WO2024167815A1

WO2024167815A1 - Method for correlating separation and mass spectral data

Info

Publication number: WO2024167815A1
Application number: PCT/US2024/014403
Authority: WO
Inventors: Spencer Clark; Mariam Elnaggar
Original assignee: Intabio LLC
Current assignee: Intabio LLC
Priority date: 2023-02-08
Filing date: 2024-02-05
Publication date: 2024-08-15
Anticipated expiration: 2025-08-08
Also published as: WO2024167819A1

Abstract

Converting a first data-set to a converted data-set using a second data-set, where the first data-set include one or more images of an isoelectric focusing of one or more analytes, where each image pixel of the images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pi) value for the analytes, where the second data-set includes one or more traces of a mobilization of the one or more analytes, and where each data point of the traces of the mobilization corresponds to a second signal intensity at a known position and/or known time for the analytes, and generating an integrated plot by correlating the converted data-set to a third data-set, where the third data-set may include mass spectral data, where the mass spectral data includes a third signal intensity as a fu tion of time

Description

METHOD FOR CORRELATING SEPARATION AND MASS SPECTRAL DATA

RELATED APPLICATIONS

[0001] The present patent application claims the priority benefit of U.S. Provisional Patent Application Ser. No. 63/444,155, filed February 8, 2023 and U.S. Provisional Patent Application Ser. No. 63/504,095, filed May 24, 2023, the content of which is hereby incorporated by reference in its entirety into this disclosure.

FIELD

[0002] The present disclosure relates generally to sample analysis, and more particularly to such analyses involving measurements through the use of mass spectrometry.

BACKGROUND

[0003] The present disclosure relates to the field of chemical analysis, and in particular, to the separation of analytes in a mixture and their subsequent analysis by mass spectrometry (MS). Separation of analyte components from a more complex analyte mixture on the basis of an inherent quality of the analytes and providing sets of fractions that are enriched for states of that quality, is a key part of analytical chemistry. For example, imaged isoelectric focusing can be used to separate a complex analyte mixture on the basis of isoelectric points (pls) of the analytes and provide valuable information about the composition of the mixture based on the imaging data collected. Such separated complex analyte mixture can then be mobilized and further analyzed by downstream MS, thus providing valuable information about the composition of the mixture based on the MS data collected. However, complications can arise when attempting to interface known enrichment methods and/or devices with other analytical equipment and/or techniques. Correlation between the two data sets can be challenging and prone to user error for a variety of reasons. For example, the two data sets may be significantly different, may use different units of measurement, or it might be unclear which portions of the data in one data set correspond to which portions of the data in the other data set.

[0004] Methods, devices, systems, and software for improving the quality of isoelectric focusing data and/or mass spectrometry data are described, as are methods, devices, systems, and software for achieving more quantitative characterization of and improved correlation between separation data and mass spectrometry data.

SUMMARY

[0005] A general aspect includes a computer-implemented method, comprising converting, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or known time for the one or more analytes; and generating at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time. In an aspect, the converted data set comprises a piecewise stretched and/or contracted signal intensity plot in a time domain where each timepoint corresponds to the position and/or the pl value.

[0006] In an aspect, the computer-implemented method includes wherein the converted data set comprises peak positions and/or pl values in the first data set as a function of a time domain. In an aspect, the converted data set comprises one or more anchor points, the one or more anchor points correlating the position and/or the pl values of the first data set to the known position and/or known time values of the second data set. In an aspect, the converted data set is further stretched, compressed, moved, or manipulated about the one or more anchor points. In an aspect, the converting of the first data set is performed by assigning the known positions and/or the known times from the second data set to one or more peaks of the first data set. In an aspect, the converting further comprises separating peaks in the first data set into a focused acidic group and/or a focused basic group and/or separating peaks in the second data set into a mobilization acidic group and/or a mobilization basic group.

[0007] In an aspect, the computer-implemented method includes ordering the peaks in the focused acidic group, the focused basic group, the mobilization acidic group, and the mobilization basic group by at least one of height, spatial position, area, or ratios of height, spatial position, or area relative to at least one corresponding marker in the first or second data set and/or to at least one corresponding analyte peak in the first or second data set. In an aspect, the computer-implemented method further includes pairing at least one first peak in the focused basic group to at least one second peak in the mobilization basic group and/or at least one first peak in the focused acidic group to at least one second peak in the mobilization acidic group. In an aspect, the pairing is performed in descending order of peak height starting with a highest peak in the focused basic group to a highest peak in the mobilization basic group and/or a highest peak in the focused acidic group to a highest peak in the mobilization acidic group.

[0008] In an aspect, the computer-implemented method includes skipping pairing of the selected peaks in the focused acidic group, the focused basic group, the mobilization acidic group, and/or the mobilization basic group which are out of spatial order. In an aspect, at least one peak in the first data set is mapped to at least one peak in the second data set. In an aspect, the at least one peak in the first data set is mapped to the at least one peak in the second data set based on one or more of criteria thresholds. In an aspect, the one or more criteria thresholds are percent relative height, percent relative position, percent relative area, and/or relative ratiometric values. In an aspect, the computer-implemented method includes calculating one or more first relative peak ratios for peaks in the first data set based on one or more peaks of the first data set and calculating one or more second relative peak ratios for peaks in the second data set based on one or more peaks of the second data set. In an aspect, the first and second one or more relative peak ratios are ratios of height, spatial position, or area. In an aspect, the computer- implemented method includes pairing one or more peaks of the first data set with one or more peaks of the second data set based on the one or more first relative peak ratios and the one or more second relative peak ratios. In an aspect, the second data set is normalized and/or interpolated prior to converting the first data set using the second data set. In an aspect, correlating the converted data set to the third data set comprises setting a time value of a tallest sample peak of the converted data to be equal to a time value from a tallest peak of the third data set.

[0009] In an aspect, correlating the converted data set to the third data set comprises aligning the converted data set to the third data set based on signal intensity. In an aspect, aligning the converted data set to the third data set can be further adjusted by a user via the graphical user interface. In an aspect, the at least one integrated plot is a pl and/or mass resolved intensity plot. In an aspect, the at least one integrated plot shows the position and/or the pl values as a function of a time domain. In an aspect, the third data set is an extracted chronogram. In an aspect, the third data set is a base peak ion (BPI) intensity plot, an extracted ion chronogram, or a multi-dimensional plot. In an aspect, generating a second integrated plot is performed by aligning the tallest analyte peak in a second conversion data set and the tallest analyte peak in the third data set. In an aspect, the second conversion data set is obtained from the first data set by converting position and/or pl values to time. In an aspect, switching from the integrated plot to the second integrated plot is performed via user input at the graphical user interface. In an aspect, generating the second integrated plot is performed automatically if the converted data set cannot be verified. In an aspect, a user is notified via the graphical user interface if the converted data set cannot be verified. In an aspect, verifying the converted data set is performed using model metrics. In an aspect, verifying the converted data set is performed using at least one of polynomial regression, quadratic regression, cubic regression, or logistic regression. In an aspect, the computer-implemented method includes obtaining the first data set, the second data set, and the third data set by performing the isoelectric focusing, mobilization, and electrospray ionization mass spectrometry using an integrated microfluidic device coupled to a mass spectrometer.

[00010] In a general aspect, a computer-implemented method is provided for displaying and/or comparing imaged capillary isoelectric focusing (icIEF) and mass spectral (MS) data for one or more analytes, the method including converting, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or known time for the one or more analytes; generating at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time; and displaying a visual representation of the at least one integrated plot via a graphical user interface.

[00011] In a general aspect, one or more non-transitory computer-readable storage media is provided, comprising instructions, which when executed by one or more computing devices, cause the one or more computing devices to: convert, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or a known time for the one or more analytes; and generate at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time.

[00012] In a general aspect, one or more non-transitory computer-readable storage media is provided, comprising instructions, which when executed by one or more computing devices, cause the one or more computing devices to: convert, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or a known time for the one or more analytes; generate at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time; and display a visual representation of the at least one integrated plot via a graphical user interface. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

DESCRIPTION OF DRAWINGS

[00013] Various aspects and embodiments of the present disclosure are shown in the drawings and described therein and elsewhere throughout the disclosure. In the drawings, like references indicate like parts.

[00014] Fig. 1 provides a schematic block diagram of the hardware components for one embodiment of the disclosed systems.

[00015] Fig. 2 provides a schematic block diagram of the software components for one embodiment of the disclosed systems. [00016] Fig. 3A depicts a gamma plot image of overall time-resolved absorbance data.

[00017] Fig. 3B provides imaged capillary isoelectric focusing (icIEF) ultraviolet (UV) absorbance data.

[00018] Fig. 4 depicts a gamma plot image of overall time-resolved absorbance data including a mobilization trace and a focused trace.

[00019] Fig. 5 provides a focused trace in Section 1 , a mobilization trace in Section 2, a flipped mobilization trace in Section 3, and an interpolated mobilization trace in Section 4. [00020] Fig. 6A provides a focused trace including peaks of analytes and markers.

[00021] Fig. 6B provides a mobilization trace including peaks of analytes and markers.

[00022] Fig. 6C provides a focused trace including peaks with assigned values for the focused acidic and focused basic groups.

[00023] Fig. 6D provides a mobilization trace including peaks with assigned values for the mobilization acidic and mobilization basic groups.

[00024] Fig. 7 provides a table depicting values for the peaks in the focused and mobilization groups and their matching.

[00025] Fig. 8 depicts a focused trace and a mobilization trace with paired peaks.

[00026] Fig. 9 provides the converted data with paired peaks and a piecewise adjustment of the data based on the paired peaks.

[00027] Fig. 10 depicts the correlation of the converted data with the MS data.

DETAILED DESCRIPTION

[00028] In various aspects and embodiments of the present disclosure, systems, components, and devices, and combinations thereof, are provided for analyzing substance samples, and particularly for analyzing of pluralities of substance samples.

[00029] Devices and methods for integration of imaged microfluidic separations (e.g., icIEF) with mass spectrometry have been previously described in, for example, published PCT Patent Application Publication No. WO 2017/095813, and U.S. Patent Application Publication No. US 2017/0176386, which are hereby incorporated by reference for all purposes. These applications describe, among other things, systems for performing imaged separation in conjunction with MS analysis. Such microfluidic systems represent a significant advancement in biologies characterization. However, in order for such systems to provide maximal benefit it would be beneficial to have methods, software and systems to aid in their operation, for example, by improving the quality of isoelectric focusing data and/or mass spectrometry data, achieving more quantitative characterization of analyte, and improving correlation between isoelectric focusing data and mass spectrometry data, as is disclosed herein.

[00030] The analysis of analytes e.g., via icIEF-MS) can be a multistep process. In an embodiment, the process may begin with separation and focusing of analytes to their isoelectric points (pls) as described in further detail below. The process may be followed by mobilization (accelerating separated ions towards the mass spectrometer and into an electrospray), also described in further detail below. In some embodiments, the mobilization may be followed by ionization for the mass spectrometry analysis. To enable monitoring throughout certain or all of the stages of the process, ultraviolet (UV) absorbance detection or other modes of detection may be utilized, also described in further detail below.

[00031] Alignment between data collected for the focusing portion (e.g., icIEF) of the analysis and for the MS analysis is critical, as both analyses may provide valuable and distinctive insight into the composition and properties of the analytes (e.g., information about the presence or absence of certain impurities, pl information, and mass spectrometric data for the analytes). However, aligning focusing data {e.g., UV absorbance traces) with MS data remains challenging and prone to user error for several reasons. For example, during mobilization, peaks may accelerate non-linearly from their focused positions — and, thus, the downstream MS trace might appear significantly different from the trace observed for the focused state. MS trace may also be a function of time, not position like the focused trace. As another example, MS detection is based on ionization and ions hitting a detector, while imaging for focusing may be based on absorption. These different detection methods are based on different properties of analytes and may have different efficiencies, may lead to different response factors, and/or result in different analytical performance characteristics — thus, the MS trace for the same mixture of analytes might look different from the focusing e.g., icIEF) trace, complicating the alignment . Further as another example, MS traces may appear to have a different number of peaks than the focusing trace due to, e.g., variations in sensitivity and/or resolution between the different modes of analysis.

[00032] In some embodiments, a solution to this problem is provided, by, e.g., converting a first data set (comprising one or more images of a spatially resolved isoelectric focusing) to a converted data set using a second data set (comprising one or more time resolved traces of a mobilization). Further, in some embodiments, at least one integrated plot is generated by correlating the converted data set to a third data set (comprising time resolved mass spectral data). Thus, in some embodiments, instead of aligning two traces that might be significantly different from each other, in different domains, and/or difficult to align e.g., spatial isoelectric focusing and temporal MS traces), a converted data set is generated and, in some embodiments, is used to generate a least one integrated plot. In some embodiments, the converted data set accounts for differences observed between isoelectric focusing and MS traces by using a mobilization data collected between the acquisition of two traces. In some embodiments, the converted data set provides more accurate data alignment between different sets of data. In some embodiments, the converted data set allows automation of data alignment, elimination of user intervention, and/or subjectivity in aligning the data.

[00033] As utilized herein, circuitry or module is “operable” to perform a function whenever the circuitry or module comprises the necessary hardware and code (if any is necessary) to perform the function, regardless of whether performance of the function is disabled or not enabled (e.g., by a user-configurable setting, factory trim, etc.).

[00034] As utilized herein, “and/or” means any one or more of the items in the list joined by “and/or.” As an example, “x and/or y” means any element of the three-element set {(x), (y), (^x, y)}- ^ln other words, “x and/or y” means “one or both of x and y.” As another example, “x, y, and/or z” means any element of the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means “one or more of x, y, and z.” As utilized herein, the term “exemplary” means serving as a non-limiting example, instance, or illustration. Further, as utilized herein, the terms “for example” and “e.g. ” set off lists of one or more non-limiting examples, instances, or illustrations.

[00035] The terminology used herein is for the purpose of describing particular examples only and is not intended to be limiting of the disclosure. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “includes,” “comprising,” “including,” “has,” “have,” “having,” and the like when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[00036] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. Thus, for example, a first element, a first component, or a first section discussed below could be termed a second element, a second component, or a second section without departing from the teachings of the present disclosure. Similarly, various spatial terms, such as “upper,” “lower,” “side,” and the like, may be used in distinguishing one element from another element in a relative manner. It should be understood, however, that components may be oriented in different manners, for example a semiconductor device may be turned sideways so that its “top” surface is facing horizontally and its “side” surface is facing vertically, without departing from the teachings of the present disclosure.

[00037] Fig. 1 provides a schematic illustration of a system hardware block diagram for one embodiment of the disclosed methods, devices, and systems. As illustrated, a system of the present disclosure may comprise one or more of the following hardware components: (i) a chemical separation system (e.g., a capillary or microfluidic device designed to perform an analyte separation, e.g., an isoelectric focusing-based separation, and one or more high-voltage power supplies), (ii) an electrospray interface for a mass spectrometer that, in some cases, may be directly integrated with the separation system (as indicated by the dashed line), (iii) a mass spectrometer, (iv) an imaging device or system, (v) a processor or computer, and (vi) a computer memory device, or any combination thereof. In some embodiments, the system may further comprise one or more capillary or microfluidic device flow controllers [e.g., programmable syringe pumps, peristaltic pumps, HPLC pumps, etc.), temperature controllers configured to maintain a specified temperature for all or a portion of a capillary or microfluidic device, additional photo sensors or image sensors (e.g., photodiodes, avalanche photodiodes, CMOS image sensors and cameras, CCD image sensors and cameras, etc.), light sources (e.g., light emitting diodes (LEDs), diode lasers, fiber lasers, gas lasers, halogen lamps, arc lamps, etc.), other types of sensors (e.g., temperature sensors, flow sensors, pH sensors, conductivity sensors, etc.), computer memory devices, computer display devices (e.g., comprising a graphical user interface), digital communication devices (e.g., intranet, internet, Wi-Fi, Bluetooth®, or other hardwired or wireless communication hardware), and the like.

[00038] In some embodiments, the system may comprise an integrated system in which a selection of functional hardware components are packaged in a fixed configuration. In some embodiments, the system may comprise a modular system in which the selection of functional hardware components may be changed in order to reconfigure the system for new applications. In some embodiments, some of these functional system components, e.g., capillaries or microfluidic devices, are replaceable or disposable components.

[00039] As noted above, any of a variety of different mass spectrometers may be utilized in different embodiments of the disclosed systems including, but not limited to, time-of- flight mass spectrometers, quadrupole mass spectrometers, ion trap or orbitrap mass spectrometers, distance-of-flight mass spectrometers, Fourier transform ion cyclotron resonance spectrometers, resonance mass measurement spectrometers, and nanomechanical mass spectrometers.

[00040] As illustrated in Fig. 2, a system of the present disclosure may comprise a plurality of software modules. For example, a system may comprise a system control software module, a data acquisition software module, a data processing software module, or any combination thereof. In general, these software modules will be configured to operate within an operating system or environment hosted by a computer processor and may communicate and share data with each other and/or the operating system.

[00041] In some embodiments, a system control software module may comprise software for: (i) coordinating the operation of the capillary- or microfluidic device-based analyte separation system with image acquisition by an imaging system, (ii) coordinating the operation of capillary- or microfluidic device-based analyte separation system with data acquisition by the mass spectrometer system, (iii) coordinating image acquisition by an imaging system with operation of the capillary- or microfluidic device-based analyte separation system and/or mass spectrometer system, (iv) providing feedback control of one or more operating parameters of an electrospray ionization setup and/or mass spectrometer based on data derived from imaging of a separation channel and/or a Taylor cone, (v) controlling data acquisition by the mass spectrometer while switching between high mass and low mass scan ranges in an alternating fashion, (vi) monitoring voltage at an ESI tip and adjusting separation circuit voltages to maintain a constant separation electric field strength (or voltage drop between the anode and cathode) and constant voltage at the ESI tip, (vii) monitoring voltage at the ESI tip and adjusting separation circuit voltages and/or mass spectrometer circuit voltages to maintain a constant electric field strength (or voltage drop) between the ESI tip and the mass spectrometer (e.g., at the inlet), or any combination thereof.

[00042] In some embodiments, a data acquisition module may comprise software for: controlling image acquisition by one or more image sensors or imaging systems, storing said image data, and providing a software interface with system control and/or data processing software modules, controlling data acquisition by one or more mass spectrometer systems, storing said mass spectrometer data (or other downstream analytical instrument), and providing a software interface with system control and/or data processing software, or any combination thereof.

[00043] In some embodiments, a data processing module may comprise software for: processing images and determining the position(s) of one or more pl standards or analyte peaks in a separation channel while the separation is being performed, after the separation is complete, or after mobilization of the pl standards and analyte peaks towards a separation channel outlet or electrospray tip, (ii) processing images and determining a velocity, an exit time, and/or an electrospray emission time for one or more pl standard or analyte peaks, (iii) processing of images of a separation channel to monitor a position of an analyte peak and images of a Taylor cone to monitor electrospray performance, where the images of the separation channel and Taylor cone are acquired either simultaneously or alternately, (iv) processing images of a Taylor cone, determining a shape, density, or other characteristic of the Taylor cone, and calculating an adjustment to be made to one or more operating parameters comprising the position (i.e., alignment and/or separation distance) of the electrospray tip or orifice relative to the mass spectrometer inlet, the fluid flow rate through the electrospray tip or orifice, the voltage between the electrospray tip or orifice and the mass spectrometer, etc., or any combination thereof, to affect a change in a quality of the mass spectrometer data; or any combination thereof.

[00044] The disclosed system and application software may be implemented using any of a variety or programming languages and environments known to those of skill in the art. Examples include, but are not limited to, C, C++, C#, PL/I, PL/S, PL/8, PL-6, SYMPL, Python, Java, LabView, Visual Basic, .NET and the like.

[00045] In some embodiments, as noted above, the data processing module may comprise image processing software for determining the positions of pl markers or separated analyte bands, for characterizing the shape, density, or other visual indicator of Taylor cone function, etc. Any of a variety of image processing algorithms known to those of skill in the art may be utilized for image pre-processing or image processing in implementing the disclosed methods and systems. Examples include, but are not limited to, Canny edge detection methods, Canny-Deriche edge detection methods, first-order gradient edge detection methods (e.g., the Sobel operator), second order differential edge detection methods, phase congruency (phase coherence) edge detection methods, other image segmentation algorithms (e.g., intensity thresholding, intensity clustering methods, intensity histogram-based methods, etc.), feature and pattern recognition algorithms (e.g., the generalized Hough transform for detecting arbitrary shapes, the circular Hough transform, etc.), and mathematical analysis algorithms (e.g., Fourier transform, fast Fourier transform, wavelet analysis, auto-correlation, Savitzky-Golay smoothing, Eigen analysis, etc.), or any combination thereof.

[00046] One or more processors or computers may be employed to implement the methods disclosed herein. The one or more processors may comprise a hardware processor such as a central processing unit (CPU), a graphic processing unit (GPU), a general-purpose processing unit, or computing platform. The one or more processors may be comprised of any of a variety of suitable integrated circuits (e.g., application specific integrated circuits (ASICs) designed specifically for implementing deep learning network architectures, or field-programmable gate arrays (FPGAs) to accelerate compute time, etc., and/or to facilitate deployment), microprocessors, emerging next-generation microprocessor designs (e.g., memristor-based processors), logic devices and the like. Although the disclosure is described with reference to a processor, other types of integrated circuits and logic devices may also be applicable. The processor may have any suitable data operation capability. For example, the processor may perform 512 bit, 256 bit, 128 bit, 64 bit, 32 bit, or 16 bit data operations. The one or more processors may be single core or multi core processors, or a plurality of processors configured for parallel processing.

[00047] The one or more processors or computers used to implement the disclosed methods may be part of a larger computer system and/or may be operatively coupled to a computer network (a “network”) with the aid of a communication interface to facilitate transmission of and sharing of data. The network may be a local area network, an intranet and/or extranet, an intranet and/or extranet that is in communication with the Internet, or the Internet. The network in some cases is a telecommunication and/or data network. The network may include one or more computer servers, which in some cases enables distributed computing, such as cloud computing . The network, in some cases with the aid of the computer system, may implement a peer-to-peer network, which may enable devices coupled to the computer system to behave as a client or a server.

[00048] The computer system may also include memory or memory locations (e.g., random-access memory, read-only memory, flash memory, Intel® Optane™ technology), electronic storage units e.g., hard disks), communication interfaces (e.g., network adapters) for communicating with one or more other systems, and peripheral devices, such as cache, other memory, data storage and/or electronic display adapters . The memory, storage units, interfaces and peripheral devices may be in communication with the one or more processors, e.g., a CPU, through a communication bus, e.g., as is found on a motherboard. The storage unit(s) may be data storage unit(s) (or data repositories) for storing data. [00049] The one or more processors, e.g., a CPU, execute a sequence of machine- readable instructions, which are embodied in a program (or software). The instructions are stored in a memory location. The instructions are directed to the CPU, which subsequently program or otherwise configure the CPU to implement the methods of the present disclosure. Examples of operations performed by the CPU include fetch, decode, execute, and write back. The CPU may be part of a circuit, such as an integrated circuit. One or more other components of the system may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[00050] The storage unit stores files, such as drivers, libraries, and saved programs. The storage unit stores user data, e.g., user-specified preferences and user-specified programs. The computer system in some cases may include one or more additional data storage units that are external to the computer system, such as located on a remote server that is in communication with the computer system through an intranet or the Internet.

[00051] Some aspects of the methods and systems provided herein are implemented by way of machine e.g., processor) executable code stored in an electronic storage location of the computer system, such as, for example, in the memory or electronic storage unit. The machine executable or machine readable code is provided in the form of software. During use, the code is executed by the one or more processors. In some cases, the code is retrieved from the storage unit and stored in the memory for ready access by the one or more processors. In some situations, the electronic storage unit is precluded, and machine-executable instructions are stored in memory. The code may be pre-compiled and configured for use with a machine having one or more processors adapted to execute the code or may be compiled at run time. The code may be supplied in a programming language that is selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[00052] Various aspects of the disclosed methods and devices may be thought of as “products” or “articles of manufacture”, e.g., “computer program or software products”, typically in the form of machine (or processor) executable code and/or associated data that is stored in a type of machine readable medium, where the executable code comprises a plurality of instructions for controlling a computer or computer system in performing one or more of the methods disclosed herein. Machine-executable code may be stored in an optical storage unit comprising an optically readable medium such as an optical disc, CD- ROM, DVD, or Blu-Ray disc. Machine-executable code may be stored in an electronic storage unit, such as memory e.g., read-only memory, random-access memory, flash memory) or on a hard disk. “Storage” type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memory chips, optical drives, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software that encodes the methods and algorithms disclosed herein.

[00053] All or a portion of the software code may at times be communicated via the Internet or various other telecommunication networks. Such communications, for example, enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, other types of media that are used to convey the software encoded instructions include optical, electrical and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and over various atmospheric links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, are also considered media that convey the software encoded instructions for performing the methods disclosed herein. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[00054] The computer system typically includes, or may be in communication with, an electronic display for providing, for example, images captured by a machine vision system. The display is typically also capable of providing a user interface (Ul). Examples of Ill’s include but are not limited to graphical user interfaces (GUIs), web-based user interfaces, and the like.

[00055] As noted above, the disclosed methods, devices, systems, and software have potential application in a variety of fields including, but not limited to, proteomics research, drug discovery and development, and clinical diagnostics. For example, the improved information content and data quality that may be achieved for separation-based electrospray ionization mass spectrometry (ESI-MS) analysis of analyte samples using the disclosed methods may be of great benefit for the characterization of biologic and biosimilar pharmaceuticals during development and/or manufacturing. Other applications may include, but are not limited to, analysis of environmental pollutants, pesticides, small molecules, metabolites, peptides, post-translational modifications, glycoforms, antibodydrug conjugates, fusion proteins, viruses, allergens, single cell organisms, and other applications.

[00056] Biologies and biosimilars are a class of drugs which include, for example, recombinant proteins, antibodies, live virus vaccines, human plasma-derived proteins, cellbased medicines, naturally sourced proteins, antibody-drug conjugates, protein-drug conjugates, and other protein drugs. The FDA and other regulatory agencies require characterization of a biologic or biosimilar and control over its quality and manufacturing processes, which may include a determination of structure, function, animal toxicity, human pharmacokinetics (PK) and pharmacodynamics (PD), clinical immunogenicity, and clinical safety and effectiveness. For biosimilars, a comparison of the proposed product and a reference product may be required. In many cases, laborious, time-intensive, and costly techniques are employed to address these requirements. Thus, there is a need for experimental techniques that allow for convenient, real-time, and relatively high-throughput analysis.

[00057] In some embodiments, the disclosed methods, devices, and systems may be used for analysis of biologies (e.g., identifying impurities and understanding how manufacturing process changes affect critical quality attributes). For example, in some instances, isoelectric point data and/or mass spectrometry data may provide important quality and/or biosimilarity information. In some embodiments, isoelectric point data and/or mass spectrometry data on an analyte pre-treated with site-specific protease may provide important information about quality and/or biosimilarity. In some embodiments, the disclosed methods, devices, and systems may be used to monitor a biologic drug manufacturing process to ensure the quality and consistency of the product by analyzing samples drawn at different points in the production process, or samples drawn from different production runs. In some embodiments, the disclosed methods, devices, and systems may be used to evaluate stability of drug product formulations. In some embodiments, the disclosed methods, devices, and systems may be used to evaluate cloned cell lines for production and quality of biological drug candidates.

[00058] The disclosed methods, devices, systems, and software may utilize any of a variety of analyte separation techniques known to those of skill in the art. For example, in some embodiments, the imaged separation may be an electrophoretic separation, such as, isoelectric focusing, capillary gel electrophoresis, capillary zone electrophoresis, isotachophoresis, capillary electrokinetic chromatography, micellar electrokinetic chromatography, flow counterbalanced capillary electrophoresis, electric field gradient focusing, dynamic field gradient focusing, and the like, that produces one or more separated analyte fractions from an analyte mixture.

[00059] In some embodiments, the separation technique may comprise isoelectric focusing (IEF), e.g., capillary isoelectric focusing (CIEF). Isoelectric focusing (or “electrofocusing”) is a technique for separating molecules by differences in their isoelectric point (pl), i.e., the pH at which they have a net zero charge. CIEF involves adding ampholyte (amphoteric electrolyte) solutions to a sample channel between reagent reservoirs containing an anode or a cathode to generate a pH gradient within a separation channel (i.e., the fluid channel connecting the electrode-containing wells) across which a separation voltage is applied. The ampholytes can be solution phase or immobilized on the surface of the channel wall. Negatively charged molecules migrate through the pH gradient in the medium toward the positive electrode while positively charged molecules move toward the negative electrode. A protein (or other molecule) that is in a pH region below its isoelectric point (pl) will be positively charged and so will migrate towards the cathode (i.e., the negatively charged electrode). The protein's overall net charge will decrease as it migrates through a gradient of increasing pH (due, for example, to protonation of carboxyl groups or other negatively charged functional groups) until it reaches the pH region that corresponds to its pl, at which point it has no net charge and so migration ceases. As a result, a mixture of proteins separates based on their relative content of acidic and basic residues and becomes focused into sharp stationary bands with each protein positioned at a point in the pH gradient corresponding to its pl. The technique is capable of extremely high resolution with proteins differing by a single charge being fractionated into separate bands. In some embodiments, isoelectric focusing may be performed in a separation channel that has been permanently or dynamically coated, e.g., with a neutral and hydrophilic polymer coating, to eliminate electroosmotic flow (EOF), allow better protein solubilization, and limit diffusion inside the capillary of fluid channel by increasing the viscosity of the electrolyte.

[00060] As noted above, the pH gradient used for capillary isoelectric focusing techniques is generated through the use of ampholytes, i.e., amphoteric molecules that contain both acidic and basic groups and that exist mostly as zwitterions within a certain range of pH. The portion of the electrolyte solution on the anode side of the separation channel is known as an “anolyte.” That portion of the electrolyte solution on the cathode side of the separation channel is known as a “catholyte.” A variety of electrolytes may be used in the disclosed methods and devices including, but not limited to, phosphoric acid, sodium hydroxide, ammonium hydroxide, glutamic acid, lysine, formic acid, dimethylamine, triethylamine, acetic acid, piperidine, diethylamine, and/or any combination thereof. The electrolytes may be used at any suitable concentration, such as 0.0001 %, 0.001%, 0.01 %, 0.1 %, 1 %, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, etc. The concentration of the electrolytes may be at least 0.0001 %, 0.001%, 0.01 %, 0.1 %, 1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90%. The concentration of the electrolytes may be at most 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1 %, 0.1%, 0.01%, 0.001 %, and 0.0001%. A range of concentrations of the electrolytes may be used, e.g., 0.1%-2%. Ampholytes can be selected from any commercial or non-commercial carrier ampholytes mixtures e.g., Servalyt pH 4-9 (Serva, Heildelberg, Germany), Beckman pH 3-10 (Beckman Instruments, Fullerton, CA, USA), Ampholine 3.5-9.5 and Pharmalyte 3-10 (both from General Electric Healthcare, Orsay, France), AESIytes (AES), FLUKA ampholyte (Thomas Scientific, Swedesboro, NJ), Biolyte (Bio-Rad, Hercules, CA)), and the like. Carrier ampholyte mixtures may comprise mixtures of small molecules (about 300 - 1 ,000 Da) containing multiple aliphatic amino and carboxylate groups that have closely spaced pl values and good buffering capacity. In the presence of an applied electric field, carrier ampholytes partition into smooth linear or non-linear pH gradients that increase progressively from the anode to the cathode.

[00061] Any of a variety of pl standards may be used in the disclosed methods and devices for calculating the isoelectric point for separated analyte peaks. For example, pl markers generally used in CIEF applications, e.g., protein pl markers and synthetic small molecule pl markers, may be used. In some instances, protein pl markers may be specific proteins with commonly accepted pl values. In some instances, the pl markers may be detectable, e.g., via imaging. A variety or combination of protein pl markers or synthetic small molecule pl markers that are commercially available, e.g., the small molecule pl markers available from Advanced Electrophoresis Solutions, Ltd. (Cambridge, Ontario, Canada), ProteinSimple, the peptide library designed by Shimura, and Slais dyes (Alcor Biosepartions), may be used.

[00062] In some embodiments, e.g., in those instances where isoelectric focusing is employed, the separated analyte bands may be mobilized towards an end of the separation channel that interfaces with a downstream analytical device, e.g., an electrospray ionization interface with a mass spectrometer. In some embodiments, mobilization of the analyte bands may be implemented by applying hydrodynamic pressure to one end of the separation channel. In some embodiments, mobilization of the analyte bands may be implemented by orienting the separation channel in a vertical position so that gravity may be employed. In some embodiments, mobilization of the analyte bands may be implemented using EOF-assisted mobilization. In some embodiments, mobilization of the analyte bands may be implemented using chemical mobilization. In some embodiments, any combination of these mobilization techniques may be employed.

[00063] In one embodiment, the mobilization step for isoelectrically focused analyte bands comprises chemical mobilization. Compared with pressure-based mobilization, chemical mobilization has the advantage of exhibiting minimal band broadening by overcoming the hydrodynamic parabolic flow profile induced by the use of pressure. Chemical mobilization may be implemented by introducing either the inlet or outlet of a separation path containing a completely or partially focused pH gradient to a conductive solution with an ion that competes with either hydronium or hydroxyl for electrophoresis into the separation path. This results in the stepwise electrokinetic displacement of the pH gradient components by disrupting the approximate zero net charge state. In the case of cathodic chemical mobilization, the supply of hydroxyls, the catholyte solution, may be replaced with a mobilization solution containing a competing anion. The competing anion can cause a drop in pH in the separation path developing a positive charge on the pH gradient components allowing them to migrate towards the cathode. Correspondingly, in anodic mobilization the supply of hydroniums, the anolyte solution is replaced with a mobilization solution containing a competing cation which increases the pH in the separation developing a negative charge of the pH gradient components allowing them to migrate towards the anode. In some embodiments, cathodic mobilization may be initiated using acidic electrolytes such as formic acid, acetic acid, carbonic acid, phosphoric acid and the like, at any suitable concentration. In some embodiments, anodic mobilization may be initiated using basic electrolytes such as ammonium hydroxide, dimethylamine, diethylamine, piperidine, sodium hydroxide and the like. In some embodiments, chemical mobilization may be initiated by adding salt, such as sodium chloride, or any other salt to the anolyte or catholyte solution. In some aspects, mobilization may be initiated using formic acid and methanol. In other embodiments, mobilization may be initiated using acetonitrile and acetic acid, for example, a composition or mobilizer comprising 25% acetonitrile and 25% acetic acid.

[00064] In a preferred embodiment, a chemical mobilization step may be initiated within a microfluidic device designed to integrate CIEF with ESI-MS by changing an electric field within the device to electrophorese a mobilization electrolyte into the separation channel. In some embodiments, the change in electric field may be implemented by connecting or disconnecting one or more electrodes attached to one or more power supplies, wherein the one or more electrodes are positioned in reagent wells on the device or integrated with fluid channels of the device. In some embodiments, the connecting or disconnecting of one or more electrodes may be controlled using a computer-implemented method and programmable switches, such that the timing and duration of the mobilization step may be coordinated with the separation step, the electrospray ionization step, and/or mass spectrometry data collection. In some embodiments, the disconnecting of one or more electrodes from the separation circuit may be implemented by using current control and setting the current to 0 pA.

[00065] In some embodiments, the movement of peaks through the separation channel (e.g., during the separation, during mobilization, etc.) can be monitored. The imaging may be LIV imaging, fluorescence imaging, transmitted light imaging, or another mode of imaging. In some embodiments, images of the separation and mobilization may be recorded at a defined rate. For example, the imaging rate may be one image per minute, one image per 30 seconds, one image per 10 seconds, one image per 5 seconds, one image per second, one image per millisecond, etc. In some embodiments, the individual images may be combined as individual frames in a “movie” showing peak formation and mobilization. In some embodiments, this movie may be saved as a GIF, AVI, MOV, MP4, or any other digital format able to save digital video data. In some embodiments, the imaging may be performed in real-time, e.g., as a separation is performed, as mobilization is performed, as electrospray is performed, etc.

[00066] In some embodiments, the time-series imaging data may be plotted on a three- dimensional or three-axis graph. One axis of the graph may represent distance e.g., physical distance or pixel position along the length of the separation channel), and one axis may represent time. In some instances, a third axis may be used to represent signal strength, intensity, or absorbance, which can be alternatively or additionally be represented by a color-scale or grayscale. In some embodiments, the x axis may be used to represent distance, the y axis to represent time, and the z axis to represent signal or absorbance. It will be appreciated that the axes may be used to represent any of the parameters e.g., distance or position along a channel, pl, intensity or absorbance, time, etc.).

[00067] The imaging data and data plotting (or other image processing) may be performed after the completion of the separation and mass spectrometry run or, in some instances, while the separation, mobilization, and mass spectrometry are performed. For instance, the computer-implemented methods or software may be configured to receive the imaging data as it is obtained, process the imaging data {e.g., to obtain intensity plots as a function of channel length) and plot the IFF data {e.g., iteratively or incrementally in a 3-dimensional plot or heat map). [00068] In some embodiments, a dynamic heat map, or gamma plot, such as shown in Fig. 3A, may be used to display a series of images (e.g., a time-series imaging data set of a focusing/separation and/or mobilization are performed in a separation channel, in which each image of the series corresponds to a different time point, as described above). As an example, in Fig. 3A, the peaks are represented as imaged analyte bands (each containing intensity or absorbance measurements) along the length of the imaged channel and plotted as a function of time. The gamma plot shows a top down image of the overall time resolved absorbance data where horizontal slices show pixels along the separation channel and vertical slices show pseudo-single point detection. In other words, each row of the gamma plot in Fig. 3A displays the position of analyte bands at a single timepoint during focusing and mobilization. Each row corresponds to an image of the separation channel and may be used to generate (or may be generated from) an electropherogram (e.g., as shown in Fig. 3B) for a given timepoint.

[00069] For each analyte peak in Fig. 3B, a gamma plot, such as the one depicted in Fig. 3A, may display a time course of the analyte peak migration. For instance, during the IEF separation, the analyte (or a plurality of analytes) may migrate from both ends of the channel to the analyte’s (or analytes’) isoelectric point(s). At the isoelectric point(s), when focusing is completed, the analyte may be enriched, resulting in a peak. Further, at the onset of mobilization, accelerated migration of each peak toward the mass spectrometer may occur. Successive images of the time series are stacked vertically, so the column axis (Y-axis) of Fig. 3A represents time, while the X-axis represents spatial resolution of bands at each point in time (e.g., the position of the analyte as a function of the position along the length of the separation channel).

[00070] Referring to Fig. 3B, in an embodiment, a focused trace (data set) with known pl markers may be obtained from the gamma plot. The focused trace may be acquired in the form of signal intensity (absorbance) versus position. The focused trace comprises one or more images of an isoelectric focusing of one or more analytes. Each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or a pl value for the one or more analytes. The peaks generated from the known pl markers in the focused data set may enable the conversion of position to pl. Consequently, pl data may be calculated and displayed as signal intensity versus pl.

[00071] Referring to Fig. 4, in an embodiment, a mobilization trace may be also formed from the gamma plot, e.g., at a given pixel or pixel range (i.e. physical distance or position along the length of the separation channel). The mobilization trace may be considered a pseudo-single point detection plot of the absorbance that passes a given pixel location, and may be selected to be closer to the mass spectrometer so that the mobilization data collected is more similar to the MS data. The mobilization trace may be formed from a vertical slice at a given pixel of the gamma plot. Each data point of the one of more traces of the mobilization corresponds to a signal intensity at a known position and/or a known time for the one or more analytes.

[00072] Referring to Fig. 5, Section 1 depicts an example of a focused absorbance trace. Fig. 5, Section 2 depicts an example of a mobilization trace. In some embodiments, a portion of the mobilization trace may be removed to account for and/or eliminate mobilization-related artifacts, prior to its further use according to the described methods. The mobilization trace may also be flipped, as depicted in Fig. 5, Section 3, to account for differences in how the focused trace and the mobilization trace are acquired and/or how the analytes move (e.g., analytes appearing on the right of the focused trace (such as depicted in Fig. 5, Section 1 ), upon mobilization, may be the first to leave the separation/focusing {e.g., icIEF) portion of an icIEF-MS system and, thus, appear on the left side of the mobilization trace (such as depicted in Fig. 5, Section 2)). Data in the mobilization trace may be basis-spline interpolated to increase the number of data points by any factor in order to have more point granularity for the peaks in the trace, resulting in clearer data. Possible factors are 100, 1000, etc. but any factor may be used. For example, in Fig. 5, Section 4, a factor of 100 is used. Marker peaks e.g., 2 pl marker peaks such as depicted in Fig. 5, Sections 1 -4) can also be found in the trace. For example, the two marker peaks may be the tallest peaks on either end of a trace above a predetermined threshold as compared to a max signal. The threshold may be up to about 75%, for example, but can vary, depending on the concentration of the marker and sample being used. For example, in some embodiments, the threshold may be 5% or 6%. In different embodiments, the threshold may be 25%. In some embodiments, pl marker peaks are found in a mobilization trace. In some embodiments, pl marker peaks are removed from the set of peaks and the remaining set is comprised of the mobilization sample peaks. In some embodiments, the relative height for each sample peak is calculated and normalized based on the tallest peak in the remaining set (with the marker peaks removed).

[00073] Referring to Figs. 6A-6D, in some embodiments, the peaks in the focused trace may be separated into a main peak, a focused acidic group {e.g., peaks 1 FA-5FA in Fig. 6C), and a focused basic group {e.g., peaks 1 FB-3FB in Fig. 6C). In some embodiments, the sample peaks in the mobilization trace may be separated into a main peak, a mobilization acidic group {e.g., peaks 1 MA-4MA in Fig. 6D), and a mobilization basic group {e.g., peaks 1 MB and 2MB in Fig. 6D). In some embodiments, the focused acidic group may be an empty group. In some embodiments, the focused basic group may be an empty group. In some embodiments, the mobilization acidic group may be an empty group. In some embodiments, the mobilization basic group may be an empty group.

[00074] In some embodiments, one or more markers are selected for the focused trace. In some embodiments, one or more markers are selected for the mobilization trace. In some embodiments, a main peak is selected; in some embodiments an acidic marker is selected; in some embodiments a basic marker is selected. In some embodiments, a focused main peak is selected for the focused trace; in some embodiments, a mobilization main peak is selected for the mobilization trace (e.g., see Figs 6A, 6C, 6D). The acidic peaks may be positioned on one side of the main peak, and the basic peaks may be positioned on the other side of the main peak. In some embodiments, a focused acidic marker may be selected; in some embodiments, a focused basic marker may be selected; in some embodiments, a mobilized acidic marker may be selected; in some embodiments, a mobilized basic marker may be selected (see, e.g., FAM, FBM, MAM, MBM in Figs 6A and 6B). Various criteria may be used to select markers — e.g., location, peak height, relative location, and/or relative peak height. The markers may be known markers as described above with reference to Fig. 3B.

[00075] Referring to Fig. 6A, the focused trace is depicted, including the focused acidic marker, focused main peak, and focused basic marker. Referring to Fig. 6B, the corresponding mobilization trace is depicted, with its markers. The peaks of each of the focused acidic group, the focused basic group, the mobilization acidic group, and the mobilization basic group may be ordered, for example, by height, spatial position, or area, (or ratios of height, spatial position, or area) relative to a marker. In some embodiments, relative spatial position may be established relative to a main peak. In some embodiments, relative spatial position may be also established relative to an acidic or basic marker. In some embodiments, relative spatial position for focused acidic peaks is established relative to the focused main peak and focused acidic marker; in some embodiments, relative spatial position for focused basic peaks is established relative to the focused main peak and focused basic marker; in some embodiments, relative spatial position for mobilized acidic peaks is established relative to the mobilized main peak and mobilized acidic marker; in some embodiments, relative spatial position for mobilized basic peaks is established relative to the mobilized main peak and mobilized basic marker. For example, D1 , depicted in Fig. 6A represents the distance from a focused peak (Pf) to the focused main peak. Dfbm is the distance from the focused main peak to the focused basic marker (FBM). For peak Pf, the distance D1 is divided by Dfbm in order to determine its relative spatial position. Similarly, a ratio may be determined for peak (Pb) using the distance D2 and the distance from the focused acidic marker to the focused main peak (Dfam). In some embodiments, a relative height ratio may also be determined for each of the peaks relative to the peak of the main peak. A similar process may be completed for the peaks of the mobilization trace relative to the mobilization marker(s) and/or the mobilization main peak, as depicted in Fig. 6B.

[00076] Figs. 6C and 6D depict an example of the peaks for each of the groups, once they are identified and ordered as disclosed above. Figs. 6C and 6D also depict the focused main peak and the mobilization main peak with their assigned values (e.g., relative height/position at time t) against which the remaining peaks may be compared.

[00077] Fig. 7 is an example of a table that depicts values for each of the peaks in Figs. 60 and 6D. In an embodiment, each of the peaks of the focused acidic, focused basic, mobilization acidic, and mobilization basic groups may be assigned values for heights and positions relative to the main focused and/or main mobilization markers. In some embodiments, peaks in the focused acidic group can then be paired with peaks in the mobilization acidic group, provided threshold criteria is met. In some embodiments, any peaks that do not match the threshold criteria or are out of spatial order will remain unmatched, and matching/pairing may continue with subsequent peaks in that group. For example, for all potential peak pairs assigned in height order, it may be ensured that peaks are assigned in the correct special order — e.g., to ensure that as focused trace peaks pls get larger or smaller, mobilization trace peaks times get smaller or larger, respectively, and/or as focused trace peaks relative positions get larger or smaller, mobilization trace peaks relative positions follow the trend e.g., get larger or smaller). If peaks appear to be out of order, the assignment of that peak may be skipped. For example, the assignment of peak 2 in the focused basic group in Fig. 7, may be skipped.

[00078] Fig. 8 provides an example of a visual depiction and additional details regarding the matching of the peaks in the groups described above. In some embodiments, once each of the peaks in the four groups have been identified and ordered, the tallest peak in the focused basic group is paired to the tallest peak in the mobilization basic group if certain threshold criteria is met. For example, peaks may be paired if they are within a certain range of the relative position. The range may be variable but can be, for example, between up to 40% of the height and/or up to 40% of the relative position, although other thresholds and any of the above criteria may be used. For example, in some embodiments, the range can be from 0% to 5%. In other embodiments, the range can be 0% to 20%, or 0% to 30%. In some embodiments, pairing is continued in descending order of peak height for the focused basic group and the mobilization basic group. In some embodiments, peaks in the focused trace may be paired to peaks of the mobilization trace using as relative position, relative height, relative area, or other ratiometric values. In some embodiments, marker peaks of the focused trace are paired to marker peaks of the mobilization trace. [00079] In some embodiments, pairing is also performed for the focused acidic group and the mobilization acidic group. In some embodiments, pairing can also be performed first for the acidic groups and then the basic groups in each of the traces. In some embodiments, if a next highest peak of the mobilization trace could be potentially paired with a next highest peak of the focused trace, but the result would pair two peaks that are out of spatial order, the peak of the mobilization trace may be skipped and the next highest peak may be picked for an attempt to be matched, provided it meets the selected criteria. [00080] Referring to Fig. 9, in some embodiments, the focused trace, and mobilization trace may be utilized to generate a converted data set which is a piecewise stretched and/or contracted signal intensity plot in a time domain where each timepoint corresponds to the position and/or the pl value. In some embodiments, the converted data set is obtained by using the mobilization trace to convert the focused trace by using anchor points that correlate the position and/or the pl values of the focused trace to the known position and/or time values of the mobilization trace. In some embodiments, the remaining data is manipulated around the anchor points by, for example, stretching, contracting, compressing, and moving the data about the anchor points. Once one or more anchor points are determined, the one or more anchor points correlate the position and/or the pl values of the first data set to the known position and/or known time values of the second data set. The converted data set is further stretched, compressed, moved, or manipulated about the one or more anchor points. In an embodiment, the anchor points may be the paired peaks as described above with regards to Fig. 8, for example, or markers such as those depicted in Figs. 6A-6D may be used as anchor points.

[00081] Referring to Fig. 10, the converted data is correlated with the MS data, resulting in an integrated plot. In some embodiments, initially, the tallest peak of the converted data is aligned with the tallest peak of the MS data. In some embodiments, the alignment may be further modified by the user via a user interface. In some embodiments, the user may revert to other types of alignment via the user interface by selecting a switch alignment button or other type of input. In some embodiments, for example, if there is insufficient data to produce an accurate or verifiable icIEF-MS alignment, the user interface may revert to a second integrated plot. In some embodiments, the second integrated plot may be obtained by converting the first data set to the time domain and then aligning the first data set in the time domain to the MS data set by aligning the tallest peak of the first data set in the time domain to the tallest peak in the MS data set. In some embodiments, generating the second integrated plot is performed automatically if the converted data set cannot be verified. In some embodiments, a user is notified via the graphical user interface if the converted data set cannot be verified. In an embodiment, verifying the converted data set is done using model metrics. For example, verifying the converted data set can be done using by, for example, polynomial regression, quadratic regression, cubic regression, or logistic regression, or similar metrics may be used.

[00082] In some embodiments, the integrated plots can yield information on mass and charge (or isoelectric points) of one or more analytes in the analyte peaks. Correlating the IEF data and MS data may be particularly useful in identifying or distinguishing one or more analyte species having a similar property (e.g., with the same charge or isoelectric point, or with the same mass) and/or having a different property. For example, two molecules with different masses may be identified in the mass spectrometer data. The two molecules may have different isoelectric points (pls) and focus in different regions of the pH gradient in IEF, or the two molecules may have the same isoelectric point (pl) and focus in the same region of the pH gradient in IEF. In the instance where the two molecules have the same pl and different masses, the correlation of the IEF and MS data may be used to distinguish the two molecules (e.g., identifying them as different species or isoforms via MS). The overlaying of the IEF data and MS data (e.g., total ion chromatogram and/or time-series ion measurements as a function of mass) on a single plot may be useful in identifying the protein isoforms by mapping the pl to the masses of one or more analyte species.

[00083] Although some aspects have been described in the context of an apparatus, it is clear that these aspects also represent a description of the corresponding method, where a block or device corresponds to a method step or a feature of a method step. Analogously, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus. Some or all of the method steps may be executed by (or using) a hardware apparatus, like for example, a processor, a microprocessor, a programmable computer or an electronic circuit. In some embodiments, some one or more of the most important method steps may be executed by such an apparatus.

[00084] Generally, embodiments of the present disclosure may be implemented through the use of computer program products embodied on computer-readable medium. Such computer program products may include instructions executable by processors and/or computing devices such as processor 204 and/or computing device 130.

[00085] While particular embodiments of the various aspects of the present disclosure have been illustrated and described, it would be apparent to those skilled in the art that various other changes and modifications can be made and are intended to fall within the spirit and scope of the present disclosure. Furthermore, although the present disclosure has been described herein in the context of particular implementations in particular environments for particular purposes, those of ordinary skill in the relevant arts will recognize that its usefulness is not limited thereto and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breadth and spirit of the present disclosure as described herein.

Claims

WHAT IS CLAIMED IS:

1 . A computer-implemented method, comprising: converting, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or known time for the one or more analytes; and generating at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time.

2. The computer-implemented method of claim 1 , wherein the converted data set comprises a piecewise stretched and/or contracted signal intensity plot in a time domain where each timepoint corresponds to the position and/or the pl value.

3. The computer-implemented method of claim 1 or claim 2, wherein the converted data set comprises peak positions and/or pl values in the first data set as a function of a time domain.

4. The computer-implemented method of claim 1 or claim 2, wherein the converted data set comprises one or more anchor points, the one or more anchor points correlating the position and/or the pl values of the first data set to the known position and/or known time values of the second data set.

5. The computer-implemented method of claim 4, wherein the converted data set is further stretched, compressed, moved, or manipulated about the one or more anchor points.

6. The computer-implemented method of claim 1 or claim 2, wherein the converting of the first data set is performed by assigning the known positions and/or the known times from the second data set to one or more peaks of the first data set.

7. The computer-implemented method of claim 1 or claim 2, wherein the converting further comprises separating peaks in the first data set into a focused acidic group and/or a focused basic group and/or separating peaks in the second data set into a mobilization acidic group and/or a mobilization basic group.

8. The computer-implemented method of claim 7, further comprising ordering the peaks in the focused acidic group, the focused basic group, the mobilization acidic group, and the mobilization basic group by at least one of height, spatial position, area, or ratios of height, spatial position, or area relative to at least one corresponding marker in the first or second data set and/or to at least one corresponding analyte peak in the first or second data set.

9. The computer-implemented method of claims 7, further comprising pairing at least one first peak in the focused basic group to at least one second peak in the mobilization basic group and/or at least one first peak in the focused acidic group to at least one second peak in the mobilization acidic group.

10. The computer-implemented method of claim 9, wherein the pairing is performed in descending order of peak height starting with a highest peak in the focused basic group to a highest peak in the mobilization basic group and/or a highest peak in the focused acidic group to a highest peak in the mobilization acidic group.

1 1 . The computer-implemented method of claims 9, further comprising skipping pairing of the peaks in the focused acidic group, the focused basic group, the mobilization acidic group, and/or the mobilization basic group which are out of spatial order.

12. The computer-implemented method of claim 1 or claim 2, wherein at least one peak in the first data set is mapped to at least one peak in the second data set.

13. The computer-implemented method of claim 12, wherein the at least one peak in the first data set is mapped to the at least one peak in the second data set based on one or more of criteria thresholds.

14. The computer-implemented method of claim 13, wherein the one or more criteria thresholds are percent relative height, percent relative position, percent relative area, and/or relative ratiometric values.

15. The computer-implemented method of claim 1 or claim 2, further comprising calculating one or more first relative peak ratios for peaks in the first data set based on one or more peaks of the first data set and calculating one or more second relative peak ratios for peaks in the second data set based on one or more peaks of the second data set.

16. The computer-implemented method of claim 15, wherein the first and second one or more relative peak ratios are ratios of height, spatial position, or area.

17. The computer-implemented method of claim 16, further comprising pairing one or more peaks of the first data set with one or more peaks of the second data set based on the one or more first relative peak ratios and the one or more second relative peak ratios.

18. The computer-implemented method of claim 1 or claim 2, wherein the first and/or second data set is normalized and/or interpolated prior to converting the first data set using the second data set.

19. The computer-implemented method of claim 1 or claim 2, wherein correlating the converted data set to the third data set comprises setting a time value of a tallest sample peak of the converted data to be equal to a time value from a tallest peak of the third data set.

20. The computer-implemented method of claim 1 or claim 2, wherein correlating the converted data set to the third data set comprises aligning the converted data set to the third data set based on signal intensity.

21 . The computer-implemented method of claim 20, wherein the aligning the converted data set to the third data set can be further adjusted by a user via a graphical user interface.

22. The computer-implemented method of claim 1 or claim 2, wherein the at least one integrated plot is a pl and/or mass resolved intensity plot.

23. The computer-implemented method of claim 1 or claim 2, wherein the at least one integrated plot shows the position and/or the pl values as a function of a time domain.

24. The computer-implemented method of claim 1 or claim 2, wherein the third data set is an extracted chronogram.

25. The computer-implemented method of claim 1 or claim 2, wherein third data set is a base peak ion (BPI) intensity plot, an extracted ion chronogram, or a multi-dimensional plot.

26. The computer-implemented method of claim 1 or claim 2, further comprising generating a second integrated plot by aligning the tallest analyte peak in a second conversion data set and the tallest analyte peak in the third data set.

27. The computer implemented method of claim 26, wherein the second conversion data set is obtained from the first data set by converting position and/or pl values to time.

28. The computer-implemented method of claim 26, further comprising switching from the integrated plot to the second integrated plot via user input at the graphical user interface.

29. The computer-implemented method of claim 26, wherein generating the second integrated plot is performed automatically if the converted data set cannot be verified.

30. The computer-implemented method of claim 26, wherein a user is notified via the graphical user interface if the converted data set cannot be verified.

31 . The computer-implemented method of claim 1 or claim 2, further comprising verifying the converted data set using model metrics.

32. The computer-implemented method of claim 31 , wherein verifying the converted data set is performed using at least one of polynomial regression, quadratic regression, cubic regression, or logistic regression.

33. The computer-implemented method of claim 1 or claim 2, further comprising, obtaining the first data set, the second data set, and the third data set by performing the isoelectric focusing, mobilization, and electrospray ionization mass spectrometry using an integrated microfluidic device coupled to a mass spectrometer.

34. A computer-implemented method for displaying and/or comparing imaged capillary isoelectric focusing (icIEF) and mass spectral (MS) data for one or more analytes, the method comprising: converting, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or known time for the one or more analytes; generating at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time; and displaying a visual representation of the at least one integrated plot via a graphical user interface.

35. One or more non-transitory computer-readable storage media comprising instructions, which when executed by one or more computing devices, cause the one or more computing devices to: convert, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each image pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or a known time for the one or more analytes; and generate at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time.

36. One or more non-transitory computer-readable storage media comprising instructions, which when executed by one or more computing devices, cause the one or more computing devices to: convert, with one or more computing devices, a first data set to a converted data set using a second data set, wherein the first data set comprises one or more images of an isoelectric focusing of one or more analytes, wherein each pixel of the one or more images of the isoelectric focusing corresponds to a first signal intensity at a position and/or an isoelectric point (pl) value for the one or more analytes, wherein the second data set comprises one or more traces of a mobilization of the one or more analytes, and wherein each data point of the one of more traces of the mobilization corresponds to a second signal intensity at a known position and/or a known time for the one or more analytes; generate at least one integrated plot by correlating the converted data set to a third data set, wherein the third data set comprises mass spectral data, wherein the mass spectral data comprises a third signal intensity as a function of time; and display a visual representation of the at least one integrated plot via a graphical user interface.