order. Specifically, these operations may not be performed in the order presented. The described operations may be performed in an order different from the described embodiment. Various additional operations may be performed, and / or the described operations may be omitted in additional embodiments.

[0022] 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). Although some elements may be referred to in the singular (e.g., "processing device"), any appropriate 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 processing device may be implemented with different ones of those operations being performed by different processing devices.

[0023] The specification uses the phrases "embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. Furthermore, the terms "comprising," "including," "having," and the like, as used with respect to the embodiments of the present disclosure, are synonymous. When used to describe a range of dimensions, the phrase "between X and Y" means a range that includes X and Y. As used in this document, "means" may refer to any individual device, collection of devices, portion of a device, or collection of portions of devices. The drawings are not necessarily drawn to scale.

[0024] Figure 1 is a block diagram of a mass spectrometer support module 1000 that determines when molecules of different sizes will elute from a chromatography column and sets methods and parameters accordingly. The mass spectrometer support module 1000 can be implemented by circuitry (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 spectrometer support module 1000 can be included in a single computing device, or can be distributed across multiple computing devices that communicate appropriately with each other. Figure 4Examples of computing devices that may implement the mass spectrometry instrument support module 1000, either alone or in combination, are discussed in conjunction with the computing device 4000 and are referenced herein. Figure 5 The mass spectrometry instrument support system 5000 discussed in the accompanying drawings provides an example of a system of interconnected computing devices, wherein the mass spectrometry instrument support module 1000 can be implemented across one or more of the computing devices. The mass spectrometry instrument support module 1000 can include sample receiving logic 1002, method and parameter determination logic 1004, processing logic 1006, and display logic 1008.

[0025] As used herein, the term "logic" may include a device for performing a set of operations associated with logic. For example, any of the logic elements included in the support module 1000 may be implemented by one or more computing devices, which are programmed with instructions to cause one or more processing devices of the computing device to perform an associated set of operations. In a specific embodiment, the logic element may include one or more non-transient computer-readable media having instructions thereon, which, when executed by one or more processing devices in one or more computing devices, cause the one or more computing devices to perform an associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements, which together perform functions associated with the module. Different logic elements in the logic elements in the module may take the same form or may take different forms. For example, some logic components in the module may be implemented by a programmed general-purpose processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different logic elements in the logic elements in the module may be associated with different instruction sets executed by one or more processing devices. A module may not contain all of the logic elements depicted in the associated figures; for example, when the module is to perform a subset of the operations discussed herein with reference to the module, the module may contain a subset of the logic elements depicted in the associated figures.

[0026] The sample receiving logic 1002 can be configured to receive samples from a queue. In some examples, the samples include analytes. The sample receiving logic 1002 can receive samples on a regular time schedule (e.g., a set number of seconds or minutes) after a certain number of samples (e.g., 20) have accumulated in the queue, receive samples according to any other suitable schedule, or receive samples on command by a user (e.g., via a GUI, such as Figure 3 GUI 3000 receives).

[0027] Method and parameter determination logic 1004 can be configured to determine instrument method and data analysis parameters based on the property of analyte.In some examples, the property of analyte includes size, mass, shape, structure or chemical composition.In particular, in certain embodiments, the user can specify the mass (that is, molecular weight) of the target analyte, or can specify the chemical structure of the target analyte (for example, in the form of FASTA files or other formats for describing nucleotide or protein sequences).When the user specifies the chemical structure of the target analyte (for example, by selecting or otherwise pointing to FASTA or other appropriate files), logic 1004 can use known technology to calculate the mass of the target analyte based on the chemical structure, and then can determine instrument method and data analysis parameters based on the mass calculated.

[0028] As described above, the logic 1004 can determine the instrument method (e.g., instrument parameters) based on the properties of the analyte (e.g., the mass of the analyte, as specified by the user). In some examples, the parameters include hardware settings, ion optics, or detector parameters. In some specific embodiments, the instrument parameters can 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, the logic 1004 can determine that for an analyte with a molecular weight between 0 kDa and 50 kDa, the scan range will be 1500-6000 m / z, the desolvation voltage can be 50 V, the trap gas setting can be 3, and the resolution can be 100,000; for an analyte with a molecular weight between 50 kDa and 300 kDa, the scan range will be 2500-10,000 m / z, the desolvation voltage can be 100 V, the trap gas setting can be 5, and the resolution can be 100,000. The rate can be 12500; for analytes with molecular weight between 300kDa and 700kDa, the scan range will be 5000-20000 m / z, the desolvation voltage can be 100V, the trap gas setting can be 6, and the resolution can be 6250; and for analytes with molecular weight between 700kDa and 1000kDa, the scan range will be 6000-24000 m / z, the desolvation voltage can be 100V, the trap gas setting can be 7, and the resolution can be 3125.

[0029] As described above, the logic 1004 can determine data analysis parameters (e.g., which algorithms to execute to analyze data from the instrument, and / or which parameters to use with the selected algorithms) based on the properties of the analyte (e.g., the mass of the analyte, as specified by the user). For example, in some embodiments, the logic 1004 can determine which of the different algorithm options to select for different data analysis processes, such as noise filtering, feature detection, mass deconvolution, and mass clustering. The logic 1004 can select from any suitable known algorithms for different ones of these processes.

[0030] In some embodiments, the logic 1004 can utilize the mass information to determine which algorithms to select. For example, the logic 1004 can select from different available mass deconvolution algorithms (e.g., the "Zscape" algorithm described in U.S. Patent 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 No. 18 / 337,183, "A Bayesian Descending Scheme for Charge State Deconvolution"). In a particular example, for analytes with molecular weights between 0 kDa and 50 kDa (having "high resolution" data, in which the mass spectrum contains features in which compound isotopomers are fully or partially separated), logic 1004 may select certain algorithms (e.g., the Zscape algorithm for mass deconvolution), and for analytes with molecular weights between 50 kDa and 1000 kDa (having "low resolution data, in which charge states can still be resolved in cases where features contain unresolved compound isotopomers), logic 1004 may select other algorithms (e.g., the BCDecon algorithm for mass deconvolution).

[0031] In some embodiments, the logic 1004 can use the mass information to set parameters for one or more selected algorithms. The association between the mass of the target analyte and the appropriate parameters can be stored in a memory available to the logic 1004 so that the logic 1004 can generate or identify appropriate parameters in response to the specified mass. The values ​​of the appropriate parameters can be generated by routine experiments for different types of analytes and condition groups and stored for access by the logic 1004. For example, for mass deconvolution, examples of parameters whose values ​​can be selected based on the mass of the target analyte can include parameters of the Zscape algorithm, parameters of the BCDecon algorithm, or parameters of any other suitable mass deconvolution algorithm.

[0032] The processing logic 1006 can be configured to process the sample for an elution time by applying instrument methods and data analysis parameters, wherein 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, the parameters are pre-optimized for a mass range and applied simultaneously with the different masses eluting from the chromatographic column.

[0033] Display logic 1008 may be displayed via a GUI (such as Figure 3 The GUI 3000 provides the user with the option of viewing the results of the processing logic 1006 or selecting a sample from the queue for the sample receiving logic 1002.

[0034] Figure 2 is a flow chart of a method 2000 for performing support operations according to various embodiments. Although the operations of the method 2000 may be described with reference to specific embodiments disclosed herein (e.g., Figure 1 The scientific instrument support module 1000 discussed in this article is referenced Figure 3 GUI 3000 discussed, this article references Figure 4 The computing device 4000 discussed and / or referenced herein Figure 5 The scientific instrument support system 5000 discussed herein is not intended to be limiting, but the method 2000 may be used in any suitable setting to perform any suitable support operations. Figure 2 The operations are each shown once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (e.g., different operations may be performed in parallel where appropriate).

[0035] At 2002, a first operation can be performed. For example, the sample receiving logic 1002 of the support module 1000 can perform the operation of 2002. The first operation can include receiving a sample including an analyte from a queue.

[0036] At 2004, a second operation can be performed. For example, the method and parameter determination logic 1004 of the support module 1000 can perform the operation of 2004. The second operation can include determining an instrument method and data analysis parameters based on the properties of the analyte.

[0037] At 2006, a third operation can be performed. For example, the processing logic 1006 of the support module 1000 can perform the operation of 2006. The third operation can include processing the sample by applying the instrument method and data analysis parameters for an elution time, wherein the mass is predicted as a function of the elution time.

[0038] As described above, in some embodiments, data analysis performed on instrument data can generate an assessment of sample quality and / or sample purity. In some embodiments, in addition to or in lieu of a numerical quality metric, a signal light color system (e.g., red, yellow, green, or other colors or visual indicators) can be employed to visually mark sample quality in an attention-grabbing manner. Figure 6 is an example of a visual indicator for marking sample quality determination results generated for a sample array disposed in a well of a tray using the instrument control and data analysis techniques disclosed herein. Although for ease of image reproduction, Figure 6 Shown in different shades of grey, but a signal light colour system (red, yellow, green) could similarly be used.

[0039] The scientific instrument support methods disclosed herein may include interaction with a human user (e.g., via the referenced Figure 5 The user's local computing device 5020 discussed above. These interactions may include providing information to the user (e.g., regarding Figure 5 information about the operation of the scientific instrument 5010, information about the 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 provide the user with an option to enter commands (e.g., to control a device such as a Figure 5 In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a display device (e.g., a display device such as a display device shown in FIG. Figure 4 4010) that provides output to the user and / or prompts the user to provide input (e.g., via the display device 4010 discussed herein). Figure 4 Other I / O devices 4012 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen. The scientific instrument support system disclosed herein may include any suitable GUI for interacting with a user.

[0040] Figure 3 An example GUI 3000 is depicted that may be used in the execution of some or all of the support methods disclosed herein, according to various embodiments. As described above, the GUI 3000 may be provided in a scientific instrument support system (e.g., as described herein with reference to Figure 5 The computing device of the scientific instrument support system 5000 discussed herein (e.g., Figure 4 The display device of the computing device 4000 discussed herein (eg, Figure 44010) and the user may use any suitable input device (e.g., including those described herein). Figure 4 The GUI 3000 may be interacted with using any of the input devices in the other I / O devices 4012 discussed above and input techniques (e.g., movement of a cursor, motion capture, facial recognition, gesture detection, voice recognition, actuation of a button, etc.).

[0041] The 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 . Figure 3 The specific number and arrangement of regions depicted in is illustrative only, and any number and arrangement of regions including any desired characteristics may be included in GUI 3000 .

[0042] The data display area 3002 can display data generated by a scientific instrument (e.g., Figure 5 The data analysis area 3004 may display the results of the 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 in the GUI 3000 (e.g., including the data output from the scientific instrument and some analysis of the data in a common graphic or area).

[0043] The scientific instrument control area 3006 may include a user-friendly interface that allows the user to control scientific instruments (e.g., Figure 5 For example, the data display area 3002 may provide the user with the option of selecting a sample from a queue for use in supporting the receiving logic 1002 of the module 1000.

[0044] The settings area 3008 may include features and functions that allow the user to control the GUI 3000 (and / or other GUIs) and / or perform common computing operations with respect to the data display area 3002 and the data analysis area 3004 (e.g., saving data on a storage device such as that described herein). Figure 4 Options discussed include storing the data on device 4004, sending data to another user, tagging data, etc.).

[0045] As described above, the scientific instrument support module 1000 may be implemented by one or more computing devices. Figure 5is a block diagram of a computing device 4000 that can 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 can be implemented by a single computing device 4000 or multiple computing devices 4000. In addition, as discussed below, the computing device 4000 (or multiple computing devices 4000) that implements the scientific instrument support module 1000 can be Figure 5 A portion of one or more of a scientific instrument 5010, a user local computing device 5020, a service local computing device 5030, or a remote computing device 5040.

[0046] Figure 4 The computing device 4000 is illustrated as having multiple components, but any one or more of these components may be omitted or duplicated to suit the application and setting. In some embodiments, some or all of the components included in the computing device 4000 may be attached to one or more motherboards and encapsulated in a housing (e.g., comprising plastic, metal, and / or other materials). In some embodiments, some of these components may be manufactured onto a single system on a chip (SoC) (e.g., the SoC may include one or more processing devices 4002 and one or more storage devices 4004). Additionally, in various embodiments, the computing device 4000 may not include a processor. Figure 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, computing device 4000 may not include display device 4010, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which display device 4010 may be coupled.

[0047] The computing device 4000 may include a processing device 4002 (e.g., one or more processing devices). 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 to convert the electronic data into other electronic data that can be stored in registers and / or memory. The processing device 4002 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0048] 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), a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In some embodiments, the storage device 4004 may include memory that shares a die with the processing device 4002. In such embodiments, the memory may be used as a 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, the storage device 4004 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processing device 4002), cause the computing device 4000 to perform any appropriate method or portion of the methods disclosed herein.

[0049] 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 other hardware and software to manage communications between the computing device 4000 and other computing devices. For example, the interface device 4006 may include circuitry for managing wireless communications that transfer 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, and the like that can transfer data through a non-solid medium using modulated electromagnetic radiation. The term does not imply that the associated device does not contain any wires, although in some embodiments, it may not contain any wires. The circuitry included in the interface device 4006 for managing wireless communications may implement any of a plurality of wireless standards or protocols, including, but not limited to, Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), the Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., the LTE-Advanced 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 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 an LTE network. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with Enhanced Data Rates 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 their derivatives, as well as any other wireless protocols designated as 3G, 4G, 5G, and higher. In some embodiments, the interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or transmit wireless communications.

[0050] In some embodiments, interface device 4006 may include a circuit for managing wired communication, such as electricity, light or any other suitable communication protocol. For example, interface device 4006 may include a circuit for supporting communication according to Ethernet technology. In some embodiments, interface device 4006 may support both wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first group of circuits of interface device 4006 may be dedicated to close range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 4006 may be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO or other. In some embodiments, a first group of circuits of interface device 4006 may be dedicated to wireless communication, and a second group of circuits of interface device 4006 may be dedicated to wired communication.

[0051] The computing device 4000 may include a battery / power circuit 4008. The battery / power circuit 4008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of the computing device 4000 to an energy source separate from the computing device 4000 (e.g., AC line power).

[0052] Computing device 4000 may include a display device 4010 (eg, multiple display devices). Display device 4010 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0053] 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, headphones, earbuds, an alarm clock, etc.), one or more audio input devices (e.g., a microphone or microphone array), a positioning device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 4000, as is 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 keyboard, a cursor control device such as a mouse, stylus, trackball, or touchpad, a barcode reader, a Quick Response (QR) code reader, or a Radio Frequency Identification (RFID) reader.

[0054] The computing device 4000 can have any suitable form factor for its applications and settings, such as a handheld or mobile computing device (e.g., a cellular phone, a smart phone, 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.

[0055] 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 example scientific instrument support system 5000 in accordance with various embodiments, in which some or all of the scientific instrument support methods disclosed herein may be performed. Figure 1 Scientific Instrument Support Module 1000 and Figure 2 The method 2000) can be implemented by one or more of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030 or the remote computing device 5040 of the scientific instrument support system 5000.

[0056] 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 aforementioned Figure 4 Any of the embodiments of the computing device 4000 discussed herein, 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 employ the embodiments described herein with reference to Figure 4 The computing device 4000 may be implemented in any suitable manner as discussed above.

[0057] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may each include a processing device 5002, a storage device 5004, and an interface device 5006. The processing device 5002 may take any suitable form, including any suitable form described herein. Figure 4 The processing device 5002 discussed above may be in the form of any of the processing devices 4002, and the processing devices 5002 included in different devices such as the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may be in the same form or in different forms. The storage device 5004 may be in any suitable form, including those described herein. Figure 4The storage device 5004 discussed above may be in the form of any of the storage devices 4004, and the storage devices 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 be in the same form or in different forms. The interface device 5006 may be in any suitable form, including those described herein. Figure 4 The interface device 4006 discussed above may be in the form of any one of the discussed embodiments, and the interface device 5006 included in different devices among 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 form or different forms.

[0058] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040 can communicate with other elements of the scientific instrument support system 5000 via a communication path 5008. The communication path 5008 can communicatively couple the interface device 5006 of different ones of the elements of the scientific instrument support system 5000, as shown, and can be a wired or wireless communication path (e.g., according to the present reference Figure 4 Any of the communication technologies discussed above for the interface device 4006 of the computing device 4000). Figure 5 The particular scientific instrument support system 5000 depicted in FIG5 includes a communication path between each pair of scientific instruments 5010, user local computing devices 5020, service local computing devices 5030, and remote computing devices 5040, but this "fully connected" implementation is merely illustrative, and in various embodiments, various of the communication paths 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 may instead communicate with the scientific instrument 5010 via the communication path 5008 between the service local computing device 5030 and the user local computing device 5020, and the communication path 5008 between the user local computing device 5020 and the scientific instrument 5010.

[0059] The user-local computing device 5020 can be a computing device that is local to the user of the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). In some embodiments, the user-local computing device 5020 can also be local to the scientific instrument 5010, but this need not be the case; for example, a user-local computing device 5020 located in a user's home or office can be remote from the scientific instrument 5010 but in communication with it such that the user can 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 can be a laptop, smartphone, or tablet device. In some embodiments, the user-local computing device 5020 can be a portable computing device.

[0060] The service-local computing device 5030 can be a computing device that is local to the entity servicing the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). For example, the service-local computing device 5030 can 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 can communicate with the scientific instrument 5010, the user-local computing device 5020, and / or the remote computing device 5040 (e.g., as discussed above, via a direct communication path 5008 or via multiple "indirect" communication paths 5008) 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., self-test results 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 can communicate with the scientific instrument 5010, the user local computing device 5020 and / or the remote computing device 5040 (e.g., as discussed above, via a direct communication path 5008 or via multiple "indirect" communication paths 5008) to transmit data to the scientific instrument 5010, the user local computing device 5020 and / or the remote computing device 5040 (e.g., to update programming instructions, such as firmware, in the scientific instrument 5010 to initiate execution of a test or calibration sequence in the scientific instrument 5010, to update programming 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's local computing device 5020 to communicate with the service local computing device 5030 to report problems with the scientific instrument 5010 or the user's local computing device 5020, to request access from a technician to improve the operation of the scientific instrument 5010, to order consumable or replacement parts associated with the scientific instrument 5010, or for other purposes.

[0061] The remote computing device 5040 can be a computing device remote from the scientific instrument 5010 and / or remote from the user's local computing device 5020 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). In some embodiments, the remote computing device 5040 can be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 can include a network attached storage device (e.g., as part of the storage device 5004). The remote computing device 5040 can store data generated by the scientific instrument 5010, perform analysis on the data generated by the scientific instrument 5010 (e.g., according to programmed instructions), facilitate communication between the user's local computing device 5020 and the scientific instrument 5010, and / or facilitate communication between the service's local computing device 5030 and the scientific instrument 5010.

[0062] In some embodiments, Figure 5 One or more of the elements of the scientific instrument support system 5000 shown may not be present. Additionally, in some embodiments, there may be Figure 5 The various components of the scientific instrument support system 5000 may be multiple elements. 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). As another example, the scientific instrument support system 5000 may include multiple scientific instruments 5010, all of which communicate with a service-local computing device 5030 and / or a remote computing device 5040; in such embodiments, the service-local computing device 5030 may monitor the multiple scientific instruments 5010, and the service-local computing device 5030 may cause updates or other information to be "broadcasted" to the multiple scientific instruments 5010 simultaneously. Different ones of the scientific instruments 5010 in the scientific instrument support system 5000 may be located close to each other (e.g., in the same room) or far away from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instrument 5010 can be connected to an Internet of Things (IoT) stack that allows command and control of the scientific instrument 5010 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications can be accessed by a user operating a user-local computing device 5020 that communicates with the scientific instrument 5010 through an intervening remote computing device 5040. In some embodiments, a manufacturer can sell the scientific instrument 5010 along with one or more associated user-local computing devices 5020 as part of a local scientific instrument computing unit 5012.

[0063] The following paragraphs provide various examples of the embodiments disclosed herein.

[0064] Embodiment A1 is a mass spectrometry support device comprising: first logic for receiving a sample from a queue, the sample comprising an analyte; second logic for determining an instrument method and data analysis parameters based on properties of the analyte; and third logic for processing the sample to a mass spectrometry elution time by applying the instrument method and data analysis parameters, wherein the mass is predicted as a function of the elution time.

[0065] Embodiment A2 includes the subject matter of embodiment A1, and further specifies that the property of the analyte comprises size, mass, shape, structure, or chemical composition.

[0066] Embodiment A3 includes the subject matter of any of Embodiments A1 and A2, and further specifies that the sample is processed by coupling size-based separation.

[0067] Embodiment A4 includes the subject matter of any one of Embodiments A1 to A3, and further specifies that the parameters comprise hardware settings, ion optics, or detector parameters.

[0068] Embodiment A5 includes the subject matter of any one of Embodiments A1 to A4, and further specifies that the parameters are pre-optimized for a mass range and applied synchronously with the different masses eluting from the chromatography column.

[0069] Embodiment A6 includes the subject matter of any one of Embodiments A1 to A5, and further specifies that the apparatus is a device for measuring the mass.

[0070] Example B1 is a scientific instrument support method, comprising: receiving an indication of the mass of a target analyte by a computing device, wherein the target analyte may be present in a sample; generating one or more instrument control parameters and one or more data processing parameters by the computing device based at least in part on the mass indication; providing the one or more instrument control parameters by the computing device for use by a mass spectrometer during analysis of the sample, wherein the mass spectrometer generates sample data based on the analysis of the sample; and providing the one or more data processing parameters by the computing device for processing the sample data.

[0071] Embodiment B2 includes the subject matter of Embodiment B1, and further comprising processing, by the computing device, the sample data using the one or more data processing parameters.

[0072] Embodiment B3 includes the subject matter of embodiment B2, and further comprising causing, by the computing device, display of a visual indicator of sample quality or sample purity based on the processed sample data.

[0073] Embodiment B4 includes the subject matter of embodiment B3, and further specifies that the visual indicator comprises a signal light color system indicating sample quality or sample purity.

[0074] Embodiment B5 includes the subject matter of any one of embodiments B1 to B4, and further specifies that the data processing parameters include an indication of at least one of a noise filtering algorithm, a feature detection algorithm, a quality deconvolution algorithm, or a quality clustering algorithm.

[0075] Embodiment B6 includes the subject matter of any one of embodiments B1 to B5, and further specifies that the data processing parameters include parameter values ​​for a specific data processing algorithm.

[0076] Embodiment B7 includes the subject matter of any one of Embodiments B1 to B6, and further specifies that the instrument control parameters include at least one of hardware settings, ion optics, or detector parameters.

[0077] Embodiment B8 includes the subject matter of any one of embodiments B1 to B7, and further specifies that receiving the indication of the mass of the target analyte comprises receiving a user specification of a chemical structure or composition of the analyte.

[0078] Embodiment B9 is a method for supporting cryo-electron microscopy (cryo-EM), comprising: receiving, by a computing device, an indication of a mass of a target analyte, wherein the target analyte may be present in a group 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 indication; causing, by the computing device, analysis of the samples in accordance with 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 the results of the sample analysis.

[0079] Embodiment B10 includes the subject matter of embodiment B9, and further provides that identifying the one or more samples for further analysis by cryo-EM comprises causing display of a visual indicator of one or more characteristics of the group of the plurality of samples.

[0080] Embodiment B11 includes the subject matter of any one of embodiments B9 to B10, and further specifies that analysis of the sample comprises coupling to size-based separation.

[0081] Embodiment B12 includes the subject matter of any one of Embodiments B9 to B10, and further comprising causing, by the computing device, a vitrification process to be performed on the identified sample.

[0082] Embodiment B13 is a method for performing a mass spectrometry process, comprising: receiving, by a computing device, an indication of a property of a target analyte, wherein the target analyte may be present in a sample and the property of the analyte includes 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 indication; and causing, by the computing device, analysis of the sample in accordance with the instrument control parameters and the data analysis parameters.

[0083] Embodiment B14 includes the subject matter of embodiment B13, and further specifies that analysis of the sample comprises size-based separation.

[0084] Embodiment B15 includes the subject matter of any one of embodiments B13 to B14, and further specifies that the parameters are pre-optimized for a mass range and applied synchronously with the different masses eluting from the chromatography column.

[0085] Embodiment B16 includes the subject matter of any one of Embodiments B13 to B14, and further comprising causing, by the computing device, display of a visual indicator of a characteristic of the sample based on the analysis.

[0086] Embodiment B17 includes the subject matter of Embodiment B16, and further specifies that the visual indicator comprises a signal light color system indicative of the characteristic.

[0087] Embodiment B18 includes the subject matter of any one of embodiments B13 to B17, 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 a target analyte having a size between 0 kilodaltons and 1000 kilodaltons.

[0088] Embodiment B19 includes the subject matter of any one of embodiments B13 to B18, and further specifies that analyzing the sample comprises size exclusion chromatography or online buffer exchange.

[0089] Embodiment B20 includes the subject matter of any one of embodiments B13 to B19, and further specifies that the target analyte is a protein.

Claims

1. A scientific instrument support method, comprising: receiving, by a computing device, an indication of a mass of a target analyte, wherein the target analyte 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 quality indication; providing, by the computing device, the one or more instrument control parameters for use by a mass spectrometer during analysis of the sample, wherein the mass spectrometer generates sample data based on the analysis of the sample; as well as The one or more data processing parameters are provided by the computing device for use in processing the sample data.

2. The scientific instrument support method according to claim 1, further comprising: The sample data is processed by the computing device using the one or more data processing parameters.

3. The scientific instrument support method according to claim 2, further comprising: Display of a visual indicator of sample quality or sample purity is caused by the computing device based on the processed sample data. 4 . The scientific instrument support method according to claim 3 , wherein the visual indicator comprises a signal light color system indicating sample quality or sample purity. 5 . The scientific instrument support method of claim 1 , wherein the data processing parameters include an indication of at least one of a noise filtering algorithm, a feature detection algorithm, a mass deconvolution algorithm, or a mass clustering algorithm. The scientific instrument support method according to claim 1 , wherein the data processing parameters include parameter values ​​for a specific data processing algorithm.

7. The scientific instrument support method of claim 1, wherein the instrument control parameters include at least one of hardware settings, ion optical devices, or detector parameters. 8 . The scientific instrument support method of claim 1 , wherein receiving the indication of the mass of a target analyte comprises receiving a user specification of a chemical structure or composition of the analyte.

9. A method for supporting cryo-electron microscopy (cryo-EM), comprising: receiving, by a computing device, an indication of a mass of a target analyte, wherein the target analyte may be present in a set of a plurality of 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 quality indication; causing analysis of the sample by the computing device according to the instrument control parameters and the data analysis parameters; as well as One or more of the samples are identified, by the computing device, for further analysis by cryo-EM based at least in part on the results of the sample analysis.

10. The method of claim 9, wherein identifying the one or more samples for further analysis by cryo-EM comprises causing display of a visual indicator of one or more characteristics of the group of the plurality of samples.

11. The method of claim 9, wherein analysis of the sample comprises coupling to size-based separation.

12. The method of claim 9, further comprising: A vitrification process is caused to be performed on the identified sample by the computing device.

13. A method of performing a mass spectrometry process, comprising: receiving, by a computing device, an indication of a property of a target analyte, wherein the target analyte may be present in a sample, and the property of the analyte comprises 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 indication; as well as Analysis of the sample is caused by the computing device according to the instrument control parameters and the data analysis parameters.

14. The method of claim 13, wherein analysis of the sample comprises size-based separation.

15. The method of claim 13, wherein the parameters are pre-optimized for a mass range and applied synchronously with the different masses eluting from the chromatography column.

16. The method of claim 13, further comprising: Display of a visual indicator of a characteristic of the sample is caused by the computing device based on the analysis.

17. The method of claim 16, wherein the visual indicator comprises a signal light color system indicating the characteristic.

18. The method of claim 13, wherein the computing device is configured to generate one or more instrument control parameters and one or more data processing parameters for a target analyte having a size between 0 kilodaltons and 1000 kilodaltons.

19. The method of claim 13, wherein analysis of the sample comprises size exclusion chromatography or online buffer exchange.

20. The method of claim 13, wherein the target analyte is a protein.

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