A system for generating gel lane plots highlighting deconvoluted masses

Abstract

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 system may include: first logic for receiving a plurality of sample data from a queue; second logic for generating, based on the sample data, a gel lane plot including a stacked row of gel spots discretized into single samples or runs on the y-axis, where the y-axis represents mass and the x-axis represents mass; third logic for regularizing the y-axis for the sample data by using a clustering technique (e.g., including a high-resolution clustering algorithm and / or a low-resolution version) to mass-cluster the sample data; and fourth logic for providing the gel lane plot for display showing deconvoluted mass spectral results for the samples.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 480,059, filed January 16, 2023, entitled "SYSTEM FOR GENERATING GEL-LANE PLOTS HIGHLIGHTING DECONVOLUTED MASSES," the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to a system for generating gel lane plots that highlight deconvoluted masses. [Background technology]

[0003] Chromatography is a technique for the separation of components of a sample mixture that exploits the different properties of the components as they interact 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. Summary of the Invention

[0004] The present invention provides a mass spectrometry support device, receiving data representing mass spectra of a plurality of samples; generating a gel lane plot based on the data, the gel lane plot including a plurality of stacked columns of gel spots, the y-axis representing mass, the x-axis being divided into a plurality of regions each corresponding to a different one of the plurality of samples, and the color or shading of the gel spots representing intensity information of the data; Provide a mass spectrometry support device, including logic, that provides the gel lane plot for display.

[0005] The present invention also provides a mass spectrometry system, comprising: a mass spectrometer; a computing device communicatively coupled to the mass spectrometer, the computing device comprising: receiving data from the mass spectrometer, the data representing mass spectra of a plurality of samples; generating a plot based on the data, the plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, the one or more spots in a lane associated with a particular sample indicating intensity information in the mass spectrum associated with that sample; A mass spectrometry system is provided that outputs the plot to a user of the mass spectrometry system.

[0006] The present invention also provides one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of a computing device, cause the computing device to: receiving data from a mass spectrometer, the data representing mass spectra of a plurality of samples; generating a gel lane plot to represent the data for display, the gel lane plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, the vertical position of a spot indicating an associated mass; For display, one or more non-transitory computer readable media are provided that generate graphics indicative of the quality or purity of individual samples based on the data. [Brief explanation of the drawings]

[0007]

[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 designate like structural elements;

[0015] The embodiments are illustrated in the figures of the accompanying drawings, by way of example, and not by way of limitation. [Figure 1] FIG. 1 is a block diagram of an exemplary mass spectrometry instrument support module for determining peak positions for a mass spectrometry dataset, according to various embodiments. [Figure 2] FIG. 1 is a flow diagram of an exemplary method for performing an assistive operation, according to various embodiments. [Figure 3] 1 is an example of a graphical user interface that may be used in implementing some or all of the assistance methods disclosed herein, according to various embodiments. [Figure 4] FIG. 1 is a block diagram of an exemplary computing device that may implement some or all of the mass spectrometry instrument-assisted methods disclosed herein, according to various embodiments. [Figure 5] FIG. 1 is a block diagram of an exemplary scientific instrument support system in which some or all of the scientific instrument support methods disclosed herein may be implemented, according to various embodiments. [Figure 6] 1 illustrates exemplary gel lane plots for multiple samples according to various embodiments disclosed herein. [Figure 7] 1 illustrates exemplary graphics for indicating sample quality and purity, according to various embodiments disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0008] 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 system may include: first logic for receiving a plurality of sample data from a queue; second logic for generating, based on the sample data, a gel lane plot whose y-axis represents mass (e.g., having mass, mass to charge (m / z), or another mass-related unit) and whose x-axis includes stacked rows of gel spots discretized into single samples or runs; third logic for regularizing the y-axis for the sample data by using a clustering technique (e.g., including a high-resolution clustering algorithm and / or a low-resolution version) to mass-cluster the sample data; and fourth logic for providing the gel lane plot for display showing deconvoluted mass spectral results for the samples.

[0009] "Gel lane" plots are particularly useful when viewing deconvoluted masses from either a single sample or across many samples. Gel lanes generally comprise stacked rows of "gel spots" (horizontal lines), where the y-axis is mass and the x-axis is discretized to single samples or runs (e.g., different regions on the x-axis correspond to different samples or runs). Several gel lanes (e.g., one per sample or run) can be arranged horizontally side-by-side, allowing for cross-sample comparisons on a single page (or view). In some embodiments, the described systems transform the x-axis relative to the y-axis, allowing more masses to be visible on a page (less data loss) because there are more pixels available per page. This favors mass over intensity (an advantage of mass spectrometry).

[0010] Mass spectra (m / z vs. intensity) have an m / z axis (mass / charge) rather than a simple mass axis. In some embodiments, a mass axis can be more expendable because mass is the chemical property that links analyte measurements to actual compound analytes. A typical deconvoluted mass spectrum (mass vs. intensity) consumes a full or partial page and is not suitable for visual comparison across or between samples. Comparing multiple spectra at once has historically been problematic and is often done either by stacking plots vertically one above the other on a page, or by a waterfall arrangement in which spectra are diagonally offset and placed on top of each other.

[0011] Furthermore, stacking plots on top of each other results in the mass axis being placed on the horizontal axis, which generally has fewer pixels per page than the vertical axis. This compresses the high-resolution mass axis into fewer pixels, resulting in data loss. This is particularly bad for large, intact molecules, whose masses can range from 50 to over 1,000,000 Da. This occurs when there are multiple stacked plots or a single figure. To reduce x-axis compression, mass spectra are often printed in landscape mode. Overlapping spectra in a waterfall plot scenario work when the data are simple but rapidly become noisy as the number of features increases. Noisy data that fluctuated from run to run also resulted in overly cluttered plots with little benefit.

[0012] In some embodiments, intensity information is displayed by coloring the "gel spots" to represent the abundance associated with the spots. The term "intensity," as used herein, can refer to the height of a peak in an m / z spectrum, the actual amount of an analyte as represented by its deconvoluted mass (sometimes referred to as abundance, which can be derived from the sum of the intensities of multiple m / z peaks in the same spectrum), relative peak height or analyte abundance (e.g., as shown in the example of FIG. 6), or any related quantity that indicates the magnitude of an indicator of the analyte present. In this scenario, darker is more abundant, and colors range from dark to light. In other scenarios, when multiple colors are used (e.g., green to red), a wider range of colors can be used. To aid in consumption, a blocked legend can be used, for example, showing four shades representing four different ranges of intensity. Intensities can be plotted directly in a compressed format (e.g., logarithmic or log10 format).

[0013] Generating these plots requires mass clustering the data together to normalize the y-axis for all sample data, ensuring that pixels for data spots are not lost during pixel reduction. High-resolution and low-resolution mass spectra may use different algorithms. Thus, the system may use a high-resolution clustering algorithm and / or a low-resolution version to improve performance. Any suitable clustering algorithm may be used for clustering, examples of which include k-means clustering or k-nearest neighbor clustering.

[0014] By plotting gel lanes horizontally across the page, masses of interest can be highlighted by drawing a highlighting line (e.g., yellow) or box around the given mass. This method allows for the display of theoretical mass in directed experiments where theoretical and experimental masses are compared.

[0015] Thus, the described system provides a compact, easy-to-use display of deconvoluted mass spectral results, similar to an electrophoretic gel, enabling unprecedented resolution and an extended dynamic range of intensities. While typical gel electrophoresis displays masses with an accuracy of several thousand daltons, mass spectrometers can accurately measure mass down to sub-dalton accuracy for high-resolution data and down to tens of daltons for low-resolution data. The dynamic range of detectable intensities is significantly improved with mass spectrometers. In addition to the gel lanes, the system includes graphics (red / yellow, green stoplights) to indicate instrument performance (QC) and overall sample quality and purity. This provides high-level, actionable data all in one view. The gel lane plots disclosed herein are more easily understandable by scientists who are not experts in mass spectrometry, improving the ease of use and accurate interpretation of data for mass spectrometry systems, thereby accelerating scientific analysis. The gel lane plots disclosed herein may be particularly valuable in applications where many samples are screened, such as drug binding studies or other molecular binding studies. For example, in molecular binding studies, the gel lane plots disclosed herein may visually represent molecular binding between two or more compounds or elements by depicting protein-bound drug and unbound drug and / or unbound protein as two or three gel lines.

[0016] In the following detailed description, reference is made to the accompanying drawings that form a part hereof, where like numerals designate like parts throughout and in which are shown by way of illustration 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.

[0017] Various operations may be described sequentially as multiple separate 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 as implying 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 the operations described may be omitted in additional embodiments.

[0018] 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 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 processing device may be implemented with different ones of the operations performed by different processing devices.

[0019] The description may include terms such as "an embodiment," "various embodiments," and "some embodiments." In the present specification, phrases such as "X" and "Y" are used, each of which may refer to one or more of the same or different embodiments. Furthermore, terms such as "comprising," "including," and "having" when used with respect to embodiments of the present disclosure are synonymous. When used to describe a range of dimensions, the phrase "between X and Y" represents a range that includes X and Y. As used herein, "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.

[0020] FIG. 1 is a block diagram of a mass spectrometry instrument support module 1000 for displaying theoretical masses in a directed experiment in which theoretical masses are compared to experimental masses. The mass spectrometry instrument support module 1000 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the mass spectrometry instrument support module 1000 may be included in a single computing device or, where appropriate, distributed across multiple computing devices in communication with each other. An example of a computing device that may implement the mass spectrometry instrument support module 1000, alone or in combination, is discussed 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 across one or more of the computing devices is discussed herein with reference to the mass spectrometry instrument support system 5000 of FIG. 5. The mass spectrometry instrument support module 1000 may include sample receiving logic 1002, gel lane plot generation logic 1004, regularization logic 1006, and display logic 1008.

[0021] As used herein, the term “logic” may include an apparatus that performs a set of operations associated with the logic. For example, any of the logic elements included in assistance module 1000 may be implemented by one or more computing devices programmed with instructions that cause one or more processing devices of the computing devices to perform the associated set of operations. In particular embodiments, a logic element may include one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term “module” may refer to a collection of one or more logic elements that together perform one or more functions associated with the module. Different logic 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 device, while other logic within the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different logic elements within a module may be associated with different sets of instructions executed by one or more processing devices. A module may not include all of the logic elements depicted in an associated figure; for example, a module may include a subset of the logic elements depicted in an associated figure when that module performs a subset of the operations discussed herein with reference to that module.

[0022] The sample receiving logic 1002 may be configured to receive a sample or multiple samples from the queue. 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 have accumulated in the queue (e.g., 20), 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).

[0023] The gel lane plot generation logic 1004 can be configured to generate, based on the sample data, a gel lane plot where the y-axis represents mass and the x-axis includes stacked columns of gel spots discretized into single samples or runs.

[0024] The regularization logic 1006 may be configured to regularize the y-axis to the sample data by using clustering techniques (e.g., including high-resolution clustering algorithms and / or low-resolution versions) to mass-cluster the sample data. In some examples, intensity information from the sample data is enhanced by applying color to the gel spots. In some embodiments, regularizing the y-axis to the sample data ensures that pixels for data spots are not lost during pixel reduction.

[0025] The display logic 1008 may provide a user, through a GUI (such as GUI 3000 in FIG. 3 ), with a gel lane plot showing deconvoluted mass spectral results for a sample or select a sample from a queue for the sample receiving logic 1002. In some cases, the gel lane plot shows a comparison between samples on a single page or view. In some cases, displaying the gel lane plot increases the number of available page pixels and reduces data loss. In some cases, the GUI also provides the user with graphics to show instrument performance (QC) and the overall quality and purity of the sample. In some cases, displaying the gel lane plot allows the user to compare the theoretical mass of a sample with the experimental mass.

[0026] Figure 6 illustrates exemplary gel lane plots for multiple samples according to various embodiments disclosed herein. In the example in Figure 6, different shading is used to indicate intensity; however, as noted above, different colors can be used similarly. In the example in Figure 6, a highlight box or line is shown around a specific mass (466 kDa) to allow comparison of theoretical and experimental masses. As illustrated in the gel lane plot in Figure 6, "white space" exists along the y-axis between observed masses, similar to results in chemical gel experiments. Many conventional methods of depicting mass spectrometry results represent "compressed" plots in which only detected masses are listed or otherwise identified. The unconventional presence of white space in the gel lane plots disclosed herein can assist users who may not be mass spectrometry experts in quickly understanding and interpreting mass spectrometry results. Figure 7 illustrates an exemplary graphic for indicating sample quality and purity. The coloring of the graphic in Figure 7 can take the form of a red, yellow, and green stoplight system, or another suitable form.

[0027] 2 is a flow diagram of a method 2000 for 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 discussed herein with reference to FIG. 1 , GUI 3000 discussed herein with reference to FIG. 3 , computing device 4000 discussed herein with reference to FIG. 4 , and / or scientific instrument support system 5000 discussed herein with reference to FIG. 5 ), but method 2000 may be used in any suitable setting 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, when suitable).

[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 plurality of sample data from a queue.

[0029] A second operation may be performed in 2004. For example, the gel lane plot generation logic 1004 of the method and support module 1000 may perform the operation of 2004. The second operation may include generating, based on the sample data, a gel lane plot where the y-axis represents mass and the x-axis includes stacked rows of gel spots discretized into single samples or runs.

[0030] A third operation may be performed in 2006. For example, the regularization logic 1006 of the assistance module 1000 may perform the operation of 2006. The third operation may include processing the sample by regularizing the y-axis for the sample data by using a clustering technique (e.g., including a high-resolution clustering algorithm and / or a low-resolution version) to mass cluster the sample data.

[0031] A fourth operation may be performed in 2008. For example, the display logic 1008 of the assistance module 1000 may perform the operation of 2008. The fourth operation may include providing a gel lane plot for display showing the deconvoluted mass spectral results for the sample.

[0032] The scientific instrument assistance methods disclosed herein may include interactions with a human user (e.g., via a user local computing device 5020 discussed herein with reference to FIG. 5 ). These interactions may include providing information to the user (e.g., information regarding the operation of a scientific instrument such as the scientific instrument 5010 of FIG. 5 , information regarding 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 implemented through a graphical user interface (GUI) including a visual display on a display device (e.g., the display device 4010 discussed herein with reference to FIG. 4 ) that provides output to the 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 the other I / O devices 4012 discussed herein with reference to FIG. 4 ). The scientific instrument support systems disclosed herein may include any suitable GUI for interaction with a user.

[0033] 3 depicts an exemplary GUI 3000 that may be used in implementing some or all of the assistance methods disclosed herein, according to various embodiments. As noted above, the GUI 3000 is displayed on a display device (e.g., the display device 4010 discussed herein with reference to FIG. 4 ) of a computing device (e.g., the computing device 4000 discussed herein with reference to FIG. 4 ) of a scientific instrument assistance system (e.g., the scientific instrument assistance system 5000 discussed herein with reference to FIG. 5 ). A user may interact with the GUI 3000 using any suitable input device (e.g., any of the input devices included in the other I / O devices 4012 discussed herein with reference to FIG. 4 ) and input technology (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 depicted in Figure 3 is merely illustrative, 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 discussed herein with reference to FIG. 5). The data analysis area 3004 may display the results of the data analysis (e.g., the results of analyzing the data illustrated in the data display area 3002 and / or other data). For example, a gel lane plot resulting from the regularization logic 1006 of the assistance 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., to include data output from the scientific instrument and some analysis of the data in 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 discussed herein with reference to FIG. 5). For example, the data display area 3002 may provide the user with the option to select sample data 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., storing data on a storage device such as storage device 4004 discussed herein with reference to FIG. 4 , transmitting data to another user, labeling data, etc.).

[0038] As noted 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 implement 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 discussed below, the computing device 4000 (or multiple computing devices 4000) that implements 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] 4 is illustrated as having several components, any one or more of which may be omitted or duplicated as appropriate for the application and configuration. In some embodiments, some or all of the components included in computing device 4000 may be mounted on one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, several of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an SoC may include one or more processing devices 4002 and one or more storage devices 4004). 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., connectors and driver circuits) to which display device 4010 may be coupled.

[0040] Computing device 4000 may include 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 that electronic data into other electronic data that may be stored in registers and / or memory. Processing device 4002 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), or other processors. integrated circuit (ASIC), central processing unit (CPU) The processing device may include a CPU, a graphics processing unit (GPU), a cryptographic processor (a dedicated processor that runs cryptographic algorithms in hardware), a server processor, or any other suitable processing device.

[0041] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include random access memory (RAM) (e.g., static RAM). The storage device 4004 may include one or more memory devices, such as static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging 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 device 4002. In such embodiments, the memory may be 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, the storage device 4004 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., processing device 4002), cause the computing device 4000 to perform any suitable of the methods disclosed herein, or portions thereof.

[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 other hardware and software for managing communications between the computing device 4000 and other computing devices. For example, the interface device 4006 may include circuitry for managing wireless communications for data transfer 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 may communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated devices do not include any wiring, although in some embodiments they may not. The circuitry included in interface device 4006 for managing wireless communications may implement any of a number of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), Institute for Electrical and Electronic Engineers (IEEE) standards, including the IEEE 802.16 standard (e.g., the IEEE 802.16-2005 Amendment), the Long-Term Evolution (LTE) project (e.g., the Advanced LTE project, the Ultra-Mobile Broadband (UMB) project (also referred to as "3GPP®2"), etc.), with any amendments, updates, and / or revisions.In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications is compatible with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA, and the like. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with a GSM 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 an 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 a Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), or LTE network. Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications The interface device 4006 may operate in accordance with the following wireless protocols: DECT (Decorative, Enhanced Cordless Telecommunications), Evolution-Data Optimized (EV-DO), and their derivatives, 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 to support 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, EV-DO, or others. 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 (e.g., AC line power) separate from computing device 4000.

[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 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 cursor control device such as a keyboard, mouse, stylus, trackball, or touchpad, a barcode reader, a Quick Response (QR) code reader, or a radio frequency identification (RFID) device. The device may include a radio frequency identification (RFID) reader.

[0047] The 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 cell 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 network 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 scientific instrument support system 5000 in which some or all of the scientific instrument support methods disclosed herein may be implemented, 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 the computing device 4000 discussed herein with reference to FIG. 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 embodiment of the embodiments of the computing device 4000 discussed herein with reference to FIG. 4.

[0050] 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 form of the processing device 4002 discussed herein with reference to Figure 4, and the processing devices 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 form or different forms. The storage device 5004 may take any suitable form, including any form of the storage device 4004 discussed herein with reference to Figure 4, 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 take the same form or different forms. The interface device 5006 may take any suitable form, including any of the forms of the interface device 4006 discussed herein with reference to FIG. 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 paths 5008. The communication paths 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 paths (e.g., via any of the communication techniques discussed herein with reference to the interface device 4006 of the computing device 4000 of FIG. 4 ). While the particular scientific instrument support system 5000 depicted in FIG. 5 includes communication paths 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 illustrative, and in various embodiments, various ones 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 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 discussed herein). In some embodiments, the user local computing device 5020 may also be local to the scientific instrument 5010, but need not be; for example, a user local computing device 5020 in a user's home or office may be remote from but in communication with the scientific instrument 5010, such that 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 (e.g., according to any of the embodiments of computing device 4000 discussed herein) that is local to an entity that provides services to the scientific instrument 5010. 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 discussed 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 discussed above) 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 programmed instructions such as firmware in the scientific instrument 5010, to initiate the performance 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, to request a visit from a technician to improve the operation of the scientific instrument 5010, to order consumables or replacement parts associated with the scientific instrument 5010, or for other purposes.

[0054] The remote computing device 5040 may be a computing device (e.g., according to any of the embodiments of computing device 4000 discussed herein) that is remote from the scientific instrument 5010 and / or from the user local computing device 5020. 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 device 5004). The remote computing device 5040 may perform analysis of data generated by the scientific instrument 5010 (e.g., according to programmed instructions), store data generated by the scientific instrument 5010 in order to facilitate communication between the user local computing device 5020 and the scientific instrument 5010, and / or facilitate communication 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 illustrated in Figure 5 may not be present. Furthermore, in some embodiments, more than one of various of the elements of the scientific instrument support system 5000 of Figure 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 servicing local computing device 5030 and / or a remote computing device 5040; in such an embodiment, the servicing local computing device 5030 may monitor these multiple scientific instruments 5010, and the servicing local computing device 5030 may "broadcast" 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 by way of 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] Example The following paragraphs provide various examples of the embodiments disclosed herein.

[0057] Example A1 is a mass analysis support device including first logic for receiving multiple sample data from a queue; second logic for generating, based on the sample data, a gel lane plot in which the y-axis represents mass and the x-axis includes stacked rows of gel spots discretized into single samples or runs; third logic for regularizing the y-axis for the sample data by using a clustering technique (e.g., including a high-resolution clustering algorithm and / or a low-resolution version) to mass-cluster the sample data; and fourth logic for providing the gel lane plot for display showing deconvoluted mass spectral results for the samples.

[0058] Example A2 includes the subject matter described in Example 1, further providing that the third logic further includes enhancing intensity information from the sample data by applying color to the gel spots.

[0059] Example A3 includes the subject matter of Example A1 or A2, further specifying that the gel lane plot increases the number of available page pixels and reduces data loss.

[0060] Example A4 includes the subject matter of any one of Examples A1-A3, and further includes the fourth logic providing graphics for display to indicate instrument performance (QC) and overall quality and purity of the sample.

[0061] Example A5 includes the subject matter of any one of Examples A1-A4, further specifying that normalizing the y-axis to the sample data ensures that pixels for the data spots are not lost during pixel reduction.

[0062] Example A6 includes the subject matter of any one of Examples A1-A5, further specifying that the representation of the gel lane plot compares the theoretical mass and the experimental mass of the sample.

[0063] Example A7 includes the subject matter of any one of Examples A1-A6, further providing that the display of the gel lane plots allows for comparison between samples on a single page or view.

[0064] Example B1 is a mass analysis support device that includes logic for receiving data representing mass spectra of a plurality of samples, generating a gel lane plot based on the data, the gel lane plot including a plurality of stacked columns of gel spots, the y-axis representing mass, the x-axis divided into a plurality of regions each corresponding to a different one of the plurality of samples, and the color or shading of the gel spots representing intensity information of the data, and providing the gel lane plot for display.

[0065] Example B2 may include the subject matter described in Example B1, and may further provide that the logic further regularizes the y-axis and data by using a clustering technique as part of generating the gel lane plot.

[0066] Example B3 can include the subject matter of example B1 or B2, and can further specify that the y-axis represents mass relative to mass or charge.

[0067] Example B4 may include the subject matter of any one of Examples B1-B3, and may further provide that the logic further provides, based on the data, a graphic indicating the quality or purity of the individual sample.

[0068] Example B5 may include the subject matter of any one of Examples B1-B4, and may further provide that the logic further generates a box or line corresponding to a particular mass and extending across a column of the gel lane plot.

[0069] Example B6 may include the subject matter of any one of Examples B1-B5, and may further provide that the logic further enables printing the gel lane plot onto a sheet of paper.

[0070] Example B7 is a mass spectrometry system including a mass spectrometer and a computing device communicatively coupled to the mass spectrometer, wherein the computing device receives data from the mass spectrometer, the data indicating mass spectra of a plurality of samples, and based on the data, generates a plot, the plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, wherein one or more spots in a lane associated with a particular sample indicate intensity information in the mass spectrum associated with that sample, and outputs the plot to a user of the mass spectrometry system.

[0071] Example B8 can include the subject matter described in Example B7 and can further provide that spots in a particular lane represent clustered data from the mass spectrum of the sample associated with that lane.

[0072] Example B9 can include the subject matter of example B7 or B8, and can further provide that the spots include a visual indicator of the intensity of one or more associated peaks in the mass spectrum.

[0073] Example B10 may include the subject matter of example B9, and may further provide that the visual indicator includes a color.

[0074] Example B11 can include the subject matter of any one of Examples B7-B10, and can further provide that the vertical position of a spot in a lane corresponds to the mass associated with the associated peak.

[0075] Example B12 may include the subject matter described in any one of Examples B7 to B11, and may further provide that the computing device is further configured to output a graphic to the user indicating the quality or purity of the individual sample based on the data.

[0076] Example B13 is one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of a computing device, cause the computing device to receive data from a mass spectrometer, the data indicating mass spectra of a plurality of samples; generate for display a gel lane plot representing the data, the gel lane plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, the vertical position of the spots indicating an associated mass; and generate for display a graphic indicating the quality or purity of the respective samples based on the data.

[0077] Example B14 may include the subject matter described in Example B13, and may further provide that the instructions, when executed, cause the computing device to further generate, for display, a box or line corresponding to a particular mass and extending across a column of the gel lane plot.

[0078] Example B15 may include the subject matter of Example B13 or B14, and may further provide that the gel lane plot includes white spaces of different lengths along the vertical length of one or more of the vertical lanes to indicate mass differences between associated spots.

[0079] Example B16 may include the subject matter of any one of Examples B13-B15, and may further provide that spots in a particular lane represent clustered data from the mass spectrum of the sample associated with that lane.

[0080] Example B17 can include the subject matter of any one of examples B13-B16, and can further provide that the spots include a visual indicator of the intensity of one or more associated peaks in the mass spectrum.

[0081] Example B18 may include the subject matter of any one of examples B13-B17, and may further provide that the visual indicator includes a color.

[0082] Example B19 may include the subject matter of any one of examples B13-B18, and may further specify that the data is received from a queue.

[0083] Example B20 can include the subject matter of any one of Examples B13-B19, and can further provide that the mass range associated with the vertical length of the lane includes at least 50 to 1,000,000 Da.

Claims

1. A mass spectrometry support device, receiving data representing mass spectra of a plurality of samples; generating a gel lane plot based on the data, the gel lane plot comprising a plurality of stacked rows of gel spots, the y-axis representing mass and the x-axis divided into a plurality of regions each corresponding to a different one of the plurality of samples, the color or shading of the gel spots representing intensity information of the data; a mass spectrometry support device including logic that provides said gel lane plot for display;

2. 10. The mass spectrometry support device of claim 1, wherein the logic further regularizes the y-axis and the data by using a clustering technique as part of generating the gel lane plot.

3. The mass analysis support system of claim 1 , wherein the y-axis represents mass or mass to charge.

4. The mass spectrometry support device of claim 1 , wherein the logic further provides a graphic indicating the quality or purity of an individual sample based on the data.

5. The logic may further comprise:

10. The mass analysis support device of claim 1, wherein the device generates a box or line corresponding to a particular mass and extending across the column of the gel lane plot.

6. 10. The mass spectrometry support device of claim 1, wherein the logic further enables printing the gel lane plot onto a sheet of paper.

7. 1. A mass spectrometry system comprising: a mass spectrometer; a computing device communicatively coupled to the mass spectrometer, the computing device comprising: receiving data from the mass spectrometer, the data representing mass spectra of a plurality of samples; generating a plot based on the data, the plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, the one or more spots in a lane associated with a particular sample indicating intensity information in the mass spectrum associated with that sample; A mass spectrometry system that outputs the plot to a user of the mass spectrometry system.

8. The mass spectrometry system of claim 7 , wherein the spots in a particular lane represent clustered data from the mass spectra of the sample associated with that lane.

9. The mass spectrometry system of claim 7 , wherein the spots comprise visual indicators of the intensities of one or more associated peaks in the mass spectrum.

10. The mass spectrometry system of claim 9 , wherein the visual indicator comprises a color.

11. 8. The mass spectrometry system of claim 7, wherein the vertical position of a spot in a lane corresponds to the mass associated with the associated peak.

12. The mass spectrometry system of claim 7 , wherein the computing device is further configured to output to the user a graphic indicating the quality or purity of an individual sample based on the data.

13. One or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of a computing device, cause the computing device to: receiving data from a mass spectrometer, the data representing mass spectra of a plurality of samples; generating a gel lane plot representing the data for display, the gel lane plot including a plurality of vertical lanes, each lane associated with a respective one of the samples, and one or more spots dispersed along the vertical length of each of the lanes, the vertical position of a spot indicating an associated mass; One or more non-transitory computer-readable media for generating, for display, a graphic indicating the quality or purity of an individual sample based on the data.

14. The instructions, when executed, further cause the computing device to:

14. The one or more non-transitory computer-readable media of claim 13, wherein the one or more non-transitory computer-readable media generate, for display, a box or line that corresponds to a particular mass and extends across a column of the gel lane plot.

15. 14. The one or more non-transitory computer-readable media of claim 13, wherein the gel lane plot includes white space of different lengths along the vertical length of one or more of the vertical lanes to indicate mass differences between associated spots.

16. 14. The one or more non-transitory computer-readable media of claim 13, wherein the spots in a particular lane represent clustered data from the mass spectrum of the sample associated with that lane.

17. 14. The one or more non-transitory computer-readable media of claim 13, wherein the spots comprise visual indicators of intensities of one or more associated peaks in the mass spectrum.

18. The one or more non-transitory computer-readable media of claim 17 , wherein the visual indicator comprises a color.

19. The one or more non-transitory computer-readable media of claim 13 , wherein the data is received from a queue.

20. 14. The one or more non-transitory computer-readable media of claim 13, wherein the mass range associated with the vertical length of a lane comprises at least 50 to 1,000,000 Da.

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